http://clinfowiki.org/wiki/api.php?action=feedcontributions&user=Ooabiri&feedformat=atomClinfowiki - User contributions [en]2024-03-28T19:43:56ZUser contributionsMediaWiki 1.22.4http://clinfowiki.org/wiki/index.php/Category:AccessibilityCategory:Accessibility2015-12-01T12:48:53Z<p>Ooabiri: Created page with "The category is collection of articles that discuss accessibility of Health Information Technology (HIT)."</p>
<hr />
<div>The category is collection of articles that discuss accessibility of Health Information Technology (HIT).</div>Ooabirihttp://clinfowiki.org/wiki/index.php/The_challenges_in_making_electronic_health_records_accessible_to_patientsThe challenges in making electronic health records accessible to patients2015-12-01T12:47:20Z<p>Ooabiri: </p>
<hr />
<div>==Introduction==<br />
There has been an increase in consumer demand for access to information and a healthcare system that may not be able to meet these demands has caused tension. In order to provide a solution to this problem there must be an understanding of the barriers that could possibly prevent patients from getting electronic access to their health data. The purpose of this paper was to review the challenges that occur with the sharing of electronic health records [[EHR]].<br />
<br />
==Current Issues==<br />
Current issues that stand in the way of providing patient EHR access include:<br />
<br />
*Cost and security concerns<br />
*Access to and custodianship of information<br />
*Defining expertise and medical authority<br />
*Determining and including relevant health information into the patient-accessible EHR<br />
*Patients’ comprehension of clinical data<br />
*Liability issues<br />
*Tensions between flexible access to data and flexible access to physicians<br />
<br />
==Future Challenges==<br />
Providing EHR access to patients could greatly benefit not only the patient but the health system as well. It could provide improvement in quality and coordination of care, improved patient compliance and reduction in system use. <br />
<br />
==References==<br />
<references/><br />
<br />
The challenges in making electronic health records accessible to patients<br />
Leslie Beard, Rebecca Schein, Dante Morra, Kumanan Wilson, Jennifer Keelan<br />
Journal of the American Medical Informatics Association Jan 2012, 19 (1) 116-120; retrieved November 22, 2015 from http://jamia.oxfordjournals.org/content/19/1/116.full DOI: 10.1136/amiajnl-2011-000261<br />
<br />
<br />
[[category: Accessibility]]<br />
[[category: EHR]]<br />
[[category: Quality of Care]]<br />
[[Category: Security]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Electronic_Dental_Records_System_AdoptionElectronic Dental Records System Adoption2015-12-01T12:27:59Z<p>Ooabiri: </p>
<hr />
<div>This is a critical review of a published article in PubMed.<br />
<br />
== Introduction ==<br />
The need for updated technology has made it necessary for dental offices to adopt the electronic dental record systems[[(EDR)]] in order to provide efficient and productive oral care. The authors mention the pressure from insurance companies to make data exchange possible digitally. <ref name="2015 Abramovicz-Finkelsztain"> Electronic Dental Records System Adoption. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/26262001 </ref><br />
<br />
The purpose of this study was to identify and evaluate the use of EDR by dental professionals in Sao Paulo city.<br />
<br />
== Methods ==<br />
The authors used a case study design with a quantitative approach. They also used the questionnaire frequently and previously used in 2006 with 31 questions and had translation for both English and Portugese to maintain the validity of the study which were asked to the dental office staff and to the dentist in two separate phone sessions.<br />
<br />
== Results ==<br />
There were great variation in the way dental data were entered and used in the dental offices contacted, in relation to the dental system used, paper or digital records, insertion of dental information, location of computers etc.<br />
<br />
==Discussion==<br />
The authors found that the majority of the dentists kept paper and digital records and conclude this to dentists may feel lack of trust with EDR systems or difficulty with nomenclature of terminologies. The pressure from insurance companies to submit claims online made the dentists do the required functionalities only and not really use the computer to gather clinical information of patients. <br />
<br />
== Conclusion ==<br />
The use of computer or tech savvy dental offices are considered competent but the dental professionals are not using the EDRs for clinical information and to be productive. So, the need for incentives and policies are suggested by the authors to better serve the dental community and provide care.<br />
<br />
== Comments ==<br />
Newer generation dentists will not have a problem adopting the EDRs as they do not fear technology and is driven by it than are older generations who fear the use of advanced technology and thereby need more reinforcement to become confident in the use of EDRs.<br />
<br />
== References ==<br />
<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Dental]]<br />
[[Category: EDR]]<br />
[[Category: Usability]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Utilizing_IHE-based_Electronic_Health_Record_Systems_for_Secondary_UseUtilizing IHE-based Electronic Health Record Systems for Secondary Use2015-12-01T12:14:49Z<p>Ooabiri: </p>
<hr />
<div>==Introduction and Objective==<br />
[[EMR|Electronic Health Records (EHRs)]] have become increasingly important in recent years in modern health. The primary use of EHRs have become more widespread, and in order to take advantage of this surge for in the secondary field such as clinical research, it is essential to define requirements for the secondary use of EHR data. The purpose of this study was to further explore the use of health data for secondary purposes; and “to propose and evaluate an IHE (Integrating the Healthcare Enterprise)-based architecture for secondary use of EHR data for medical research, decision-making in health politics, and quality assurance." <ref name="Holze & Gall 2011">Holzer, K., & Gall, W. (2011). Utilizing IHE-based Electronic Health Record systems for secondary use. Methods of information in medicine, 50(4), 319.</ref><br />
<br />
==Methods==<br />
A deduction of eight core requirements for secondary use of EHR data was made from published literature. An analysis of the IHE profile MPQ (Multi-Patient Queires) was carried out. The architecture for patient-centered cross-domain document retrieval was examined.<br />
<br />
==Results==<br />
A proposal was made to establish an IHE-based architecture for patient-centered cross-domain [[Secondary use of EMR|secondary use of EHR]] data. The architecture was evaluated based on the eight core requirements and showed a positive outcome on six, and partial fulfillment of two requirements.<br />
<br />
==Discussion and Conclusion==<br />
The re-use of electronic health data in EHRs for research and other secondary fields hold great value for the future, hence further research in this area is essential.<br />
<br />
==My Comments==<br />
As the paper stated, EHR systems are an essential part of the modern healthcare system and it is necessary to carry out further research into methods to utilize clinical data from EHR for secondary use.<br />
<br />
== Related Articles ==<br />
<br />
* [[Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research]]<br />
* [[Secondary Use of EHR: Data Quality Issues and Informatics Opportunities]]<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category: EHR]]<br />
[[Category: CIS]]<br />
[[Category: Usability]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/User_PermissionUser Permission2015-11-26T15:51:23Z<p>Ooabiri: </p>
<hr />
<div>Also called user right or user privilege or user authorization, is the authorization given by system administrators (or designee) that define what the end-user can access (and/or type of access) within the network or system (such as data files, applications, printers and scanners). For example, a clinical staff that works in the using floor will be privileged to view a clinical information that will enable them do their work but not have access to the billing information[http://www.pcmag.com/encyclopedia/term/58231/user-permissions].</div>Ooabirihttp://clinfowiki.org/wiki/index.php/User_PermissionUser Permission2015-11-26T15:49:45Z<p>Ooabiri: Created page with "Also called User right or User privilege or user authorizations, is the authorization given by system administrators (or designee) that define what the end-user can access (an..."</p>
<hr />
<div>Also called User right or User privilege or user authorizations, is the authorization given by system administrators (or designee) that define what the end-user can access (and/or type of access) within the network or system (such as data files, applications, printers and scanners). For example, a clinical staff that works in the using floor will be privileged to view a clinical information that will enable them do their work but not have access to the billing information[http://www.pcmag.com/encyclopedia/term/58231/user-permissions].</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Terms_related_to_privacy,_confidentiality,_and_securityTerms related to privacy, confidentiality, and security2015-11-26T15:44:55Z<p>Ooabiri: </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 />
* [[User Permission]]<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>Ooabirihttp://clinfowiki.org/wiki/index.php/Mobile_phone_diabetes_project_led_to_improved_glycemic_control_and_net_savings_for_Chicago_plan_participantsMobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants2015-11-22T23:26:10Z<p>Ooabiri: /* Related Articles */</p>
<hr />
<div>This is a review of the research article authored by Nundy, S., Dick, J. J., Chou, C. H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014)<br />
<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Patients with chronic diseases spend additional time in healthcare settings where resources are directed instead of around the patients and the community. Chronic diseases just like diabetes remain a leading cause of preventable morbidity, mortality and excess costs. Quality outcomes for these patients are largely determined by the activities they engage in outside of their follow up clinical encounters with their providers. The activities include taking medications, eating healthy meals, signs and symptoms monitoring and engaging in regular physical activities. Because a greater number of patients now have smart phones, mobile phone shown to be promising platform for engaging chronic disease patients in these activities <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref>.<br />
<br />
== Method ==<br />
<br />
The authors presented the results of a quasi-experimental (two-group pre-post) study of a behavioral intervention program (called CareSmart) among Chicago health plan participant and non-participant adults with diabetes. Study was conducted between May 2012 and February 2013. CareSmart is mobile Health ([[mHealth | mHealth]]) diabetes program that provide self-management support and team-based care management through automated text messages. The study population included all adult health plan members with diagnosis of Type 1 & 2 diabetes. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Results ==<br />
<br />
The authors reported <br />
* Statistically significant improvements in glycemic control and patients’ satisfaction with overall care in mHealth participants. <br />
* 64% of mHealth participants agreed that phone calls from nurses were helpful for education. <br />
* 70% of mHealth participants agreed that phone calls from nurses were helpful in the navigation of healthcare. <br />
* A net cost savings of $437 per mHealth participant and overall total of $32, 388 (8.8% savings) over pre-period costs were reported. <br />
* The number and cost of outpatient visits for the mHealth participants were significantly reduced. <br />
* Non-statistical significance decrease in the emergency service and hospital usage and costs.<br />
<br />
== Conclusion ==<br />
<br />
Mobile Health programs can support the aim of improving patients’ experience, population health and reducing per capita healthcare costs. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Remarks about the article ==<br />
<br />
This study is show how mobile technology can be leveraged to make existing health system resources more efficient in supporting chronic disease care. The study also emphasized self-management instead of clinical care. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Related Articles ==<br />
* [[Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes]]<br />
* [[Effect of Home Blood Pressure Telemonitoring and Pharmacist Management On Blood Pressure Control: The HyperLink Cluster Randomized Trial]]<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: mHealth]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: HIT]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Mobile_phone_diabetes_project_led_to_improved_glycemic_control_and_net_savings_for_Chicago_plan_participantsMobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants2015-11-22T23:09:19Z<p>Ooabiri: </p>
<hr />
<div>This is a review of the research article authored by Nundy, S., Dick, J. J., Chou, C. H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014)<br />
<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Patients with chronic diseases spend additional time in healthcare settings where resources are directed instead of around the patients and the community. Chronic diseases just like diabetes remain a leading cause of preventable morbidity, mortality and excess costs. Quality outcomes for these patients are largely determined by the activities they engage in outside of their follow up clinical encounters with their providers. The activities include taking medications, eating healthy meals, signs and symptoms monitoring and engaging in regular physical activities. Because a greater number of patients now have smart phones, mobile phone shown to be promising platform for engaging chronic disease patients in these activities <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref>.<br />
<br />
== Method ==<br />
<br />
The authors presented the results of a quasi-experimental (two-group pre-post) study of a behavioral intervention program (called CareSmart) among Chicago health plan participant and non-participant adults with diabetes. Study was conducted between May 2012 and February 2013. CareSmart is mobile Health ([[mHealth | mHealth]]) diabetes program that provide self-management support and team-based care management through automated text messages. The study population included all adult health plan members with diagnosis of Type 1 & 2 diabetes. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Results ==<br />
<br />
The authors reported <br />
* Statistically significant improvements in glycemic control and patients’ satisfaction with overall care in mHealth participants. <br />
* 64% of mHealth participants agreed that phone calls from nurses were helpful for education. <br />
* 70% of mHealth participants agreed that phone calls from nurses were helpful in the navigation of healthcare. <br />
* A net cost savings of $437 per mHealth participant and overall total of $32, 388 (8.8% savings) over pre-period costs were reported. <br />
* The number and cost of outpatient visits for the mHealth participants were significantly reduced. <br />
* Non-statistical significance decrease in the emergency service and hospital usage and costs.<br />
<br />
== Conclusion ==<br />
<br />
Mobile Health programs can support the aim of improving patients’ experience, population health and reducing per capita healthcare costs. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Remarks about the article ==<br />
<br />
This study is show how mobile technology can be leveraged to make existing health system resources more efficient in supporting chronic disease care. The study also emphasized self-management instead of clinical care. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Related Articles ==<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: mHealth]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: HIT]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Mobile_phone_diabetes_project_led_to_improved_glycemic_control_and_net_savings_for_Chicago_plan_participantsMobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants2015-11-22T23:08:32Z<p>Ooabiri: /* Results */</p>
<hr />
<div>This is a review for the research article authored by Nundy, S., Dick, J. J., Chou, C. H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014)<br />
<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Patients with chronic diseases spend additional time in healthcare settings where resources are directed instead of around the patients and the community. Chronic diseases just like diabetes remain a leading cause of preventable morbidity, mortality and excess costs. Quality outcomes for these patients are largely determined by the activities they engage in outside of their follow up clinical encounters with their providers. The activities include taking medications, eating healthy meals, signs and symptoms monitoring and engaging in regular physical activities. Because a greater number of patients now have smart phones, mobile phone shown to be promising platform for engaging chronic disease patients in these activities <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref>.<br />
