http://clinfowiki.org/wiki/api.php?action=feedcontributions&user=Nneka.nwaeme&feedformat=atomClinfowiki - User contributions [en]2024-03-28T11:01:48ZUser contributionsMediaWiki 1.22.4http://clinfowiki.org/wiki/index.php/Transforming_the_EHR_into_a_knowledge_platform_to_ensure_improved_health_and_healthcareTransforming the EHR into a knowledge platform to ensure improved health and healthcare2015-11-30T05:02:59Z<p>Nneka.nwaeme: </p>
<hr />
<div>== First Review ==<br />
<br />
This is a review of the white paper by Ian Chuang, M.D. for [http://www.himss.org/ Health Information Management Systems Society (HIMSS)] entitled, ''Transforming the EHR into a knowledge platform to ensure improved health and healthcare''. <br />
=== Background and Purpose ===<br />
<br />
The focus of this whitepaper is to promote EHR implementation best practices and emphasizes how an EHR system provides a knowledge platform for [[CDS|clinical decision support]] and optimization of care processes.<br />
<br />
=== Discussion ===<br />
<br />
Data should seamlessly be available at the multiple times during a patient encounter when care decisions are being made. With a paper system, information is often missing or there is an overwhelming amount of information and care is compromised. Clinical decision support should be designed to guide clinicians with relevant, timely information at the appropriate point in the workflow. “What is presented, how it is presented and when it is presented are crucial to the user acceptance and improve clinical outcomes” (p. 3, <ref name="Chuang 2014”>< Chuang, I., Health Information Management Systems Society (HIMSS). (October, 2014). Transforming the EHR into a knowledge platform to ensure improved health and healthcare. Retrieved from http://s3.amazonaws.com/rdcms-himss/files/production/public/FileDownloads/2014-10-17%20Netsmart%20White%20Paper%20Transforming%20the%20EHR.pdf<br />
</ref><br />
).<br />
<br />
Knowledge flow is the integration of decision support into care processes in a way that creates a process of continuous improvement. Elements to be considered to ensure proper knowledge flow:<br />
<br />
*Data availability (at the point of care)<br />
*Data accessibility downstream (who will need the data later?)<br />
*Data understandability and usability<br />
*Data transformation (data to information to knowledge)<br />
*Data deliverability (passive, proactive or combination)<br />
<br />
=== Conclusion ===<br />
<br />
Clinical decision support should be an integral part of EHR design and should allow for varying levels of presentation. Ideally, the level of clinical decision support (CDS) should be configurable by the user. The lowest level is a straightforward, passive reminder. Informative CDS gives additional description. Educational CDS might offer extensive evidence-based enabling the provider to take specific actions for optimization of patient care. <br />
<br />
=== Comments ===<br />
<br />
The author makes an interesting point that designers should shift thinking towards knowledge flow instead of the tradition focus on data flow. So, given a specific end goal (measureable outcome improvement) design in what key decisions must be incorporated into the workflow to ensure the goal is met. These knowledge-oriented flows are designed into the system beforehand with the possibility of 'learning' through constant quality improvement.<br />
<br />
== Second Review ==<br />
<br />
Add next review here.<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category:Reviews]]<br />
[[Category:EHR]]<br />
[[Category:CDS]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category:Methodologies and Frameworks]]<br />
[[Category: Meaningful Use]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Computerized_Physician_Order_Entry_with_Clinical_Decision_Support_in_Long-Term_Care_Facilities:_Costs_and_Benefits_to_StakeholdersComputerized Physician Order Entry with Clinical Decision Support in Long-Term Care Facilities: Costs and Benefits to Stakeholders2015-11-30T04:51:51Z<p>Nneka.nwaeme: </p>
<hr />
<div>== Introduction ==<br />
The computerized physician order entry [[CPOE | (CPOE)]] with clinical decision support [[CDS | (CDS)]] is being encouraged as a way to improve quality of care and patient safety. This article analyzes the costs and benefits of the CPOE in long-term care (LTC) facilities such as nursing home. LTC facilities are the setting of care for a growing number of our nation's older population. CPOE is beneficial in minimizing adverse drug events which are an increasingly recognized safety and quality concern in this population.<br />
<br />
== Stakeholders in LTC settings ==<br />
In an LTC setting physician, pharmacies and laboratory usually have a fee-for-service arrangement, whereas nurses are employed by LTC <ref> Subramanian, S., Hoover, S., Gilman, B., Field, T. S., Mutter, R., & Gurwitz, J. H. (2007). Computerized physician order entry with clinical decision support in long‐term care facilities: costs and benefits to stakeholders. Journal of the American Geriatrics Society, 55(9), 1451-1457</ref>. Medicate provides around half of LTC payments.<br />
=== Identifying potential cost and benefits ===<br />
*Acquisition cost: purchasing of new system, hardware, software, installing, integration with existing laboratory and pharmacy systems and initial staff training.<br />
*Annual cost: maintenance and upgrades of the system, licensing fees and ongoing staff training. <br />
<br />
== Factors affecting the cost and benefits ==<br />
===Factors related to Health Information Technology Software===<br />
*The cost and the potential benefits both raise as the functionality of the system goes higher.<br />
*There can be unintended consequences of using the system, which if not addressed can affect the quality of care and patient safety negatively.<br />
*The physician might not be inclined to learn and use various COPE system at various LTC as they decrease their productivity.<br />
===Factors related to the Healthcare System===<br />
*The cost and benefit will also be affected by the level of connectivity and communication amongst various systems like pharmacy, laboratories, other LTC facilities and users. <br />
*In LTC setting many stakeholder are off-site, the greater the interoperability of the various systems the better probability of benefits form the COPE system.<br />
*The early adopter of COPE with CDS system seems to have higher cost, thus suitable incentives needs provided in order to increase early adoption of these systems.<br />
===LTC Facility Characteristics===<br />
*The upfront cost for adopting COPE system will be greatly affected by the level of existing information technology in the LTC facility.<br />
*The size of the LTC facilities and organization will also play an important role in deterring cost-benefit ration.<br />
*The cost and potential benefits of the system to an LTC will be directly affected by initial purchase price of the system.<br />
*The type of resident population in an LTC.<br />
<br />
Although, all the stakeholder bear increase in cost to some degree, the cost and the benefits of are not shared equally. It appears that the LTC facility and physician are bearing the cost in term of finance and time devoted for installing and using the system. Whereas, the residents and the payers’ benefit from the system. To increase the adoption of HIT systems the payers’ will need to share their benefits and incentives will needed to offset the increase cost burden on LTC facility and physician.<br />
<br />
==Related Articles==<br />
[[Barriers Over Time to Full Implementation of Health Information Exchange in the United States]]<br />
<br />
[[Effects of computerized clinical decision support systems on practitioner performance and patient outcomes]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: CDS]]<br />
[[Category: CDSS]]<br />
[[Category: CPOE]]<br />
[[Category:HI5313-2015-FALL]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Computerized_Physician_Order_Entry_with_Clinical_Decision_Support_in_Long-Term_Care_Facilities:_Costs_and_Benefits_to_StakeholdersComputerized Physician Order Entry with Clinical Decision Support in Long-Term Care Facilities: Costs and Benefits to Stakeholders2015-11-30T04:51:18Z<p>Nneka.nwaeme: </p>
<hr />
<div>== Introduction ==<br />
The computerized physician order entry [[CPOE | (CPOE)]] with clinical decision support [[CDS | (CDS)]] is being encouraged as a way to improve quality of care and patient safety. This article analyzes the costs and benefits of the CPOE in long-term care (LTC) facilities such as nursing home. LTC facilities are the setting of care for a growing number of our nation's older population. CPOE is beneficial in minimizing adverse drug events which are an increasingly recognized safety and quality concern in this population.<br />
<br />
== Stakeholders in LTC settings ==<br />
In an LTC setting physician, pharmacies and laboratory usually have a fee-for-service arrangement, whereas nurses are employed by LTC <ref> Subramanian, S., Hoover, S., Gilman, B., Field, T. S., Mutter, R., & Gurwitz, J. H. (2007). Computerized physician order entry with clinical decision support in long‐term care facilities: costs and benefits to stakeholders. Journal of the American Geriatrics Society, 55(9), 1451-1457</ref>. Medicate provides around half of LTC payments.<br />
=== Identifying potential cost and benefits ===<br />
*Acquisition cost: purchasing of new system, hardware, software, installing, integration with existing laboratory and pharmacy systems and initial staff training.<br />
*Annual cost: maintenance and upgrades of the system, licensing fees and ongoing staff training. <br />
<br />
== Factors affecting the cost and benefits ==<br />
===Factors related to Health Information Technology Software===<br />
*The cost and the potential benefits both raise as the functionality of the system goes higher.<br />
*There can be unintended consequences of using the system, which if not addressed can affect the quality of care and patient safety negatively.<br />
*The physician might not be inclined to learn and use various COPE system at various LTC as they decrease their productivity.<br />
===Factors related to the Healthcare System===<br />
*The cost and benefit will also be affected by the level of connectivity and communication amongst various systems like pharmacy, laboratories, other LTC facilities and users. <br />
*In LTC setting many stakeholder are off-site, the greater the interoperability of the various systems the better probability of benefits form the COPE system.<br />
*The early adopter of COPE with CDS system seems to have higher cost, thus suitable incentives needs provided in order to increase early adoption of these systems.<br />
===LTC Facility Characteristics===<br />
*The upfront cost for adopting COPE system will be greatly affected by the level of existing information technology in the LTC facility.<br />
*The size of the LTC facilities and organization will also play an important role in deterring cost-benefit ration.<br />
*The cost and potential benefits of the system to an LTC will be directly affected by initial purchase price of the system.<br />
*The type of resident population in an LTC.<br />
<br />
Although, all the stakeholder bear increase in cost to some degree, the cost and the benefits of are not shared equally. It appears that the LTC facility and physician are bearing the cost in term of finance and time devoted for installing and using the system. Whereas, the residents and the payers’ benefit from the system. To increase the adoption of HIT systems the payers’ will need to share their benefits and incentives will needed to offset the increase cost burden on LTC facility and physician.<br />
<br />
==Related Articles==<br />
[[Barriers Over Time to Full Implementation of Health Information Exchange in the United States]]<br />
[[Effects of computerized clinical decision support systems on practitioner performance and patient outcomes]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: CDS]]<br />
[[Category: CDSS]]<br />
[[Category: CPOE]]<br />
[[Category:HI5313-2015-FALL]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Syndromic_Surveillance_Using_Ambulatory_Electronic_Health_RecordsSyndromic Surveillance Using Ambulatory Electronic Health Records2015-11-30T04:27:22Z<p>Nneka.nwaeme: </p>
<hr />
<div>===Introduction===<br />
Syndromic Surveillance is the monitoring of specific syndromes, such as the flu or gastrointestinal disease. Data sources for syndromic surveillance include emergency department chief complaints, and more recently with their wide implementation, [[EMR|Electronic Health Records (EHRs)]]. This study focuses specifically on the syndromes of flu and gastrointestinal disease.<br />
<br />
===Methods===<br />
Two syndromic surveillance systems were used in the Institute for Family Health (IFH), which is comprised of 13 community health centers in Manhattan and Bronx, NY. One of the systems consisted of searching for structured (specifically [[ICD]]-10 coded) data in health records, while the second one analyzed narrative text. The data obtained was compared to the NYC Dept. of Health and Mental Hygiene Emergency Dept. Chief Complaint Surveillance System and also to NYC WHO data for positive influenza isolate results. WHO data was not available for gastrointestinal disease.<br />
<br />
===Results===<br />
The authors found that a syndromic surveillance system using structured EHR data had a cross-correlation of 0.89 to Emergency Department (ED) data for influenza like illness (ILI) and 0.81 for gastrointestinal infectious disease (GID). When the system used structured data, there was a 0.84 cross-correlation to ED data for ILI, but 0.47 to ED data for GID. <br />
<br />
===Discussion===<br />
The study design had many flaws:<br />
<br />
-EHR variables had to be mapped to terms in the syndrome definitions, requiring engineers experienced with the IFH Epic system.<br />
<br />
-The degree of system tailoring did not allow for conclusions about structured v. narrative data. Goals and context should be considered when choosing one over the other.<br />
<br />
-Only E.D. chief complaint data was available to compare for GID results from the surveillance systems.<br />
<br />
===My Comments===<br />
There were some parts of this article that were difficult to follow, examples of the ED chief complaint data would have greatly aided in understanding the comparison between the syndromic surveillance systems data and ED data. However, I agree that the authors did succeed in showing that EHRs are a good data source for syndromic surveillance.<ref name="Hripcsak et al. 2009"> Hripcsak, G., Soulakis, N.O., Li, L., Morrison, F.P., Lai, A.M., Friedman, C., ..., Mostashari, F.(2009). Syndromic Surveillance Using Ambulatory Electronic Health Records. Journal of American Medical Informatics Association, 16. DOI 10.1197/jamia.M2922</ref><br />
<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Public Health]]<br />
[[Category: EHR]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/MitigationMitigation2015-11-25T23:49:23Z<p>Nneka.nwaeme: </p>
<hr />
<div>According to the Federal Emergency Management Agency (FEMA), mitigation is "the effort to reduce loss of life and property by lessening the impact of disasters.” It involves taking immediate action to reduce adverse human and financial consequences. These actions include analyzing risk, reducing risk and insuring against risk. <ref name=”FEMA”> https://www.fema.gov/what-mitigation </ref><br />
<br />
Under HIPAA’s privacy rule at 45 C.F.R. § 164.530(f), a covered entity must mitigate, to the extent possible, any harmful effects that are known to the covered entity and that result from a use or disclosure of personal health information (PHI) in violation of its own privacy policies and procedures or the Privacy Rule by the covered entity or its business associates. Therefore, mitigation is required, where feasible, for known harmful effects caused by the covered entity’s own workforce misusing or disclosing electronic PHI or by such misuse or wrongful disclosure by a health information organization that is a business associate of the covered entity. While appropriate steps to mitigate harm caused by an improper use or disclosure in an electronic environment will vary based on a sum of the circumstances, some mitigation steps to consider include: <ref name=: “The HIPAA Privacy Rule and Electronic Health Information Exchange in a Networked Environment”> http://www.hhs.gov/ocr/privacy/hipaa/understanding/special/healthit/accountability.pdf </ref><br />
<br />
*Identifying the cause of the violation and amending privacy policies and technical procedures, as necessary, to guarantee it does not happen again; <br />
*Contacting the network administrator, as well as other potentially affected entities, to try to salvage or otherwise limit the further distribution of improperly disclosed information; <br />
*Notifying the individual of the violation if the individual needs to take self-protective measures to ameliorate or avoid the harm, as in the case of potential identify theft.<br />
<br />
==References==<br />
<references/></div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/MitigationMitigation2015-11-25T23:48:26Z<p>Nneka.nwaeme: </p>
<hr />
<div>According to the Federal Emergency Management Agency (FEMA),” mitigation is the effort to reduce loss of life and property by lessening the impact of disasters.” It involves taking immediate action to reduce adverse human and financial consequences. These actions include analyzing risk, reducing risk and insuring against risk. <ref name=”FEMA”> https://www.fema.gov/what-mitigation </ref><br />
<br />
Under HIPAA’s privacy rule at 45 C.F.R. § 164.530(f), a covered entity must mitigate, to the extent possible, any harmful effects that are known to the covered entity and that result from a use or disclosure of personal health information (PHI) in violation of its own privacy policies and procedures or the Privacy Rule by the covered entity or its business associates. Therefore, mitigation is required, where feasible, for known harmful effects caused by the covered entity’s own workforce misusing or disclosing electronic PHI or by such misuse or wrongful disclosure by a health information organization that is a business associate of the covered entity. While appropriate steps to mitigate harm caused by an improper use or disclosure in an electronic environment will vary based on a sum of the circumstances, some mitigation steps to consider include: <ref name=: “The HIPAA Privacy Rule and Electronic Health Information Exchange in a Networked Environment”> http://www.hhs.gov/ocr/privacy/hipaa/understanding/special/healthit/accountability.pdf </ref><br />
<br />
*Identifying the cause of the violation and amending privacy policies and technical procedures, as necessary, to guarantee it does not happen again; <br />
*Contacting the network administrator, as well as other potentially affected entities, to try to salvage or otherwise limit the further distribution of improperly disclosed information; <br />
*Notifying the individual of the violation if the individual needs to take self-protective measures to ameliorate or avoid the harm, as in the case of potential identify theft.<br />
<br />
==References==<br />
<references/></div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/MitigationMitigation2015-11-25T23:47:38Z<p>Nneka.nwaeme: Created page with "According to the Federal Emergency Management Agency (FEMA),” mitigation is the effort to reduce loss of life and property by lessening the impact of disasters.” It involv..."</p>
<hr />
<div>According to the Federal Emergency Management Agency (FEMA),” mitigation is the effort to reduce loss of life and property by lessening the impact of disasters.” It involves taking immediate action to reduce adverse human and financial consequences. These actions include analyzing risk, reducing risk and insuring against risk. <ref name=”FEMA”> https://www.fema.gov/what-mitigation </ref>. <br />
<br />
Under HIPAA’s privacy rule at 45 C.F.R. § 164.530(f), a covered entity must mitigate, to the extent possible, any harmful effects that are known to the covered entity and that result from a use or disclosure of personal health information (PHI) in violation of its own privacy policies and procedures or the Privacy Rule by the covered entity or its business associates. Therefore, mitigation is required, where feasible, for known harmful effects caused by the covered entity’s own workforce misusing or disclosing electronic PHI or by such misuse or wrongful disclosure by a health information organization that is a business associate of the covered entity. While appropriate steps to mitigate harm caused by an improper use or disclosure in an electronic environment will vary based on a sum of the circumstances, some mitigation steps to consider include <ref name=: “The HIPAA Privacy Rule and Electronic Health Information Exchange in a Networked Environment”> http://www.hhs.gov/ocr/privacy/hipaa/understanding/special/healthit/accountability.pdf </ref>. <br />
<br />
*Identifying the cause of the violation and amending privacy policies and technical procedures, as necessary, to guarantee it does not happen again; <br />
*Contacting the network administrator, as well as other potentially affected entities, to try to salvage or otherwise limit the further distribution of improperly disclosed information; <br />
*Notifying the individual of the violation if the individual needs to take self-protective measures to ameliorate or avoid the harm, as in the case of potential identify theft.<br />
<br />
==References==<br />
<references/></div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Terms_related_to_privacy,_confidentiality,_and_securityTerms related to privacy, confidentiality, and security2015-11-25T23:19:00Z<p>Nneka.nwaeme: </p>
<hr />
<div>== Important terms related to patient privacy, data confidentiality, and security ==<br />
<br />
* [[Acceptable Use Policy (AUP)]]<br />
* [[Access control]]<br />
* [[Access Logs]]<br />
* [[Administrative Safeguards]]<br />
* [[Anonymization of data]]<br />
* [[Antivirus program]]<br />
* [[ARRA]]<br />
* [[Attestation]]<br />
* [[Audit trail]]<br />
* [[Authentication]]<br />
* [[Authorization ]]<br />
* [[Autonomy]]<br />
* [[Audit trails]]<br />
* [[Availability]]<br />
* [[Backup]]<br />
* [[Biometrics]]<br />
* [[Black hat hacker]]<br />
* [[Blacklisting]]<br />
* [[Break Glass]]<br />
* [[Business Associates]]<br />
* [[Business continuity]]<br />
* [[Business escrow]]<br />
* [[Certificates]]<br />
* [[Co-mingled records]]<br />
* [[Confidentiality]]<br />
* [[Contingency Plan]]<br />
* [[Cookies]]<br />
* [[Covered Entities]]<br />
* [[Cryptographic Checksum]]<br />
* [[Data Breach]]<br />
* [[Data confidentiality]]<br />
* [[Data re-identification]]<br />
* [[Data security]]<br />
* [[Data integrity]]<br />
* [[Data Governance]]<br />
* [[Data Retention]]<br />
* [[Data Use Agreement]]<br />
* [[Decrypting]]<br />
* [[De-Identified Data]]<br />
* [[Digital Signature]]<br />
* [[Disaster Recovery Plan]]<br />
* [[Disclosure]]<br />
* [[Emancipated Minor]]<br />
* [[FHIR]]<br />
* [[Identifiable Health Data]]<br />
* [[Identity (SQL)]]<br />
* [[Institutional Review Board (IRB)]]<br />
* [[Electronic Signature]]<br />
* [[Encryption]]<br />
* [[FERPA]]<br />
* [[Firewall]]<br />
* [[FTP (File Transfer Protocol)]]<br />
* [[Freedom of Information Act (FIOA)]]<br />
* [[Genetic Information]]<br />
* [[Grey Hat Hacker]]<br />
* [[Hacker]]<br />
* [[Health Insurance Portability and Accountability Act (HIPAA)]]<br />
* [[Homelessness]]<br />
* [[Hot Sites]]<br />
* [[HTTPS protocol]]<br />
* [[Identification Card]]<br />
* [[In loco parentis]]<br />
* [[Information Security Officer (ISO)]]<br />
* [[Integrity]]<br />
* [[JavaScript]]<br />
* [[Limited Data Set ]]<br />
* [[Malware]]<br />
* [[Malicious Software]]<br />
* [[Marketing]]<br />
* [[Medical Record Number]]<br />
* [[Minimum Necessary]]<br />
* [[Mission Critical]]<br />
* [[Mitigation]]<br />
* [[Non-repudiation]]<br />
* [[Password]]<br />
* [[Password change policy]]<br />
* [[Patient privacy]]<br />
* [[Patient Safety and Quality Improvement Act]]<br />
* [[Personal identifiers]]<br />
* [[Personally identifiable data]]<br />
* [[Phishing]]<br />
* [[Pretty Good Privacy]]<br />
* [[Privacy]]<br />
* [[Private key]]<br />
* [[Protected Health Information (PHI)]]<br />
* [[Provider Identity Theft]]<br />
* [[Proxy access]]<br />
* [[Proxy server]]<br />
* [[Psychotherapy Notes]]<br />
* [[Public key]]<br />
* [[Remote login]]<br />
* [[Right to Privacy]]<br />
* [[Risk Assessment]]<br />
* [[Risk analysis]]<br />
* [[Risk mitigation]]<br />
* [[Role-based access]]<br />
* [[Rootkit]]<br />
* [[Secure FTP]]<br />
* [[Secure Multipurpose Internet Mail Extensions]]<br />
* [[Secure Sockets Layer]]<br />
* [[Security audit]]<br />
* [[Security flaw]]<br />
* [[Security Policy]]<br />
* [[Security Rule]]<br />
* [[Security Standards]]<br />
* [[Spoofing]]<br />
* [[Spyware]]<br />
* [[System Assessment]]<br />
* [[System Security]]<br />
* [[SSH]]<br />
* [[TCP/IP]]<br />
* [[Temporal Key Integrity Protocol (TKIP)]]<br />
* [[Trojan horse]]<br />
* [[Transport Layer Security]]<br />
* [[Treatment, Payment and Operation (TPO)]]<br />
* [[Two-factor authentication]]<br />
* [[Unemancipated Minor]]<br />
* [[Virtual Private Network]]<br />
* [[Virus]]<br />
* [[White hat hacker]]<br />
* [[Wi-Fi Protected Access (WPA)]]<br />
* [[Wired Equivalent Privacy (WEP)]]<br />
* [[Worm]]<br />
<br />
<br />
[[Category: Definition]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Cognitive_and_usability_engineering_methods_for_the_evaluation_of_clinical_information_systemsCognitive and usability engineering methods for the evaluation of clinical information systems2015-11-25T11:24:49Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “Cognitive and usability engineering methods for the evaluation of clinical information systems” by Andre W. Kushniruk. <ref name= “Kushniruk 2003”> Cognitive and usability engineering methods for the evaluation of clinical information systems. Journal of Biomedical Informatics 37 (2004) 56-76 http://dx.doi.org/10.1016/j.jbi.2004.01.003 </ref><br />
<br />
<br />
==Introduction==<br />
Healthcare policy and decision makers are requesting solid evidence to justify the need for investing in health information systems. This demand necessitates adequate [[evaluation]] of information systems. In recent times, a wide range of approaches and methodologies for evaluation of information systems have developed ranging from controlled clinical trials to use of questionnaires and interviews with users. <br />
<br />
[[Usability]] is generally defined as “the capacity of a system to allow users to carry out their tasks safely, effectively, efficiently, and enjoyably.” Currently, usability engineering has emerged to address the need for systems evaluation and improvement. <br />
<br />
In this paper, the authors focused on methods of evaluation that have arisen cognitive and usability engineering that can be applied during a system’s development in order to provide feedback and direction for its ultimate design. <br />
<br />
==Need for New Evaluation Methodologies for Health Systems==<br />
The authors indicated that traditional, outcome-based evaluations include quantitative assessments of the economic impact, accuracy, safety, and reliability of completed information systems. It was noted that such studies have pre-defined outcome measures which are measured after the system has been deployed in some setting. However, the problem is that if the outcome such studies are negative, then there is often no way of knowing the reason for this outcome. <br />