<br />
== Method ==<br />
<br />
The authors presented the results of a quasi-experimental (two-group pre-post) study of a behavioral intervention program (called CareSmart) among Chicago health plan participant and non-participant adults with diabetes. Study was conducted between May 2012 and February 2013. CareSmart is mobile Health ([[mHealth | mHealth]]) diabetes program that provide self-management support and team-based care management through automated text messages. The study population included all adult health plan members with diagnosis of Type 1 & 2 diabetes. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Results ==<br />
<br />
The authors reported <br />
* Statistically significant improvements in glycemic control and patients’ satisfaction with overall care in mHealth participants. <br />
* 64% of mHealth participants agreed that phone calls from nurses were helpful for education. <br />
* 70% of mHealth participants agreed that phone calls from nurses were helpful in the navigation of healthcare. <br />
* A net cost savings of $437 per mHealth participant and overall total of $32, 388 (8.8% savings) over pre-period costs were reported. <br />
* The number and cost of outpatient visits for the mHealth participants were significantly reduced. <br />
* Non-statistical significance decrease in the emergency service and hospital usage and costs.<br />
<br />
== Conclusion ==<br />
<br />
Mobile Health programs can support the aim of improving patients’ experience, population health and reducing per capita healthcare costs. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Remarks about the article ==<br />
<br />
This study is show how mobile technology can be leveraged to make existing health system resources more efficient in supporting chronic disease care. The study also emphasized self-management instead of clinical care. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Related Articles ==<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: mHealth]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: HIT]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Mobile_phone_diabetes_project_led_to_improved_glycemic_control_and_net_savings_for_Chicago_plan_participantsMobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants2015-11-22T23:07:44Z<p>Ooabiri: /* Conclusion */</p>
<hr />
<div>This is a review for the research article authored by Nundy, S., Dick, J. J., Chou, C. H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014)<br />
<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Patients with chronic diseases spend additional time in healthcare settings where resources are directed instead of around the patients and the community. Chronic diseases just like diabetes remain a leading cause of preventable morbidity, mortality and excess costs. Quality outcomes for these patients are largely determined by the activities they engage in outside of their follow up clinical encounters with their providers. The activities include taking medications, eating healthy meals, signs and symptoms monitoring and engaging in regular physical activities. Because a greater number of patients now have smart phones, mobile phone shown to be promising platform for engaging chronic disease patients in these activities <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref>.<br />
<br />
== Method ==<br />
<br />
The authors presented the results of a quasi-experimental (two-group pre-post) study of a behavioral intervention program (called CareSmart) among Chicago health plan participant and non-participant adults with diabetes. Study was conducted between May 2012 and February 2013. CareSmart is mobile Health ([[mHealth | mHealth]]) diabetes program that provide self-management support and team-based care management through automated text messages. The study population included all adult health plan members with diagnosis of Type 1 & 2 diabetes. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Results ==<br />
<br />
The authors reported<br />
** Statistically significant improvements in glycemic control and patients’ satisfaction with overall care in mHealth participants. <br />
** 64% of mHealth participants agreed that phone calls from nurses were helpful for education. <br />
** 70% of mHealth participants agreed that phone calls from nurses were helpful in the navigation of healthcare. <br />
** A net cost savings of $437 per mHealth participant and overall total of $32, 388 (8.8% savings) over pre-period costs were reported. <br />
** The number and cost of outpatient visits for the mHealth participants were significantly reduced. <br />
** Non-statistical significance decrease in the emergency service and hospital usage and costs.<br />
<br />
== Conclusion ==<br />
<br />
Mobile Health programs can support the aim of improving patients’ experience, population health and reducing per capita healthcare costs. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Remarks about the article ==<br />
<br />
This study is show how mobile technology can be leveraged to make existing health system resources more efficient in supporting chronic disease care. The study also emphasized self-management instead of clinical care. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Related Articles ==<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: mHealth]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: HIT]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Mobile_phone_diabetes_project_led_to_improved_glycemic_control_and_net_savings_for_Chicago_plan_participantsMobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants2015-11-22T23:07:29Z<p>Ooabiri: /* Results */</p>
<hr />
<div>This is a review for the research article authored by Nundy, S., Dick, J. J., Chou, C. H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014)<br />
<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Patients with chronic diseases spend additional time in healthcare settings where resources are directed instead of around the patients and the community. Chronic diseases just like diabetes remain a leading cause of preventable morbidity, mortality and excess costs. Quality outcomes for these patients are largely determined by the activities they engage in outside of their follow up clinical encounters with their providers. The activities include taking medications, eating healthy meals, signs and symptoms monitoring and engaging in regular physical activities. Because a greater number of patients now have smart phones, mobile phone shown to be promising platform for engaging chronic disease patients in these activities <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref>.<br />
<br />
== Method ==<br />
<br />
The authors presented the results of a quasi-experimental (two-group pre-post) study of a behavioral intervention program (called CareSmart) among Chicago health plan participant and non-participant adults with diabetes. Study was conducted between May 2012 and February 2013. CareSmart is mobile Health ([[mHealth | mHealth]]) diabetes program that provide self-management support and team-based care management through automated text messages. The study population included all adult health plan members with diagnosis of Type 1 & 2 diabetes. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Results ==<br />
<br />
The authors reported<br />
** Statistically significant improvements in glycemic control and patients’ satisfaction with overall care in mHealth participants. <br />
** 64% of mHealth participants agreed that phone calls from nurses were helpful for education. <br />
** 70% of mHealth participants agreed that phone calls from nurses were helpful in the navigation of healthcare. <br />
** A net cost savings of $437 per mHealth participant and overall total of $32, 388 (8.8% savings) over pre-period costs were reported. <br />
** The number and cost of outpatient visits for the mHealth participants were significantly reduced. <br />
** Non-statistical significance decrease in the emergency service and hospital usage and costs.<br />
<br />
== Conclusion ==<br />
<br />
Mobile Health programs can support the aim of improving patients’ experience, population health and reducing per capita healthcare costs. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref> <br />
<br />
== Remarks about the article ==<br />
<br />
This study is show how mobile technology can be leveraged to make existing health system resources more efficient in supporting chronic disease care. The study also emphasized self-management instead of clinical care. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Related Articles ==<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: mHealth]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: HIT]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Mobile_phone_diabetes_project_led_to_improved_glycemic_control_and_net_savings_for_Chicago_plan_participantsMobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants2015-11-22T23:05:37Z<p>Ooabiri: </p>
<hr />
<div>This is a review for the research article authored by Nundy, S., Dick, J. J., Chou, C. H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014)<br />
<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Patients with chronic diseases spend additional time in healthcare settings where resources are directed instead of around the patients and the community. Chronic diseases just like diabetes remain a leading cause of preventable morbidity, mortality and excess costs. Quality outcomes for these patients are largely determined by the activities they engage in outside of their follow up clinical encounters with their providers. The activities include taking medications, eating healthy meals, signs and symptoms monitoring and engaging in regular physical activities. Because a greater number of patients now have smart phones, mobile phone shown to be promising platform for engaging chronic disease patients in these activities <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref>.<br />
<br />
== Method ==<br />
<br />
The authors presented the results of a quasi-experimental (two-group pre-post) study of a behavioral intervention program (called CareSmart) among Chicago health plan participant and non-participant adults with diabetes. Study was conducted between May 2012 and February 2013. CareSmart is mobile Health ([[mHealth | mHealth]]) diabetes program that provide self-management support and team-based care management through automated text messages. The study population included all adult health plan members with diagnosis of Type 1 & 2 diabetes. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Results ==<br />
<br />
The authors reported<br />
<br />
**Statistically significant improvements in glycemic control and patients’ satisfaction with overall care in mHealth participants. <br />
** 64% of mHealth participants agreed that phone calls from nurses were helpful for education. <br />
** 70% of mHealth participants agreed that phone calls from nurses were helpful in the navigation of healthcare. <br />
** A net cost savings of $437 per mHealth participant and overall total of $32, 388 (8.8% savings) over pre-period costs were reported. <br />
** The number and cost of outpatient visits for the mHealth participants were significantly reduced. <br />
** Non-statistical significance decrease in the emergency service and hospital usage and costs. <br />
<br />
== Conclusion ==<br />
<br />
Mobile Health programs can support the aim of improving patients’ experience, population health and reducing per capita healthcare costs. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref> <br />
<br />
== Remarks about the article ==<br />
<br />
This study is show how mobile technology can be leveraged to make existing health system resources more efficient in supporting chronic disease care. The study also emphasized self-management instead of clinical care. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Related Articles ==<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: mHealth]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: HIT]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Mobile_phone_diabetes_project_led_to_improved_glycemic_control_and_net_savings_for_Chicago_plan_participantsMobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants2015-11-22T23:05:07Z<p>Ooabiri: </p>
<hr />
<div>This is a review for the research article authored by Nundy, S., Dick, J. J., Chou, C. H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014)<br />
<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Patients with chronic diseases spend additional time in healthcare settings where resources are directed instead of around the patients and the community. Chronic diseases just like diabetes remain a leading cause of preventable morbidity, mortality and excess costs. Quality outcomes for these patients are largely determined by the activities they engage in outside of their follow up clinical encounters with their providers. The activities include taking medications, eating healthy meals, signs and symptoms monitoring and engaging in regular physical activities. Because a greater number of patients now have smart phones, mobile phone shown to be promising platform for engaging chronic disease patients in these activities <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref>.<br />
<br />
== Method ==<br />
<br />
The authors presented the results of a quasi-experimental (two-group pre-post) study of a behavioral intervention program (called CareSmart) among Chicago health plan participant and non-participant adults with diabetes. Study was conducted between May 2012 and February 2013. CareSmart is mobile Health ([[mHealth | mHealth]]) diabetes program that provide self-management support and team-based care management through automated text messages. The study population included all adult health plan members with diagnosis of Type 1 & 2 diabetes. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Results ==<br />
<br />
The authors reported<br />
<br />
** Statistically significant improvements in glycemic control and patients’ satisfaction with overall care in mHealth participants. <br />
** 64% of mHealth participants agreed that phone calls from nurses were helpful for education. <br />
** 70% of mHealth participants agreed that phone calls from nurses were helpful in the navigation of healthcare. <br />
** A net cost savings of $437 per mHealth participant and overall total of $32, 388 (8.8% savings) over pre-period costs were reported. <br />
** The number and cost of outpatient visits for the mHealth participants were significantly reduced. <br />
** Non-statistical significance decrease in the emergency service and hospital usage and costs. <br />
<br />
== Conclusion ==<br />
<br />
Mobile Health programs can support the aim of improving patients’ experience, population health and reducing per capita healthcare costs. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref> <br />
<br />
== Remarks about the article ==<br />
<br />
This study is show how mobile technology can be leveraged to make existing health system resources more efficient in supporting chronic disease care. The study also emphasized self-management instead of clinical care. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Related Articles ==<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: mHealth]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: HIT]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Mobile_phone_diabetes_project_led_to_improved_glycemic_control_and_net_savings_for_Chicago_plan_participantsMobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants2015-11-22T23:03:43Z<p>Ooabiri: Created page with "This is a review for the research article authored by Nundy, S., Dick, J. J., Chou, C. H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014) == Introduction == Patients wi..."</p>
<hr />
<div>This is a review for the research article authored by Nundy, S., Dick, J. J., Chou, C. H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014)<br />
<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Patients with chronic diseases spend additional time in healthcare settings where resources are directed instead of around the patients and the community. Chronic diseases just like diabetes remain a leading cause of preventable morbidity, mortality and excess costs. Quality outcomes for these patients are largely determined by the activities they engage in outside of their follow up clinical encounters with their providers. The activities include taking medications, eating healthy meals, signs and symptoms monitoring and engaging in regular physical activities. Because a greater number of patients now have smart phones, mobile phone shown to be promising platform for engaging chronic disease patients in these activities <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref>.<br />
<br />
== Method ==<br />
<br />
The authors presented the results of a quasi-experimental (two-group pre-post) study of a behavioral intervention program (called CareSmart) among Chicago health plan participant and non-participant adults with diabetes. Study was conducted between May 2012 and February 2013. CareSmart is mobile Health ([[mHealth | mHealth]]) diabetes program that provide self-management support and team-based care management through automated text messages. The study population included all adult health plan members with diagnosis of Type 1 & 2 diabetes. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Results ==<br />
<br />
The authors reported <br />
** Statistically significant improvements in glycemic control and patients’ satisfaction with overall care in mHealth participants. <br />
** 64% of mHealth participants agreed that phone calls from nurses were helpful for education. <br />
** 70% of mHealth participants agreed that phone calls from nurses were helpful in the navigation of healthcare. <br />
** A net cost savings of $437 per mHealth participant and overall total of $32, 388 (8.8% savings) over pre-period costs were reported. <br />
** The number and cost of outpatient visits for the mHealth participants were significantly reduced. <br />
** Non-statistical significance decrease in the emergency service and hospital usage and costs. <br />
<br />
== Conclusion ==<br />
<br />
Mobile Health programs can support the aim of improving patients’ experience, population health and reducing per capita healthcare costs. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref> <br />
<br />
== Remarks about the article ==<br />
<br />
This study is show how mobile technology can be leveraged to make existing health system resources more efficient in supporting chronic disease care. The study also emphasized self-management instead of clinical care. <ref name = "2014, Nundy et al.">Nundy, 2014. Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034376/</ref><br />
<br />
== Related Articles ==<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: mHealth]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: HIT]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Category:Ethics_and_LegalityCategory:Ethics and Legality2015-11-16T03:44:06Z<p>Ooabiri: Created page with "This Category is used to group articles that address ethics and legal issues related to the use of Health information Technologies."</p>
<hr />
<div>This Category is used to group articles that address ethics and legal issues related to the use of Health information Technologies.</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Ethics_and_the_Electronic_Health_Record_in_Dental_School_ClinicsEthics and the Electronic Health Record in Dental School Clinics2015-11-16T03:42:31Z<p>Ooabiri: </p>
<hr />
<div> <br />
This is a critical review of a published article in PubMed.<br />
==Abstract==<br />
Electronic health records (EHRs) are a major development in the practice of dentistry, and dental schools and den-<br />
tal curricula have bene tted from this technology. Patient data entry, storage, retrieval, transmission, and archiving have been streamlined, and the potential for teledentistry and improvement in epidemiological research is beginning to be realized. How- ever, maintaining patient health information in an electronic form has also changed the environment in dental education, setting up potential ethical dilemmas for students and faculty members. The purpose of this article is to explore some of the ethical issues related to EHRs, the advantages and concerns related to the use of computers in the dental operatory, the impact of the EHR on the doctor-patient relationship, the introduction of web-based EHRs, the link between technology and ethics, and potential solu- tions for the management of ethical concerns related to EHRs in dental schools.<br />
==Summary==<br />
=== Introduction ===<br />
The increasing use of [[electronic health record (EHR)]] brings with it the issues of ethics challenging their use in a dental school environment. The privacy and security of the health information of a patient are protected by legislation such as the Health Insurance Portability and Accountability Act (HIPAA) of 1996 and the [[ARRA|Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009]].<ref name="2011Cederberg">Ethics and the Electronic Health Record in Dental School Clinics. http://www.jdentaled.org/content/76/5/584.full.pdf+html </ref><br />
<br />
The authors in this article explored the challenges with the introduction, use, ethical issues, advantages, and impact of EHR in dental operatory and potential solutions for managing these ethical concerns.<br />
<br />
=== Ethical issues ===<br />
The authors relayed the ethical issues related to the electronic form of patient health information. They reported the disadvantage of issues related to breach of patient information by dental students or faculty member involved with improper access to the health information, or hacking into or stealing passwords of the faculty or modifying the codes of procedures done etc. While paper based records have their limitations when compared to electronic records, the alterations or breaches are easier to hide in the digital environment.<br />
<br />
===Using computers in the operatory ===<br />
The authors reported that 55.6 per cent of dentists had workstations in their operator as published by the American Dental Association (ADA) in 2006. The use of computers in the operatory takes a way face time with the patients and the resultant depersonalization could be considered an ethical breach.<br />
<br />
=== EHRs in the Educational Environment===<br />
The authors are concerned about the use of EHR in the dental educational environment and recommended the need for understanding the level of computer knowledge of student dentists and developing the dental school curriculum to include workflows and learning software systems in use in each individual dental school. They found that patient doctor relationship is at stake when the doctor is using the computer for most part of the dental visit to enter the patient information which led to patient distrust and later led to malpractice lawsuits.<br />
<br />
=== EHR challenges in the Dental School Environment ===<br />
The authors reported of surveys that showed many dental students involved in some short of cheating in EHR and this could have been to steal faculty passwords to change dental codes of procedures done, unethical attitude with regard to patient info in an EHR are some of the challenges.<br />
<br />
=== Potential Solutions ===<br />
The authors mention some potential solutions some of which are already in use which reiterate the importance of maintaining ethical integrity while providing efficient dental care to patients. The use of video surveillance while students use an EHR, grades in ethics to be an important part of providing clinical care, upholding professionalism in the courses and ethical modeling by the school faculty and its members. <br />
<br />
=== Conclusion ===<br />
The authors explored some difficult issues related to the use of EHR in dental environments. The increasing use of EHR means there needs to be increased ethical modeling and professionalism while using an EHR to maintain its integrity and usefulness. <br />
<br />
=== Comments ===<br />
Due to the efficiency of the electronic availability of patient health information, the need to enhance [[Security|security]] and ethics of using an electronic health record must be reinforced on a regular basis in order to safeguard the EHR.<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:EHR]]<br />
[[Category:Ethics and Legality]]<br />
[[Category:HIPAA]]<br />
[[Category: Usability]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Clinical_decision_support_systems:_A_discussion_of_quality,_safety_and_legal_liability_issuesClinical decision support systems: A discussion of quality, safety and legal liability issues2015-11-16T03:35:36Z<p>Ooabiri: </p>
<hr />
<div>==Introduction== <br />
The paper examines safety and quality practices currently in use and proposes various options to deal with legal exposure associated with [[CDS|Clinical Decision Support Systems CDSSs]].<br />
<br />
==Quality and safety engineering==<br />
Quality and safety standards such as ISO 9000 and IEC 61508 are being widely adopted in software engineering to achieve best practices in designing and development of systems <ref>Fox, J., & Thomson, R. (2002). Clinical decision support systems: a discussion of quality, safety and legal liability issues. In Proceedings of the AMIA Symposium (p. 265). American Medical Informatics Association. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244432/pdf/procamiasymp00001-0306.pdf </ref>. However, medical software is a complex technology and current standards are unable to assure safety.<br />
<br />
==Risk and liability assessment==<br />
Legal liability of the provider of CDSSs is currently unclear but even in absence of case law in legal view they will have duty of care towards patients and clinical professionals. Suppliers depend on disclaimer and insurance for legal and financial protection.<br />
<br />
==Quality and safety protocol==<br />
The authors propose the use of flexible framework for quality and safety of CDSS application rather than a “one size fits all” approach. They suggest that Hazards and Operability Analysis (HAZOP) can be used to assess the risk levels. The quality of CDSSs will need to be assessed in two parts: the technology used and the knowledge content within the CDSSs. <br />
<br />
==Safety management==<br />
With unusual circumstances and lack of resources, a well-developed and functioning CDSS can give inappropriate clinical suggestion. Thus such avoidable hazards should be assessed and their management should be implemented during the safety consideration phase. <br />
*Safety by design: Author suggests use both of “safety life cycle” and “usual quality life cycle”.<br />
*Operational safety: Can be achieved by limiting access, audit trails (Black box function) and guardian function. <br />
*The safety case: Documentation of HAZOP related analysis, method, scope and safety related design decisions should be made available to all users.<br />
*Safety culture: This need to be developed so all staff is committed to safety and understand their actions and decisions will affect the patient.<br />
<br />
==Conclusion==<br />
This paper provides various options for management of quality and safety of CDSSs to enable its providers to demonstrate their duty of care. <br />
<br />
==Comments==<br />
CDSS is widely getting accepted in the clinical field as a tool to improve safety and quality of patient care. Unfortunately, the CDSS itself, its use, the surrounding circumstances, and the user all may lead to possible harm sometimes. There has been no clear legal guidelines established as to the liability issues that may arise in future. This paper examines such issues and suggest various way the safety of CDSS can be enhanced as well as legal liability issues can be dealt with.<br />
<br />
==References==<br />
<references><br />
<br />
[[Category: Reviews]]<br />
[[Category: CDS]]<br />
[[Category: CDSS]]<br />
[[Category: Ethics and Legality]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category: Quality of Care]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Cost-Effectiveness_of_a_Computerized_Provider_Order_Entry_System_in_Improving_Medication_Safety_Ambulatory_CareCost-Effectiveness of a Computerized Provider Order Entry System in Improving Medication Safety Ambulatory Care2015-11-11T12:57:54Z<p>Ooabiri: </p>
<hr />
<div>This is a review of the 2014 paper by Forrester, et al. <ref name="forrester">Forrester, S. H., Hepp, Z., Roth, J. A., Wirtz, H. S., & Devine, E. B. (2014). Cost-effectiveness of a computerized provider order entry system in improving medication safety ambulatory care. Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 17(4), 340–349. http://doi.org/10.1016/j.jval.2014.01.009</ref><br />
<br />
<br />
== Abstract ==<br />
<br />
*Background—[[Computerized provider order entry]](CPOE) is the process of entering physician orders directly into an electronic health record. Although CPOE has been shown to improve medication safety and reduce health care costs, these improvements have been demonstrated largely in the inpatient setting; the cost-effectiveness in the ambulatory setting remains uncertain.<br />
<br />
*Objective—The objective was to estimate the cost-effectiveness of CPOE in reducing medication errors and adverse drug events (ADEs) in the ambulatory setting.<br />
<br />
*Methods—We created a decision-analytic model to estimate the cost-effectiveness of CPOE in a midsized (400 providers) multidisciplinary medical group over a 5-year time horizon— 2010 to 2014— the time frame during which health systems are implementing CPOE to meet Meaningful Use criteria. We adopted the medical group’s perspective and utilized their costs, changes in efficiency, and actual number of medication errors and ADEs. One-way and probabilistic sensitivity analyses were conducted. Scenario analyses were explored.<br />
<br />
*Results—In the base case, CPOE dominated paper prescribing, that is, CPOE cost $18 million less than paper prescribing, and was associated with 1.5 million and 14,500 fewer medication errors and ADEs, respectively, over 5 years. In the scenario that reflected a practice group of five providers, CPOE cost $265,000 less than paper prescribing, was associated with 3875 and 39 fewer medication errors and ADEs, respectively, over 5 years, and was dominant in 80% of the simulations.<br />
<br />
== Background ==<br />
<br />
The authors seek to estimate the cost-effectiveness of CPOE implementation in an ambulatory setting by constructing an analytic model. <br />
<br />
== Methods ==<br />
<br />
A cost-effectiveness model was constructed by projecting the expected cost savings from decreased ADE's and medication errors, over an expected 5-year run cycle. Total group analysis was based on the entire group of 400 prescribers, small-group analysis was also performed. <br />
<br />
== Results ==<br />
<br />
The "base model" projected $18 million in savings with CPOE, and would be associated with 1.5 million fewer errors and 14,500 fewer ADEs. In a small, 5-provider model, the projection was that CPOE would save $265,000 and<br />
result in 3,875 fewer medication errors and 39 fewer ADEs over a 5-year span. <br />
<br />
== Conclusion ==<br />
<br />
The projections indicate that deployment of CPOE in the mid-size, ambulatory setting is likely to be cost-effective. Ambulatory providers, even in small groups, should expect to see a reasonable benefit over 5 years.<br />
<br />
== Comments ==<br />
<br />
It's an interesting approach, and a reasonable way to approach cost-effectiveness in the ambulatory setting, which has not been studied as well as the inpatient setting. Whether this "makes the case" for a small ambulatory provider would be difficult to guess, but it seems like a well-thought-out analysis that would boost confidence heading into a CPOE deployment. <br />
<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category: CPOE]]<br />
[[Category: Benefits and Costs]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Cost-Effectiveness_of_a_Computerized_Provider_Order_Entry_System_in_Improving_Medication_Safety_Ambulatory_CareCost-Effectiveness of a Computerized Provider Order Entry System in Improving Medication Safety Ambulatory Care2015-11-11T12:55:08Z<p>Ooabiri: </p>
<hr />
<div>This is a review of the 2014 paper by Forrester, et al. <ref name="forrester">Forrester, S. H., Hepp, Z., Roth, J. A., Wirtz, H. S., & Devine, E. B. (2014). Cost-effectiveness of a computerized provider order entry system in improving medication safety ambulatory care. Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 17(4), 340–349. http://doi.org/10.1016/j.jval.2014.01.009</ref><br />
<br />
<br />
== Abstract ==<br />
<br />
*Background—[[Computerized provider order entry]](CPOE) is the process of entering physician orders directly into an electronic health record. Although CPOE has been shown to improve medication safety and reduce health care costs, these improvements have been demonstrated largely in the inpatient setting; the cost-effectiveness in the ambulatory setting remains uncertain.<br />
<br />
*Objective—The objective was to estimate the cost-effectiveness of CPOE in reducing medication errors and adverse drug events (ADEs) in the ambulatory setting.<br />
<br />
*Methods—We created a decision-analytic model to estimate the cost-effectiveness of CPOE in a midsized (400 providers) multidisciplinary medical group over a 5-year time horizon— 2010 to 2014— the time frame during which health systems are implementing CPOE to meet Meaningful Use criteria. We adopted the medical group’s perspective and utilized their costs, changes in efficiency, and actual number of medication errors and ADEs. One-way and probabilistic sensitivity analyses were conducted. Scenario analyses were explored.<br />
<br />
*Results—In the base case, CPOE dominated paper prescribing, that is, CPOE cost $18 million less than paper prescribing, and was associated with 1.5 million and 14,500 fewer medication errors and ADEs, respectively, over 5 years. In the scenario that reflected a practice group of five providers, CPOE cost $265,000 less than paper prescribing, was associated with 3875 and 39 fewer medication errors and ADEs, respectively, over 5 years, and was dominant in 80% of the simulations.<br />
<br />
== Background ==<br />
<br />
The authors seek to estimate the cost-effectiveness of CPOE implementation in an ambulatory setting by constructing an analytic model. <br />
<br />
== Methods ==<br />
<br />
A cost-effectiveness model was constructed by projecting the expected cost savings from decreased ADE's and medication errors, over an expected 5-year run cycle. Total group analysis was based on the entire group of 400 prescribers, small-group analysis was also performed. <br />
<br />
== Results ==<br />
<br />
The "base model" projected $18 million in savings with CPOE, and would be associated with 1.5 million fewer errors and 14,500 fewer ADEs. In a small, 5-provider model, the projection was that CPOE would save $265,000 and<br />
result in 3,875 fewer medication errors and 39 fewer ADEs over a 5-year span. <br />