<br />
In addition, the authors suggested that one of the most widely used methods for evaluating health information systems continues to be the use of questionnaires, either as the primary method of data collection in system evaluations, or alternatively as one of several types of data collected in multi-method evaluations of information systems. Questionnaires used for assessing the results of a system may not reveal how a technology under study fits into the context of actual system use. They are also limited in providing detailed information about the process of use of a system in performing complex tasks. Questionnaires contain items that are pre-determined by the investigators and consequently are of limited value in identifying new or emergent issues in the use of a system that the investigators have not previously thought of. Furthermore, by asking subjects to rate a system, using a questionnaire typically presented sometime after system’s use, the results are subject to problems of the subject’s recall of their experience in using the system.<br />
<br />
Despite these prospective limitations, these approaches are still comprehensively used forms of data collection for gathering system requirements upon which systems are developed and also for evaluating the effects of newly introduced health information systems.<br />
<br />
==Cognitive Task Analysis in Biomedical Informatics==<br />
Cognitive task analysis (CTA) is an emerging approach to the evaluation of medical systems that represents an assimilation of work from the field of systems engineering and cognitive research in medicine. It is concerned with characterizing the decision making and reasoning skills and information processing needs of subjects as they perform activities and perform tasks involving the processing of complex information. CTA has also been applied in the design of systems in order to create a better understanding of human information needs in development of systems<br />
In health care, these tasks might consist of activities such as a physician entering data into an information system or a nurse accessing on-line guidelines to help in management of a patient.<br />
<br />
==Usability Engineering in Biomedical Informatics==<br />
In health care settings, a number of researchers have begun to apply methods adapted from usability engineering towards the design and evaluation of clinical information systems. This has included work in developing portable and low cost methods for analyzing use of health care information systems, along with a focus on developing principled qualitative and quantitative methods for analyzing usability data resulting from such study.<br />
<br />
There are a number of specific methods associated with usability engineering and leading among these is usability testing. Usability testing refers to “the evaluation of information systems that involves testing of participants (i.e., subjects) who are representative of the target user population, as they perform representative tasks using an information technology (e.g., physicians using a CPR system to record patient data) in a particular clinical context”. During the evaluation, all user–computer interactions are typically recorded (e.g., video recordings made of all computer screens or user activities and actions). Types of evaluations using this approach can vary from formal, controlled laboratory studies of users, to less formal approaches.<br />
<br />
Information from usability testing regarding user problems, preferences, suggestions and work practices is applied not only towards the end of system development (to ensure that systems are effective, efficient and sufficiently enjoyable to achieve acceptance), but throughout the development cycle to ensure that the development process leads to operative end products.<br />
The typical system development life cycle is characterized by the following phases, which define major activities involved in developing software: (1) project planning, (2) analysis (involving gathering of system requirements), (3) design of the system, (4) implementation, and (5) system support/maintenance.<br />
<br />
There are a number of types of usability tests, based on when in the development life cycle they are applied: (1) Exploratory Tests—conducted early in the system development cycle to test preliminary design concepts using prototypes or storyboards. (2) Testing of prototypes used during requirements gathering. (3) Assessment Tests—conducted early or midway through the development cycle to provide iterative feedback into evolving design of prototypes or systems. (4) Validation Tests—conducted to ensure that completed software products are acceptable regarding predefined acceptance measures. (5) Comparison Tests—conducted at any stage to compare design alternatives or possible solutions.<br />
<br />
==Usability testing approaches to the evaluation of clinical information system==<br />
* Phase 1: Identification of evaluation objectives<br />
* Phase 2: Sample selection and study design<br />
* Phase 3: Selection of representative experimental tasks and contexts.<br />
* Phase 4: Selection of background questionnaires<br />
*Phase 5: Selection of the evaluation environment<br />
*Phase 6: Data collection video recording and recording of thought processes<br />
*Phase 7: Analysis of the process data<br />
*Phase 8: Interpretation of findings<br />
*Phase 9: Iterative input into design<br />
<br />
==Heuristic Evaluation and Usability Heuristics==<br />
Heuristic evaluation is a usability inspection method in which the system is evaluated on the basis of well-tested design principles such as visibility of system status, user control and freedom, consistency and standards, flexibility, and efficiency of use. This methodology was developed by Jakob Nielsen. There are several stages to carrying out a heuristic evaluation:<br />
* Heuristic 1: Visibility of system status<br />
* Heuristic 2: Match the system to the real World.<br />
* Heuristic 3: User control and freedom.<br />
* Heuristic 4: Consistency and standards.<br />
* Heuristic 5: Error prevention<br />
* Heuristic 6: Minimize memory load—support recognition rather than recall<br />
* Heuristic 7: Flexibility and efficiency of use.<br />
* Heuristic 8: Aesthetic and minimalist design.<br />
* Heuristic 9: Help users recognize, diagnose and recover from errors.<br />
* Heuristic 10: Help and documentation.<br />
<br />
==Usability inspection approaches to the evaluation of clinical information systems==<br />
Several types of inspection methods have appeared in the literature:<br />
<br />
*Heuristic evaluation: involves having usability specialists judge the user interface and system functionality as to whether they conform to established principles of usability and good design. Heuristic evaluation basically involves the estimation of the usability of a system by a user interface expert who systematically examines a system or interface using a set of heuristics. <br />
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*Guideline reviews: can be considered to be a hybrid between heuristic evaluation and standard software inspection, where the interface or system being evaluated is checked for conformance with a comprehensive set of usability guidelines. <br />
<br />
*Pluralistic Walkthroughs: involve conducting review meetings where users, developers and analysts step through specific scenarios together and discuss usability issues that they feel might arise<br />
*Consistency Inspections: refer to an evaluation of a system in terms of how consistent it is with other related designs (or other systems belonging to a similar family of products). <br />
<br />
*Standard Inspections: involve an expert on system standards inspecting the interface with regard to compliance with some specified usability or system standards. <br />
<br />
*The Cognitive Walkthrough: is a method which applies principles from the study of cognitive psychology to simulate the cognitive processes and user actions needed to carry out specific tasks using a computer system<br />
<br />
The authors found the heuristic evaluation and the cognitive walkthrough to be most useful for adaptation in order to evaluate health information systems.<br />
<br />
==Advances in Usability Evaluations in Biomedical Informatics==<br />
In recent years a number of trends have occurred in the refinement and application of the methodological approaches described in this paper. These include advances in the following areas: (a) application and extension of the approaches to the distance analysis of the use of systems over the World Wide Web, (b) automation of some of the key components in the analysis of data, (c) extension to evaluation of mobile applications, and (d) advances in conducting evaluations in naturalistic or simulated environments.<br />
<br />
==Conclusion==<br />
In summary the authors argued that conventional methods for evaluating health information systems have limitations and that they could benefit by supplementing them with newer types of evaluation emerging from cognitive science and usability engineering. It was noted that a challenge for future work on evaluation of health information systems lies in the integration of data collected from multiple evaluation methods.<br />
<br />
==References==<br />
<references/><br />
<br />
==Comments==<br />
This was a particularly extensive article that detailed crucial information about methods of evaluating health information systems. Researchers, hospitals and policy makers would find this article useful, especially in assessing the necessity and value of health information systems.<br />
<br />
==Related Articles==<br />
[[Complementary methods of system usability evaluation: surveys and observations during software design and development cycles]]<br />
<br />
<br />
[[Category: Usability]]<br />
[[Category: CIS]]<br />
[[Category: Evaluation]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Cognitive_and_usability_engineering_methods_for_the_evaluation_of_clinical_information_systemsCognitive and usability engineering methods for the evaluation of clinical information systems2015-11-24T22:23:29Z<p>Nneka.nwaeme: Created page with "This is a systematic review of the article entitled “Cognitive and usability engineering methods for the evaluation of clinical information systems” by Andre W. Kushniruk...."</p>
<hr />
<div>This is a systematic review of the article entitled “Cognitive and usability engineering methods for the evaluation of clinical information systems” by Andre W. Kushniruk. <ref name= “Kushniruk 2003”> Cognitive and usability engineering methods for the evaluation of clinical information systems. [Journal of Biomedical Informatics 37 (2004) 56-76] http://dx.doi.org/10.1016/j.jbi.2004.01.003 </ref></div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Implementation_of_an_evidence-based_order_set_to_impact_initial_antibiotic_time_intervals_in_adult_febrile_neutropeniaImplementation of an evidence-based order set to impact initial antibiotic time intervals in adult febrile neutropenia2015-11-19T07:43:43Z<p>Nneka.nwaeme: /* Related Articles */</p>
<hr />
<div>==Introduction==<br />
In this article we learn about complications that occur in cancer patients who undergo chemotherapy such as febrile neutropenia and how the use of standardized order sets in the [[EHR]] can help this group of "patients ensure they receive prompt treatment for infection". <ref name="order set">Implementation of an evidence-based order set to impact initial antibiotic time intervals in adult febrile neutropenia,http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/?term=Implementation+of+an+evidence-based+order+set+to+impact+initial+antibiotic+time+intervals+in+adult+febrile+neutropenia,Best, J. T., Frith, K., Anderson, F., Rapp, C. G., Rioux, L., & Ciccarello, C. (2011, November). In Oncology nursing forum (Vol. 38, No. 6, pp. 661-668)Chicago</ref> Febrile neutropenia is defined as an oncological emergency where the patient presents with fever accompanied by an absolute neutrophil level of under 500/mcl. The purpose is to avoid delays in the initiation of antibiotic treatment, delays in transferring patient to the inpatient unit, and delays in the administration of such medications. <ref name="order set"></ref><br />
<br />
==Methods/Materials==<br />
* Retrospective chart reviewed was done and patients who had cancer and febrile neutropenia were selected.<br />
* Evidence based guidelines were researched and applied. They included appropriate use of antibiotics, laboratory tests and microbiology tests.<br />
* Development of standardized order sets that "use appropriate antibiotics" including adjustment guidelines based on the disease characteristics.<br />
* Interdisciplinary team was formed to plan, develop and implement the clinical guidelines with standardized order sets <br />
* The Independent-Samples T test for equality of means was used for statistical measurement.<ref name="order set"></ref><br />
<br />
==Findings==<br />
The findings were based on the antibiotic time interval. They found a significant decrease in the time the antibiotic was ordered and the time it was administrated. The length of stay in the hospital of most patients was reduced. However, there was also several confounding factors that influence the results. These were patients who were newly diagnosed and required longer stay as well as those who developed complications.<br />
<br />
==Conclusion==<br />
The use of [[Evidence-based medicine]] is important in providing essential tools that will help the clinician provide better health care outcomes in patients<ref name="order set"></ref>. The implementation of EBM will cause a change process for those who will adopt and adapt to this effective method that is aimed at reducing delayed diagnosis and delayed treatment that can eventually lead to medical errors.<br />
<br />
==Related Articles==<br />
<br />
[[Evidence-based management of ambulatory electronic health record system implementation: an assessment of conceptual support and qualitative evidence]]<br />
<br />
[[Implementation of electronic chemotherapy ordering: an opportunity to improve evidence-based oncology care]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category: EHR implementation project]]<br />
[[Category: Evidence Based Medicine (EBM)]]<br />
[[Category: Order Sets]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Grand_challenges_in_clinical_decision_supportGrand challenges in clinical decision support2015-11-19T07:32:19Z<p>Nneka.nwaeme: /* Related Articles */</p>
<hr />
<div>This is a perspective by Dean F. Sittig, Adam Wright, Jerome A. Osheroff, Blackford Middleton, Jonathan M. Teich, Joan S. Ash, Emily Campbell, & David W. Bates. (2008) in the Journal of Biomedical Informatics entitled “Grand challenges in clinical decision support.” <ref name="Sittig"> Sittig, et.al. (2008). Grand Challenges in Clinical Decision Support. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2660274/ . </ref><br />
<br />
== Introduction ==<br />
<br />
The paper is a viewpoint about common challenges in clinical decision support (CDS). According to the authors, an advanced level of [[clinical decision support systems]] are needed to achieve a high quality of care, such as [[interoperability]] and development of longitudinal electronic health records [[EHR]] for all patients. Despite the potential benefits, the implementation and use of CDS in routine practice is lacking. This article will list the top 10 “grand challenges” under three large categories that were identified by the authors and clinicians by utilizing an iterative and consensus-building process.<br />
<br />
== The Top 10 Clinical Decision Support Grand Challenges ==<br />
<br />
<br />
<br />
=== Improve the effectiveness of CDS interventions ===<br />
<br />
'''1.''' <u>'''Improve the human-computer interface'''</u><br />
A poor human-computer interface is one of the common reasons why clinicians override unsolicited CDS alerts. Similarly, CDS alerts without a follow-up or recommendations further interrupts the workflow, increases frustration, and subsequently forms a barrier to adoption. Therefore, clearer information displays and CDS alerts with follow-up action will reduce challenges in CDS. <br />
<br />
'''2.''' <u>'''Summarize patient-level information'''</u><br />
It is impossible for physicians to retain and recall all the patient information during a visit. A succinct summary is needed on each patient to reduce cognitive load and improve decision making. <br />
<br />
'''3.''' <u>'''Prioritize and filter recommendations to the user'''</u><br />
Automatically prioritize recommendations according to a ‘multi-attribute utility model’ by uniting patient- and provider-specific information to consider predictable mortality or morbidity reduction, patient preferences, cost of care, efficacy of the test or therapy, patient tolerability, workflow position, services covered by insurance plans, genetic and genomic concerns, clinician’s experience, and other issues.<br />
<br />
'''4.''' <u>'''Combine recommendations for patients with co-morbidities'''</u><br />
Incorporating two or more guidelines help improve care outcome in patients with comorbidities. This challenge requires new combinatorial, logical, or semantic approaches. <br />
<br />
'''5.''' <u>'''Use freetext information to drive clinical decision support'''</u><br />
A CDS with an automated text processing ability can significantly improve the quality of care as at least 50% of the patient data is documented in an unstructured format.<br />
<br />
=== Create new CDS interventions ===<br />
<br />
'''6.''' <u>'''Prioritize CDS content development and implementation'''</u><br />
One of the disadvantages of CDS is the high cost for implementation and content development. Prioritizing the content can greatly reduce the cost and enable widespread use of CDS. <br />
<br />
'''7.''' <u>'''Mine large clinical databases to create new CDS'''</u><br />
Mere incorporation of guidelines in the CDS content is not enough. A complete, highly scientific, rigorously [[Evidence-based medicine]] large clinical database is needed to improve CDS interventions.<br />
<br />
=== Disseminate existing CDS knowledge and interventions ===<br />
<br />
'''8.''' <u>'''Disseminate best practices in CDS design, development, and implementation'''</u><br />
Sharing or exchange of experiences about design, development, implementation, maintenance, and evaluation can facilitate in developing high-level CDS. <br />
<br />
'''9.''' <u>'''Create an architecture for sharing executable CDS modules and services'''</u><br />
Standardization of CDS interfaces enables its utilization more effectively.<br />
<br />
'''10.''' <u>'''Create internet-accessible clinical decision support repositories'''</u><br />
Design CDS that are easy to download, maintain, modify, install, upgrade and used on any Certification Commission for Healthcare Information Technology (CCHIT)-certified EHR product.<br />
<br />
== Conclusion ==<br />
<br />
In order to achieve the full benefits of CDS, the stakeholders have to overcome common challenges described in this paper. Improving the efficacy of current CDS interventions, creating new CDS interventions, and pooling of resources through sharing CDS related experiences are essential for improved health outcomes. <br />
<br />
== Comments ==<br />
<br />
I chose this paper because of its unique and thorough presentation. The common challenges of CDS are well addressed and a concise solution is offered to each challenge. <br />
<br />
== Related Articles ==<br />
<br />
*[[Lessons learned from implementing service-oriented clinical decision support at four sites: A qualitative study]]<br />
<br />
*[[Barriers and facilitators to the uptake of computerized clinical decision support systems in specialty hospitals: protocol for a qualitative cross-sectional study]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: CDS]]<br />
[[Category: CDSS]]<br />
[[Category: EHR]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Evaluation_of_medication_errors_via_a_computerized_physician_order_entry_system_in_an_inpatient_renal_transplant_unitEvaluation of medication errors via a computerized physician order entry system in an inpatient renal transplant unit2015-11-19T07:22:42Z<p>Nneka.nwaeme: </p>
<hr />
<div>Article Review Marfo, K., Garcia, D., Khalique, S., Berger, K., Lu, A. (2011). Evaluation of medication errors via a computerized physician order entry system in an inpatient renal transplant unit.<ref name= "Evaluation of Medication Errors">https://www.dovepress.com/evaluation-of-medication-errors-via-a-computerized-physician-order-ent-peer-reviewed-article-TRRM./</ref><br />
<br />
<br />
==Background==<br />
This article speaks about the medication errors in an inpatient renal transplant unit. Medication errors happen every day and in just about every healthcare institution. Medication errors have reduced significantly when healthcare institutions started using computerized physician order entry ([[CPOE|CPOE]]). However, in a complex unit such as renal care, even having a CPOE still has a high risk of medication errors. This is due to the complex medication regimen and specialized skills required. <br />
<br />
==Methods==<br />
There was a 10-day audit done with a 28-day follow-up period. Time periods were selected at random to review medication dispense in the CPOE. The medication errors were then documented when the even deviated from the standard in written transplant protocols. <br />
<br />
==Results==<br />
103 medication errors were found and of those 68 were identified as kidney transplant recipients. The most common medication that was associated with the errors were with immunosuprressants. From the 10-day audit, 66% of the 43 medication errors encountered. 19% and 16% covered dispensing and administration errors. The follow-up 28-day audit 57% was identified in 60 medication errors.<br />
<br />
==Conclusion==<br />
In conclusion, this article states that even with a CPOE active, there needs to be a specific system geared towards renal transplant patients, Further research is needed to assess the impact on having a CPOE system in the transplant unit.<br />
<br />
==Related Articles==<br />
[[Comprehensive Analysis of a Medication Dosing Error Related to CPOE]]<br />
<br />
[[Evaluation of Outpatient Computerized Physician Medication Order Entry Systems: A Systematic Review]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category:Reviews]]<br />
[[Category:CPOE]]<br />
[[Category:Medication Errors]]<br />
[[Category:HI5313-2015-FALL]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Implementation_Pearls_from_a_New_Guidebook_on_Improving_Medication_Use_and_Outcomes_with_Clinical_Decision_SupportImplementation Pearls from a New Guidebook on Improving Medication Use and Outcomes with Clinical Decision Support2015-11-19T00:13:02Z<p>Nneka.nwaeme: </p>
<hr />
<div>===Introduction===<br />
<br />
Studies reveal that applying [[CDS|CDS]] to medication-related challenges has shown successful results. Despite these successes, studies also have shown that even with advanced clinical systems and CDS, [[ADE|Adverse drug event]] can persist. To help ensure that CDS is successfully deployed many diverse organizational and individual stakeholders came together to synthesize CDS implementation best practices. A central premise of the recommended implementation approach is that successfully applying CDS to address targeted objectives requires that the CDS five rights must be addressed. The CDS intervention must deliver the right information, to the right person, in the right format, through the right channel, at the right point in workflow.<ref name="2009 Sirajuddin">Implementation Pearls from a New Guidebook on Improving Medication Use and Outcomes with Clinical Decision Support. Effective CDS is Essential for Addressing Healthcare Performance Improvement Imperatives. Sirajuddin AM1, Osheroff JA, Sittig DF, Chuo J, Velasco F, Collins DA. J Healthc Inf Manag. 2009 Fall;23(4):38-45. http://www.ncbi.nlm.nih.gov/pubmed/?term=Implementation+Pearls+from+a+New+Guidebook+on+Improving+Medication+Use+and+Outcomes+with+Clinical+Decision+Support%3A </ref><br />
<br />
====THE CDS FIVE RIGHTS OVERVIEW====<br />
<br />
• Right information – components include pertinent to clinician and patient at hand, Address specific information need or action at hand, Current, believable and trusted by recipient, Contain the appropriate level of detail.<br />
<br />
• Right person- The healthcare team is comprised of several key players such as physicians, nurses, pharmacists and others—each of which play different patient care roles, and has different decision support needs and opportunities. Identifying the connection between the right information and the right person is the basis of the second CDS right.<br />
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• Right CDS intervention format- it is important to consider the full portfolio of CDS intervention types in addressing specific CDS objective; such as an alert, order set or reference information to answer a clinical question.<br />
<br />
• Right channel- the communication channel through which the right information will hopefully be conveyed to the right person in the right format. For example, a clinical information system such as an electronic medical record, personal health record or a more general channel such as the internet or a mobile device.<br />
<br />
• Right point in the workflow- understanding of the workflow that a planned CDS intervention is designed to support is essential for intervention success. For example, at time of decision, action, need.<br />
<br />
====OTHER PEARLS FOR SUCCESSFUL CDS PROGRAMS====<br />
<br />
Overview of selected best practices for setting up a successful CDS program to address medication use, and other priority targets.<br />
<br />
• Establish a solid foundation for CDS efforts- Creating a shared understanding of basic concepts and approaches and of improvement and intervention opportunities and strategies. Another key foundation component is quantifying baseline performance levels around the targets to be addressed with CDS and producing some commitment to targeted benefits.<br />
<br />
• Deploy CDS for maximum acceptance-<br />
When end-users are involved in developing CDS interventions that become tools that can help them achieve their important goals (or are at least aligned in some way with them), the resulting interventions will be much more likely to be used and useful.<br />
<br />
• Devote adequate attention and resources to measurement-<br />
Many CDS programs do not devote adequate attention to measuring intervention effects—both intended and unintended – and thereby miss important improvement opportunities. Issues to track for CDS interventions include structural measures, processes and outcome measures.<br />
<br />
• Manage knowledge asset and decisions proactively-<br />
As an organization’s CDS program evolves over time, there is an increasing volume of knowledge assets such as order sets, rules, referential content, as well as decisions related to those assets, which are essential to proactively manage.<br />
<br />
===Conclusion===<br />
<br />
The CDS five rights approach provides a framework for successfully linking CDS intervention features with specific objectives the interventions are intended to address. This also includes establishing a foundation for CDS, deploying CDS for maximum acceptance, measuring CDS effects and proactively managing CDS knowledge assets and decisions.<br />
<br />
===References===<br />
<references/><br />
<br />
[[Category : Reviews]]<br />
[[Category : CDS]]<br />
[[Category: Adverse drug event]]<br />
[[Category: HI5313-2015-FALL]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Evaluation_of_causes_and_frequency_of_medication_errors_during_information_technology_downtimeEvaluation of causes and frequency of medication errors during information technology downtime2015-11-19T00:07:37Z<p>Nneka.nwaeme: </p>
<hr />
<div>== Introduction ==<br />
Clinical information systems have shown significant benefits in terms of quality of care and patient safety. However, it is not completely devoid of errors. Although [[computerized physician order entry]] (CPOE) has shown a significant decrease in medication errors, there are still instances where fatal errors have resulted using these systems. Medication errors in CIS can be due to various factors, such as entering data or instruction in the wrong field and disruption in flow of information due to system malfunctions and/or system downtime. In this article authors specifically report medication errors that are caused due to [[IT Downtime – A Cultural Shift|system downtime]].<br />
<ref name ="Koppel 2005"> Koppel, 2005. IT Role of computerized physician order entry systems in facilitating medication errors. http://www.ncbi.nlm.nih.gov/pubmed/15755942</ref><br />
<br />
== Methods ==<br />
The authors conduct a survey of 78 hospitals in Ohio, in which survey participants were directly involved in supporting and maintaining information based technologies. <br />
== Results ==<br />
Survey response rate was 41%. Two main reasons for uncontrollable downtime was computer virus in eight hospitals and natural disaster in 4 hospitals. A total of 39 medication errors were reported in a 12-month period and in CPOE and the worst of which required intervention to prevent patient harm. It is worth noting that all the hospitals had some kind of a back up system to provide support in downtime.<br />
== Conclusions ==<br />
Despite having back up plans in place to ensure smooth operation during downtime, medication errors were caused and in some cases it extended the length of hospital stay and in others needed intervention to prevent patient harm. This suggests that further efforts are needed to address downtime workflow. <br />
== Comments ==<br />
The uniqueness of this article is that it studies medication errors specifically during information technology downtime. It is noteworthy that all the hospitals had downtime plans and some back up system to provide patient care during downtime despite which errors occurred. This further emphasizes that having a back up plan is not sufficient. It is essential to test the back up plans and continuously modify it to obtain better results.<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: EHR]]<br />
[[Category: CPOE]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category:Medication Errors]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Understanding_keys_to_successful_implementation_of_electronic_decision_support_in_rural_hospitals:_analysis_of_a_pilot_study_for_antimicrobial_prescribingUnderstanding keys to successful implementation of electronic decision support in rural hospitals: analysis of a pilot study for antimicrobial prescribing2015-11-18T23:59:26Z<p>Nneka.nwaeme: </p>
<hr />
<div><br />
<br />
== Background ==<br />
[[CDS|Clinical Decision Support]] (CDS) provides clinicians the opportunity to have [[evidence-based medicine]] information at the right moment to enhance medical decision making. Despite its potential in reducing medical errors, improve clinical outcomes and increase healthcare quality, CDSS are still not widely used by clinicians. Factors such as complexity, lack of adequate training and support as well as increase cost, are constantly cited by clinicians as barriers preventing CDSS implementation.<br />
<br />
== Introduction ==<br />
Antimicrobial agents constitute a major portion of hospital pharmacy expenditures, accounting for 20% to 50% of the total budget. Rural hospitals are specially in great disadvantage regarding CDSS implementations because of factors such as insufficient resources, limited clinical information systems as well as limited access to infectious disease physicians providing advice or assistance. Therefore, internet-based decision support tools offer to clinicians an option to provide adequate antimicrobial prescribing advice to those individuals in rural communities lacking access to other complex decision support systems.<ref name="Stevenson et al">Understanding Keys to Successful Implementation of Electronic Decision Support in Rural Hospitals: Analysis of a Pilot Study for Antimicrobial Prescribing http://ca3cx5qj7w.search.serialssolutions.com/OpenURL_local?sid=Entrez:PubMed&id=pmid:16280394</ref><br />
<br />
== Study design ==<br />
Pretest/Post-test<br />
<br />
== Methods ==<br />
A therapeutic clinical decision support system for the management of infectious diseases called "Antibiotic Assistant" was used during this study. Antibiotic assistant provides patient-specific antimicrobial recommendations based on factors such as co-morbid conditions, recommendations based on demographics characteristics, vitals signs and results of microbiology studies. Five rural hospitals from southwest Idaho were selected; selection was based on various factors such as involvement in a local rural health network as well as geographic proximity to the research team. Participants accessed an internet based platform during the study; this platform was developed and supported by the Centers for Medicare & Medicaid Services. Each prescribing clinician at the different hospitals was asked to introduce patients' data into the Internet-based decision support tool (antibiotic assistant) and to implement the recommendations when making therapeutic and dosing decisions. An antimicrobial management team (AMT) consisting of a nurse, pharmacist and infection control staff was formed in each hospital to prevent the under-utilization the decision support tool as well as to ensure that a clinician was aware of the CDSS recommendations during the first 24 hours of a patient's hospital admission.<br />
<br />
== Results ==<br />
First, physicians were reluctant to use the internet-based decision support tool because of perceived length of time required for log in and overall system run time. It was also reported that computers were not constantly located in patient care areas. Despite the formation of the Antimicrobial management teams to obtain CDSS recommendations in a timely fashion and provide that information to clinicians, transfer of information failed to occur in 3 of the participating hospitals. In another participating hospital, clinicians decided not to follow the CDSS recommendations most of the times; this was attributed to the mechanism of information communication. In the first hospital (A), clinicians were notified of recommendations in 32% of the cases in which the prescribed drugs differed from those recommended; it was noticed that antimicrobial orders were modified in 50% of those cases. In contrast, physicians in the second hospital (B) were notified of the recommendations by the AMT in 71% of the cases; However, they followed the recommendations in only 24%. It was noticed that the local hospital environment and community discouraged that member of the AMT (Non-physicians) questioned clinicians' decisions. <br />
<br />
== Conclusion ==<br />
Cultural factors represented a barrier for the implementation of electronic decision support tools. Although cultural factors have recently surged as possible barriers for the uptake of electronic decision support tools, this is not the first study to show that we may be overlooking cultural factors when developing and implementing ITS. <br />
<br />
== References ==<br />
<references/> <br />
<br />
[[Category: Reviews]]<br />
[[Category: Technologies]]<br />
[[Category: CDSS]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: Evidence Based Medicine (EBM)]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Clinical_Decision_Support_Systems_for_the_Practice_of_Evidence-based_MedicineClinical Decision Support Systems for the Practice of Evidence-based Medicine2015-11-11T23:52:56Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “Clinical Decision Support Systems for the Practice of Evidence-based Medicine” by Ida Sim, MD, PhD <ref name= “Sim 2001”> Clinical Decision Support Systems for the Practice of Evidence-based Medicine. [J Am Med Inform Assoc. 2001 Nov-Dec; 8(6): 527–534] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC130063/ </ref><br />
<br />
<br />
==Background==<br />
[[Clinical decision support systems]] (CDSSs) are recognized for their capacity to reduce medical errors and increase quality of care. Similarly, evidence based medicine has also been noted as a means to improve clinical outcomes. Therefore the use of CDSSs to encourage evidence-based medicine is highly regarded as a means to secure improvement in health care. <br />
The authors of this article sought to specify the actions of the Evidence and Decision Support Track of the Spring 200 AMIA Symposium. The goal of the symposium was to evaluate the challenges facing the implementation of CDSS-facilitated evidence based medicine. In particular, the authors provided research and policy recommendations increasing the development and adoption of CDSSs for evidence-based medicine.<br />
<br />
==Definitions==<br />
*Evidence-based medicine: involves the management of indiviudal patients through clinical expertise in collaboration with the judicious use the current best evidence from clinical care research and scientific literature.<br />
*Clinical decision support system (CDSS): software designed to directly facilitate clinical decision-making.<br />
*Evidence-adaptive CDSS: these systems contain a clinical knowledge base that is derived from and reflects the most current evidence from research literature and practice-based sources.<br />
<br />
==Process==<br />
The following process was used to derive five areas of focus that are critical to the goal of increased adoption of CDSSs for evidence based medicine:<br />
<br />
*Collection of literature-based and practice-based research evidence into machine-interpretable formats appropriate for CDSS use.<br />
*Institution of a technical and methodological basis for applying research evidence to individual patients at point of care.<br />
*Assessment of clinical effects and costs of CDSSs.<br />
*Promotion of the effectual implementation and use of CDSSs that have demonstrated positive clinical performance or outcomes.<br />
*Establishment of policies that offer incentives for implementing CDSSs to improve health care quality.<br />
<br />
==The Role of Evidence in Evidence-adaptive CDSSs==<br />
The authors noted that Clinical Decision Support Systems can only be as useful as the strength of the underlying evidence base. Also, this evidence must be current, accessible and machine interpretable. <br />
The following are the types of evidence that are encourage evidence-adaptive CDSSs:<br />
*Literature-based Evidence<br />
*Practice-based Evidence<br />
*Patient-directed Evidence<br />
<br />
==Recommendations==<br />
The following steps were recommended for researchers, developers and implementers in order to increase adoption of evidence-adaptive CDSSs:<br />
<br />
===Recommendations for Clinical and Informatics Researchers===<br />
*Conduct better quality clinical research on the effectiveness of clinical interventions especially in primary care settings.<br />
*Develop better methods for interpreting results from a wide range of study designs.<br />
*Develop machine-interpretable repositories of guidelines that can be linked to current evidence-based repositories.<br />
*Build standard interfaces among repositories to allow evidence to be linked automatically to among other systems.<br />
*Develop an informatics infrastructure for practice-based research networks to collect practice-based evidence.<br />
<br />
===Recommendations for Researchers and Developers===<br />
* Continue development of a comprehensive, expressive clinical vocabulary that can scale from administrative to clinical decision support needs.<br />
* Explore and develop automatic methods for updating CDSS knowledge bases to reflect the current state and quality of the literature-based evidence.<br />
* Develop models of decision making that can simultaneously accommodate the beliefs, perspectives, and values of multiple decision makers, including those of physicians and patients.<br />
<br />
===Recommendations for Current CDSS Developers===<br />
* Adopt and use standard vocabularies and standards for knowledge representation as they become available.<br />
* Integrate CDSSs with electronic medical records and other relevant systems using appropriate interoperability standards such as HL7.<br />
<br />
===Recommendation for Policy Makers, Manufacturers and Organizations===<br />
* Fund development and demonstration of inter-organizational sharing of evidence-based knowledge and its application in diverse CDSSs.<br />
<br />
==Evaluation of Clinical Decision Support Systems==<br />
Even with the promise of CDSSs for improving care, evaluations have shown that CDSSs have only a modest capacity to improve transitional measures such as guideline adherence and drug dosing accuracy. In other words, the effect of CDSSs on clinical outcomes remains uncertain. <br />
<br />
===Recommendations for Evaluators===<br />
* Evaluate CDSSs using an approach that identifies both benefits and unanticipated problems related to CDSS implementation and use.<br />
* Conduct more CDSS evaluations in actual practice settings.<br />
* Establish partnerships between academic groups and community practices to conduct evaluations.<br />
<br />
===Recommendations for CDSS Implementers===<br />
* Establish a CDSS implementation team composed of clinicians, information technologists, managers, and evaluators to work together to adapt and implement the CDSS.<br />
* Plan for work flow re-engineering, organizational, and social issues and incorporate change management techniques into system development and implementation.<br />
<br />
===Recommendations for Policy Makers===<br />
* Develop financial and reimbursement policies that provide incentives for health-care providers to implement and use CDSSs.<br />
* Develop and implement financial and reimbursement policies that reward the acquisition of measurable quality goals, as might be achieved by CDSSs.<br />
* Promote organization and leadership across the health care and clinical research sectors to leverage informatics promotion and development efforts by government, industry, AMIA, and others.<br />
<br />
==Conclusions==<br />
The combination of CDSS technology with evidence-based medicine brings together two potentially powerful methods for improving health care quality. Literature-based and practice-based evidence must be collected into computable knowledge bases, technical and methodological foundations for evidence-adaptive CDSSs must be developed and maintained, and public policies must be established to finance the implementation of electronic medical records and CDSSs and to reward health care quality improvement through the use of evidence-based CDSSs.<br />
<br />
==References==<br />
<references/><br />
<br />
==Related Articles==<br />
[[Evidence-based management of ambulatory electronic health record system implementation: an assessment of conceptual support and qualitative evidence]]<br />
<br />
==Comments==<br />
This article provides an excellent overview of the use of clinical decision support in collaboration with evidence-based medicine to provide excellent quality of care. Policy makers, organizations, researchers and innovators alike would find this study useful, especially in acquiring foundational knowledge of this topic.<br />
<br />
[[Category: CDS]]<br />
<br />
[[Category: Evidence Based Medicine (EBM)]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Clinical_Decision_Support_Systems_for_the_Practice_of_Evidence-based_MedicineClinical Decision Support Systems for the Practice of Evidence-based Medicine2015-11-11T23:49:57Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “Clinical Decision Support Systems for the Practice of Evidence-based Medicine” by Ida Sim, MD, PhD <ref name= “Sim 2001”> Clinical Decision Support Systems for the Practice of Evidence-based Medicine. [J Am Med Inform Assoc. 2001 Nov-Dec; 8(6): 527–534] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC130063/ </ref><br />
<br />
<br />
==Background==<br />
Clinical decision support systems (CDSSs) are recognized for their capacity to reduce medical errors and increase quality of care. Similarly, evidence based medicine has also been noted as a means to improve clinical outcomes. Therefore the use of CDSSs to encourage evidence-based medicine is highly regarded as a means to secure improvement in health care. <br />
The authors of this article sought to specify the actions of the Evidence and Decision Support Track of the Spring 200 AMIA Symposium. The goal of the symposium was to evaluate the challenges facing the implementation of CDSS-facilitated evidence based medicine. In particular, the authors provided research and policy recommendations increasing the development and adoption of CDSSs for evidence-based medicine.<br />
<br />
==Definitions==<br />
*Evidence-based medicine: involves the management of indiviudal patients through clinical expertise in collaboration with the judicious use the current best evidence from clinical care research and scientific literature.<br />
*Clinical decision support system (CDSS): software designed to directly facilitate clinical decision-making.<br />
*Evidence-adaptive CDSS: these systems contain a clinical knowledge base that is derived from and reflects the most current evidence from research literature and practice-based sources.<br />
<br />
==Process==<br />
The following process was used to derive five areas of focus that are critical to the goal of increased adoption of CDSSs for evidence based medicine:<br />
<br />
*Collection of literature-based and practice-based research evidence into machine-interpretable formats appropriate for CDSS use.<br />
*Institution of a technical and methodological basis for applying research evidence to individual patients at point of care.<br />
*Assessment of clinical effects and costs of CDSSs.<br />
*Promotion of the effectual implementation and use of CDSSs that have demonstrated positive clinical performance or outcomes.<br />
*Establishment of policies that offer incentives for implementing CDSSs to improve health care quality.<br />
<br />
==The Role of Evidence in Evidence-adaptive CDSSs==<br />
The authors noted that Clinical Decision Support Systems can only be as useful as the strength of the underlying evidence base. Also, this evidence must be current, accessible and machine interpretable. <br />
The following are the types of evidence that are encourage evidence-adaptive CDSSs:<br />
*Literature-based Evidence<br />
*Practice-based Evidence<br />
*Patient-directed Evidence<br />
<br />
==Recommendations==<br />
The following steps were recommended for researchers, developers and implementers in order to increase adoption of evidence-adaptive CDSSs:<br />
<br />
===Recommendations for Clinical and Informatics Researchers===<br />
*Conduct better quality clinical research on the effectiveness of clinical interventions especially in primary care settings.<br />
*Develop better methods for interpreting results from a wide range of study designs.<br />
*Develop machine-interpretable repositories of guidelines that can be linked to current evidence-based repositories.<br />
*Build standard interfaces among repositories to allow evidence to be linked automatically to among other systems.<br />
*Develop an informatics infrastructure for practice-based research networks to collect practice-based evidence.<br />
<br />
===Recommendations for Researchers and Developers===<br />
* Continue development of a comprehensive, expressive clinical vocabulary that can scale from administrative to clinical decision support needs.<br />
* Explore and develop automatic methods for updating CDSS knowledge bases to reflect the current state and quality of the literature-based evidence.<br />
* Develop models of decision making that can simultaneously accommodate the beliefs, perspectives, and values of multiple decision makers, including those of physicians and patients.<br />
<br />
===Recommendations for Current CDSS Developers===<br />
* Adopt and use standard vocabularies and standards for knowledge representation as they become available.<br />
* Integrate CDSSs with electronic medical records and other relevant systems using appropriate interoperability standards such as HL7.<br />
<br />
===Recommendation for Policy Makers, Manufacturers and Organizations===<br />
* Fund development and demonstration of inter-organizational sharing of evidence-based knowledge and its application in diverse CDSSs.<br />
<br />
==Evaluation of Clinical Decision Support Systems==<br />
Even with the promise of CDSSs for improving care, evaluations have shown that CDSSs have only a modest capacity to improve transitional measures such as guideline adherence and drug dosing accuracy. In other words, the effect of CDSSs on clinical outcomes remains uncertain. <br />
<br />
===Recommendations for Evaluators===<br />
* Evaluate CDSSs using an approach that identifies both benefits and unanticipated problems related to CDSS implementation and use.<br />
* Conduct more CDSS evaluations in actual practice settings.<br />
* Establish partnerships between academic groups and community practices to conduct evaluations.<br />
<br />
===Recommendations for CDSS Implementers===<br />
* Establish a CDSS implementation team composed of clinicians, information technologists, managers, and evaluators to work together to adapt and implement the CDSS.<br />
* Plan for work flow re-engineering, organizational, and social issues and incorporate change management techniques into system development and implementation.<br />
<br />
===Recommendations for Policy Makers===<br />
* Develop financial and reimbursement policies that provide incentives for health-care providers to implement and use CDSSs.<br />
* Develop and implement financial and reimbursement policies that reward the acquisition of measurable quality goals, as might be achieved by CDSSs.<br />
* Promote organization and leadership across the health care and clinical research sectors to leverage informatics promotion and development efforts by government, industry, AMIA, and others.<br />
<br />
==Conclusions==<br />
The combination of CDSS technology with evidence-based medicine brings together two potentially powerful methods for improving health care quality. Literature-based and practice-based evidence must be collected into computable knowledge bases, technical and methodological foundations for evidence-adaptive CDSSs must be developed and maintained, and public policies must be established to finance the implementation of electronic medical records and CDSSs and to reward health care quality improvement through the use of evidence-based CDSSs.<br />
<br />
==References==<br />
<references/><br />
<br />
==Related Articles==<br />
[[Evidence-based management of ambulatory electronic health record system implementation: an assessment of conceptual support and qualitative evidence]]<br />
<br />
==Comments==<br />
This article provides an excellent overview of the use of clinical decision support in collaboration with evidence-based medicine to provide excellent quality of care. Policy makers, organizations, researchers and innovators alike would find this study useful, especially in acquiring foundational knowledge of this topic.<br />
<br />
[[Category: CDS]]<br />
<br />
[[Category: Evidence Based Medicine (EBM)]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Clinical_Decision_Support_Systems_for_the_Practice_of_Evidence-based_MedicineClinical Decision Support Systems for the Practice of Evidence-based Medicine2015-11-11T22:13:56Z<p>Nneka.nwaeme: Created page with "This is a systematic review of the article entitled “Clinical Decision Support Systems for the Practice of Evidence-based Medicine” by Ida Sim, MD, PhD <ref name= “Sim 2..."</p>
<hr />
<div>This is a systematic review of the article entitled “Clinical Decision Support Systems for the Practice of Evidence-based Medicine” by Ida Sim, MD, PhD <ref name= “Sim 2001”> Clinical Decision Support Systems for the Practice of Evidence-based Medicine. [J Am Med Inform Assoc. 2001 Nov-Dec; 8(6): 527–534] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC130063/ </ref></div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Comprehensive_Analysis_of_a_Medication_Dosing_Error_Related_to_CPOEComprehensive Analysis of a Medication Dosing Error Related to CPOE2015-11-04T22:10:10Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “Comprehensive Analysis of a Medication Dosing Error Related to CPOE” by Jan Horsky <ref name= “Horskey 2005”> Comprehensive Analysis of a Medication Dosing Error Related to CPOE. [J Am Med Inform Association 2005;12:377–382. DOI 10.1197/jamia.M1740] http://dx.doi.org/10.1197/jamia.M1740 </ref>.<br />
<br />
==Introduction==<br />
New drugs that manage or relieve previously untreated diseases have come about due to new innovations in pharmacology research. These advancements in drug therapy have led to increased incidence of [[Adverse drug event]] (ADEs) due to avoidable causes such as prescribing errors. <br />
[[Computerized physician order entry]] (CPOE) systems are known to drastically minimize the incidence of ADEs by confirming legibility of orders and integrating clinical decision support ([[CDS]]) such as checking for allergies.<br />
<br />
However, the progressive effect of CPOE on prescribing safety can be compromised by the advent of new forms of errors. These errors are related to the intricacy of the human-computer interaction and may be a consequence of poor user training or inadequate understanding of data handling by a CPOE application. <br />
<br />
Understanding the acuity of users at crucial stages of an incident that occurred during the use of CPOE is extremely beneficial to the process of characterizing cognitively based errors.<br />
In this article, the case of a serious medication error that occurred at a large academic medical institution is described and a synopsis of how the error was analyzed is discussed. The authors hope that characterization of the entire process of the error will provide key insight and recommendations for improving CPOE systems and clinical ordering procedures.<br />
<br />
==Case Description==<br />
*An elderly man was admitted to a medical intensive care unit with septic shock and respiratory failure then transferred to a pulmonary service unit.<br />
*On a Saturday morning, Provider A diagnosed the patient as hypokalemic after observing a low serum KCL in the setting of renal insufficiency.<br />
*Provider A decided to replete the patient’s KCL by providing 40 mEq of KCL via an IV route over a period of 4 hours as indicated by institutional guidelines.<br />
*After the order was entered, Provider A realized that the patient already had an IV fluid line and subsequently decided to provide KCL as an additive to the currently running IV fluid.<br />
*Provider A then entered a new order for infusion of 100 mEq of KCL in 1 liter of D5W solution at a rate of 75ml/hr. <br />