<br />
== Conclusion ==<br />
<br />
The projections indicate that deployment of CPOE in the mid-size, ambulatory setting is likely to be cost-effective. Ambulatory providers, even in small groups, should expect to see a reasonable benefit over 5 years.<br />
<br />
== Comments ==<br />
<br />
It's an interesting approach, and a reasonable way to approach cost-effectiveness in the ambulatory setting, which has not been studied as well as the inpatient setting. Whether this "makes the case" for a small ambulatory provider would be difficult to guess, but it seems like a well-thought-out analysis that would boost confidence heading into a CPOE deployment. <br />
<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category: CPOE]]<br />
[[Category: Benefits and Cost]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Effect_of_home_telemonitoring_on_glycemic_and_blood_pressure_control_in_primary_care_clinic_patients_with_diabetesEffect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes2015-11-11T12:38:02Z<p>Ooabiri: </p>
<hr />
<div>This is a review for the research article authored by Wakefield, B. J., Koopman, R. J., Keplinger, L. E., Bomar, M., Bernt, B., Johanning, J. L., ... & Mehr, D. R. (2014)<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
The traditional face to face co-management of diabetes and hypertension patients is complex but can be augmented with in-home monitoring technologies that enable primary care providers to monitor these patients more frequently. These in-home monitoring can increase the number of non-clinic visit generated data points that help make changes to plan of care as need arise. The aim of this study was to evaluate the short-term effectiveness of these in-home devices in transmitting hemoglobin A1C (A1C) and systolic BP measurements (SBP) <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
== Method ==<br />
The authors used a single-center randomized controlled clinical trial that compared in-home monitoring of patient type 2 diabetes A1C (≥8%) and SBP (>130 mmHg) (n= 55) and usual care of these patients (n=53) for a period of 12 weeks at six University of Missouri Family and Internal Medicine Clinics queried from an electronic medical record every 2 weeks during the study enrolment phase. Study subjects recruitment occurred between May 2009 and August 2010 from an initial pool of 1,343 subjects with 108 agreeing to participate. Of which 53 and 55 subjects were randomly assigned to intervention and control respectively <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
<br />
== Results ==<br />
The authors found no statistically significant difference in A1C or SBP between the two groups at baseline, at 3 and 6 months follow up periods though there was gender difference in SBP at baseline <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
== Conclusion ==<br />
The author concluded that practices need to be selective in their use of [[Telemedicine| telemonitoring]] of patients because the addition of telemonitoring technology alone is unlikely to lead to improvements in the targeted outcome in the short-terms <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>.<br />
<br />
<br />
== Remarks about the article == <br />
The article shows that healthcare information technology (HIT) alone is not sufficient to bring about improvement in healthcare outcomes. It important to point out that other similar studies found significant difference in the same outcome of interest. <br />
<br />
== Related Articles ==<br />
* [[Effect of Home Blood Pressure Telemonitoring and Pharmacist Management On Blood Pressure Control: The HyperLink Cluster Randomized Trial]]<br />
* Stone, R. A., Rao, R. H., Sevick, M. A., Cheng, C., Hough, L. J., Macpherson, D. S., … Derubertis, F. R. (2010). Active care management supported by home telemonitoring in veterans with type 2 diabetes: the DiaTel randomized controlled trial. Diabetes Care, 33(3), 478–484. http://doi.org/10.2337/dc09-1012<br />
* Bosworth, H. B., Olsen, M. K., Grubber, J. M., Neary, A. M., Orr, M. M., Powers, B. J., … Oddone, E. Z. (2009). Two self-management interventions to improve hypertension control: a randomized trial. Annals of Internal Medicine, 151(10), 687–95. http://doi.org/10.7326/0003-4819-151-10-200911170-00148<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Telemedicine]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Effect_of_home_telemonitoring_on_glycemic_and_blood_pressure_control_in_primary_care_clinic_patients_with_diabetesEffect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes2015-11-11T12:37:04Z<p>Ooabiri: </p>
<hr />
<div>This is a review for the research article authored by Wakefield, B. J., Koopman, R. J., Keplinger, L. E., Bomar, M., Bernt, B., Johanning, J. L., ... & Mehr, D. R. (2014)<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
The traditional face to face co-management of diabetes and hypertension patients is complex but can be augmented with in-home monitoring technologies that enable primary care providers to monitor these patients more frequently. These in-home monitoring can increase the number of non-clinic visit generated data points that help make changes to plan of care as need arise. The aim of this study was to evaluate the short-term effectiveness of these in-home devices in transmitting hemoglobin A1C (A1C) and systolic BP measurements (SBP) <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
== Method ==<br />
The authors used a single-center randomized controlled clinical trial that compared in-home monitoring of patient type 2 diabetes A1C (≥8%) and SBP (>130 mmHg) (n= 55) and usual care of these patients (n=53) for a period of 12 weeks at six University of Missouri Family and Internal Medicine Clinics queried from an electronic medical record every 2 weeks during the study enrolment phase. Study subjects recruitment occurred between May 2009 and August 2010 from an initial pool of 1,343 subjects with 108 agreeing to participate. Of which 53 and 55 subjects were randomly assigned to intervention and control respectively <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
<br />
== Results ==<br />
The authors found no statistically significant difference in A1C or SBP between the two groups at baseline, at 3 and 6 months follow up periods though there was gender difference in SBP at baseline <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
== Conclusion ==<br />
The author concluded that practices need to be selective in their use of [[Telemedicine| telemonitoring]] of patients because the addition of telemonitoring technology alone is unlikely to lead to improvements in the targeted outcome in the short-terms <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>.<br />
<br />
<br />
== Remarks about the article == <br />
The article shows that healthcare information technology (HIT) alone is not sufficient to bring about improvement in healthcare outcomes. It important to point out that other similar studies found significant difference in the same outcome of interest. <br />
<br />
== Related Articles ==<br />
* [[Effect of Home Blood Pressure Telemonitoring and Pharmacist Management On Blood Pressure Control: The HyperLink Cluster Randomized Trial]]<br />
* Stone, R. A., Rao, R. H., Sevick, M. A., Cheng, C., Hough, L. J., Macpherson, D. S., … Derubertis, F. R. (2010). Active care management supported by home telemonitoring in veterans with type 2 diabetes: the DiaTel randomized controlled trial. Diabetes Care, 33(3), 478–484. http://doi.org/10.2337/dc09-1012<br />
* Bosworth, H. B., Olsen, M. K., Grubber, J. M., Neary, A. M., Orr, M. M., Powers, B. J., … Oddone, E. Z. (2009). Two self-management interventions to improve hypertension control: a randomized trial. Annals of Internal Medicine, 151(10), 687–95. http://doi.org/10.7326/0003-4819-151-10-200911170-00148<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Effect_of_home_telemonitoring_on_glycemic_and_blood_pressure_control_in_primary_care_clinic_patients_with_diabetesEffect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes2015-11-11T12:34:03Z<p>Ooabiri: </p>
<hr />
<div>This is a review for the research article authored by Wakefield, B. J., Koopman, R. J., Keplinger, L. E., Bomar, M., Bernt, B., Johanning, J. L., ... & Mehr, D. R. (2014)<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
The traditional face to face co-management of diabetes and hypertension patients is complex but can be augmented with in-home monitoring technologies that enable primary care providers to monitor these patients more frequently. These in-home monitoring can increase the number of non-clinic visit generated data points that help make changes to plan of care as need arise. The aim of this study was to evaluate the short-term effectiveness of these in-home devices in transmitting hemoglobin A1C (A1C) and systolic BP measurements (SBP) <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home [[Telemedicine| telemonitoring]] on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
== Method ==<br />
The authors used a single-center randomized controlled clinical trial that compared in-home monitoring of patient type 2 diabetes A1C (≥8%) and SBP (>130 mmHg) (n= 55) and usual care of these patients (n=53) for a period of 12 weeks at six University of Missouri Family and Internal Medicine Clinics queried from an electronic medical record every 2 weeks during the study enrolment phase. Study subjects recruitment occurred between May 2009 and August 2010 from an initial pool of 1,343 subjects with 108 agreeing to participate. Of which 53 and 55 subjects were randomly assigned to intervention and control respectively <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
<br />
== Results ==<br />
The authors found no statistically significant difference in A1C or SBP between the two groups at baseline, at 3 and 6 months follow up periods though there was gender difference in SBP at baseline <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
== Conclusion ==<br />
The author concluded that practices need to be selective in their use of telemonitoring of patients because the addition of telemonitoring technology alone is unlikely to lead to improvements in the targeted outcome in the short-terms <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>.<br />
<br />
<br />
== Remarks about the article == <br />
The article shows that healthcare information technology (HIT) alone is not sufficient to bring about improvement in healthcare outcomes. It important to point out that other similar studies found significant difference in the same outcome of interest. <br />
<br />
== Related Articles ==<br />
* [[Effect of Home Blood Pressure Telemonitoring and Pharmacist Management On Blood Pressure Control: The HyperLink Cluster Randomized Trial]]<br />
* Stone, R. A., Rao, R. H., Sevick, M. A., Cheng, C., Hough, L. J., Macpherson, D. S., … Derubertis, F. R. (2010). Active care management supported by home telemonitoring in veterans with type 2 diabetes: the DiaTel randomized controlled trial. Diabetes Care, 33(3), 478–484. http://doi.org/10.2337/dc09-1012<br />
* Bosworth, H. B., Olsen, M. K., Grubber, J. M., Neary, A. M., Orr, M. M., Powers, B. J., … Oddone, E. Z. (2009). Two self-management interventions to improve hypertension control: a randomized trial. Annals of Internal Medicine, 151(10), 687–95. http://doi.org/10.7326/0003-4819-151-10-200911170-00148<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Effect_of_home_telemonitoring_on_glycemic_and_blood_pressure_control_in_primary_care_clinic_patients_with_diabetesEffect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes2015-11-11T12:09:49Z<p>Ooabiri: </p>
<hr />
<div>Wakefield, B. J., Koopman, R. J., Keplinger, L. E., Bomar, M., Bernt, B., Johanning, J. L., ... & Mehr, D. R. (2014)<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
The traditional face to face co-management of diabetes and hypertension patients is complex but can be augmented with in-home monitoring technologies that enable primary care providers to monitor these patients more frequently. These in-home monitoring can increase the number of non-clinic visit generated data points that help make changes to plan of care as need arise. The aim of this study was to evaluate the short-term effectiveness of these in-home devices in transmitting hemoglobin A1C (A1C) and systolic BP measurements (SBP) <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
== Method ==<br />
The authors used a single-center randomized controlled clinical trial that compared in-home monitoring of patient type 2 diabetes A1C (≥8%) and SBP (>130 mmHg) (n= 55) and usual care of these patients (n=53) for a period of 12 weeks at six University of Missouri Family and Internal Medicine Clinics queried from an electronic medical record every 2 weeks during the study enrolment phase. Study subjects recruitment occurred between May 2009 and August 2010 from an initial pool of 1,343 subjects with 108 agreeing to participate. Of which 53 and 55 subjects were randomly assigned to intervention and control respectively. <br />
<br />
<br />
== Results ==<br />
The authors found no statistically significant difference in A1C or SBP between the two groups at baseline, at 3 and 6 months follow up periods though there was gender difference in SBP at baseline. <br />
<br />
== Conclusion ==<br />
The author concluded that practices need to be selective in their use of telemonitoring of patients because the addition of telemonitoring technology alone is unlikely to lead to improvements in the targeted outcome in the short-terms.<br />
<br />
<br />
== Remarks about the article == <br />
The article shows that healthcare information technology (HIT) alone is not sufficient to bring about improvement in healthcare outcomes. It important to point out that other similar studies found significant difference in the same outcome of interest. <br />
<br />
== Related Articles ==<br />
* Stone, R. A., Rao, R. H., Sevick, M. A., Cheng, C., Hough, L. J., Macpherson, D. S., … Derubertis, F. R. (2010). Active care management supported by home telemonitoring in veterans with type 2 diabetes: the DiaTel randomized controlled trial. Diabetes Care, 33(3), 478–484. http://doi.org/10.2337/dc09-1012<br />
* Bosworth, H. B., Olsen, M. K., Grubber, J. M., Neary, A. M., Orr, M. M., Powers, B. J., … Oddone, E. Z. (2009). Two self-management interventions to improve hypertension control: a randomized trial. Annals of Internal Medicine, 151(10), 687–95. http://doi.org/10.7326/0003-4819-151-10-200911170-00148<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Effect_of_home_telemonitoring_on_glycemic_and_blood_pressure_control_in_primary_care_clinic_patients_with_diabetesEffect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes2015-11-11T12:08:49Z<p>Ooabiri: Created page with "Wakefield, B. J., Koopman, R. J., Keplinger, L. E., Bomar, M., Bernt, B., Johanning, J. L., ... & Mehr, D. R. (2014) == Introduction == The traditional face to face co-man..."</p>
<hr />
<div>Wakefield, B. J., Koopman, R. J., Keplinger, L. E., Bomar, M., Bernt, B., Johanning, J. L., ... & Mehr, D. R. (2014)<br />
<br />
<br />
<br />
<br />
== Introduction ==<br />
The traditional face to face co-management of diabetes and hypertension patients is complex but can be augmented with in-home monitoring technologies that enable primary care providers to monitor these patients more frequently. These in-home monitoring can increase the number of non-clinic visit generated data points that help make changes to plan of care as need arise. The aim of this study was to evaluate the short-term effectiveness of these in-home devices in transmitting hemoglobin A1C (A1C) and systolic BP measurements (SBP) <ref name = "2014, Wakefield et al.">Wakefield, 2014. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes http://online.liebertpub.com/doi/abs/10.1089/tmj.2013.0151</ref>. <br />
<br />
== Method ==<br />
The authors used a single-center randomized controlled clinical trial that compared in-home monitoring of patient type 2 diabetes A1C (≥8%) and SBP (>130 mmHg) (n= 55) and usual care of these patients (n=53) for a period of 12 weeks at six University of Missouri Family and Internal Medicine Clinics queried from an electronic medical record every 2 weeks during the study enrolment phase. Study subjects recruitment occurred between May 2009 and August 2010 from an initial pool of 1,343 subjects with 108 agreeing to participate. Of which 53 and 55 subjects were randomly assigned to intervention and control respectively. <br />
<br />
<br />
== Results ==<br />
The authors found no statistically significant difference in A1C or SBP between the two groups at baseline, at 3 and 6 months follow up periods though there was gender difference in SBP at baseline. <br />
<br />
== Conclusion ==<br />
The author concluded that practices need to be selective in their use of telemonitoring of patients because the addition of telemonitoring technology alone is unlikely to lead to improvements in the targeted outcome in the short-terms.<br />
<br />
<br />
== Remarks about the article == <br />
The article shows that healthcare information technology (HIT) alone is not sufficient to bring about improvement in healthcare outcomes. It important to point out that other similar studies found significant difference in the same outcome of interest. <br />
<br />
== Related Articles ==<br />