*The order for 40 mEq of KCL through IV was supposed to be discontinued at this point but Provider A mistakenly discontinued a similar order entered by another clinician from two days earlier.<br />
*Provider A then received notification from the pharmacy department that the dose of 100 mEq of KCL in 1 liter of D5W was higher than the maximum allowed for the facility. <br />
*Provider A discontinued the order for 100 mEq of KCL in 1 liter of D5W and wrote a new order for 80mq/L KClr.<br />
*This new order for 80mq/L KClr was supposed to deliver 1L of fluid however the order did not specify stop time or maximum volume of fluid to be delivered.<br />
*As a result, the fluid continued to be administered for 36 hours. Unfortunately, Provider A unintentionally caused the patient to receive a total of 256 mEq KCL over 36 hours.<br />
*On Sunday morning, there was a change in coverage. Provider A asked Provider B to check the patient’s KCl level.<br />
*Provider B reviewed the patient’s most recent serum KCl which was taken on Saturday morning (before the infusion of potassium). The value was 3.1 mEq/ L which indicated that the patient was hypokalemic. Provider B did not realize that the lab result was indicative of the patient’s potassium status prior to unnecessary KCl repletion.<br />
*Provider B then ordered 60mEq KCl by injection to be given even while the previous potassium drip was still running.<br />
*Order entry logs revealed that another dose of 40mEq KCl IV injection was also ordered by Provider B but no clear evidence from sources indicate that it was actually given.<br />
*Therefore, the patient received a total volume of 316 mEq KCl over 42 hours.<br />
*On Monday, when the patient’s potassium levels were checked, the patient was found to be dangerously hypokalemic with a serum potassium level of 7.8 mEq/L.<br />
*Once the errors were discovered, immediate measures were taken and the patient was treated.<br />
<br />
==Methods and Examples==<br />
The case was reviewed by the hospital Significant Event Committee and experts in cognitive evaluation of information systems. The mission was to identify possible cognitive errors in the chain actions that led to the [[medication error]] and make suggestions to change system interface design and user training in order to eliminate the chance of a similar event. Three significant methods were used in order to create a reconstruction of events that took place.<br />
===Analysis of Order Entry Logs===<br />
All medication orders for the patient over the three days that the incident took place were evaluated. From this analysis, it was discovered that Provider A interacted with the order entry system on three occasions within a 2-hour period. Provider B interacted with the system on three separate occasions, manipulating four orders within the span of an hour. Inappropriate use of CPOE application was also uncovered, such as the use of free-text comment field to limit total fluid volume to 1 liter.<br />
===Visual and Cognitive Evaluation of Ordering Screens===<br />
Unfortunately, the data captured by computer entry logs did not have any information regarding what values were visible on the screen at the time the orders were being filled. Six orders were identified as being potentially erroneous but it was uncertain what the users’ motives were for activating and discontinuing them. Other inconsistencies in visual layout, screen control behavior, and ordering clarity were examined.<br />
===Semi-structured Interviews with Clinicians===<br />
The purpose of these interviews was to integrate the collected data with personal observations and to discover how clinicians interpreted information available to them while using the order entry system. Also, any verbal exchanges with the patient and an explanation for the changes in the order were examined. <br />
<br />
==Results and Discussion==<br />
It was found that this medication error occurred as a result of several factors:<br />
*Misconceptions about the relation between intravenous volume and time duration<br />
*Sub-optimal display of IV bolus injection and medicated fluid drip orders<br />
*Misconception of latest and “dated” laboratory results<br />
*Lack of certain automated checking functions in the order entry system<br />
*Inadequate training of safe and efficient ordering practices<br />
<br />
===Specific Recommendations for System and Ordering Procedure Changes===<br />
The hospital’s Medication Safety and Informatics Committee made the following recommendations for changes:<br />
*Screens for ordering continuous IV fluid drips and drips of limited volume need to be clearly distinct so that the ordering of each is unambiguous.<br />
*Screens that list active medication orders also should list IV drip orders.<br />
*Laboratory results review screen needs to clearly visually indicate when the most recent results are not from the current day.<br />
*Add an alert that would inform users, ordering potassium (drip or bolus) when the patient already has another active order for potassium.<br />
*Add an alert informing users ordering potassium when there has not been a serum potassium value recorded in the past 12 hours or the most recent potassium value is greater than 4.0. This would reduce the likelihood of ordering potassium when the patient is hyperkalemic.<br />
*Make other minor changes to increase the consistency of ordering screen behavior.<br />
*Training for the order entry application should not be limited to procedural knowledge but should emphasize conceptual understanding and safe entry strategies.<br />
<br />
==Conclusion==<br />
The basis of this medical error was as a result of failures in interaction among human and system agents. The classes of errors that we described are likely to occur in similar systems at other institutions. Sophisticated information systems require comprehensive analyses of human errors for design changes that accentuate clarity of communicated information and employ useful safeguards against patient injury.<br />
<br />
==Comments==<br />
This article is very useful for understanding medical errors based on user cognition while using order entry systems. Extensive research needs to be done in order to enhance visual display, cognition-friendly functions and decision support in health information technology systems.<br />
<br />
==References==<br />
<references/><br />
<br />
==Related Articles==<br />
[[Medication errors: prevention using information technology systems]]<br />
<br />
[[Category: CPOE]]<br />
[[Category: Medication Errors]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Comprehensive_Analysis_of_a_Medication_Dosing_Error_Related_to_CPOEComprehensive Analysis of a Medication Dosing Error Related to CPOE2015-11-04T22:09:12Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “Comprehensive Analysis of a Medication Dosing Error Related to CPOE” by Jan Horsky <ref name= “Horskey 2005”> Comprehensive Analysis of a Medication Dosing Error Related to CPOE. [J Am Med Inform Association 2005;12:377–382. DOI 10.1197/jamia.M1740] http://dx.doi.org/10.1197/jamia.M1740 </ref>.<br />
<br />
==Introduction==<br />
New drugs that manage or relieve previously untreated diseases have come about due to new innovations in pharmacology research. These advancements in drug therapy have led to increased incidence of [[Adverse drug event]] (ADEs) due to avoidable causes such as prescribing errors. <br />
[[Computerized physician order entry]] (CPOE) systems are known to drastically minimize the incidence of ADEs by confirming legibility of orders and integrating clinical decision support ([[CDS]]) such as checking for allergies.<br />
<br />
However, the progressive effect of CPOE on prescribing safety can be compromised by the advent of new forms of errors. These errors are related to the intricacy of the human-computer interaction and may be a consequence of poor user training or inadequate understanding of data handling by a CPOE application. <br />
<br />
Understanding the acuity of users at crucial stages of an incident that occurred during the use of CPOE is extremely beneficial to the process of characterizing cognitively based errors.<br />
In this article, the case of a serious medication error that occurred at a large academic medical institution is described and a synopsis of how the error was analyzed is discussed. The authors hope that characterization of the entire process of the error will provide key insight and recommendations for improving CPOE systems and clinical ordering procedures.<br />
<br />
==Case Description==<br />
*An elderly man was admitted to a medical intensive care unit with septic shock and respiratory failure then transferred to a pulmonary service unit.<br />
*On a Saturday morning, Provider A diagnosed the patient as hypokalemic after observing a low serum KCL in the setting of renal insufficiency.<br />
*Provider A decided to replete the patient’s KCL by providing 40 mEq of KCL via an IV route over a period of 4 hours as indicated by institutional guidelines.<br />
*After the order was entered, Provider A realized that the patient already had an IV fluid line and subsequently decided to provide KCL as an additive to the currently running IV fluid.<br />
*Provider A then entered a new order for infusion of 100 mEq of KCL in 1 liter of D5W solution at a rate of 75ml/hr. <br />
*The order for 40 mEq of KCL through IV was supposed to be discontinued at this point but Provider A mistakenly discontinued a similar order entered by another clinician from two days earlier.<br />
*Provider A then received notification from the pharmacy department that the dose of 100 mEq of KCL in 1 liter of D5W was higher than the maximum allowed for the facility. <br />
*Provider A discontinued the order for 100 mEq of KCL in 1 liter of D5W and wrote a new order for 80mq/L KClr.<br />
*This new order for 80mq/L KClr was supposed to deliver 1L of fluid however the order did not specify stop time or maximum volume of fluid to be delivered.<br />
*As a result, the fluid continued to be administered for 36 hours. Unfortunately, Provider A unintentionally caused the patient to receive a total of 256 mEq KCL over 36 hours.<br />
*On Sunday morning, there was a change in coverage. Provider A asked Provider B to check the patient’s KCl level.<br />
*Provider B reviewed the patient’s most recent serum KCl which was taken on Saturday morning (before the infusion of potassium). The value was 3.1 mEq/ L which indicated that the patient was hypokalemic. Provider B did not realize that the lab result was indicative of the patient’s potassium status prior to unnecessary KCl repletion.<br />
*Provider B then ordered 60mEq KCl by injection to be given even while the previous potassium drip was still running.<br />
*Order entry logs revealed that another dose of 40mEq KCl IV injection was also ordered by Provider B but no clear evidence from sources indicate that it was actually given.<br />
*Therefore, the patient received a total volume of 316 mEq KCl over 42 hours.<br />
*On Monday, when the patient’s potassium levels were checked, the patient was found to be dangerously hypokalemic with a serum potassium level of 7.8 mEq/L.<br />
*Once the errors were discovered, immediate measures were taken and the patient was treated.<br />
<br />
<br />
==Methods and Examples==<br />
The case was reviewed by the hospital Significant Event Committee and experts in cognitive evaluation of information systems. The mission was to identify possible cognitive errors in the chain actions that led to the [[medication error]] and make suggestions to change system interface design and user training in order to eliminate the chance of a similar event. Three significant methods were used in order to create a reconstruction of events that took place.<br />
===Analysis of Order Entry Logs===<br />
All medication orders for the patient over the three days that the incident took place were evaluated. From this analysis, it was discovered that Provider A interacted with the order entry system on three occasions within a 2-hour period. Provider B interacted with the system on three separate occasions, manipulating four orders within the span of an hour. Inappropriate use of CPOE application was also uncovered, such as the use of free-text comment field to limit total fluid volume to 1 liter.<br />
===Visual and Cognitive Evaluation of Ordering Screens===<br />
Unfortunately, the data captured by computer entry logs did not have any information regarding what values were visible on the screen at the time the orders were being filled. Six orders were identified as being potentially erroneous but it was uncertain what the users’ motives were for activating and discontinuing them. Other inconsistencies in visual layout, screen control behavior, and ordering clarity were examined.<br />
===Semi-structured Interviews with Clinicians===<br />
The purpose of these interviews was to integrate the collected data with personal observations and to discover how clinicians interpreted information available to them while using the order entry system. Also, any verbal exchanges with the patient and an explanation for the changes in the order were examined. <br />
<br />
==Results and Discussion==<br />
It was found that this medication error occurred as a result of several factors:<br />
*Misconceptions about the relation between intravenous volume and time duration<br />
*Sub-optimal display of IV bolus injection and medicated fluid drip orders<br />
*Misconception of latest and “dated” laboratory results<br />
*Lack of certain automated checking functions in the order entry system<br />
*Inadequate training of safe and efficient ordering practices<br />
<br />
===Specific Recommendations for System and Ordering Procedure Changes===<br />
The hospital’s Medication Safety and Informatics Committee made the following recommendations for changes:<br />
*Screens for ordering continuous IV fluid drips and drips of limited volume need to be clearly distinct so that the ordering of each is unambiguous.<br />
*Screens that list active medication orders also should list IV drip orders.<br />
*Laboratory results review screen needs to clearly visually indicate when the most recent results are not from the current day.<br />
*Add an alert that would inform users, ordering potassium (drip or bolus) when the patient already has another active order for potassium.<br />
*Add an alert informing users ordering potassium when there has not been a serum potassium value recorded in the past 12 hours or the most recent potassium value is greater than 4.0. This would reduce the likelihood of ordering potassium when the patient is hyperkalemic.<br />
*Make other minor changes to increase the consistency of ordering screen behavior.<br />
*Training for the order entry application should not be limited to procedural knowledge but should emphasize conceptual understanding and safe entry strategies.<br />
<br />
==Conclusion==<br />
The basis of this medical error was as a result of failures in interaction among human and system agents. The classes of errors that we described are likely to occur in similar systems at other institutions. Sophisticated information systems require comprehensive analyses of human errors for design changes that accentuate clarity of communicated information and employ useful safeguards against patient injury.<br />
<br />
==Comments==<br />
This article is very useful for understanding medical errors based on user cognition while using order entry systems. Extensive research needs to be done in order to enhance visual display, cognition-friendly functions and decision support in health information technology systems.<br />
<br />
==References==<br />
<references/><br />
<br />
==Related Articles==<br />
[[Medication errors: prevention using information technology systems]]<br />
<br />
[[Category: CPOE]]<br />
[[Category: Medication Errors]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Comprehensive_Analysis_of_a_Medication_Dosing_Error_Related_to_CPOEComprehensive Analysis of a Medication Dosing Error Related to CPOE2015-11-04T20:58:45Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “Comprehensive Analysis of a Medication Dosing Error Related to CPOE” by Jan Horsky <ref name= “Horskey 2005”> Comprehensive Analysis of a Medication Dosing Error Related to CPOE. [J Am Med Inform Association 2005;12:377–382. DOI 10.1197/jamia.M1740] http://dx.doi.org/10.1197/jamia.M1740 </ref>.<br />
<br />
<br />
==Introdution==<br />
New drugs that manage or relieve previously untreated diseases have come about due to new innovations in pharmacology research. These advancements in drug therapy have led to increased incidence of [[Adverse drug event]] (ADEs) due to avoidable causes such as prescribing errors. <br />
[[Computerized physician order entry]] (CPOE) systems are known to drastically minimize the incidence of ADEs by confirming legibility of orders and integrating clinical decision support ([[CDS]]) such as checking for allergies.<br />
<br />
However, the progressive effect of CPOE on prescribing safety can be compromised by the advent of new forms of errors. These errors are related to the intricacy of the human-computer interaction and may be a consequence of poor user training or inadequate understanding of data handling by a CPOE application. <br />
<br />
Understanding the acuity of users at crucial stages of an incident that occurred during the use of CPOE is extremely beneficial to the process of characterizing cognitively based errors.<br />
In this article, the case of a serious medication error that occurred at a large academic medical institution is described and a synopsis of how the error was analyzed is discussed. The authors hope that characterization of the entire process of the error will provide key insight and recommendations for improving CPOE systems and clinical ordering procedures.<br />
<br />
<br />
==Case Description==<br />
*An elderly man was admitted to a medical intensive care unit with septic shock and respiratory failure then transferred to a pulmonary service unit.<br />
*On a Saturday morning, Provider A diagnosed the patient as hypokalemic after observing a low serum KCL in the setting of renal insufficiency.<br />
*Provider A decided to replete the patient’s KCL by providing 40 mEq of KCL via an IV route over a period of 4 hours as indicated by institutional guidelines.<br />
*After the order was entered, Provider A realized that the patient already had an IV fluid line and subsequently decided to provide KCL as an additive to the currently running IV fluid.<br />
*Provider A then entered a new order for infusion of 100 mEq of KCL in 1 liter of D5W solution at a rate of 75ml/hr. <br />
*The order for 40 mEq of KCL through IV was supposed to be discontinued at this point but Provider A mistakenly discontinued a similar order entered by another clinician from two days earlier.<br />
*Provider A then received notification from the pharmacy department that the dose of 100 mEq of KCL in 1 liter of D5W was higher than the maximum allowed for the facility. <br />
*Provider A discontinued the order for 100 mEq of KCL in 1 liter of D5W and wrote a new order for 80mq/L KClr.<br />
*This new order for 80mq/L KClr was supposed to deliver 1L of fluid however the order did not specify stop time or maximum volume of fluid to be delivered.<br />
*As a result, the fluid continued to be administered for 36 hours. Unfortunately, Provider A unintentionally caused the patient to receive a total of 256 mEq KCL over 36 hours.<br />
*On Sunday morning, there was a change in coverage. Provider A asked Provider B to check the patient’s KCl level.<br />
*Provider B reviewed the patient’s most recent serum KCl which was taken on Saturday morning (before the infusion of potassium). The value was 3.1 mEq/ L which indicated that the patient was hypokalemic. Provider B did not realize that the lab result was indicative of the patient’s potassium status prior to unnecessary KCl repletion.<br />
*Provider B then ordered 60mEq KCl by injection to be given even while the previous potassium drip was still running.<br />
*Order entry logs revealed that another dose of 40mEq KCl IV injection was also ordered by Provider B but no clear evidence from sources indicate that it was actually given.<br />
*Therefore, the patient received a total volume of 316 mEq KCl over 42 hours.<br />
*On Monday, when the patient’s potassium levels were checked, the patient was found to be dangerously hypokalemic with a serum potassium level of 7.8 mEq/L.<br />
*Once the errors were discovered, immediate measures were taken and the patient was treated.</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Comprehensive_Analysis_of_a_Medication_Dosing_Error_Related_to_CPOEComprehensive Analysis of a Medication Dosing Error Related to CPOE2015-10-31T09:42:59Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “Comprehensive Analysis of a Medication Dosing Error Related to CPOE” by Jan Horsky <ref name= “Horskey 2005”> Comprehensive Analysis of a Medication Dosing Error Related to CPOE. [J Am Med Inform Association 2005;12:377–382. DOI 10.1197/jamia.M1740] http://dx.doi.org/10.1197/jamia.M1740 </ref>.<br />
<br />
<br />
==Introdution==<br />
New drugs that manage or relieve previously untreated diseases have come about due to new innovations in pharmacology research. These advancements in drug therapy have led to increased incidence of [[Adverse drug event]] (ADEs) due to avoidable causes such as prescribing errors. <br />
[[Computerized physician order entry]] (CPOE) systems are known to drastically minimize the incidence of ADEs by confirming legibility of orders and integrating clinical decision support ([[CDS]]) such as checking for allergies.<br />
<br />
However, the progressive effect of CPOE on prescribing safety can be compromised by the advent of new forms of errors. These errors are related to the intricacy of the human-computer interaction and may be a consequence of poor user training or inadequate understanding of data handling by a CPOE application. <br />
<br />
Understanding the acuity of users at crucial stages of an incident that occurred during the use of CPOE is extremely beneficial to the process of characterizing cognitively based errors.<br />
In this article, the case of a serious medication error that occurred at a large academic medical institution is described and a synopsis of how the error was analyzed is discussed. The authors hope that characterization of the entire process of the error will provide key insight and recommendations for improving CPOE systems and clinical ordering procedures.</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Comprehensive_Analysis_of_a_Medication_Dosing_Error_Related_to_CPOEComprehensive Analysis of a Medication Dosing Error Related to CPOE2015-10-31T09:41:17Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “Comprehensive Analysis of a Medication Dosing Error Related to CPOE” by Jan Horsky <ref name= “Horskey 2005”> Comprehensive Analysis of a Medication Dosing Error Related to CPOE. [J Am Med Inform Association 2005;12:377–382. DOI 10.1197/jamia.M1740] http://dx.doi.org/10.1197/jamia.M1740 </ref>.<br />
<br />
<br />
==Introdution==<br />
New drugs that manage or relieve previously untreated diseases have come about due to new innovations in pharmacology research. These advancements in drug therapy have led to increased incidence of [adverse drug events] (ADEs) due to avoidable causes such as prescribing errors. <br />
[[Computerized physician order entry]] (CPOE) systems are known to drastically minimize the incidence of ADEs by confirming legibility of orders and integrating clinical decision support ([[CDS]]) such as checking for allergies.<br />
<br />
However, the progressive effect of CPOE on prescribing safety can be compromised by the advent of new forms of errors. These errors are related to the intricacy of the human-computer interaction and may be a consequence of poor user training or inadequate understanding of data handling by a CPOE application. <br />
<br />
Understanding the acuity of users at crucial stages of an incident that occurred during the use of CPOE is extremely beneficial to the process of characterizing cognitively based errors.<br />
In this article, the case of a serious medication error that occurred at a large academic medical institution is described and a synopsis of how the error was analyzed is discussed. The authors hope that characterization of the entire process of the error will provide key insight and recommendations for improving CPOE systems and clinical ordering procedures.</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Comprehensive_Analysis_of_a_Medication_Dosing_Error_Related_to_CPOEComprehensive Analysis of a Medication Dosing Error Related to CPOE2015-10-31T09:01:27Z<p>Nneka.nwaeme: Created page with "This is a systematic review of the article entitled “Comprehensive Analysis of a Medication Dosing Error Related to CPOE” by Jan Horsky <ref name= “Horskey 2005”> Comp..."</p>
<hr />
<div>This is a systematic review of the article entitled “Comprehensive Analysis of a Medication Dosing Error Related to CPOE” by Jan Horsky <ref name= “Horskey 2005”> Comprehensive Analysis of a Medication Dosing Error Related to CPOE. [J Am Med Inform Association 2005;12:377–382. DOI 10.1197/jamia.M1740] http://dx.doi.org/10.1197/jamia.M1740 </ref>.</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Electronic_health_record_-_based_triggers_to_detect_potential_delays_in_cancer_diagnosisElectronic health record - based triggers to detect potential delays in cancer diagnosis2015-10-28T01:13:56Z<p>Nneka.nwaeme: /* Related Articles */</p>
<hr />
<div><ref>Murphy, D., Laximisan, A., Reis, B., Thomas, E., Esquivel, A., Forjuoh, S., . . . Singh, H. (n.d.).Electronic health record-based triggers to detect potential delays in cancer diagnosis. BMJ Quality and Safety, 23(1). http://qualitysafety.bmj.com/content/23/1/8.short</ref><br />
<br />
<br />
This article focuses on finding triggers to prevent to the delay in the diagnosing of diseases.<br />
<br />
== Introduction ==<br />
The early detection of cancer is something that can be very beneficial to treating cancer. The purpose of this article is to develop and evaluate trigger algorithms to electronically flag medical records of patients. The cancers that were focused on were colorectal cancer and prostate cancer.<br />
<br />
== Methods ==<br />
<br />
Retrospective data was mined from two integrated health systems with comprehensive electronic health records. Using the data mining algorithm, patient records with specific demographics were searched: abnormal prostate-specific antigen, positive fecal occult blood test, iron-deficiency anemia, and hematochezia.<br />
<br />
== Results ==<br />
<br />
Over 290,000 patients were reviewed using the algorithm between January and December 2009. Overall, over 1564 triggers were found. The study also discovered that there were many triggers that were not found during check-ups were discovered using the algorithm.<br />
<br />
== Conclusion ==<br />
<br />
Using algorithms like this, cancer triggers can be discovered and, in turn, can help diagnose cancers earlier for treatment. Some limitations of the study include the range of the study. The study was only conducted at two sites. The same methods were used at both sites during this study and they may not be able to be applied at other sites. The study also failed to discover why these triggers were being missed in the first place. Many said the reason was the difficulty getting the appropriate appointments with specialists and patients failing to show up for appointments.<br />
<br />
== Related Articles ==<br />
Murphy, D. R., Wu, L., Thomas, E. J., Forjuoh, S. N., Meyer, A. N., & Singh, H. (2015). Electronic trigger-based intervention to reduce delays in diagnostic evaluation for cancer: a cluster randomized controlled trial. Journal of Clinical Oncology, JCO-2015. http://jco.ascopubs.org/content/early/2015/08/21/JCO.2015.61.1301.long<br />
<br />
[[Electronic medical records and quality of cancer care]]<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category:Reviews]]<br />
[[Category:EHR]]<br />
[[Category:CDS]]<br />
[[Category:HI5313-2015-FALL]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Implementation_of_multiple-domain_covering_computerized_decision_support_systems_in_primary_care:_a_focus_group_study_on_perceived_barriersImplementation of multiple-domain covering computerized decision support systems in primary care: a focus group study on perceived barriers2015-10-28T00:03:56Z<p>Nneka.nwaeme: /* Related Articles */</p>
<hr />
<div>== Introduction ==<br />
[[CDS|Clinical decision support systems (CDSS)]] are becoming widespread in use in different healthcare settings. However, confirmation that shows the actual usage of the systems is still hard to come by. This particular study aims to find out what kind of obstacles keep the CDSSs from being used, particularly by primary care practitioners.<br />
<br />
== Methods ==<br />
The study took place in the Netherlands. The CDSS that was involved is called NHGDoc; it “provides GPs, GP trainees and PNs evidence-based and, on the basis of structured data in the EHRS (also known as Electronic Medical Records ([[EMR]])), patient-specific advices during consultation in terms of patient data registration, drug prescription and management.”<ref name="barrier">Lugtenberg, M., Weenink, J.-W., van der Weijden, T., Westert, G. P., & Kool, R. B. (2015). Implementation of multiple-domain covering computerized decision support systems in primary care: a focus group study on perceived barriers. BMC Medical Informatics & Decision Making, 15(82). http://doi.org/10.1186/s12911-015-0205-z</ref> The study design used was qualitative that used three focus groups.<ref name="barrier"></ref> The focus group involved a discussion about “perceived barriers” that the members could name.<ref name="barrier"></ref><br />
<br />
== Results ==<br />
From the barriers mentioned, three different types emerged: knowledge-related barriers, barriers related to the evaluation of the features of the CDSS (source and content, format/lay out, and functionality), and external barriers interacting with the CDSS (patient-related and environmental factors).<ref name="barrier"></ref> Within the knowledge-related barriers, the main concern was due to not understanding features of the CDSS. Within the evaluation of the features of the CDSS, three concerns were mentioned: <br />
* the source and content of the CDSS<br />
* the format or lay out of the CDSS content<br />
* the functionality of the CDSS.<ref name="barrier"></ref> <br />
<br />
Within the external barriers interacting with the CDSS, two concerns were mentioned: patient-related factors and environmental factors.<ref name="barrier"></ref><br />
<br />
== Discussion ==<br />
The presence of a multitude of barriers highlights a key component to low usage of CDSS by providers. With knowledge being a component of the barriers, good introductions to the CDSS is key. However, a key limitation of the study is the small sample size.<br />
<br />
== Comments ==<br />
The study provides a good survey into what kind of barriers are perceived by the minds of the providers. Even with the limitations of the study, the barriers found can be used as a guide to ask other providers about what barriers may be present; this also helps with future studies as well, acting as a guide for understanding and finding barriers.<br />
<br />
== Related Articles ==<br />
<br />
[[Barriers and facilitators to the uptake of computerized clinical decision support systems in specialty hospitals: protocol for a qualitative cross-sectional study]]<br />
<br />
[[What may help or hinder the implementation of computerized decision support systems (CDSSs): a focus group study with physicians.]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: CDS]]<br />
[[Category: CDSS]]<br />
[[Category: CIS]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/A_Tale_Of_Two_Large_Community_Electronic_Health_Record_Extension_ProjectsA Tale Of Two Large Community Electronic Health Record Extension Projects2015-10-20T17:53:42Z<p>Nneka.nwaeme: /* The New York Primary Care Information Project */</p>
<hr />
<div>This is a systematic review of the article entitled “A Tale of Two Large Community Electronic Health Record Extension Projects” by Farzad Mostashari <ref name= “Mostashari 2009”> A Tale of Two Large Community Electronic Health Record Extension Projects. [Health Affairs 28, no. 2 (2009): 345–356; 10.1377/hlthaff.28.2.345] http://content.healthaffairs.org/content/28/2/345.full.pdf+html </ref>. <br />
<br />
==Introduction==<br />
<br />
The nationwide adoption of [[Electronic Health Records]] (EHRs) is an essential constituent of federal and state health reform, however adoption rates are quite low. The Massachusetts eHealth Collaborative (MAeHC) and the New York City Primary Care Information Project (PCIP) exemplify efforts to overcome poor EHR implementation rates in community health centers (CHCs) and independent physician organizations. The MAeHC and PCIP projects were able achieve extensive adoption of EHRs across their network. This article provides insight into both of these large-scale projects, and offers viewpoints on complementary approaches and lessons learned.<br />
<br />
==The Massachusetts eHealth Collaborative==<br />
<br />
===Origins===<br />
MAeHC was instituted in 2004 as a non-profit organization with a mission to “facilitate ubiquitous adoption of EHRs in the commonwealth of Massachusetts”. It originates from the three-prong leadership of the American College of Physicians, the Massachusetts Medical Society and a $50 million investment from Blue Cross Blue Shield of Massachusetts.<br />
<br />
===Community-based focus===<br />
On December 6, 2004, MAeHC initiated recruitment for three pilot programs in greater Brockton, greater Newburyport, and northern Berkshire County in order to evaluate the costs, merits, drawbacks, and barriers to widespread implementation of EHRs and [[Health Information Exchange]] (HIE). <br />
<br />
===Building an organization===<br />
In order to fulfill the great expectations of statewide EHR implementation, MAeHC created the following organizational model:<br />
<br />
(1). Senior relationship managers: works with community leaders to provide cooperative approach to the local program<br />
<br />
(2). Practice consultants: facilitates workflow redesign<br />
<br />
(3). Project managers: tracks project milestones<br />
<br />
(4). Technical managers: provides technical expertise such as design and configuration of EHR systems and interfaces<br />
<br />
(5). Other essential functions such as database management, evaluation, communication, accounting and bookkeeping<br />
<br />
===Choosing vendors===<br />
MAeHC selected and deployed four vendors for their pilot project: Allscripts, GE Centricity, eClinicalWorks, and NextGen. In addition, the design and configuration of hardware was maintained by MAeHC technical experts and Concordant, an integration vendor.<br />
<br />
===Current Status===<br />
MAeHCs launched its first EHR system in March 2006. A year and a half later, MAeHC was able to bring 97 percent of its participants onto EHRs under the guidance of practice consultants. Post-implementation, MAeHC has focused on:<br />
<br />
(1). Improving adoption rates of low usage providers<br />
<br />
(2). Encouraging more physicians to use EHR functions<br />
<br />
(3). Arranging clinical documentation across providers for improved performance evaluation<br />
<br />
==The New York Primary Care Information Project==<br />
<br />
===Origins===<br />
PCIP was created in 2005 by New York City Department of Health and Mental Hygiene as an initiative to improve population health in less privileged communities through health information technology. The program has three areas of concentration:<br />
<br />
(1). Prevention through the use of information systems<br />
<br />
(2). Updates in care management and practice workflows<br />
<br />
(3). Compensation for prevention and management of chronic disease<br />
<br />
===Developing and deploying a quality-focused EHR===<br />
The city of New York made it a priority to provide EHRs in all community health centers (CHCs) by the end of 2009 because only three out of twenty-nine CHCs were using EHRs. In March 2007, the city nominated eClinical Works as their choice vendor for EHRs based on its high quality and track record with MAeHC. By October 1, 2008, PCIP had attained EHR systems for over 1,400 providers.<br />
<br />
===Supporting office redesign and quality improvement activities===<br />
PCIP provided numerous implementation services such as project management, clinical workflow analysis, interface development and vendor relations. Billing and EMR consultants were also available to troubleshoot and teach best practices.<br />
<br />
===Creating a framework for a pay-for-prevention system===<br />
Establishment of integrated EHR systems enable participants in PCIP to send summaries of their quality measures to a Quality Reporting System for the city. The data obtained from these quality and performance assessments allow PCIP to provide up to $200, 000 per physician in EHR-enabled practices for good management practices such as a well-managed cardiovascular patient. <br />
<br />
===Future Plans===<br />
PCIP intends to continue to expand prevention-oriented EHRs as well as provide patients with EHR-linked personal health records.<br />
<br />
==Contrasting Approaches==<br />
<br />
MAeHC promotes continuity of care within specified communities while PCIP embraces improvements in preventative care and chronic disease management in a population.<br />
<br />
===Outreach===<br />
MAeHC strived to intervene in three communities with an objective of attaining 100 percent EHR coverage while PCIPs target is to recruit 25-30 percent of high volume primary care providers serving the less privileged of New York City.<br />
<br />
===Practice contributions===<br />
MAeHC funds all direct costs of EHR implementation while PCIP only covers the cost of software and training. PCIP also requires a $4,000 cash contribution from each provider, which is placed in a quality improvement fund.<br />
<br />
===How many EHR Vendors?===<br />
MAeHC consents to providers choosing from among four vendors. PCIS only allows one vendor.<br />
<br />
==Lessons Learned: Why it is Harder Than it Looks==<br />
*A minority of implementations sites will consume the majority of resources.<br />
*Creating scalable solutions such as standardization of implementation processes is challenging.<br />
*Establishing electronic interfaces for [health information exchange] and [[interoperability]] is difficult.<br />
*Widespread EHR implementation may be a prerequisite for improved public health, quality of care and health system efficiency but may not be sufficient enough to achieve this ultimate goal. <br />
<br />
==Conclusion==<br />
Although the initiation and completion of an EHR implementation is difficult, it is achievable. Strong leadership and millions of dollars are needed as well as knowledgeable managers and teams. Most importantly, the collaboration of vendors and communities to discover scalable solutions is vital to widespread EHR adoption.<br />
<br />
==Comments==<br />
This article is essential reading for clinicians, hospitals, vendors and stakeholders in the health industry. An overall “big picture” about the nuts and bolts of EHR implementation in two different settings is provided. This information would prove useful to any community or health entity that desires to take on the enormous task of EHR implementation.<br />
<br />
==References==<br />
<references/><br />
<br />
==Related Articles==<br />
[[Perceived efficiency impacts following electronic health record implementation: an exploratory study of an urban community health center network]]<br />
<br />
[[Category: EHR]]<br />
[[Category:EHR implementation project]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/A_Tale_Of_Two_Large_Community_Electronic_Health_Record_Extension_ProjectsA Tale Of Two Large Community Electronic Health Record Extension Projects2015-10-20T17:50:58Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “A Tale of Two Large Community Electronic Health Record Extension Projects” by Farzad Mostashari <ref name= “Mostashari 2009”> A Tale of Two Large Community Electronic Health Record Extension Projects. [Health Affairs 28, no. 2 (2009): 345–356; 10.1377/hlthaff.28.2.345] http://content.healthaffairs.org/content/28/2/345.full.pdf+html </ref>. <br />
<br />
==Introduction==<br />
<br />
The nationwide adoption of [[Electronic Health Records]] (EHRs) is an essential constituent of federal and state health reform, however adoption rates are quite low. The Massachusetts eHealth Collaborative (MAeHC) and the New York City Primary Care Information Project (PCIP) exemplify efforts to overcome poor EHR implementation rates in community health centers (CHCs) and independent physician organizations. The MAeHC and PCIP projects were able achieve extensive adoption of EHRs across their network. This article provides insight into both of these large-scale projects, and offers viewpoints on complementary approaches and lessons learned.<br />
<br />
==The Massachusetts eHealth Collaborative==<br />
<br />
===Origins===<br />
MAeHC was instituted in 2004 as a non-profit organization with a mission to “facilitate ubiquitous adoption of EHRs in the commonwealth of Massachusetts”. It originates from the three-prong leadership of the American College of Physicians, the Massachusetts Medical Society and a $50 million investment from Blue Cross Blue Shield of Massachusetts.<br />
<br />
===Community-based focus===<br />
On December 6, 2004, MAeHC initiated recruitment for three pilot programs in greater Brockton, greater Newburyport, and northern Berkshire County in order to evaluate the costs, merits, drawbacks, and barriers to widespread implementation of EHRs and [[Health Information Exchange]] (HIE). <br />
<br />
===Building an organization===<br />
In order to fulfill the great expectations of statewide EHR implementation, MAeHC created the following organizational model:<br />
<br />
(1). Senior relationship managers: works with community leaders to provide cooperative approach to the local program<br />
<br />
(2). Practice consultants: facilitates workflow redesign<br />
<br />
(3). Project managers: tracks project milestones<br />
<br />
(4). Technical managers: provides technical expertise such as design and configuration of EHR systems and interfaces<br />
<br />
(5). Other essential functions such as database management, evaluation, communication, accounting and bookkeeping<br />
<br />
===Choosing vendors===<br />
MAeHC selected and deployed four vendors for their pilot project: Allscripts, GE Centricity, eClinicalWorks, and NextGen. In addition, the design and configuration of hardware was maintained by MAeHC technical experts and Concordant, an integration vendor.<br />
<br />
===Current Status===<br />
MAeHCs launched its first EHR system in March 2006. A year and a half later, MAeHC was able to bring 97 percent of its participants onto EHRs under the guidance of practice consultants. Post-implementation, MAeHC has focused on:<br />
<br />
(1). Improving adoption rates of low usage providers<br />
<br />
(2). Encouraging more physicians to use EHR functions<br />
<br />
(3). Arranging clinical documentation across providers for improved performance evaluation<br />
<br />
==The New York Primary Care Information Project==<br />
<br />
PCIP was created in 2005 by New York City Department of Health and Mental Hygiene as an initiative to improve population health in less privileged communities through health information technology. The program has three areas of concentration:<br />
<br />
(1). Prevention through the use of information systems<br />
<br />
(2). Updates in care management and practice workflows<br />
<br />
(3). Compensation for prevention and management of chronic disease<br />
<br />
===Developing and deploying a quality-focused EHR===<br />
The city of New York made it a priority to provide EHRs in all community health centers (CHCs) by the end of 2009 because only three out of twenty-nine CHCs were using EHRs. In March 2007, the city nominated eClinical Works as their choice vendor for EHRs based on its high quality and track record with MAeHC. By October 1, 2008, PCIP had attained EHR systems for over 1,400 providers.<br />
<br />
===Supporting office redesign and quality improvement activities===<br />
PCIP provided numerous implementation services such as project management, clinical workflow analysis, interface development and vendor relations. Billing and EMR consultants were also available to troubleshoot and teach best practices.<br />
<br />
===Creating a framework for a pay-for-prevention system===<br />
Establishment of integrated EHR systems enable participants in PCIP to send summaries of their quality measures to a Quality Reporting System for the city. The data obtained from these quality and performance assessments allow PCIP to provide up to $200, 000 per physician in EHR-enabled practices for good management practices such as a well-managed cardiovascular patient. <br />
<br />
===Future Plans===<br />
PCIP intends to continue to expand prevention-oriented EHRs as well as provide patients with EHR-linked personal health records.<br />
<br />
==Contrasting Approaches==<br />
<br />
MAeHC promotes continuity of care within specified communities while PCIP embraces improvements in preventative care and chronic disease management in a population.<br />
<br />
===Outreach===<br />
MAeHC strived to intervene in three communities with an objective of attaining 100 percent EHR coverage while PCIPs target is to recruit 25-30 percent of high volume primary care providers serving the less privileged of New York City.<br />
<br />
===Practice contributions===<br />
MAeHC funds all direct costs of EHR implementation while PCIP only covers the cost of software and training. PCIP also requires a $4,000 cash contribution from each provider, which is placed in a quality improvement fund.<br />
<br />
===How many EHR Vendors?===<br />
MAeHC consents to providers choosing from among four vendors. PCIS only allows one vendor.<br />
<br />
==Lessons Learned: Why it is Harder Than it Looks==<br />
*A minority of implementations sites will consume the majority of resources.<br />
*Creating scalable solutions such as standardization of implementation processes is challenging.<br />
*Establishing electronic interfaces for [health information exchange] and [[interoperability]] is difficult.<br />
*Widespread EHR implementation may be a prerequisite for improved public health, quality of care and health system efficiency but may not be sufficient enough to achieve this ultimate goal. <br />
<br />
==Conclusion==<br />
Although the initiation and completion of an EHR implementation is difficult, it is achievable. Strong leadership and millions of dollars are needed as well as knowledgeable managers and teams. Most importantly, the collaboration of vendors and communities to discover scalable solutions is vital to widespread EHR adoption.<br />
<br />
==Comments==<br />
This article is essential reading for clinicians, hospitals, vendors and stakeholders in the health industry. An overall “big picture” about the nuts and bolts of EHR implementation in two different settings is provided. This information would prove useful to any community or health entity that desires to take on the enormous task of EHR implementation.<br />
<br />
==References==<br />
<references/><br />
<br />
==Related Articles==<br />
[[Perceived efficiency impacts following electronic health record implementation: an exploratory study of an urban community health center network]]<br />
<br />
[[Category: EHR]]<br />
[[Category:EHR implementation project]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Identifying_Best_Practices_for_Clinical_Decision_Support_and_Knowledge_Management_in_the_FieldIdentifying Best Practices for Clinical Decision Support and Knowledge Management in the Field2015-10-20T11:08:24Z<p>Nneka.nwaeme: </p>
<hr />
<div>===Introduction===<br />
<br />
Clinical decision support (CDS) provided through computerized provider order entry is essential to enhancing health care quality. Little research about [[CDS|CDS]] has been conducted in community hospitals. Because many problems with CDS are related to behavioral, organizational, and cognitive issues, the Provider Order Entry Team (POET) based at Oregon Health & Science University in Portland is conducting two multi-site studies about these issues, one in community hospitals and the other in ambulatory settings. The purpose is to identify best practices for CDS implementation and knowledge management. <ref name="2010 Joan">Identifying Best Practices for Clinical Decision Support and Knowledge Management in the Field http://www.researchgate.net/publication/236323784_Identifying_Best_Practices_for_Clinical_Decision_Support_and_Knowledge_Management_in_the_Field</ref><br />
<br />
===Methods===<br />
<br />
A series of ethnographic studies were conducted to gather information from nine diverse organizations. Using the Rapid Assessment Process methodology, surveys, interviews, and observations were conducted by a multi-disciplinary team over a period of two years in eight different geographic regions of the U.S.A. A template organizing method for an expedited analysis of the data was first utilized, followed by a deeper and more time consuming interpretive approach. <br />
<br />
===Results===<br />
Five major categories of best practices that require careful consideration while carrying out the planning, implementation, and knowledge management processes related to CDS was identified.<br />
Best Practice 1: View CDS broadly/ define CDS broadly,<br />
Best Practice 2: Move forward with simple CDS no matter what your size,<br />
Best Practice 3: Focus on “inline” CDS. An inline CDS is that which notifies an intermediary such as a nurse or pharmacist rather than a physician,<br />
Best Practice 4: Use what is available from your vendor but plan to customize the CDS,<br />
Best Practice 5: Plan knowledge management processes early,<br />
<br />
===Conclusion===<br />
<br />
The five best practices categories represented a high level view that can be useful to all types of organizations planning to implement CPOE with CDS. All of these best practices depend on planning ahead, ideally prior to CPOE implementation. All of the best practices also involved the availability of skilled informatics specialists and clinician leaders.<br />
<br />
===Comments===<br />
<br />
This is a good study that took the best practices for CDS and knowledge management from many pioneer organizations. Lessons learned through these pioneers can provide valuable guidance so that CDS can eventually have optimal impact on health care quality in many more organizations in the future.<br />
<br />
===References===<br />
<br />
<references/><br />
<br />
[[Category : Reviews]]<br />
<br />
[[Category : CDS]]<br />
<br />
[[Category: Meaningful Use]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/A_Tale_Of_Two_Large_Community_Electronic_Health_Record_Extension_ProjectsA Tale Of Two Large Community Electronic Health Record Extension Projects2015-10-20T06:52:06Z<p>Nneka.nwaeme: Created page with "This is a systematic review of the article entitled “A Tale of Two Large Community Electronic Health Record Extension Projects” by Farzad Mostashari <ref name= “Mostasha..."</p>
<hr />
<div>This is a systematic review of the article entitled “A Tale of Two Large Community Electronic Health Record Extension Projects” by Farzad Mostashari <ref name= “Mostashari 2009”> A Tale of Two Large Community Electronic Health Record Extension Projects. [Health Affairs 28, no. 2 (2009): 345–356; 10.1377/hlthaff.28.2.345] http://content.healthaffairs.org/content/28/2/345.full.pdf+html </ref>.</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Enabling_Better_Interoperability_for_HealthCare:_Lessons_in_Developing_a_Standards_Based_Application_Programing_Interface_for_Electronic_Medical_Record_SystemsEnabling Better Interoperability for HealthCare: Lessons in Developing a Standards Based Application Programing Interface for Electronic Medical Record Systems2015-10-20T03:10:09Z<p>Nneka.nwaeme: </p>
<hr />
<div>Review: ''Enabling Better Interoperability for HealthCare: Lessons in Developing a Standards Based Application Programing Interface for Electronic Medical Record Systems''<ref>Kasthurirathne, S., Mamlin, B., Kumara, H., Grieve, G., & Biondich, P. (2015). Enabling Better Interoperability for HealthCare: Lessons in Developing a Standards Based Application Programing Interface for Electronic Medical Record Systems. ''J Med Syst Journal of Medical Systems, 39''(182). http://dx.doi.org/10.1007/s10916-015-0356-6</ref><br />
<br />
==Abstract==<br />
The authors developed an [https://en.wikipedia.org/wiki/Application_programming_interface API] to generalize interaction with [[OpenMRS]] using [[FHIR]] and "prevent ... developers from having to learn or work with a domain specific OpenMRS API". The work is intended to provide a model for retiring legacy/domain-specific APIs in favor of generalized, domain-independent APIs for EMRs implementing FHIR.<br />
<br />
==Objectives==<br />
The authors introduce FHIR in the context of the history and limitations of the [[HL7]] v2 and v3 standards, and present it as a viable alternative that can address the problems that the earlier HL7 standards are known for. They offer it as a remedy for healthcare data fragmentation. Specifically, they note that the native API of OpenMRS, an EHR "system that is widely used across resource poor settings", has a number of limitations built-in because of its design. In particular, the API is domain specific, so developers have to learn the domain to use the API; on the other hand, the API is extensible, so some "domain objects can be generic". The authors describe how FHIR can be applied as an API whereas standards such as [[Clinical Document Architecture (CDA)]] are tied to documents. FHIR can support documents but also "other resource exchange frameworks including messaging, searches and operations." Additionally, there is work being done on a "CDA on FHIR initiative" which will allow FHIR to represent CDA objects. Though discussing the benefits of FHIR the authors note that OpenMRS is not readily capable of integrating with FHIR resources; they therefore set out to develop an API that can be used to extend OpenMRS to provide [[interoperability]] and to work toward specifying a new native FHIR API for OpenMRS.<br />
<br />
==Materials and Methods==<br />
This section first discusses the background of OpenMRS, FHIR, and the current status of OpenMRS interoperability. The authors note that OpenMRS was not architected with interoperability specifically in mind, so the "ad hoc efforts that were less synchronized with long term architectural goals" resulted in a fragmented set of available add-ons for OpenMRS, many of which duplicated each other's effort.<br />
<br />
===FHIR Development Efforts===<br />