** Stone, R. A., Rao, R. H., Sevick, M. A., Cheng, C., Hough, L. J., Macpherson, D. S., … Derubertis, F. R. (2010). Active care management supported by home telemonitoring in veterans with type 2 diabetes: the DiaTel randomized controlled trial. Diabetes Care, 33(3), 478–484. http://doi.org/10.2337/dc09-1012<br />
** Bosworth, H. B., Olsen, M. K., Grubber, J. M., Neary, A. M., Orr, M. M., Powers, B. J., … Oddone, E. Z. (2009). Two self-management interventions to improve hypertension control: a randomized trial. Annals of Internal Medicine, 151(10), 687–95. http://doi.org/10.7326/0003-4819-151-10-200911170-00148<br />
<br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Electronic_Health_Records_in_Four_Community_Physician_Practices:_Impact_on_Quality_and_Cost_of_CareElectronic Health Records in Four Community Physician Practices: Impact on Quality and Cost of Care2015-11-08T16:47:10Z<p>Ooabiri: </p>
<hr />
<div>==Introduction==<br />
[[Health information technology|Health information technology (HIT)]] has been seen as a way to lower the cost of patient care while improving the quality. People who advocate HIT have suggested that technology such as clinical decision support [[CDS]] have many benefits including an increase in adherence to guidelines, improvements in health status, and lower costs. Electronic health records (EHRs) have been one of the most discussed forms of HIT and the Institute of Medicine (IOM) has given eight core functionalities that these systems should have. Some of these functionalities include:<br />
<br />
*Health information and data storage<br />
*Management of lab and imaging tests results<br />
*Electronic ordering <br />
*Clinical decision support<br />
*Interoperability <br />
*Administrative processing for things such a billing <br />
<br />
There have only been a few studies that have shown the impact of HIT on cost and quality of care. A study was done to analyze the impact of an EHR implementation in four private practice settings. <ref name =”Impact on quality and cost of care”> Electronic Health Record in Four Community Physician Practices : Impact on Quality and Cost of Care W. Pete Welch, Dawn Bazarko, Kimberly Ritten, Yo Burgess, Robert Harmon, Lewis G. Sandy Journal of the American Medical Informatics Association May 2007, 14 (3) 320-328; Retrieved on November 4, 2015 from http://jamia.oxfordjournals.org/content/14/3/320 DOI: 10.1197/jamia.M2125</ref><br />
<br />
==Methods==<br />
Quantitative and qualitative data were collected for this study. Telephone calls and site visits were used to collect the qualitative study. Various technical functionalities were discussed including: diagnosis, lab, radiology, and decision support, reminders and patient education capabilities. Barriers faced during implementation were also discussed. Differences in quality of care and costs were measured before and after EHR adoption to collect quantitative data. The specific diseases that were focused on in this study were hypertension, hyperlipidemia, diabetes, and other heart conditions. The cost per care episode over a year and the rate of clinical guideline adherence were used to measure the differences in cost & quality. <br />
<br />
==Results==<br />
The implementation of the EHR had a positive impact on the quality of care regarding hypertension and hyperlipidemia, but no impact on diabetes and coronary artery disease. <br />
<br />
==Conclusion==<br />
This study was able to show that adherence to guidelines increased with the use of EHRs. More research is needed to study the impact on quality and the cost of care over a longer time period. <br />
<br />
==References==<br />
<References/><br />
<br />
[[Category: Benefits_and_Costs]]<br />
[[Category: CDSS]]<br />
[[Category: EHR]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: Quality_of_Care]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Electronic_Health_Records_in_Four_Community_Physician_Practices:_Impact_on_Quality_and_Cost_of_CareElectronic Health Records in Four Community Physician Practices: Impact on Quality and Cost of Care2015-11-08T16:44:28Z<p>Ooabiri: </p>
<hr />
<div>==Introduction==<br />
[[Health information technology|Health information technology (HIT)]] has been seen as a way to lower the cost of patient care while improving the quality. People who advocate HIT have suggested that technology such as clinical decision support [[CDS]] have many benefits including an increase in adherence to guidelines, improvements in health status, and lower costs. Electronic health records (EHRs) have been one of the most discussed forms of HIT and the Institute of Medicine (IOM) has given eight core functionalities that these systems should have. Some of these functionalities include:<br />
<br />
*Health information and data storage<br />
*Management of lab and imaging tests results<br />
*Electronic ordering <br />
*Clinical decision support<br />
*Interoperability <br />
*Administrative processing for things such a billing <br />
<br />
There have only been a few studies that have shown the impact of HIT on cost and quality of care. A study was done to analyze the impact of an EHR implementation in four private practice settings. <ref name =”Impact on quality and cost of care”> Electronic Health Record in Four Community Physician Practices : Impact on Quality and Cost of Care W. Pete Welch, Dawn Bazarko, Kimberly Ritten, Yo Burgess, Robert Harmon, Lewis G. Sandy Journal of the American Medical Informatics Association May 2007, 14 (3) 320-328; Retrieved on November 4, 2015 from http://jamia.oxfordjournals.org/content/14/3/320 DOI: 10.1197/jamia.M2125</ref><br />
<br />
==Methods==<br />
Quantitative and qualitative data were collected for this study. Telephone calls and site visits were used to collect the qualitative study. Various technical functionalities were discussed including: diagnosis, lab, radiology, and decision support, reminders and patient education capabilities. Barriers faced during implementation were also discussed. Differences in quality of care and costs were measured before and after EHR adoption to collect quantitative data. The specific diseases that were focused on in this study were hypertension, hyperlipidemia, diabetes, and other heart conditions. The cost per care episode over a year and the rate of clinical guideline adherence were used to measure the differences in cost & quality. <br />
<br />
==Results==<br />
The implementation of the EHR had a positive impact on the quality of care regarding hypertension and hyperlipidemia, but no impact on diabetes and coronary artery disease. <br />
<br />
==Conclusion==<br />
This study was able to show that adherence to guidelines increased with the use of EHRs. More research is needed to study the impact on quality and the cost of care over a longer time period. <br />
<br />
==References==<br />
<References/><br />
<br />
[[Category: Benefits_and_Costs]]<br />
[[Category: EHR]]<br />
[[Category: CDSS]]<br />
[[Category: HI5313-2015-FALL]]<br />
[[Category: Quality_of_Care]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Category:System_Uptime_and_DowntimeCategory:System Uptime and Downtime2015-11-08T16:24:32Z<p>Ooabiri: Created page with "This category is used organize articles that address Health information Technology related downtime and uptime."</p>
<hr />
<div>This category is used organize articles that address Health information Technology related downtime and uptime.</div>Ooabirihttp://clinfowiki.org/wiki/index.php/IT_Downtime_%E2%80%93_A_Cultural_ShiftIT Downtime – A Cultural Shift2015-11-08T15:26:02Z<p>Ooabiri: </p>
<hr />
<div>== Introduction ==<br />
According to Dr. Weed, the four main goals of [[EMR|electronic health record]] include immediate access to patient data, access to data from multiple care episodes, standardized data organization and help hospitals plan and organize capacity and patient flow. <br />
There is an ongoing realization of benefits of Electronic Health Records (EHR) as well as other associated issues such as unplanned downtime. Conventional Information Technology (IT) departments have multiple back-up options to ensure workflow and data safety during downtime. However, EHRs are yet to achieve that level of downtime safety. There have been many reported incidents of EHR downtime, which has gained attention due to inconveniences caused in the workflow. This study outlines the measures taken at University Health Network, a large academic research hospital at Toronto, Canada.<br />
<ref name ="Caesar 2015"> Caesar, 2015. IT Downtime – A Cultural Shift. http://www.ncbi.nlm.nih.gov/pubmed/26168390</ref><br />
<br />
== Methods ==<br />
The research group formed an organization wide IT downtime committee that included representatives from nursing, physicians, laboratory, pharmacy, radiology, facilities, switchboard, IT and administration. They conducted tabletop exercises. More importantly, the EHR downtime is not considered as an IT problem alone; instead, it is considered as both IT and Clinical team problem.<br />
== Results ==<br />
The tabletop downtime exercises emphasized that communication is extremely critical to function during EHR downtimes. Further, it emphasized that personnel need to be trained or practice how to effectively communicate during IT downtime. They included an escalation matrix so that as soon as a physician notices trouble with EHR they can escalate the matter and communicate the severity of problems at different levels so that action is taken on time. Paper work was the main source of documentation during downtime.<br />
Finally the authors propose plans to fill the gap in EHRs of patients once the hospital recovers from downtime. First they proposed a centralized plan where a team was deployed to fill data into computers. Soon, however, it was realized that centralization of such plan took quite an effort and financially was not very cheap. Then they proposed a de-centralized plan where each department could fill in data at their convenience, which raised concerns about quality. Finally, depending on the needs, to some departments recovery team was deployed and for others it was not. <br />
== Conclusions ==<br />
Orchestrating workflow during IT downtime is an organizational effort and must be a shared responsibility of both IT and Clinical teams. <br />
Centralized accountability is necessary to ensure a standardized protocol and coordinated efforts throughout the organization and to foster continuous learning to bridge the gap.<br />
Finally, IT downtime preparedness is a continuous planning, practicing, responding, recovering and debriefing process. Downtime knowledge needs to be embedded in staff training and practiced and verified regularly.<br />
<br />
== Comments ==<br />
It is another study where paper records are considered as ultimate back up plan to continue work during EHR downtime. The downtime protocol proposed in this article is similar to the HELP system downtime protocol in some aspects. One interesting proposal here is that the downtime is not just viewed as IT issue instead it is considered organizational. Also the institution pays emphasis to update patient data to EHR after downtime recovery, which in my opinion is necessary as more practices rely on EHR. <br />
== Reference ==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category:HI5313-2015-FALL]]<br />
[[Category: EHR]]<br />
[[Category: IT]]<br />
[[Category: System Uptime and Downtime]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Simulated_electronic_health_record_(Sim-EHR)_curriculum:_teaching_EHR_skills_and_use_of_the_EHR_for_disease_management_and_preventionSimulated electronic health record (Sim-EHR) curriculum: teaching EHR skills and use of the EHR for disease management and prevention2015-11-08T14:44:58Z<p>Ooabiri: </p>
<hr />
<div>=First Review=<br />
[[Simulated Electronic Health Record (Sim-EHR) Curriculum: Teaching EHR Skills and Use of the EHR for Disease Management and Prevention]]<br />
<br />
=Second Review=<br />
==Introduction==<br />
The introduction of the [[Electronic Health Records]] is now an important skill that needs to be taught to every student. In this study the purpose was to create a simulated Electronic Health Record (Sim-EHR) curriculum for medical students during training. [[Evidence based medicine]] is integrated into the system to aid the students in caring and experiencing the real life use of EHR in a simulated setting treating virtual patients with complex health care needs. <br />
<br />
==Methods==<br />
Oregon Health and Science University implemented the Sim-EHR system for their medical students in 2011.<ref name="sim-EHR">Simulated electronic health record (Sim-EHR) curriculum: teaching EHR skills and use of the EHR for disease management and prevention,http://ca3cx5qj7w.search.serialssolutions.com/OpenURL_local?sid=Entrez:PubMed&id=pmid:24448035,Milano, Christina E., et al,Academic medicine: journal of the Association of American Medical Colleges 89.3 (2014): 399</ref> <br />
* The development of the curriculum was created with general and specific objectives. <br />
* Assessment measures were created.<br />
* Simulated charts and virtual patients were created<br />
* Implementation in the Family medicine clerkship<br />
<br />
==Results==<br />
An assessment for students were measure during the implementation of the system. 406 students completed the simulated charts during the study. They measured each students ability to demonstrate skills in chart maintenance, knowledge of evidence based medical guidelines of chronic diseases and their management, and their ability to submit adequate orders/labs/imaging.<br />
<br />
==Conclusion== <br />
The Sim-EHR remains in use until the completing of this study. The curriculum was also implemented over four hours of the OHSU internal medicine clinic orientations in July 2012 and July 2013; as of January 2014, 21 interns had completed simulated charts. <ref name="sim-EHR"></ref> Feedback of faculty and students proved successful. "The Sim-EHR curriculum can be adapted to teach many aspects of medical care and for use with learners at many different levels."<ref name="sim-EHR"></ref><br />
<br />
==Comments==<br />
It is important to incorporate the use of EHR and the use of meaningful data during their training years for compliance of medicare and medicaid purposes. The integration of [[Evidence based medicine]] in the EHR is also essential to practice good clinical guidelines.<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: Training and User Support]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Results_from_simulated_data_sets:_probabilistic_record_linkage_outperforms_deterministic_record_linkageResults from simulated data sets: probabilistic record linkage outperforms deterministic record linkage2015-11-05T00:03:25Z<p>Ooabiri: /* Introduction */</p>
<hr />
<div>Tromp, M., Ravelli, A. C., Bonsel, G. J., Hasman, A., & Reitsma, J. B. (2011)<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Record linkage is possible when a combined set of partially identifying variables from different sources are combined to uniquely identify a patient. Two frequently applied strategies are the deterministic record linkage (DRL) and [[Analysis of a Probabilistic Record Linkage Technique without Human Review| probabilistic record linkage (PRL)]]. With DRL, all or predefined subset of linkage variables have to agree to consider a pair as a link. The PRL approach uses a probability based weights of agreement or disagreement between the paired variables to match or mismatch a patient. Two types of errors can occur in linked records-False Nonlink which is the failure to link two records that truly belong to the same person. False Link is linking two records that belong to different persons. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Method ==<br />
<br />
The authors used simulated data sets with varying amount of registration errors and discriminating power of linking variables that mimicked a range of realistic scenarios. They created a total of 10 scenarios. For each scenario, they compared the results of the two linkage strategies. Two datasets with four linking variables and specified sample size were used in the study. They stimulated 100 datasets by comparing the true status of linkage with that linkage result using either of the two methods. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Results ==<br />
<br />
The probabilistic strategy outperformed the deterministic strategy in all scenarios. In linking situations with few or no errors with a powerful discriminating key, the simple deterministic full linkage strategy perform as good as that of probabilistic approach. The full deterministic strategy produced the lowest number of false positive links at the expense of missing considerable number of matches. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Conclusion==<br />
<br />
The PLR was found to be more flexible and provides data about the quality of the linkage process that can minimize the degree of linkage errors per given data. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Remarks about the article ==<br />
<br />
The article is very important study that can be helpful in helping organization decide which strategy adopt given the characteristics of data to be linked and the inherited shortcomings of both strategies. The article structure made it very difficult to read. It doesn’t have clearly spelled out conclusion except in the abstract. <br />
<br />
==Related Topics==<br />
<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
[[Improving record linkage performance in the presence of missing linkage data]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Results_from_simulated_data_sets:_probabilistic_record_linkage_outperforms_deterministic_record_linkageResults from simulated data sets: probabilistic record linkage outperforms deterministic record linkage2015-11-04T23:26:19Z<p>Ooabiri: /* Introduction */</p>
<hr />