The development goal was to build software to "enable the management of OpenMRS data as FHIR resources." The software is meant to be domain independent. The authors worked to identify existing FHIR libraries that could be used in their project so that they could focus on developing the integration between OpenMRS and FHIR instead of developing an implementation of FHIR first. They selected the [http://jamesagnew.github.io/hapi-fhir/ HAPI-FHIR] library on the basis of its active development community which they judged to be most likely to be able to sustain development and support of the library in the longer term.<br />
<br />
===Architecting the FHIR Module===<br />
The authors wrote their FHIR module as an independent API and did not use the existing web services layer of OpenMRS. They discuss the technical details of the API in some depth, including the development of their own web services layer.<br />
<br />
==Results==<br />
The authors were successful in developing their API. It provides access to "FHIR Patient, Person, Location, Observation, Encounter and Allergy Intolerance resources" from OpenMRS in XML and JSON formats. They have published their software with an open-source license in the repository of extensions to OpenMRS.<br />
<br />
===SMART on FHIR for OpenMRS===<br />
The team also demonstrated the interoperability of their API by integrating an OpenMRS instance with an instance of the [[SMART_uses_for_public_health | SMART]] Pediatric Growth Chart application. A graphical representation of the integration workflow is presented in the article.<br />
<br />
==Discussion==<br />
The authors recommend that their approach be adopted by the OpenMRS project, as their success "highlighted the value of moving towards a domain independent and standards based API that supports interoperability", whereas the existing OpenMRS API is constrained in these areas. They recognize, though, that integrating the API is technically challenging, so they propose a development roadmap that is meant to allow for a rolling, phased integration.<br />
<br />
==Conclusion==<br />
The authors are realistic in noting that in order for their API and the dataflow model it represents to be adopted, FHIR itself has to be adopted more broadly in the healthcare IT market, that the OpenMRS community has to embrace it, and that FHIR has to move beyond its current status as a draft standard; they note that this combination of factors could take many years to coalesce. They optimistically state, though, that even if FHIR never takes off, their work can still be of value in that it represents an advance in the understanding of development of standards-compliant and domain-nonspecific APIs for open source healthcare projects, even if another standard is ultimately adopted.<br />
<br />
==Comment==<br />
The article demonstrates an interesting point that allows the reader to know that HIT is approaching the integration and interoperability of the [[EHR]] system through many programs and softwares. The [[FHIR]] is one step further in the advancement of the adoption and implementation. <br />
<br />
==References==<br />
<references/><br />
<br />
[[Category:Reviews]]<br />
[[Category:EHR]]<br />
[[Category:Interoperability]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category:HIE]]<br />
[[Category:Interface, Usability and Accessibility]]<br />
[[Category:Meaningful Use]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Automated_electronic_medical_record_sepsis_detection_in_the_emergency_departmentAutomated electronic medical record sepsis detection in the emergency department2015-10-20T02:46:04Z<p>Nneka.nwaeme: </p>
<hr />
<div>== Abstract ==<br />
<br />
“Background. While often first treated in the emergency department (ED), identification of sepsis is difficult. [[EMR|Electronic medical record]] (EMR) clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an EMR-based, automated sepsis identification system. Methods. We tested an EMR-based sepsis identification tool at a major academic, urban ED with 64,000 annual visits. The EMR system collected vital sign and laboratory test information on all ED patients, triggering a “sepsis alert” for those with ≥2 SIRS (systemic inflammatory response syndrome) criteria (fever, tachycardia, tachypnea, leukocytosis) plus ≥1 major organ dysfunction (SBP ≤ 90 mm Hg, lactic acid ≥2.0 mg/dL). We confirmed the presence of sepsis through manual review of physician, nursing, and laboratory records. We also reviewed a random selection of ED cases that did not trigger a sepsis alert. We evaluated the diagnostic accuracy of the sepsis identification tool. Results. From January 1 through March 31, 2012, there were 795 automated sepsis alerts. We randomly selected 300 cases without a sepsis alert from the same period. The true prevalence of sepsis was 355/795 (44.7%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value of the sepsis alert was 44.7% (95% CI [41.2–48.2%]). Pneumonia and respiratory infections (38%) and urinary tract infection (32.7%) were the most common infections among the 355 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (26.1%), traumatic (25.7%), and cardiovascular (20.0%) conditions. Rates of hospital admission were: true-positive sepsis alert 91.0%, false-positive alert 83.0%, no sepsis alert 5.7%. Conclusions. This ED EMR-based automated sepsis identification system was able to detect cases with sepsis. Automated EMR-based detection may provide a viable strategy for identifying sepsis in the ED.” <ref name="Nguyen">Nguyen, S. Q., Mwakalindile, E., Booth, J. S., Hogan, V., Morgan, J., Prickett, C. T., … Wang, H. E. (2014). Automated electronic medical record sepsis detection in the emergency department. PeerJ, 2, e343. http://doi.org/10.7717/peerj.343</ref><br />
<br />
== Purpose ==<br />
<br />
The purpose of this retrospective study was to test a [[CDS|clinical decision support (CDS)]] tool aimed at detecting the presence of sepsis in patients in the emergency department.<br />
<br />
== Methods ==<br />
<br />
The researchers developed the sepsis CDS tool using the Cerner EMR used at the University of Alabama at Birmingham hospital. The tool returned a “’sepsis alert’ if the EMR identified two or more Systemic Inflammatory Response Syndrome (SIRS) criteria and at least one sign of shock” in a patient. To determine the accuracy of the tool, the researchers then combed through the positive alerts and manually confirmed the presence (or absence) of sepsis in the patients. They also randomly checked 300 records of patients that were not flagged by the tool to ensure they did not (or did) have sepsis.<br />
<br />
== Results ==<br />
<br />
The sepsis CDS tool gave a positive alert in 795 patients, of which 355 were manually confirmed by the researchers. This gave a PPV of 44.7%. Review of the 300 randomly checked records showed that none had sepsis which gave an estimated NPV of 100%.<br />
<br />
== Discussion ==<br />
<br />
The researchers were encouraged by the findings of this study. They also felt the number of false positives (440) of the tool was not too high due to the difficulty in diagnosing sepsis. In addition, they noted there are many other diseases that can exhibit SIRS criteria and possibly trigger a false positive alert. Another important finding is that a large majority of patients that triggered an alert were eventually admitted to the hospital. Thus, they postulated that the tool could also be used as an indicator for how sick a patient is and the need for hospitalization.<br />
<br />
== Thoughts ==<br />
<br />
This study provides another example of how a CDS tool can aid a busy ED physician in determining if a patient has sepsis or has the need for hospitalization. More research needs to be done with regards to this tool though. A prospective study would be beneficial in addition to reviewing more non-alert patient records. The sample sizes are rather small.<br />
<br />
== Reference ==<br />
<br />
<references/><br />
<br />
<br />
[[Category:Reviews]]<br />
[[Category:CDS]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category:EHR implementation project]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Personal_health_records:_a_randomized_trial_of_effects_on_elder_medication_safetyPersonal health records: a randomized trial of effects on elder medication safety2015-10-20T02:36:55Z<p>Nneka.nwaeme: </p>
<hr />
<div>==First Review==<br />
<br />
This is a review for <br />
"Personal health records: a randomized trial of effects on elder medication safety".<br />
<ref name= "A Chrischilles, Juan Pablo Hourcade, William Doucette, David Eichmann, Brian Gryzlak, Ryan Lorentzen, Kara Wright, Elena Letuchy, Michael Mueller, Karen Farris, Barcey Levy (2014)"><br />
Journal of the American Medical Informatics Association Jul 2014, 21 (4) 679-686; DOI: 10.1136/amiajnl-2013-002284 </ref><br />
<br />
=== Introduction ===<br />
<br />
The purpose of the study was to examine the impact of a [[PHR|personal health record (PHR)]] on medication-use among older adults.<br />
<br />
=== Methods ===<br />
<br />
A PHR was designed and pretested in a 6-month randomized controlled trial with older adults. After completing mailed baseline questionnaires, eligible computer users aged 65 and over were randomized 3:1 to be given access to a PHR. Follow-up questionnaires measured change from baseline medication use, medication reconciliation behaviors, and medication management problems.<br />
<br />
=== Results ===<br />
<br />
Older adults were interested in keeping track of their health and medication information. A majority (55.2%) logged into the PHR and used it, but only 16.1% used it frequently. At follow-up, those randomized to the PHR group were significantly less likely to use multiple non-steroidal anti-inflammatory drugs—the most common warning generated by the system (viewed by 23% of participants). Compared with low/non-users, high users reported significantly more changes in medication use and improved medication reconciliation behaviors, and recognized significantly more side effects, but there was no difference in use of inappropriate medications or adherence measures.<br />
<br />
=== Conclusions ===<br />
<br />
PHRs can engage older adults for better medication self-management; however, features that motivate continued use will be needed. Longer-term studies of continued users will be required to evaluate the impact of these changes in behavior on patient health outcomes.<br />
<br />
=== Comments ===<br />
<br />
In this day and age when almost everything is easily accessible from our smartphones, it's hard to understand why more participants of the study did not engage in continued use of the PHR. I agree that longer studies are needed to understand how to engage and motivate for continued use in order to evaluate the impact of any changes in behavior and patient health outcomes. <br />
<br />
<br />
== Reference ==<br />
<br />
<references/><br />
[[Category:Reviews]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category:Patient Safety]]<br />
[[Category:PHR]]<br />
[[Category: Medication-Based Safety Rules]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Attitudes_and_perceptions_of_pediatric_residents_on_transitioning_to_CPOEAttitudes and perceptions of pediatric residents on transitioning to CPOE2015-10-17T03:44:26Z<p>Nneka.nwaeme: </p>
<hr />
<div>''' Creating Attitudes and perceptions of pediatric residents on transitioning to CPOE '''<br />
<br />
<ref name="CPOE">Shriner, A.R., Webber, E.C.,Creating Attitudes and perceptions of pediatric residents on transitioning to CPOE. http://ca3cx5qj7w.search.serialssolutions.com/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Attitudes+and+perceptions+of+pediatric+residents+on+transitioning+to+CPOE&rft.jtitle=Applied+clinical+informatics&rft.au=Shriner%2C+A+R&rft.au=Webber%2C+E+C&rft.date=2014&rft.eissn=1869-0327&rft.volume=5&rft.issue=3&rft.spage=721&rft_id=info:pmid/25298812&rft.externalDocID=25298812&paramdict=en-US</ref><br />
<br />
=== Background ===<br />
<br />
This article focuses on the implementation of Computer Provider Order Entry, ([[CPOE|CPOE]]), being used by new resident physicians. A study was done over a period of 6 months and 12 months to see how their attitudes differed.<br />
<br />
=== Methods ===<br />
<br />
During this study, resident physicians were able to use the CPOE instead of the old handwritten orders. Prior to using the CPOE, they had pre-conceptions of how the system could benefit them. After using the CPOE for 6 months, they were asked to give their opinions about the system and the same thing after using the system for 12 months.<br />
<br />
=== Results ===<br />
<br />
The results of this study are as follows. After te 6 month period, only 48% said they favoed the CPOE, while 42% did not care for the CPOE. After the 12 month period was up, 80% of the residents favored the CPOE. The other percentage would rather revert back to paper ordering. The resident physicians had many different views about the improvement of patient care using the CPOE. Some stated that their was litte to no improvement while others said their was some improved aspects. <br />
<br />
=== Conclusion ===<br />
<br />
In conclusion, the residents had a preference for the CPOE over the traditional paper ordering. The majority of them did find that the CPOE helped patient improvement. <br />
<br />
=== Related Articles===<br />
<br />
[[Principles for a Successful Computerized Physician Order Entry Implementation.]]<br />
<br />
<br />
=== References ===<br />
<references/><br />
<br />
<br />
[[Category: Reviews ]]<br />
[[Category: CPOE ]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/The_Relationship_between_Electronic_Health_Record_Use_and_Quality_of_Care_over_TimeThe Relationship between Electronic Health Record Use and Quality of Care over Time2015-10-13T20:47:43Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “The Relationship between Electronic Health Record Use and Quality of Care over Time” by Li Zhou <ref name= “Zhou 2009”> The Relationship between Electronic Health Record Use and Quality of Care over Time. DOI: http://dx.doi.org/10.1197/jamia.M3128 457-464 </ref>. <br />
<br />
==Introduction==<br />
Electronic health records(EHRs) have a great likelihood to enhance the quality of health care by offering real-time access to patients’ health information, tracking patients over time to make certain that they obtain guideline-recommended care, and offering decision-support mechanisms to minimize medical errors. However studies suggest that that simply having an EHR may not be adequate enough to improve quality and safety of health care. Additionally, it is possible that quality and safety benefits of EHR adoption and use may be time-dependent, perhaps taking several years after implementation to take place, as users become more knowledgeable about EHR applications. This article evaluated how the quality of care delivered in ambulatory care practices varied according to duration of EHR adoption and usage.<br />
<br />
==Methods==<br />
The study design involved two data sources: (1) a statewide survey of physicians’ adoption and use of EHR and (2) statewide data on physicians’ quality of care as indicated by their performance on widely used quality measures. <br />
===Statewide Survey of Physicians’ Use of Electronic Health Records===<br />
1,181 respondents were surveyed for this portion of the analysis. Respondents specified how long they had been associated with their main practice and if their main practice had an EHR. If a practice was presently using an EHR, respondents specified when their practice first began using it and designated which EHR features were available and, if available, the degree to which they used each feature. Also, in order to evaluate financial considerations, respondents were asked to signify whether their practice’s income or their personal earnings were eligible for incentive payments for quality of care, patient satisfaction, adoption of [[health information technology]] (HIT), or actual use of HIT.<br />
===Statewide Data on Physicians’ Quality of Care===<br />
Four years of data was collected on 445 physician respondents pertaining to n six previously defined clinical categories of quality from 2001-2005. If a physician pointed out in the 2005 survey that a feature was available in his or her EHR system, the author assumed that the feature had been available since the time when the practice first began using an EHR. The same theory was also applied to the extent of usage of the EHR feature. Based on these assumptions, projections for EHR adoption and availability and use of EHR core functions by year were obtained. <br />
<br />
==Results==<br />
===Characteristics of Survey Respondents===<br />
Physicians practicing in a metropolitan setting and in groups with more physicians were found to be more likely to have an EHR.<br />
=== EHR Adoption and Use of EHR Functions===<br />
137 physicians provided the year in which their practice first began using an EHR. By 2005, the average duration of using EHR in this study population was found to be 4.8 years. Also, the availability and use of core EHR functions increased over time from 2000 to 2005<br />
=== Quality Performance and EHR Adoption===<br />
Quality performance between EHR users and non-users regardless of when their EHRs were implemented was evaluated. For all 6 clinical conditions categories, there was no found difference in performance between EHR users and non-users.<br />
=== Financial Considerations Regarding EHR Usage and Quality of Care===<br />
It was found that having an EHR was not associated to physicians’ reported financial incentives for patient satisfaction or clinical quality.<br />
<br />
==Discussion==<br />
This study examined the relationship between EHRs and health care quality, particularly taking into consideration the changes in association over a period of time. No confirmation that quality of care improved with a longer interval of EHR usage was found. The results imply that merely implementing EHRs is unlikely to result in enhanced quality. Other approaches, such as paying more for higher quality care and ensuring that physicians are using EHRs to their full capacity through education and workflow renovation may be necessary. However, several studies have demonstrated that decision support delivered through electronic records can improve quality of care. For this study, usage of decision support among EHR users was quite low at only 23.5% in 2005, compared to its availability, which was 65.0% amid EHR adopters. As a result, it was agreed that quality of care improvement is achievable when EHRs are coupled with other system supports such as decision support and order entry. <br />
Several limitations were found to be of significance in this study:<br />
* Unknown factors may have masked true associations.<br />
* Even though the measures used in this study have been extensively used by researchers and other healthcare related entities, they are derived from claims data. Actual clinical data may provide a more precise representation of the quality of physician care.<br />
* EHR adoption and usage were self-reported by physicians, and social prestige bias may have led physicians to overrate actual EHR usage.<br />
* The survey was carried out in a single state therefore generalizing the findings to the rest of the United States may be inadequate.<br />
<br />
==Conclusion==<br />
There was no found association between length of time using an EHR and quality of ambulatory care. Also, EHR use was not linked with improved quality of care. Strategies to increase the efficient use of [clinical decision support] and other potential tools to improve quality of care should be considered. Future studies may be needed to re-evaluate the relationships between the quality of care and EHR use over time.<br />
<br />
==Comments==<br />
This study provides a straightforward qualitative and quantitative analysis of whether or not EHR usage provides improved quality of care over a period of time. Despite a few limitations to the study, it is evident that an EHR use alone will not necessarily enhance quality of care. Rather, the incorporation of system support tools such as clinical decision support and [[computerized physician order entry]] leads to better quality improvement outcomes. However, more research needs to be done in order to fully assess the benefits of EHR system tools in improving quality of care over a period of time.<br />
<br />
==References==<br />
<references/><br />
<br />
==Related Articles==<br />
[[Impact of electronic health record systems on information integrity: quality and safety implications]]<br />
<br />
[[The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview]]<br />
<br />
<br />
[[Category: EHR]]<br />
[[Category: Usability]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/The_Relationship_between_Electronic_Health_Record_Use_and_Quality_of_Care_over_TimeThe Relationship between Electronic Health Record Use and Quality of Care over Time2015-10-13T20:43:32Z<p>Nneka.nwaeme: /* Related Articles */</p>
<hr />
<div>This is a systematic review of the article entitled “The Relationship between Electronic Health Record Use and Quality of Care over Time” by Li Zhou <ref name= “Zhou 2009”> The Relationship between Electronic Health Record Use and Quality of Care over Time. DOI: http://dx.doi.org/10.1197/jamia.M3128 457-464 </ref>. <br />
<br />
==Introduction==<br />
Electronic health records(EHRs) have a great likelihood to enhance the quality of health care by offering real-time access to patients’ health information, tracking patients over time to make certain that they obtain guideline-recommended care, and offering decision-support mechanisms to minimize medical errors. However studies suggest that that simply having an EHR may not be adequate enough to improve quality and safety of health care. Additionally, it is possible that quality and safety benefits of EHR adoption and use may be time-dependent, perhaps taking several years after implementation to take place, as users become more knowledgeable about EHR applications. This article evaluated how the quality of care delivered in ambulatory care practices varied according to duration of EHR adoption and usage.<br />
<br />
==Methods==<br />
The study design involved two data sources: (1) a statewide survey of physicians’ adoption and use of EHR and (2) statewide data on physicians’ quality of care as indicated by their performance on widely used quality measures. <br />
===Statewide Survey of Physicians’ Use of Electronic Health Records===<br />
1,181 respondents were surveyed for this portion of the analysis. Respondents specified how long they had been associated with their main practice and if their main practice had an EHR. If a practice was presently using an EHR, respondents specified when their practice first began using it and designated which EHR features were available and, if available, the degree to which they used each feature. Also, in order to evaluate financial considerations, respondents were asked to signify whether their practice’s income or their personal earnings were eligible for incentive payments for quality of care, patient satisfaction, adoption of [[health information technology]] (HIT), or actual use of HIT.<br />
===Statewide Data on Physicians’ Quality of Care===<br />
Four years of data was collected on 445 physician respondents pertaining to n six previously defined clinical categories of quality from 2001-2005. If a physician pointed out in the 2005 survey that a feature was available in his or her EHR system, the author assumed that the feature had been available since the time when the practice first began using an EHR. The same theory was also applied to the extent of usage of the EHR feature. Based on these assumptions, projections for EHR adoption and availability and use of EHR core functions by year were obtained. <br />
<br />
==Results==<br />
===Characteristics of Survey Respondents===<br />
Physicians practicing in a metropolitan setting and in groups with more physicians were found to be more likely to have an EHR.<br />
=== EHR Adoption and Use of EHR Functions===<br />
137 physicians provided the year in which their practice first began using an EHR. By 2005, the average duration of using EHR in this study population was found to be 4.8 years. Also, the availability and use of core EHR functions increased over time from 2000 to 2005<br />
=== Quality Performance and EHR Adoption===<br />
Quality performance between EHR users and non-users regardless of when their EHRs were implemented was evaluated. For all 6 clinical conditions categories, there was no found difference in performance between EHR users and non-users.<br />
=== Financial Considerations Regarding EHR Usage and Quality of Care===<br />
It was found that having an EHR was not associated to physicians’ reported financial incentives for patient satisfaction or clinical quality.<br />
<br />
==Discussion==<br />
This study examined the relationship between EHRs and health care quality, particularly taking into consideration the changes in association over a period of time. No confirmation that quality of care improved with a longer interval of EHR usage was found. The results imply that merely implementing EHRs is unlikely to result in enhanced quality. Other approaches, such as paying more for higher quality care and ensuring that physicians are using EHRs to their full capacity through education and workflow renovation may be necessary. However, several studies have demonstrated that decision support delivered through electronic records can improve quality of care. For this study, usage of decision support among EHR users was quite low at only 23.5% in 2005, compared to its availability, which was 65.0% amid EHR adopters. As a result, it was agreed that quality of care improvement is achievable when EHRs are coupled with other system supports such as decision support and order entry. <br />
Several limitations were found to be of significance in this study:<br />
* Unknown factors may have masked true associations.<br />
* Even though the measures used in this study have been extensively used by researchers and other healthcare related entities, they are derived from claims data. Actual clinical data may provide a more precise representation of the quality of physician care.<br />
* EHR adoption and usage were self-reported by physicians, and social prestige bias may have led physicians to overrate actual EHR usage.<br />
* The survey was carried out in a single state therefore generalizing the findings to the rest of the United States may be inadequate.<br />
<br />
==Conclusion==<br />
There was no found association between length of time using an EHR and quality of ambulatory care. Also, EHR use was not linked with improved quality of care. Strategies to increase the efficient use of [clinical decision support] and other potential tools to improve quality of care should be considered. Future studies may be needed to re-evaluate the relationships between the quality of care and EHR use over time.<br />
<br />
==Comments==<br />
This study provides a straightforward qualitative and quantitative analysis of whether or not EHR usage provides improved quality of care over a period of time. Despite a few limitations to the study, it is evident that an EHR use alone will not necessarily enhance quality of care. Rather, the incorporation of system support tools such as clinical decision support and [[computerized physician order entry]] leads to better quality improvement outcomes. However, more research needs to be done in order to fully assess the benefits of EHR system tools in improving quality of care over a period of time.<br />
<br />
==References==<br />
<references/><br />
<br />
==Related Articles==<br />