<div>Tromp, M., Ravelli, A. C., Bonsel, G. J., Hasman, A., & Reitsma, J. B. (2011)<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Record [[linkage|linkage]] is possible when a combined set of partially identifying variables from different sources are combined to uniquely identify a patient. Two frequently applied strategies are the deterministic record linkage (DRL) and probabilistic record linkage (PRL). With DRL, all or predefined subset of linkage variables have to agree to consider a pair as a link. The PRL approach uses a probability based weights of agreement or disagreement between the paired variables to match or mismatch a patient. Two types of errors can occur in linked records-False Nonlink which is the failure to link two records that truly belong to the same person. False Link is linking two records that belong to different persons. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Method ==<br />
<br />
The authors used simulated data sets with varying amount of registration errors and discriminating power of linking variables that mimicked a range of realistic scenarios. They created a total of 10 scenarios. For each scenario, they compared the results of the two linkage strategies. Two datasets with four linking variables and specified sample size were used in the study. They stimulated 100 datasets by comparing the true status of linkage with that linkage result using either of the two methods. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Results ==<br />
<br />
The probabilistic strategy outperformed the deterministic strategy in all scenarios. In linking situations with few or no errors with a powerful discriminating key, the simple deterministic full linkage strategy perform as good as that of probabilistic approach. The full deterministic strategy produced the lowest number of false positive links at the expense of missing considerable number of matches. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Conclusion==<br />
<br />
The PLR was found to be more flexible and provides data about the quality of the linkage process that can minimize the degree of linkage errors per given data. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Remarks about the article ==<br />
<br />
The article is very important study that can be helpful in helping organization decide which strategy adopt given the characteristics of data to be linked and the inherited shortcomings of both strategies. The article structure made it very difficult to read. It doesn’t have clearly spelled out conclusion except in the abstract. <br />
<br />
==Related Topics==<br />
<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
[[Improving record linkage performance in the presence of missing linkage data]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Results_from_simulated_data_sets:_probabilistic_record_linkage_outperforms_deterministic_record_linkageResults from simulated data sets: probabilistic record linkage outperforms deterministic record linkage2015-11-04T23:24:55Z<p>Ooabiri: /* Introduction */</p>
<hr />
<div>Tromp, M., Ravelli, A. C., Bonsel, G. J., Hasman, A., & Reitsma, J. B. (2011)<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Record linkage|Record linkage]] is possible when a combined set of partially identifying variables from different sources are combined to uniquely identify a patient. Two frequently applied strategies are the deterministic record linkage (DRL) and probabilistic record linkage (PRL). With DRL, all or predefined subset of linkage variables have to agree to consider a pair as a link. The PRL approach uses a probability based weights of agreement or disagreement between the paired variables to match or mismatch a patient. Two types of errors can occur in linked records-False Nonlink which is the failure to link two records that truly belong to the same person. False Link is linking two records that belong to different persons. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Method ==<br />
<br />
The authors used simulated data sets with varying amount of registration errors and discriminating power of linking variables that mimicked a range of realistic scenarios. They created a total of 10 scenarios. For each scenario, they compared the results of the two linkage strategies. Two datasets with four linking variables and specified sample size were used in the study. They stimulated 100 datasets by comparing the true status of linkage with that linkage result using either of the two methods. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Results ==<br />
<br />
The probabilistic strategy outperformed the deterministic strategy in all scenarios. In linking situations with few or no errors with a powerful discriminating key, the simple deterministic full linkage strategy perform as good as that of probabilistic approach. The full deterministic strategy produced the lowest number of false positive links at the expense of missing considerable number of matches. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Conclusion==<br />
<br />
The PLR was found to be more flexible and provides data about the quality of the linkage process that can minimize the degree of linkage errors per given data. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Remarks about the article ==<br />
<br />
The article is very important study that can be helpful in helping organization decide which strategy adopt given the characteristics of data to be linked and the inherited shortcomings of both strategies. The article structure made it very difficult to read. It doesn’t have clearly spelled out conclusion except in the abstract. <br />
<br />
==Related Topics==<br />
<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
[[Improving record linkage performance in the presence of missing linkage data]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Results_from_simulated_data_sets:_probabilistic_record_linkage_outperforms_deterministic_record_linkageResults from simulated data sets: probabilistic record linkage outperforms deterministic record linkage2015-11-04T23:24:15Z<p>Ooabiri: </p>
<hr />
<div>Tromp, M., Ravelli, A. C., Bonsel, G. J., Hasman, A., & Reitsma, J. B. (2011)<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Record linkage|Record linkage]]is possible when a combined set of partially identifying variables from different sources are combined to uniquely identify a patient. Two frequently applied strategies are the deterministic record linkage (DRL) and probabilistic record linkage (PRL). With DRL, all or predefined subset of linkage variables have to agree to consider a pair as a link. The PRL approach uses a probability based weights of agreement or disagreement between the paired variables to match or mismatch a patient. Two types of errors can occur in linked records-False Nonlink which is the failure to link two records that truly belong to the same person. False Link is linking two records that belong to different persons. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Method ==<br />
<br />
The authors used simulated data sets with varying amount of registration errors and discriminating power of linking variables that mimicked a range of realistic scenarios. They created a total of 10 scenarios. For each scenario, they compared the results of the two linkage strategies. Two datasets with four linking variables and specified sample size were used in the study. They stimulated 100 datasets by comparing the true status of linkage with that linkage result using either of the two methods. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Results ==<br />
<br />
The probabilistic strategy outperformed the deterministic strategy in all scenarios. In linking situations with few or no errors with a powerful discriminating key, the simple deterministic full linkage strategy perform as good as that of probabilistic approach. The full deterministic strategy produced the lowest number of false positive links at the expense of missing considerable number of matches. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Conclusion==<br />
<br />
The PLR was found to be more flexible and provides data about the quality of the linkage process that can minimize the degree of linkage errors per given data. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Remarks about the article ==<br />
<br />
The article is very important study that can be helpful in helping organization decide which strategy adopt given the characteristics of data to be linked and the inherited shortcomings of both strategies. The article structure made it very difficult to read. It doesn’t have clearly spelled out conclusion except in the abstract. <br />
<br />
==Related Topics==<br />
<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
[[Improving record linkage performance in the presence of missing linkage data]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Results_from_simulated_data_sets:_probabilistic_record_linkage_outperforms_deterministic_record_linkageResults from simulated data sets: probabilistic record linkage outperforms deterministic record linkage2015-11-04T23:22:15Z<p>Ooabiri: Created page with "Tromp, M., Ravelli, A. C., Bonsel, G. J., Hasman, A., & Reitsma, J. B. (2011) == Introduction == Record linkage is possible when a combined set of partially identifying va..."</p>
<hr />
<div>Tromp, M., Ravelli, A. C., Bonsel, G. J., Hasman, A., & Reitsma, J. B. (2011)<br />
<br />
<br />
<br />
== Introduction ==<br />
<br />
Record linkage is possible when a combined set of partially identifying variables from different sources are combined to uniquely identify a patient. Two frequently applied strategies are the deterministic record linkage (DRL) and probabilistic record linkage (PRL). With DRL, all or predefined subset of linkage variables have to agree to consider a pair as a link. The PRL approach uses a probability based weights of agreement or disagreement between the paired variables to match or mismatch a patient. Two types of errors can occur in linked records-False Nonlink which is the failure to link two records that truly belong to the same person. False Link is linking two records that belong to different persons. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Method ==<br />
<br />
The authors used simulated data sets with varying amount of registration errors and discriminating power of linking variables that mimicked a range of realistic scenarios. They created a total of 10 scenarios. For each scenario, they compared the results of the two linkage strategies. Two datasets with four linking variables and specified sample size were used in the study. They stimulated 100 datasets by comparing the true status of linkage with that linkage result using either of the two methods. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Results ==<br />
<br />
The probabilistic strategy outperformed the deterministic strategy in all scenarios. In linking situations with few or no errors with a powerful discriminating key, the simple deterministic full linkage strategy perform as good as that of probabilistic approach. The full deterministic strategy produced the lowest number of false positive links at the expense of missing considerable number of matches. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Conclusion==<br />
<br />
The PLR was found to be more flexible and provides data about the quality of the linkage process that can minimize the degree of linkage errors per given data. <ref name = "2011, Tromp et al.">Tromp, 2011. Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage http://www.jclinepi.com/article/S0895-4356(10)00225-8/pdf/</ref><br />
<br />
== Remarks about the article ==<br />
<br />
The article is very important study that can be helpful in helping organization decide which strategy adopt given the characteristics of data to be linked and the inherited shortcomings of both strategies. The article structure made it very difficult to read. It doesn’t have clearly spelled out conclusion except in the abstract. <br />
<br />
==Related Topics==<br />
<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
[[Improving record linkage performance in the presence of missing linkage data]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Early_cost_and_safety_benefits_of_an_inpatient_electronic_health_recordEarly cost and safety benefits of an inpatient electronic health record2015-10-27T10:39:18Z<p>Ooabiri: </p>
<hr />
<div>==Introduction== <br />
<br />
There have been various reasons for hospitals to implement electronic health records (EHRs). Some of these reasons include penalties for healthcare facilities that do not demonstrate [[meaningful Use]] as well as financial benefits. In order to improve the quality of care delivered to patients as well as their safety, organizations such as the Institute of Medicine and Leapfrog have pushed for the implementation of EHRs, specifically the use of the computerized provider order entry (CPOE) function. <ref name= “EHR cost and safety benefits”>Early cost and safety benefits of an inpatient electronic health record<br />
Jonathan A Zlabek, Jared W Wickus, Michelle A Mathiason<br />
Journal of the American Medical Informatics Association Mar 2011, 18 (2) 169-172; retrieved October 21, 2015 from http://jamia.oxfordjournals.org/content/18/2/169 DOI: 10.1136/jamia.2010.007229 </ref><br />
<br />
==Methods==<br />
<br />
An inpatient EHR was implemented at Gundersen Lutheran Medical Center, a 325-bed hospital in Wisconsin. The EHR was implemented on November 1, 2008 and the [[CPOE]] function was added on February 4, 2009. Data were collected for a period of 1 year before and after EHR implementation. Measures of safety included medication events and measures of cost of care included paper use, laboratory and imaging tests, and transcription costs. Measures of quality that were studied included length of stay and readmissions that occurred within 30 days. <br />
<br />
==Results==<br />
<br />
The study demonstrated a decrease in paper consumption due to the use of electronic documentation, as well as a decrease in transcription costs. The use of the EHR was also able to identify potential medication errors that would not have been identified without the use of an EHR. It decreased the rate of medication errors as well. Overall it was found that the implantation of an EHR, specifically with a CPOE function, reduced cost of care and improved patient safety.<br />
<br />
==Related Articles==<br />
*[[CPOE]]<br />
*[[Computer physician order entry: benefits, costs, and issues.]]<br />
<br />
==References==<br />
<br />
<references/><br />
<br />
[[category: EHR]]<br />
[[Category: Meaningful Use]]<br />
[[category: CPOE]]<br />
[[category: HI5313-2015-FALL]]<br />
[[category: Benefits and Costs]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Costs_and_benefits_of_health_information_technologyCosts and benefits of health information technology2015-10-27T10:25:10Z<p>Ooabiri: </p>
<hr />
<div>==Introduction==<br />
<br />
The article discussed a literature review of the value of discrete HIT functions and systems in various healthcare settings. The authors found that some evidence suggests [[Health information technology]] (HIT) can improve efficiency, cost-effectiveness, quality, and safety by making best practice guidelines and evidence databases immediately available to clinicians, and by making computerized patient information available throughout a health care network, but little is known about the factors required for community practices to successfully implement off-the-shelf systems. <ref name = “Shekelle”> Shekelle, P. Martin, S., Keeler, E. (2006) Costs and Benefits of Health Information Technology: Evidence Reports/Technology Assessments, No. 132. </ref><br />
<br />
==Background==<br />
<br />
Leap Frog Group, Center for Medicare and Medicaid Services [[CMS]], Office of Disease Prevention and Health Research and Quality ODPHP and the [[Agency for Healthcare Research and Quality (AHRQ)| Agency for Healthcare Quality (AHRQ)]] [http://www.ahrq.gov/] requested an evidence report on costs and benefits of HIT systems to evaluate the evidence regarding the value of discrete HIT functions and systems in healthcare settings. Key questions were related to: <br />
* What are the costs and benefits of interoperability for providers and payors and what is the critical information required by decision makers<br />
* What is the framework for developing level/bundles for functionality by payer/purchase<br />
* What analytic methods can be used to produce evidence of cost and benefits for providers and payors<br />
* What are the barriers that providers and systems encounter in implementation of [[EHR|EHR]] system<br />
<br />
==Method==<br />
<br />
The authors conducted electronic searches of PubMed, Cochrane Registries and Cochrane Database of Reviews of effectiveness (DARE) and private industry articles published starting from 1995. There were 856 studies screened and 256 were included in the final analysis.<br />
<br />
==Results==<br />
<br />
Of the 256 studies the categories were not mutually exclusive and were categorized as: <ref name = “Shekelle”> Shekelle, P. Martin, S., Keeler, E. (2006) Costs and Benefits of Health Information Technology: Evidence Reports/Technology Assessments, No. 132. </ref><br />
* 156 related to [[CDS|decision support]], <br />
* 84 assessed the electronic medical record<br />
* 30 were about [[CPOE|CPOE]] (categories are not mutually exclusive) <br />
* 124 assessed the effects on outpatient or ambulatory setting<br />
* 82 assessed use in the hospital or inpatient setting<br />
* 97 studies used a randomized design<br />
* 11 were other [[Randomized controlled trial (RCT)|controlled clinical trails]]<br />
* 33 used a pre-post design<br />
* 20 used a time series<br />
* 17 were case studies with a concurrent control <br />
Of the 211 hypothesis-testing studies reviewed, 82 contained cost data.<br />
<br />
==Conclusion==<br />
<br />
Based on the reviews the authors concluded HIT can help transform the delivery of health care, making it safer, more effective, and more efficient. They noted some organizations have realized major gains through the implementation of multifunctional, interoperable HIT systems. The authors also concluded widespread implementation of HIT has been limited by a lack of generalizable knowledge about what types of HIT and implementation methods to utilize. Costs and benefits of health information technology.<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: HIT]]<br />
[[Category: Benefits and Costs]]</div>Ooabirihttp://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 diagnosis2015-10-27T10:17:59Z<p>Ooabiri: </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 early. 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 can in turn can help diagnosed cancers earlier for treatment. Some limitations to this study include the range of the study. The study was only conducted at two sites, mean the same methods used during this study 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 />
1. 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 />
==References==<br />
<references/><br />
<br />
[[Category:Reviews]]<br />
[[Category:EHR]]<br />
[[Category:CDS]]<br />
[[Category:HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Performance_of_probabilistic_method_to_detect_duplicate_individual_case_safety_reportsPerformance of probabilistic method to detect duplicate individual case safety reports2015-10-21T23:01:32Z<p>Ooabiri: /* Related Topics */</p>
<hr />
<div>==Background==<br />
<br />
The duplication of case reports is problematic and misleading when attempting to conduct effective pharmacovigilance. The sources of these duplicates are multifactorial and there are different approaches to detecting these duplicates. This article evaluated the [[Analysis of a Probabilistic Record Linkage Technique without Human Review| probabilistic record]] or [[Patient Matching Algorithms| patient matching]] approach. <ref name = "2014, Tregunno et al.">Tregunno, 2014. Performance of probabilistic method to detect duplicate individual case safety reports. http://www.ncbi.nlm.nih.gov/pubmed/24627310</ref><br />
<br />
==Method==<br />
<br />
<br />
The authors used a likelihood-based detection algorithm (VigiMatch®) that computes a match score (the probability that the two likely records relate same entity) for each pair of likely duplicated records was utilized. VigiMatch®was applied on the WHO global individual case safety reports database (vigiBase®) that contain over 8 million reports of suspected adverse effects of drugs reactions from 112 countries but this study focused on three countries (UK, Spain and Denmark).<br />
<br />
==Results==<br />
<br />
<br />
The vigiMatch achieved a very high predictive value for confirmed duplicates for each data set that range from 82% to 32%. The use of rule-based duplicates detection failed in significant proportion. The sources of duplication varied by country but similarities were observed. <br />
<br />
<br />
==Conclusion==<br />
<br />
<br />
The vigiMatch hit-miss approach detected duplicates that were missed by rule-based methodology resulting in reduced suspected duplicates and improved manual reviews accuracy. <br />
<br />
<br />
<br />
==Remarks about the article==<br />
<br />
<br />
My concern is that the authors did not consider the contextual differences among the countries selected for the study. There could have been that the data collection, analysis and reporting plan was different for each country and this could have affected the reported data to vigiBase®. While this article will serve as a base for my project, the context of Nigeria is very different from the European countries.<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Improving record linkage performance in the presence of missing linkage data]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Analysis_of_a_Probabilistic_Record_Linkage_Technique_without_Human_ReviewAnalysis of a Probabilistic Record Linkage Technique without Human Review2015-10-21T23:00:52Z<p>Ooabiri: /* Related Topics */</p>
<hr />
<div>Article by: Grannis, S. J., Overhage, J. M., Hui, S., & McDonald, C. J. (2003)<br />
<br />
<br />
== Introduction ==<br />
<br />
Record linkage is the process of combining information from two or more databases about an individual, family, or entity. A method of record linkage is the probabilistic linkage without human intervention. With this methodology, an algorithm is used to generate a match of the likelihood score, which is compared to a predetermined threshold for which, if this likelihood score is above a link is established and below it is a non-link. <ref name = "2003, Grannis et al.">Grannis, 2003. Analysis of a Probabilistic Record Linkage Technique without Human Review http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479910/</ref><br />
<br />
== Method ==<br />
The authors compared the performance of a deterministic method (from a previous study) to an unsupervised probabilistic method using the say gold-standard datasets for two hospital [[Registry| registries]]. In this particular study, the authors generated match likelihood scores for each record-pair using the Felligi-Sunter model which sums the component weights of each identifier in the record pair. Each pair was labeled as linked or non-linked. To ensure non-human review, the authors used an estimator function using the Expectation Maximization (EM) [http://www.cs.utah.edu/~piyush/teaching/EM_algorithm.pdf]<br />
<br />
== Results ==<br />
The authors reported a 99.98% and 99.80% true link and identifier agreement for registry A for manual review and EM estimator respectively. For registry B, they reported 99.99% and 99.89% for manual review and EM estimator respectively. The authors also reported an improvement in the sensitivity and specificity with use of the probabilistic method over the deterministic method (about 6 to 7 percent improvement in sensitivities with minimal decrease in specificity). <br />
<br />
<br />
== Conclusion ==<br />
In record linkage in which human intervention is not practical or possible, the use of the EM algorithm accurately estimated linkage parameters. <br />
<br />
== Remarks about the article ==<br />
The methodology used in this study is limited to small datasets. The methodology is limited in that the authors didn’t take into consideration minor spelling variation and topographical errors in data. It would have been helpful as well for the authors to include a website where reviewers and critics can reproduce or run their algorithm on sample datasets to test out accuracy as reported.<br />
<br />
In addition, this article was published in 2003 when it was more likely to have several department in a hospital to assign unique patient identifiers for each area. In Radiology for example, an "imaging number" was assigned to each patient in addition to their [[Medical Record Number|medical record number]]. The effort to make sure patients have only one record containing all their history continues to be front center today as it was in 2003.<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
[[Improving record linkage performance in the presence of missing linkage data]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Analysis_of_a_Probabilistic_Record_Linkage_Technique_without_Human_ReviewAnalysis of a Probabilistic Record Linkage Technique without Human Review2015-10-21T23:00:28Z<p>Ooabiri: </p>
<hr />
<div>Article by: Grannis, S. J., Overhage, J. M., Hui, S., & McDonald, C. J. (2003)<br />
<br />
<br />
== Introduction ==<br />
<br />
Record linkage is the process of combining information from two or more databases about an individual, family, or entity. A method of record linkage is the probabilistic linkage without human intervention. With this methodology, an algorithm is used to generate a match of the likelihood score, which is compared to a predetermined threshold for which, if this likelihood score is above a link is established and below it is a non-link. <ref name = "2003, Grannis et al.">Grannis, 2003. Analysis of a Probabilistic Record Linkage Technique without Human Review http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479910/</ref><br />
<br />
== Method ==<br />
The authors compared the performance of a deterministic method (from a previous study) to an unsupervised probabilistic method using the say gold-standard datasets for two hospital [[Registry| registries]]. In this particular study, the authors generated match likelihood scores for each record-pair using the Felligi-Sunter model which sums the component weights of each identifier in the record pair. Each pair was labeled as linked or non-linked. To ensure non-human review, the authors used an estimator function using the Expectation Maximization (EM) [http://www.cs.utah.edu/~piyush/teaching/EM_algorithm.pdf]<br />
<br />
== Results ==<br />
The authors reported a 99.98% and 99.80% true link and identifier agreement for registry A for manual review and EM estimator respectively. For registry B, they reported 99.99% and 99.89% for manual review and EM estimator respectively. The authors also reported an improvement in the sensitivity and specificity with use of the probabilistic method over the deterministic method (about 6 to 7 percent improvement in sensitivities with minimal decrease in specificity). <br />
<br />
<br />
== Conclusion ==<br />
In record linkage in which human intervention is not practical or possible, the use of the EM algorithm accurately estimated linkage parameters. <br />
<br />
== Remarks about the article ==<br />
The methodology used in this study is limited to small datasets. The methodology is limited in that the authors didn’t take into consideration minor spelling variation and topographical errors in data. It would have been helpful as well for the authors to include a website where reviewers and critics can reproduce or run their algorithm on sample datasets to test out accuracy as reported.<br />
<br />
In addition, this article was published in 2003 when it was more likely to have several department in a hospital to assign unique patient identifiers for each area. In Radiology for example, an "imaging number" was assigned to each patient in addition to their [[Medical Record Number|medical record number]]. The effort to make sure patients have only one record containing all their history continues to be front center today as it was in 2003.<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
[[Improving record linkage performance in the presence of missing linkage data]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Analysis_of_a_Probabilistic_Record_Linkage_Technique_without_Human_ReviewAnalysis of a Probabilistic Record Linkage Technique without Human Review2015-10-21T22:59:07Z<p>Ooabiri: </p>
<hr />
<div>Article by: Grannis, S. J., Overhage, J. M., Hui, S., & McDonald, C. J. (2003)<br />
<br />
<br />
== Introduction ==<br />
<br />
Record linkage is the process of combining information from two or more databases about an individual, family, or entity. A method of record linkage is the probabilistic linkage without human intervention. With this methodology, an algorithm is used to generate a match of the likelihood score, which is compared to a predetermined threshold for which, if this likelihood score is above a link is established and below it is a non-link. <ref name = "2003, Grannis et al.">Grannis, 2003. Analysis of a Probabilistic Record Linkage Technique without Human Review http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479910/</ref><br />
<br />
== Method ==<br />
The authors compared the performance of a deterministic method (from a previous study) to an unsupervised probabilistic method using the say gold-standard datasets for two hospital [[Registry| registries]]. In this particular study, the authors generated match likelihood scores for each record-pair using the Felligi-Sunter model which sums the component weights of each identifier in the record pair. Each pair was labeled as linked or non-linked. To ensure non-human review, the authors used an estimator function using the Expectation Maximization (EM) [http://www.cs.utah.edu/~piyush/teaching/EM_algorithm.pdf]<br />
<br />
== Results ==<br />
The authors reported a 99.98% and 99.80% true link and identifier agreement for registry A for manual review and EM estimator respectively. For registry B, they reported 99.99% and 99.89% for manual review and EM estimator respectively. The authors also reported an improvement in the sensitivity and specificity with use of the probabilistic method over the deterministic method (about 6 to 7 percent improvement in sensitivities with minimal decrease in specificity). <br />
<br />
<br />
== Conclusion ==<br />
In record linkage in which human intervention is not practical or possible, the use of the EM algorithm accurately estimated linkage parameters. <br />
<br />
== Remarks about the article ==<br />
The methodology used in this study is limited to small datasets. The methodology is limited in that the authors didn’t take into consideration minor spelling variation and topographical errors in data. It would have been helpful as well for the authors to include a website where reviewers and critics can reproduce or run their algorithm on sample datasets to test out accuracy as reported.<br />
<br />
In addition, this article was published in 2003 when it was more likely to have several department in a hospital to assign unique patient identifiers for each area. In Radiology for example, an "imaging number" was assigned to each patient in addition to their [[Medical Record Number|medical record number]]. The effort to make sure patients have only one record containing all their history continues to be front center today as it was in 2003.<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Analysis_of_a_Probabilistic_Record_Linkage_Technique_without_Human_ReviewAnalysis of a Probabilistic Record Linkage Technique without Human Review2015-10-21T22:58:38Z<p>Ooabiri: </p>
<hr />
<div>Article by: Grannis, S. J., Overhage, J. M., Hui, S., & McDonald, C. J. (2003)<br />
<br />
<br />
== Introduction ==<br />
<br />
Record linkage [[Improving record linkage performance in the presence of missing linkage data]] is the process of combining information from two or more databases about an individual, family, or entity. A method of record linkage is the probabilistic linkage without human intervention. With this methodology, an algorithm is used to generate a match of the likelihood score, which is compared to a predetermined threshold for which, if this likelihood score is above a link is established and below it is a non-link. <ref name = "2003, Grannis et al.">Grannis, 2003. Analysis of a Probabilistic Record Linkage Technique without Human Review http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479910/</ref><br />
<br />
== Method ==<br />
The authors compared the performance of a deterministic method (from a previous study) to an unsupervised probabilistic method using the say gold-standard datasets for two hospital [[Registry| registries]]. In this particular study, the authors generated match likelihood scores for each record-pair using the Felligi-Sunter model which sums the component weights of each identifier in the record pair. Each pair was labeled as linked or non-linked. To ensure non-human review, the authors used an estimator function using the Expectation Maximization (EM) [http://www.cs.utah.edu/~piyush/teaching/EM_algorithm.pdf]<br />
<br />
== Results ==<br />
The authors reported a 99.98% and 99.80% true link and identifier agreement for registry A for manual review and EM estimator respectively. For registry B, they reported 99.99% and 99.89% for manual review and EM estimator respectively. The authors also reported an improvement in the sensitivity and specificity with use of the probabilistic method over the deterministic method (about 6 to 7 percent improvement in sensitivities with minimal decrease in specificity). <br />
<br />
<br />
== Conclusion ==<br />
In record linkage in which human intervention is not practical or possible, the use of the EM algorithm accurately estimated linkage parameters. <br />
<br />
== Remarks about the article ==<br />
The methodology used in this study is limited to small datasets. The methodology is limited in that the authors didn’t take into consideration minor spelling variation and topographical errors in data. It would have been helpful as well for the authors to include a website where reviewers and critics can reproduce or run their algorithm on sample datasets to test out accuracy as reported.<br />
<br />
In addition, this article was published in 2003 when it was more likely to have several department in a hospital to assign unique patient identifiers for each area. In Radiology for example, an "imaging number" was assigned to each patient in addition to their [[Medical Record Number|medical record number]]. The effort to make sure patients have only one record containing all their history continues to be front center today as it was in 2003.<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Analysis_of_a_Probabilistic_Record_Linkage_Technique_without_Human_ReviewAnalysis of a Probabilistic Record Linkage Technique without Human Review2015-10-21T22:56:22Z<p>Ooabiri: </p>
<hr />