[[Impact of electronic health record systems on information integrity: quality and safety implications]]<br />
<br />
[[The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/The_Relationship_between_Electronic_Health_Record_Use_and_Quality_of_Care_over_TimeThe Relationship between Electronic Health Record Use and Quality of Care over Time2015-10-13T20:39:10Z<p>Nneka.nwaeme: Created page with "This is a systematic review of the article entitled “The Relationship between Electronic Health Record Use and Quality of Care over Time” by Li Zhou <ref name= “Zhou 200..."</p>
<hr />
<div>This is a systematic review of the article entitled “The Relationship between Electronic Health Record Use and Quality of Care over Time” by Li Zhou <ref name= “Zhou 2009”> The Relationship between Electronic Health Record Use and Quality of Care over Time. DOI: http://dx.doi.org/10.1197/jamia.M3128 457-464 </ref>. <br />
<br />
==Introduction==<br />
Electronic health records(EHRs) have a great likelihood to enhance the quality of health care by offering real-time access to patients’ health information, tracking patients over time to make certain that they obtain guideline-recommended care, and offering decision-support mechanisms to minimize medical errors. However studies suggest that that simply having an EHR may not be adequate enough to improve quality and safety of health care. Additionally, it is possible that quality and safety benefits of EHR adoption and use may be time-dependent, perhaps taking several years after implementation to take place, as users become more knowledgeable about EHR applications. This article evaluated how the quality of care delivered in ambulatory care practices varied according to duration of EHR adoption and usage.<br />
<br />
==Methods==<br />
The study design involved two data sources: (1) a statewide survey of physicians’ adoption and use of EHR and (2) statewide data on physicians’ quality of care as indicated by their performance on widely used quality measures. <br />
===Statewide Survey of Physicians’ Use of Electronic Health Records===<br />
1,181 respondents were surveyed for this portion of the analysis. Respondents specified how long they had been associated with their main practice and if their main practice had an EHR. If a practice was presently using an EHR, respondents specified when their practice first began using it and designated which EHR features were available and, if available, the degree to which they used each feature. Also, in order to evaluate financial considerations, respondents were asked to signify whether their practice’s income or their personal earnings were eligible for incentive payments for quality of care, patient satisfaction, adoption of [[health information technology]] (HIT), or actual use of HIT.<br />
===Statewide Data on Physicians’ Quality of Care===<br />
Four years of data was collected on 445 physician respondents pertaining to n six previously defined clinical categories of quality from 2001-2005. If a physician pointed out in the 2005 survey that a feature was available in his or her EHR system, the author assumed that the feature had been available since the time when the practice first began using an EHR. The same theory was also applied to the extent of usage of the EHR feature. Based on these assumptions, projections for EHR adoption and availability and use of EHR core functions by year were obtained. <br />
<br />
==Results==<br />
===Characteristics of Survey Respondents===<br />
Physicians practicing in a metropolitan setting and in groups with more physicians were found to be more likely to have an EHR.<br />
=== EHR Adoption and Use of EHR Functions===<br />
137 physicians provided the year in which their practice first began using an EHR. By 2005, the average duration of using EHR in this study population was found to be 4.8 years. Also, the availability and use of core EHR functions increased over time from 2000 to 2005<br />
=== Quality Performance and EHR Adoption===<br />
Quality performance between EHR users and non-users regardless of when their EHRs were implemented was evaluated. For all 6 clinical conditions categories, there was no found difference in performance between EHR users and non-users.<br />
=== Financial Considerations Regarding EHR Usage and Quality of Care===<br />
It was found that having an EHR was not associated to physicians’ reported financial incentives for patient satisfaction or clinical quality.<br />
<br />
==Discussion==<br />
This study examined the relationship between EHRs and health care quality, particularly taking into consideration the changes in association over a period of time. No confirmation that quality of care improved with a longer interval of EHR usage was found. The results imply that merely implementing EHRs is unlikely to result in enhanced quality. Other approaches, such as paying more for higher quality care and ensuring that physicians are using EHRs to their full capacity through education and workflow renovation may be necessary. However, several studies have demonstrated that decision support delivered through electronic records can improve quality of care. For this study, usage of decision support among EHR users was quite low at only 23.5% in 2005, compared to its availability, which was 65.0% amid EHR adopters. As a result, it was agreed that quality of care improvement is achievable when EHRs are coupled with other system supports such as decision support and order entry. <br />
Several limitations were found to be of significance in this study:<br />
* Unknown factors may have masked true associations.<br />
* Even though the measures used in this study have been extensively used by researchers and other healthcare related entities, they are derived from claims data. Actual clinical data may provide a more precise representation of the quality of physician care.<br />
* EHR adoption and usage were self-reported by physicians, and social prestige bias may have led physicians to overrate actual EHR usage.<br />
* The survey was carried out in a single state therefore generalizing the findings to the rest of the United States may be inadequate.<br />
<br />
==Conclusion==<br />
There was no found association between length of time using an EHR and quality of ambulatory care. Also, EHR use was not linked with improved quality of care. Strategies to increase the efficient use of [clinical decision support] and other potential tools to improve quality of care should be considered. Future studies may be needed to re-evaluate the relationships between the quality of care and EHR use over time.<br />
<br />
==Comments==<br />
This study provides a straightforward qualitative and quantitative analysis of whether or not EHR usage provides improved quality of care over a period of time. Despite a few limitations to the study, it is evident that an EHR use alone will not necessarily enhance quality of care. Rather, the incorporation of system support tools such as clinical decision support and [[computerized physician order entry]] leads to better quality improvement outcomes. However, more research needs to be done in order to fully assess the benefits of EHR system tools in improving quality of care over a period of time.<br />
<br />
==References==<br />
<references/><br />
<br />
==Related Articles==</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Patient_Safety:_Improving_Safety_with_Information_TechnologyPatient Safety: Improving Safety with Information Technology2015-10-12T03:36:37Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a systematic review of the article entitled “Patient Safety: Improving Safety with Information Technology” by David W. Bates, MD <ref name= “Bates 2003”> Bates, D. Improving Safety with Information Technology. New Engl J Med 2003 348:2526-2534 http://www.nejm.org/doi/pdf/10.1056/NEJMsa020847</ref>.<br />
<br />
===Introduction===<br />
Bates introduces information technology (IT) as a significant solution to the ever-growing need for quality and safety in health care and encourages the medical industry to follow the example of other industries in pursuit of mass customization and individualization. He also proposed that sophistication of computers and software has a great potential to minimize harm or injury caused during medical care. In this article, Bates provides an assessment of the current status of information technology with regard to safety and studies the consequences of the implementation of IT for health care, research, and policy.<br />
===Ways that Information Technology can Reduce Errors===<br />
Three approaches were discussed: <br />
* Preventing errors and adverse events<br />
* Expediting a quick response after an adverse event has ensued<br />
* Following up with adverse events and providing feedback.<br />
<br />
===Improving Communication===<br />
Bates described poor communication, especially those that result from inadequate “handoffs” between providers as one of the most common factors contributing to the incidence of adverse events. Modern technology such as computerized coverage systems for signing out, hand-held personal digital devices and wireless access to electronic medical records were determined to be plausible solutions to enhance information exchange. The article also explained that information systems can automatically recognize and quickly communicate problems to clinicians such as an abnormal lab value unlike traditional systems in which such results are communicated to a unit secretary. <br />
===Providing Access to Information===<br />
Bates noted that access to reference information such as medical literature, textbooks and Medline database are now readily available on computers and point-of-care devices. <br />
===Requiring Information and Assisting with Calculations===<br />
A primary advantage of IT such “forcing function” was discussed. Forcing function was described as a means to control the way a task is performed. Examples of constraints named in the article include legible order entry and restricted medication dosing/route of administration. Bates termed forcing function as one of the principal means in which [[computerized physician order entry]] reduces the rate of errors.<br />
===Monitoring===<br />
In this section, Bates discusses how computer applications can identify problems and track relations and trends, which can allow clinicians to intervene before an adverse outcome can occur.<br />
<br />
===Decision Support===<br />
Tools that have minimize diagnostic and treatment errors in various clinical settings were evaluated. Information systems were said to assist with work flow by providing access to key information on patients as laboratory values, by calculating weight-based doses of medications, or by flagging patients that have an order for imaging where intravenous contrast material may be unsuitable.<br />
<br />
===Rapid Response to and Tracking of Adverse Events===<br />
Bates assesses the importance of information technology tools such as computerized prescribing, in combination with electronic medical records and clinical decision support with preventing, tracking and providing early intervention for adverse events. For example, one study found that the use of clinical decision support prevented 44% of misses for a team of clinicians. <br />
===Medication Safety and the Prevention of Errors===<br />
Bates found that the use of computerized physician order entry with clinical decision support greatly reduced common factors contributing towards misinformation and subsequent medication error such as illegible orders, errors of calculation, and errors in transcription.<br />
===Summary of Approaches to Prevention===<br />
Few comprehensive studies have been conducted to assess the benefits of IT in improving safety in health care. Bates indicated that more research is needed to fully understand how best to provide the effective patient safety tools through IT. <br />
===Barriers and Directions for Improvement===<br />
It was found that despite major opportunities for improvement and development of patient safety, the adoption of information technology in health care remains slow. Factors contributing to these limitations include: <br />
*Financial Barriers<br />
*Lack of Standards<br />
*Cultural Barriers<br />
===Conclusions===<br />
The article was concluded by stating that the current difficulties in medical care can be improved by increased use of information technology. Information technology can drastically improve patient safety by providing structured actions such as computerized physician order entry, evidence-based clinical decision support and catching errors.<br />
===Comments===<br />
This article provides an excellent introduction to the mandate to improve patient safety through the use of information technology. Bates provides a thorough assessment of information technology as an essential tool for clinician to provide safe health care.<br />
===References===<br />
<references/><br />
<br />
==Related Articles==<br />
[[The Long Road to Patient Safety: A Status Report on Patient Safety Systems]]<br />
<br />
Measuring and improving patient safety through health information technology: The Health IT Safety Framework.<br />
[http://qualitysafety.bmj.com/content/early/2015/09/13/bmjqs-2015-004486.long]<br />
<br />
[[Category: Medication Errors]]<br />
[[Category: Technology]]<br />
[[Category: CIS]][[Category:HI5313-2015-FALL]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Ontology_driven_decision_support_for_the_diagnosis_of_mild_cognitive_impairmentOntology driven decision support for the diagnosis of mild cognitive impairment2015-10-11T14:30:48Z<p>Nneka.nwaeme: </p>
<hr />
<div>==First Review==<br />
===Introduction===<br />
This paper focuses on the development of an ontology for mild cognitive impairment (MCI). Alzheimer’s Disease (AD) can be diagnosed with reasonable accuracy at the stage of dementia, which is a point at which little can be done to achieve a favorable outcome. Hence there is a keen interest in being able to diagnose AD before the dementia stage is reached. Studies at Mayo’s Alzheimer’s Disease Research Center (ADRC) show that 8 out of 10 patients with MCI will convert to AD. Being able to accurately diagnose MCI can help with diagnosing AD pre-dementia stage.<br />
Prior methods for diagnosing MCI used observation based criteria which are subject to bias and could result in misdiagnosis<ref name="main_article"> Zhang, X., Hu, B., Ma, X., Moore, P., & Chen, J. (2014). Ontology driven decision support for the diagnosis of mild cognitive impairment. Computer Methods and Programs in Biomedicine, 113(3), 781–791. http://doi.org/10.1016/j.cmpb.2013.12.023</ref>. To remove the possibility of subjectivity resulting in misdiagnosis, an ontology for MCI containing specialized MRI knowledge about the cortical thickness of the brain structure was created to be used by clinical decision support systems when analyzing brain MRI scans for a patient.<br />
<br />
===Method===<br />
The components of their framework consist of a MCI knowledge repository, an inference mechanism (rule sets extracted using machine learning algorithms), a feature obtaining process (measurements of the cortical thickness) and data processing mechanism<br />
<ref name="main_article"></ref>. The inference mechanism uses the C4.5 algorithm and it was trained using MRI data for 187 MCI patients and 177 non-MCI patients who served as normal controls.<br />
<br />
===Results===<br />
They obtained a sensitivity score of 80.2% and with 10-fold cross validation, they were able to show that it performed better than other algorithms like support vector machines (SVM) and Bayesian network (BN) and back propagation (BP) <ref name="main_article"></ref>.<br />
<br />
==Comments==<br />
This is an extensive study using machine learning algorithms with MRI data for patient diagnosis. Validation using independent data sets would be important for clinical translation.<br />
<br />
==Second review==<br />
Write something here<br />
<br />
==Related Articles==<br />
[[Improving Clinical Practice Using Clinical Decision Support Systems: A Systematic Review of Trials to Identify Features Critical to Success]]<br />
<br />
[[Clinical Decision Support Systems (CDSS) for preventive management of COPD patients]]<br />
<br />
[[Clinical Decision Support for Early Recognition of Sepsis]]<br />
<br />
===References===<br />
<references/><br />
[[Category: Reviews]]<br />
[[Category: CDS]]<br />
[[Category:Evidence Based Medicine (EBM)]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Factors_associated_with_difficult_electronic_health_record_implementation_in_office_practiceFactors associated with difficult electronic health record implementation in office practice2015-10-11T13:53:05Z<p>Nneka.nwaeme: </p>
<hr />
<div>==Abstract==<br />
<br />
Physicians running their own practice are required to demonstrate the meaningful use http://clinfowiki.org/wiki/index.php/Meaningful_Use of health information technology (HIT) as of 2015. The consequence of not doing so would cost financial loss for the physician’s practice mainly due to the reduction of Medicaid reimbursements. Another consequence of not using an electronic health record (EHR) would be that the physician’s office would not be able to deliver the standard of care now required under the healthcare reform. Physicians with their own practice are more likely to have difficulty with EHR implementation than physicians who are employed at a facility. Health IT Regional Extension Centers ([[HITREC]]) now exist throughout the country, through the federal government. The use of outside help to aid office physicians in the implementation of EHRs might result in a more successful implementation. A study was done using data from practices in three different communities participating in the Massachusetts eHealth collaborative (MAeHC) EHR implementation project. The purpose of this study was to identify the factors that cause difficulty in the process of EHR implementation. <ref name= ‘EHR implementation”>Factors associated with difficult electronic health record implementation in office practice Marshall Fleurant, Rachel Kell, Chelsea Jenter, Lynn A Volk, Fang Zhang, David W Bates, Steven R Simon Journal of the American Medical Informatics Association Jul 2012, 19 (4) 541-544, retrieved October 7, 2015, from http://jamia.oxfordjournals.org/content/19/4/541 DOI: 10.1136/amiajnl-2011-000689</ref><br />
<br />
==Methods==<br />
<br />
Data that was collected includes the attitudes, demographics and practice characteristics of physicians. A pre and post intervention survey was given to the practices in three different communities to measure the physician’s perception of the difficulties of EHR implementation. MAeHC installed EHR in physician’s practices between the year of 2006 and 2008. MAeHC also provided the offices with on-site consultation to aid in the workflow design and integration of the EHR. Technical support was also provided. A total of 167 physician practices participated in this study. The pre-intervention survey was given in 2005 and had the goal of measuring physicians’ attitudes towards perceptions of quality of care, demographics, practice characteristics and attitudes towards the use of computers in healthcare. The post-survey was given after the completion of the project in 2009.<br />
<br />
==Results==<br />
<br />
All 163 physicians were able to complete both surveys in 2005 and 2009. Out of the 163, 54 physicians reported that the implementation process was very difficult. Eighty-four physicians reported the process to be somewhat difficult and 18 reported the no difficulty in the process. <br />
<br />
==Conclusions==<br />
<br />
Physicians who were employed by practices experienced an easier time with EHR implementation. The physicians who had ownership stake in their practices perceived EHR implementation to be more difficult compared to other physicians. It was also realized the important role that office staff play in the implementation of EHR. EHR implementation in the future should include the needs of the physicians who own the practice and should also focus on the critical role that office staff members play in the transformation process. <br />
<br />
==Related Articles==<br />
[[Implementing Health Information Technology to Improve the Process of Health Care Delivery: A Case Study]]<br />
<br />
==References==<br />
<br />
<references/><br />
<br />
<br />
<br />
<br />
[[Category: EHR]]<br />
[[Category: EHR implementation project]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Impact_of_a_clinical_decision_support_system_for_high-alert_medications_on_the_prevention_of_prescription_errorsImpact of a clinical decision support system for high-alert medications on the prevention of prescription errors2015-10-09T03:37:31Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a review of the paper by Lee, et al. <ref name ="lee">http://dx.doi.org/10.1016/j.ijmedinf.2014.08.006 Lee, J., Han, H., Ock, M., Lee, S., Lee, S., & Jo, M.-W. (2014). Impact of a clinical decision support system for high-alert medications on the prevention of prescription errors. International Journal of Medical Informatics, 83(12), 929–940. <br />
</ref><br />
<br />
== Background ==<br />
<br />
The authors evaluated the introduction of a [[CDS]] for high-risk medications in the setting of a very large (2700 bed) tertiary medical center. The goal was to improve the safe administration of five medications: regular insulin, potassium chloride, and the anticoagulants warfarin, heparin and urokinase. <br />
<br />
== Methods == <br />
<br />
Prescribing data, and intervention log data was analyzed for the six months prior to, and following deployment of the CDS.<br />
<br />
== Results ==<br />
<br />
Improvements were noted in ordering dilutents for IV medications and maximum-dose monitoring. Orders that omitted dilution fluids for insulin and KCl dropped from 12,878 to 0 cases. Orders that exceeded the maximum dose of a drug dropped from 214 to 9 cases. Following adjustments in the program, no orders exceeded the maximum-dose recommendation.<br />
<br />
== Conclusion ==<br />
<br />
Implementing the CDS did improve safe ordering of several high-risk medications. Some minor errors in the program had to adjusted after deployment, and there were some unusual errors noted that appeared to be user workarounds. <br />
<br />
== Discussion == <br />
<br />
Data on effectiveness of CDS continues to accumulate. This paper demonstrates the achievement of a reasonable goal. It is perhaps more interesting to look at the unexpected consequences of CDS implementation to help other entities plan such a deployment. <br />
<br />
==Related Articles==<br />
[[Medication errors: prevention using information technology systems]]<br />
<br />
== References ==<br />
<references/><br />
<br />
<br />
[[Category:Reviews]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category:CDS]]<br />
[[Category:Medication Errors]]<br />
[[Category:EHR]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Does_Health_Information_Exchange_Reduce_Redundant_ImagingDoes Health Information Exchange Reduce Redundant Imaging2015-10-09T03:25:15Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a review of Lammers’ article "Does Health Information Exchange Reduce Redundant Imaging? Evidence From Emergency Departments".<ref name="Lammers 2014">Lammers, E., Adler-Milstein, J., & Kocher, K. (2014). Does Health Information Exchange Reduce Redundant Imaging? Evidence From Emergency Departments. Medical Care, 52(3), 227-234. Retrieved October 5, 2015 from http://journals.lww.com/lww-medicalcare/pages/default.aspx</ref> <br />
<br />
== Background ==<br />
[[HIE|Health Information Exchanges (HIE)]] are supposed to be enhance the continuity of patient care. It allows the sharing of patient data between different points of care. In an ideal world, HIEs should provide great benefit including quality gains and cost savings. Despite this notion, there has been limited supporting evidence and research done to prove that HIEs produce these results. The purpose of the article is to evaluate the use of HIE and whether it is associated with a decline in repeat imaging in emergency departments. <ref name="Lammers 2014"> </ref><br />
<br />
== Methods ==<br />
The methodology used for this research was used to compare the effects and trends of 37 EDs utilized by HIE during a time period to 410 EDs that did not participate in an HIE. The 3 imaging orders accounted for were CT Scans(computed tomography), ultrasounds, and chest x-rays. The data used came from the State Emergency Department Databases for California and Florida in 2007-2010 along with HIMSS data of hospitals participating in HIE. The article defined repeat image test as the same test done in the same body region within 30 days at unaffiliated EDs.<ref name="Lammers 2014"> </ref><br />
<br />
== Results ==<br />
From the samples, they discovered that there were repeats of the following 14.7% of CTs, 20.7 of Ultrasounds, 19.5% of chest x-rays. HIE was then associated to reduced probability of repeats in all 3 tests with about 95% confidence level. <ref name="Lammers 2014"> </ref><br />
<br />
== Conclusion ==<br />
Based on the results, they have found a relationship between HIE and repeat imaging in an ED environment. Thus HIE can be a potential tool in decreasing redundant medical services, creating savings in cost and care. <ref name="Lammers 2014"> </ref><br />
<br />
== Comments == <br />
This is an interesting article as it is the first of its kind to assess and provide evidence of the benefits of HIE. By decreasing redundant tests (among other things), HIE can reduce the costs of healthcare. However, many organizations have been slow to adopt due to strict data sharing policies and lack of trust between providers. I believe more studies like this need to be done to prove the value and [[EMR_Benefits:_HIE|benefits]] of HIE.<br />
<br />
==Related Articles==<br />
[[Health information exchange and patient safety]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category:Reviews]]<br />
[[Category:HIE]]<br />
[[Category:Interoperability]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Patient_Safety:_Improving_Safety_with_Information_TechnologyPatient Safety: Improving Safety with Information Technology2015-10-08T14:27:03Z<p>Nneka.nwaeme: </p>
<hr />
<div>==Introduction==<br />
This is a systematic review of the article entitled “Patient Safety: Improving Safety with Information Technology” by David W. Bates, MD <ref name= “Bates 2003”> Bates, D. Improving Safety with Information Technology. New Engl J Med 2003 348:2526-2534 http://www.nejm.org/doi/pdf/10.1056/NEJMsa020847</ref>.<br />
==Overview==<br />
Bates introduces information technology (IT) as a significant solution to the ever-growing need for quality and safety in health care and encourages the medical industry to follow the example of other industries in pursuit of mass customization and individualization. He also proposed that sophistication of computers and software has a great potential to minimize harm or injury caused during medical care. In this article, Bates provides an assessment of the current status of information technology with regard to safety and studies the consequences of the implementation of IT for health care, research, and policy.<br />
==Ways that Information Technology can Reduce Errors==<br />
Three approaches to reducing error with IT were discussed: <br />
*Preventing errors and adverse events<br />
* Expediting a quick response after an adverse event has ensued<br />
* Following up with adverse events and providing feedback.<br />
==Improving Communication==<br />
Bates described poor communication, especially those that result from inadequate “handoffs” between providers as one of the most common factors contributing to the incidence of adverse events. Modern technology such as computerized coverage systems for signing out, hand-held personal digital devices and wireless access to electronic medical records were determined to be plausible solutions to enhance information exchange. The article also explained that information systems can automatically recognize and quickly communicate problems to clinicians such as an abnormal lab value unlike traditional systems in which such results are communicated to a unit secretary. <br />
==Providing Access to Information==<br />
Bates noted that access to reference information such as medical literature, textbooks and Medline database are now readily available on computers and point-of-care devices. <br />
==Requiring Information and Assisting with Calculations==<br />
A primary advantage of IT such “forcing function” was discussed. Forcing function was described as a means to control the way a task is performed. Examples of constraints named in the article include legible order entry and restricted medication dosing/route of administration. Bates termed forcing function as one of the principal means in which [[computerized physician order entry]] reduces the rate of errors.<br />
==Monitoring==<br />
In this section, Bates discusses how computer applications can identify problems and track relations and trends, which can allow clinicians to intervene before an adverse outcome happens.<br />
==Decision Support==<br />
Tools that have been developed in order to minimize diagnostic and treatment errors in various clinical settings were evaluated. Information systems were said to assist with work flow by providing access to key information on patients as laboratory values, by calculating weight-based doses of medications, or by flagging patients that have an order for imaging where intravenous contrast material may be unsuitable.<br />
==Rapid Response to and Tracking of Adverse Events==<br />
Bates assesses the importance of information technology tools such as computerized prescribing, in combination with electronic medical records and clinical decision support with preventing, tracking and providing early intervention for adverse events. For example, one study found that the use of clinical decision support prevented 44% of misses for a team of clinicians. <br />
==Medication Safety and the Prevention of Errors==<br />
Bates found that the use of computerized physician order entry with clinical decision support greatly reduced common factors contributing towards misinformation and subsequent medication error such as illegible orders, errors of calculation, and errors in transcription.<br />
==Summary of Approaches to Prevention==<br />
Few comprehensive studies have been conducted to assess the benefits of IT in improving safety in health care. Bates indicated that more research is needed to fully understand how best to provide the effective patient safety tools through IT. <br />
==Barriers and Directions for Improvement==<br />
It was found that despite major opportunities for improvement and development of patient safety, the adoption of information technology in health care remains slow. Factors contributing to these limitations include: <br />
*Financial Barriers<br />
*Lack of Standards<br />
*Cultural Barriers<br />
==Conclusions==<br />
The article was concluded by stating that the current difficulties in medical care can be improved by increased use of information technology. Information technology can drastically improve patient safety by providing structured actions such as computerized physician order entry, evidence-based clinical decision support and catching errors.<br />
==Comments==<br />
This article provides an excellent introduction to the mandate to improve patient safety through the use of information technology. Bates provides a thorough assessment of information technology as an essential tool for clinician to provide safe health care.<br />
==References==<br />
<references/><br />
==Related Articles==<br />
[[The Long Road to Patient Safety: A Status Report on Patient Safety Systems]]<br />
[[Category: Medication Errors]]<br />
[[Category: Technology]]<br />
[[Category: CIS]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Patient_Safety:_Improving_Safety_with_Information_TechnologyPatient Safety: Improving Safety with Information Technology2015-10-08T14:16:28Z<p>Nneka.nwaeme: Created page with "==Introduction== This is a systematic review of the article entitled “Patient Safety: Improving Safety with Information Technology” by David W. Bates, MD <ref name= “Bat..."</p>
<hr />
<div>==Introduction==<br />
This is a systematic review of the article entitled “Patient Safety: Improving Safety with Information Technology” by David W. Bates, MD <ref name= “Bates 2003”> Bates, D. Improving Safety with Information Technology. New Engl J Med 2003 348:2526-2534 http://www.nejm.org/doi/pdf/10.1056/NEJMsa020847</ref>.<br />
==Overview==<br />
Bates introduces information technology (IT) as a significant solution to the ever-growing need for quality and safety in health care and encourages the medical industry to follow the example of other industries in pursuit of mass customization and individualization. He also proposed that sophistication of computers and software has a great potential to minimize harm or injury caused during medical care. In this article, Bates provides an assessment of the current status of information technology with regard to safety and studies the consequences of the implementation of IT for health care, research, and policy.<br />
==Ways that Information Technology can Reduce Errors==<br />
Three approaches to reducing error with IT were discussed: <br />
*Preventing errors and adverse events<br />
* Expediting a quick response after an adverse event has ensued<br />
* Following up with adverse events and providing feedback.<br />
==Improving Communication==<br />
Bates described poor communication, especially those that result from inadequate “handoffs” between providers as one of the most common factors contributing to the incidence of adverse events. Modern technology such as computerized coverage systems for signing out, hand-held personal digital devices and wireless access to electronic medical records were determined to be plausible solutions to enhance information exchange. The article also explained that information systems can automatically recognize and quickly communicate problems to clinicians such as an abnormal lab value unlike traditional systems in which such results are communicated to a unit secretary. <br />
==Providing Access to Information==<br />
Bates noted that access to reference information such as medical literature, textbooks and Medline database are now readily available on computers and point-of-care devices. <br />
==Requiring Information and Assisting with Calculations==<br />
A primary advantage of IT such “forcing function” was discussed. Forcing function was described as a means to control the way a task is performed. Examples of constraints named in the article include legible order entry and restricted medication dosing/route of administration. Bates termed forcing function as one of the principal means in which [[computerized physician order entry]] reduces the rate of errors.<br />
==Monitoring==<br />
In this section, Bates discusses how computer applications can identify problems and track relations and trends, which can allow clinicians to intervene before an adverse outcome happens.<br />
==Decision Support==<br />
Tools that have been developed in order to minimize diagnostic and treatment errors in various clinical settings were evaluated. Information systems were said to assist with work flow by providing access to key information on patients as laboratory values, by calculating weight-based doses of medications, or by flagging patients that have an order for imaging where intravenous contrast material may be unsuitable.<br />
==Rapid Response to and Tracking of Adverse Events==<br />
Bates assesses the importance of information technology tools such as computerized prescribing, in combination with electronic medical records and clinical decision support with preventing, tracking and providing early intervention for adverse events. For example, one study found that the use of clinical decision support prevented 44% of misses for a team of clinicians. <br />
==Medication Safety and the Prevention of Errors==<br />
Bates found that the use of computerized physician order entry with clinical decision support greatly reduced common factors contributing towards misinformation and subsequent medication error such as illegible orders, errors of calculation, and errors in transcription.<br />
==Summary of Approaches to Prevention==<br />
Few comprehensive studies have been conducted to assess the benefits of IT in improving safety in health care. Bates indicated that more research is needed to fully understand how best to provide the effective patient safety tools through IT. <br />
==Barriers and Directions for Improvement==<br />
It was found that despite major opportunities for improvement and development of patient safety, the adoption of information technology in health care remains slow. Factors contributing to these limitations include: <br />
*Financial Barriers<br />
*Lack of Standards<br />
*Cultural Barriers<br />
==Conclusions==<br />
The article was concluded by stating that the current difficulties in medical care can be improved by increased use of information technology. Information technology can drastically improve patient safety by providing structured actions such as computerized physician order entry, evidence-based clinical decision support and catching errors.<br />
==Comments==<br />
This article provides an excellent introduction to the mandate to improve patient safety through the use of information technology. Bates provides a thorough assessment of information technology as an essential tool for clinician to provide safe health care.<br />
==References==<br />
<references/><br />
==Related Articles==<br />
[[The Long Road to Patient Safety: A Status Report on Patient Safety Systems]]<br />
[[Category: Medication Errors]]<br />
[[Category: Technology]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Impact_of_electronic_health_record_systems_on_information_integrity:_quality_and_safety_implicationsImpact of electronic health record systems on information integrity: quality and safety implications2015-10-06T09:54:10Z<p>Nneka.nwaeme: </p>
<hr />
<div>This is a review of the 2013 article, “Impact of Electronic Health Record Systems on Information Integrity: Quality and Safety Implications”, by Bowman, Sue, MJ, RHIA, CCS, FAHIMA.<br />
<br />
==Introduction==<br />
<br />
There are many benefits associated with the implementation and use of [[EMR|Electronic Health Records]], but there are also risks and consequences as well, especially when it comes to design and use. Sue Bowman reviews and discusses a few of the risks and consequences associated with poor design and improper use of EHR’s in this article.<br />
<br />
==EHR Adoption issues==<br />
<br />
American healthcare is eager to see the benefits associated with the adoption of Health Information Technology (Health IT), current it appears costly and inadequately regulated. “Many EHR’s have been developed from erroneous or incomplete design specifications; are dependent on unreliable hardware or software platforms; and have programming errors or bugs."<Ref name="2013 Bowman">Bowman 2013. Impact of Electronic Health Record Systems on Information Integrity: Quality and Safety Implications http://search.proquest.com.ezproxyhost.library.tmc.edu/docview/1507286703?pq-origsite=summon&accountid=7034</Ref><br />
<br />
==Risks and Consequences==<br />
<br />
Adopting a poorly designed EHR or improperly utilizing an EHR leaves many healthcare providers vulnerable to several risks. These risks include software failure, usability issues, and inappropriate document capture, which can lead to instances of healthcare fraud and abuse. <br />
<br />
==Methods to reduce risk and consequence==<br />
<br />
While the benefits of EHR’s outweigh the risks and consequences, addressing the risks and consequences now will help improve the future of EHR use. Implementing and regulating standards for the design and utilization of EHR’s can drastically minimize the risks and consequences being experienced with poor design and improper use of EHR’s today. <br />
<br />
==Comments==<br />
<br />
Fraud is major concern in today’s healthcare. EHR’s provide and opportunity for fraud when poorly implemented or if utilized incorrectly. Addressing these issues has not been regulated or properly managed amidst the adoption of Health IT. Addressing issues that provide opportunity for fraud and abuse will help to ensure the privacy and security of Personal Identifiable Information (PII) and Personal Health Information (PHI), as well the affordability of Health IT.<br />
<br />
==Related Articles==<br />
[[The impact of electronic health records on healthcare quality: a systematic review and meta-analysis]]<br />
<br />
==References==<br />
<br />
<References/><br />
<br />
[[Category: Reviews]]<br />
[[Category: EHR]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/An_Observational_Study_of_the_Impact_of_a_Computerized_Physician_Order_Entry_System_on_the_Rate_of_Medication_Errors_in_an_Orthopaedic_Surgery_UnitAn Observational Study of the Impact of a Computerized Physician Order Entry System on the Rate of Medication Errors in an Orthopaedic Surgery Unit2015-10-06T09:04:02Z<p>Nneka.nwaeme: </p>
<hr />
<div>==Background==<br />
To determine the impact of [[CPOE| computer provider order entry (CPOE)]] on [[medication errors|medication errors]] during prescription, dispensing, and administration of medications in an orthopaedic surgery unit. <ref name ="2015 Hernandez"> Hernandez, 2015. An Observational Study of the Impact of a Computerized Physician Order Entry System on the Rate of Medication Errors in an Orthopaedic Surgery Unit. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/26207363</ref><br />
<br />
==Methods==<br />
In a teaching hospital in Paris, France, a before-after observational study was performed comparing the number of medication errors in prescribing, dispensing, and administration before and after the implementation of CPOE. <br />
<br />
==Results==<br />
111 patients were observed the pre- CPOE implementation period while 86 patients were observed post- CPOE implementation period. The use of CPOE had a 92% decrease in prescribing errors and a 17.5% decrease in prescribing errors. There was no significant difference found in dispensing errors. <br />
<br />
==Conclusion==<br />
The implementation of CPOE reduced the amount of prescribing and administration errors. <br />
<br />
==Comments==<br />
This study has proven that usage of CPOE in an Orthopaedic Surgery Unit reduces the amount of medication errors, which will increase patient safety and quality of care.<br />
<br />
==Related Articles==<br />
[[Medication errors: prevention using information technology systems]]<br />
<br />
==References==<br />
<References/><br />
<br />
[[Category: Reviews]]<br />
[[Category:CPOE]]<br />
[[Category:Medication Error]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/The_impact_of_computerized_provider_order_entry_on_medication_errors_in_a_multispecialty_group_practiceThe impact of computerized provider order entry on medication errors in a multispecialty group practice2015-10-06T08:43:19Z<p>Nneka.nwaeme: /* Related Links */</p>
<hr />
<div>Article review by Devine, Emily Beth, Hansen, Ryan N., Wilson-Norton, Jennifer L., Lawless, N. M., Fisk, ALbert W., Blough, David K., Martin, Diane P., and Sullivan Sean D. The artice is named "The Impact of Computerized Provider Order Entry on Medication Error in a mulitispecialty group practice." <ref name="CPOE"> (2010). The Impact of Computerized Provider Order Entry on Medication Error in a mulitispecialty group practice. J AM Meed Inform Association, 78-84. doi:10.1197/jamia.M328 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995630/</ref><br />
<br />
== Background ==<br />
<br />
Computerized Physician Order Entry [[CPOE|(CPOE)]] is a system physicians use for the treatment of their patients. Errors when medication is involved, has been a major problems. [[CPOE|(CPOE)]] was created to eliminate as many medication errors as possible.<br />
<br />
== Methods ==<br />
<br />
A pre-test and post-test study was done to evaluate for medication errors, rates, types. and severity of errors. The pre-test were handwritten by physcians and the post-test was done using the [[CPOE|(CPOE)]] method. The handwritten prescriptions and orders were prone to have more errors due to not being legible, spelling errors, and the use of incorrect abbreviations. Data was collected from multiple sources using both methods to see what the outcome of errors would be. <br />
<br />
== Results ==<br />
<br />
After all of the tests were done, it was concluded that the computerized system had far less errors than the handwritten method. The reduction of errors improved patient care. <br />
<br />
== Conclusion ==<br />
<br />
The use of a computerized system helped reduce medication errors tremendously. More and more companies are using a [[CPOE|(CPOE)]].<br />
<br />
== Related Links==<br />
[[The_impact_of_electronic_health_records_on_healthcare_quality:_a_systematic_review_and_meta-analysis]]<br />
<br />
[[Medication errors: prevention using information technology systems]]<br />
<br />
== References ==<br />
<references/><br />
[[Category: Reviews]]<br />
[[Category: CPOE]]<br />
[[Category: Medication Errors]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/The_impact_of_computerized_provider_order_entry_on_medication_errors_in_a_multispecialty_group_practiceThe impact of computerized provider order entry on medication errors in a multispecialty group practice2015-10-06T08:43:06Z<p>Nneka.nwaeme: /* Related Links */</p>
<hr />
<div>Article review by Devine, Emily Beth, Hansen, Ryan N., Wilson-Norton, Jennifer L., Lawless, N. M., Fisk, ALbert W., Blough, David K., Martin, Diane P., and Sullivan Sean D. The artice is named "The Impact of Computerized Provider Order Entry on Medication Error in a mulitispecialty group practice." <ref name="CPOE"> (2010). The Impact of Computerized Provider Order Entry on Medication Error in a mulitispecialty group practice. J AM Meed Inform Association, 78-84. doi:10.1197/jamia.M328 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995630/</ref><br />
<br />
== Background ==<br />
<br />
Computerized Physician Order Entry [[CPOE|(CPOE)]] is a system physicians use for the treatment of their patients. Errors when medication is involved, has been a major problems. [[CPOE|(CPOE)]] was created to eliminate as many medication errors as possible.<br />
<br />
== Methods ==<br />
<br />
A pre-test and post-test study was done to evaluate for medication errors, rates, types. and severity of errors. The pre-test were handwritten by physcians and the post-test was done using the [[CPOE|(CPOE)]] method. The handwritten prescriptions and orders were prone to have more errors due to not being legible, spelling errors, and the use of incorrect abbreviations. Data was collected from multiple sources using both methods to see what the outcome of errors would be. <br />
<br />
== Results ==<br />
<br />
After all of the tests were done, it was concluded that the computerized system had far less errors than the handwritten method. The reduction of errors improved patient care. <br />
<br />
== Conclusion ==<br />
<br />
The use of a computerized system helped reduce medication errors tremendously. More and more companies are using a [[CPOE|(CPOE)]].<br />
<br />
== Related Links==<br />
*[[The_impact_of_electronic_health_records_on_healthcare_quality:_a_systematic_review_and_meta-analysis]]<br />
<br />
*[[Medication errors: prevention using information technology systems]]<br />
<br />
== References ==<br />
<references/><br />
[[Category: Reviews]]<br />
[[Category: CPOE]]<br />
[[Category: Medication Errors]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Pharmacovigilance_using_clinical_notesPharmacovigilance using clinical notes2015-10-06T08:03:09Z<p>Nneka.nwaeme: </p>
<hr />
<div>== First review ==<br />
<br />
===Introduction===<br />
Physicians prescribe medications based on their diagnosis of a patient’s condition to help them get better,eventually recover from their condition if not chronic or help them manage the condition if it is chronic. According to the article, 50% <ref name="main_article">Pharmacovigilance using clinical notes: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815419/</ref> of adverse events during hospital stays are a result of drug related events. These events increase a patient’s time in the hospital, which costs both the patient, the hospital and sometime clinician who are responsible for their care, dearly. Some of these safety issues aren’t usually detected until after market approval when they’ve been in use by the general public. Coded discharge and insurance claims data have been used to try to detect drug safety issue, but experts believe more than 90% of the data needed to detect safety issues could be missing from the data <ref name="main_article"></ref>.<br />
The writers of this article propose an approach that uses free text clinical notes to detect safety issues involving drugs. Their method uses annotation and medical terminologies to transform free text clinical notes into a de-identified patient feature matrix. The generated matrices serve as input for a high-throughput process that detects drug–adverse event associations and adverse events associated with drug–drug interactions, which in most cases is before official alerts are issued.<br />
<br />
===Methods===<br />
They pulled data from Stanford Translational Research Integrated Database<br />
which contains 1.8 million patients with 19 million documented encounters and more than 11 million unstructured clinical notes. A references standard was created using known drug-adverse event associations consisting of two sets (single-drug adverse events and two-drug case), to test the performance of their methods <ref name="main_article"></ref>. <br />
<br />
They used a two step approach to test their methods. The first step helped them flag putative signals while the second step helped them flag for false positives. For the first step they computed a raw association in the form of an unadjusted OR, followed by adjustment for potential confounders, using the patient-feature matrix. In the second step they adjusted for confounding using specific patient factors like; age,gender, ethnicity, comorbidity and coprescription frequency to calculate the propensity score <ref name="main_article"></ref>.The propensity score indicates the probability of a patient being exposed to a drug.<br />
<br />
===Results===<br />
They were able to detect drug-adverse event associations by reproducing the association between rofecoxib and myocardial infarction <ref name="main_article"></ref>. In reproducing the association, they obtained an odds ratio of 1.31 (95% confidence interval (CI): 1.16–1.45) which correlates with previously reported associations.They were also able to detect adverse drug-drug interactions with an AUC performance of 81.5%<br />
<br />
===Conclusion===<br />
Applying data mining techniques to clinical notes for the purpose of pharmacovigilance is not only feasible but a smarter and faster way to detect adverse events related to drugs. <br />
<br />
==Related Articles==<br />
[[Towards Meaningful Medication-Related Clinical Decision Support: Recommendations for an Initial Implementation]]<br />
<br />
== Second review ==<br />
Write something here!<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category: Drug-drug interaction]]<br />
[[Category: Adverse drug event]]</div>Nneka.nwaemehttp://clinfowiki.org/wiki/index.php/Pharmacovigilance_using_clinical_notesPharmacovigilance using clinical notes2015-10-06T07:51:51Z<p>Nneka.nwaeme: /* Introduction */</p>
<hr />
<div>== First review ==<br />
<br />
===Introduction===<br />
Physicians prescribe medications based on their diagnosis of a patient’s condition to help them get better,eventually recover from their condition if not chronic or help them manage the condition if it is chronic. According to the article, 50% <ref name="main_article">Pharmacovigilance using clinical notes: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815419/</ref> of adverse events during hospital stays are a result of drug related events. These events increase a patient’s time in the hospital, which costs both the patient, the hospital and sometime clinician who are responsible for their care, dearly. Some of these safety issues aren’t usually detected until after market approval when they’ve been in use by the general public. Coded discharge and insurance claims data have been used to try to detect drug safety issue, but experts believe more than 90% of the data needed to detect safety issues could be missing from the data <ref name="main_article"></ref>.<br />
The writers of this article propose an approach that uses free text clinical notes to detect safety issues involving drugs. Their method uses annotation and medical terminologies to transform free text clinical notes into a de-identified patient feature matrix. The generated matrices serve as input for a high-throughput process that detects drug–adverse event associations and adverse events associated with drug–drug interactions, which in most cases is before official alerts are issued.<br />
<br />
===Methods===<br />
They pulled data from Stanford Translational Research Integrated Database<br />
which contains 1.8 million patients with 19 million documented encounters and more than 11 million unstructured clinical notes. A references standard was created using known drug-adverse event associations consisting of two sets (single-drug adverse events and two-drug case), to test the performance of their methods <ref name="main_article"></ref>. <br />
<br />
They used a two step approach to test their methods. The first step helped them flag putative signals while the second step helped them flag for false positives. For the first step they computed a raw association in the form of an unadjusted OR, followed by adjustment for potential confounders, using the patient-feature matrix. In the second step they adjusted for confounding using specific patient factors like; age,gender, ethnicity, comorbidity and coprescription frequency to calculate the propensity score <ref name="main_article"></ref>.The propensity score indicates the probability of a patient being exposed to a drug.<br />
<br />
===Results===<br />
They were able to detect drug-adverse event associations by reproducing the association between rofecoxib and myocardial infarction <ref name="main_article"></ref>. In reproducing the association, they obtained an odds ratio of 1.31 (95% confidence interval (CI): 1.16–1.45) which correlates with previously reported associations.They were also able to detect adverse drug-drug interactions with an AUC performance of 81.5%<br />
<br />
===Conclusion===<br />
Applying data mining techniques to clinical notes for the purpose of pharmacovigilance is not only feasible but a smarter and faster way to detect adverse events related to drugs. <br />
<br />
== Second review ==<br />
Write something here!<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category: Drug-drug interaction]]</div>Nneka.nwaeme