<div>Article by: Grannis, S. J., Overhage, J. M., Hui, S., & McDonald, C. J. (2003)<br />
<br />
<br />
== Introduction ==<br />
<br />
[[Improving record linkage performance in the presence of missing linkage data]]Record linkage is the process of combining information from two or more databases about an individual, family, or entity. A method of record linkage is the probabilistic linkage without human intervention. With this methodology, an algorithm is used to generate a match of the likelihood score, which is compared to a predetermined threshold for which, if this likelihood score is above a link is established and below it is a non-link. <ref name = "2003, Grannis et al.">Grannis, 2003. Analysis of a Probabilistic Record Linkage Technique without Human Review http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479910/</ref><br />
<br />
== Method ==<br />
The authors compared the performance of a deterministic method (from a previous study) to an unsupervised probabilistic method using the say gold-standard datasets for two hospital [[Registry| registries]]. In this particular study, the authors generated match likelihood scores for each record-pair using the Felligi-Sunter model which sums the component weights of each identifier in the record pair. Each pair was labeled as linked or non-linked. To ensure non-human review, the authors used an estimator function using the Expectation Maximization (EM) [http://www.cs.utah.edu/~piyush/teaching/EM_algorithm.pdf]<br />
<br />
== Results ==<br />
The authors reported a 99.98% and 99.80% true link and identifier agreement for registry A for manual review and EM estimator respectively. For registry B, they reported 99.99% and 99.89% for manual review and EM estimator respectively. The authors also reported an improvement in the sensitivity and specificity with use of the probabilistic method over the deterministic method (about 6 to 7 percent improvement in sensitivities with minimal decrease in specificity). <br />
<br />
<br />
== Conclusion ==<br />
In record linkage in which human intervention is not practical or possible, the use of the EM algorithm accurately estimated linkage parameters. <br />
<br />
== Remarks about the article ==<br />
The methodology used in this study is limited to small datasets. The methodology is limited in that the authors didn’t take into consideration minor spelling variation and topographical errors in data. It would have been helpful as well for the authors to include a website where reviewers and critics can reproduce or run their algorithm on sample datasets to test out accuracy as reported.<br />
<br />
In addition, this article was published in 2003 when it was more likely to have several department in a hospital to assign unique patient identifiers for each area. In Radiology for example, an "imaging number" was assigned to each patient in addition to their [[Medical Record Number|medical record number]]. The effort to make sure patients have only one record containing all their history continues to be front center today as it was in 2003.<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]<br />
[[Category: HI5313-2015-FALL]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Improving_record_linkage_performance_in_the_presence_of_missing_linkage_dataImproving record linkage performance in the presence of missing linkage data2015-10-21T22:52:39Z<p>Ooabiri: </p>
<hr />
<div>Article by:Ong, T. C., Mannino, M. V., Schilling, L. M., & Kahn, M. G. (2014)<br />
<br />
<br />
<br />
== Introduction ==<br />
In the absence of accurate and universal [[patient identifier]]s, record linkage methods use non-unique fields to link two or more records belonging to the same individual. These quasi-identifiers (e.g. date of birth, lastname, place of birth, etc) are fields that when combined together may uniquely identify an individual. In the healthcare setting, such identifiers may be missing due to a multitude of reasons, making record linkage very difficult. In relational databases used to store medical records, two or more records are linked using a primary key that must be unique and should not be missing but missing data are the fact of life in healthcare research. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Background ==<br />
There are two main approaches to matching two or more records using identifiers: deterministic and probabilistic. Deterministic methods link records based on exact agreement/disagreement of a combination of quasi-identifiers. Deterministic approaches are unable to match records with typographical or phonetic errors. Probabilistic methods calculate a likelihood score to determine if two records refer to the same person. The most common method is the Fellegi-Sunter (FS) method which considers each pair of quasi-identifier in a record pair to be either match or un-match based on the assigned matching weights. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref> <br />
<br />
== Method ==<br />
The authors used the open-source Fine-grained record Linkage (FRIL) software that extends the distance algorithms and FS scoring methods to develop three methods (Weight Redistribution, Distance Imputation and Linkage Expansion). Weight Redistribution removes fields with missing data sets and redistributes the weights based on the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in linkage field. To test this methodology, the authors created two paired datasets initially containing 5000 records each that contains 9 fields with simulated values with varying corruption rates. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Results ==<br />
The method developed in this research did better than previous methods of record linkage. This study had a sensitivity ranging from .895 to .992 and Positive predictive values (PPV) ranging from 0.865 to 1.00 in data sets with low corruption rate. The authors also found increased corruption rates lead to decreased sensitivity in all methods. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Conclusion==<br />
Depending on the performance goal of the record linkage process, the three new methods responded well to big data sets with the missing values in some fields but none has 100% sensitivity with 100% specificity. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Remarks about the article ==<br />
The methodology used in this study address a lot of the issues from the previously reviewed similar studies. First it can be used with missing values in some fields. Second their methods are hybrid of deterministic and probabilistic methods. The authors gave access to the source codes, datasets, and the documentation used in the research (https://github.com/recordlinkagerep/missingdataproject). This is helpful in reproducing their results using their datasets or applying to other similar data sets. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Improving_record_linkage_performance_in_the_presence_of_missing_linkage_dataImproving record linkage performance in the presence of missing linkage data2015-10-21T22:49:56Z<p>Ooabiri: </p>
<hr />
<div>Article by:Ong, T. C., Mannino, M. V., Schilling, L. M., & Kahn, M. G. (2014)<br />
<br />
<br />
<br />
== Introduction ==<br />
In the absence of accurate and universal patient identifiers, record linkage methods use non-unique fields to link two or more records belonging to the same individual. These quasi-identifiers (e.g. date of birth, lastname, place of birth, etc) are fields that when combined together may uniquely identify an individual. In the healthcare setting, such identifiers may be missing due to a multitude of reasons, making record linkage very difficult. In relational databases used to store medical records, two or more records are linked using a primary key that must be unique and should not be missing but missing data are the fact of life in healthcare research. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Background ==<br />
There are two main approaches to matching two or more records using identifiers: deterministic and probabilistic. Deterministic methods link records based on exact agreement/disagreement of a combination of quasi-identifiers. Deterministic approaches are unable to match records with typographical or phonetic errors. Probabilistic methods calculate a likelihood score to determine if two records refer to the same person. The most common method is the Fellegi-Sunter (FS) method which considers each pair of quasi-identifier in a record pair to be either match or un-match based on the assigned matching weights. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref> <br />
<br />
== Method ==<br />
The authors used the open-source Fine-grained record Linkage (FRIL) software that extends the distance algorithms and FS scoring methods to develop three methods (Weight Redistribution, Distance Imputation and Linkage Expansion). Weight Redistribution removes fields with missing data sets and redistributes the weights based on the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in linkage field. To test this methodology, the authors created two paired datasets initially containing 5000 records each that contains 9 fields with simulated values with varying corruption rates. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Results ==<br />
The method developed in this research did better than previous methods of record linkage. This study had a sensitivity ranging from .895 to .992 and Positive predictive values (PPV) ranging from 0.865 to 1.00 in data sets with low corruption rate. The authors also found increased corruption rates lead to decreased sensitivity in all methods. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Conclusion==<br />
Depending on the performance goal of the record linkage process, the three new methods responded well to big data sets with the missing values in some fields but none has 100% sensitivity with 100% specificity. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Remarks about the article ==<br />
The methodology used in this study address a lot of the issues from the previously reviewed similar studies. First it can be used with missing values in some fields. Second their methods are hybrid of deterministic and probabilistic methods. The authors gave access to the source codes, datasets, and the documentation used in the research (https://github.com/recordlinkagerep/missingdataproject). This is helpful in reproducing their results using their datasets or applying to other similar data sets. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Improving_record_linkage_performance_in_the_presence_of_missing_linkage_dataImproving record linkage performance in the presence of missing linkage data2015-10-21T22:49:11Z<p>Ooabiri: </p>
<hr />
<div>Article by:Ong, T. C., Mannino, M. V., Schilling, L. M., & Kahn, M. G. (2014)<br />
<br />
<br />
<br />
== Introduction ==<br />
In the absence of accurate and universal patient identifiers, record linkage methods use non-unique fields to link two or more records belonging to the same individual. These quasi-identifiers (e.g. date of birth, lastname, place of birth, etc) are fields that when combined together may uniquely identify an individual. In the healthcare setting, such identifiers may be missing due to a multitude of reasons, making record linkage very difficult. In relational databases used to store medical records, two or more records are linked using a primary key that must be unique and should not be missing but missing data are the fact of life in healthcare research. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Background ==<br />
There are two main approaches to matching two or more records using identifiers: deterministic and probabilistic. Deterministic methods link records based on exact agreement/disagreement of a combination of quasi-identifiers. Deterministic approaches are unable to match records with typographical or phonetic errors. Probabilistic methods calculate a likelihood score to determine if two records refer to the same person. The most common method is the Fellegi-Sunter (FS) method which considers each pair of quasi-identifier in a record pair to be either match or un-match based on the assigned matching weights. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref> <br />
<br />
== Method ==<br />
The authors used the open-source Fine-grained record Linkage (FRIL) software that extends the distance algorithms and FS scoring methods to develop three methods (Weight Redistribution, Distance Imputation and Linkage Expansion). Weight Redistribution removes fields with missing data sets and redistributes the weights based on the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in linkage field. To test this methodology, the authors created two paired datasets initially containing 5000 records each that contains 9 fields with simulated values with varying corruption rates. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Results ==<br />
The method developed in this research did better than previous methods of record linkage. This study had a sensitivity ranging from .895 to .992 and Positive predictive values (PPV) ranging from 0.865 to 1.00 in data sets with low corruption rate. The authors also found increased corruption rates lead to decreased sensitivity in all methods. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
== Conclusion==<br />
Depending on the performance goal of the record linkage process, the three new methods responded well to big data sets with the missing values in some fields but none has 100% sensitivity with 100% specificity. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf<br />
<br />
== Remarks about the article ==<br />
The methodology used in this study address a lot of the issues from the previously reviewed similar studies. First it can be used with missing values in some fields. Second their methods are hybrid of deterministic and probabilistic methods. The authors gave access to the source codes, datasets, and the documentation used in the research (https://github.com/recordlinkagerep/missingdataproject). This is helpful in reproducing their results using their datasets or applying to other similar data sets. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf/</ref><br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Improving_record_linkage_performance_in_the_presence_of_missing_linkage_dataImproving record linkage performance in the presence of missing linkage data2015-10-21T22:46:53Z<p>Ooabiri: </p>
<hr />
<div>Article by:Ong, T. C., Mannino, M. V., Schilling, L. M., & Kahn, M. G. (2014)<br />
<br />
<br />
<br />
== Introduction ==<br />
In the absence of accurate and universal patient identifiers, record linkage methods use non-unique fields to link two or more records belonging to the same individual. These quasi-identifiers (e.g. date of birth, lastname, place of birth, etc) are fields that when combined together may uniquely identify an individual. In the healthcare setting, such identifiers may be missing due to a multitude of reasons, making record linkage very difficult. In relational databases used to store medical records, two or more records are linked using a primary key that must be unique and should not be missing but missing data are the fact of life in healthcare research. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf<br />
<br />
== Background ==<br />
There are two main approaches to matching two or more records using identifiers: deterministic and probabilistic. Deterministic methods link records based on exact agreement/disagreement of a combination of quasi-identifiers. Deterministic approaches are unable to match records with typographical or phonetic errors. Probabilistic methods calculate a likelihood score to determine if two records refer to the same person. The most common method is the Fellegi-Sunter (FS) method which considers each pair of quasi-identifier in a record pair to be either match or un-match based on the assigned matching weights. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf <br />
<br />
== Method ==<br />
The authors used the open-source Fine-grained record Linkage (FRIL) software that extends the distance algorithms and FS scoring methods to develop three methods (Weight Redistribution, Distance Imputation and Linkage Expansion). Weight Redistribution removes fields with missing data sets and redistributes the weights based on the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in linkage field. To test this methodology, the authors created two paired datasets initially containing 5000 records each that contains 9 fields with simulated values with varying corruption rates. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf<br />
<br />
== Results ==<br />
The method developed in this research did better than previous methods of record linkage. This study had a sensitivity ranging from .895 to .992 and Positive predictive values (PPV) ranging from 0.865 to 1.00 in data sets with low corruption rate. The authors also found increased corruption rates lead to decreased sensitivity in all methods. <br />
<br />
== Conclusion==<br />
Depending on the performance goal of the record linkage process, the three new methods responded well to big data sets with the missing values in some fields but none has 100% sensitivity with 100% specificity. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf<br />
<br />
== Remarks about the article ==<br />
The methodology used in this study address a lot of the issues from the previously reviewed similar studies. First it can be used with missing values in some fields. Second their methods are hybrid of deterministic and probabilistic methods. The authors gave access to the source codes, datasets, and the documentation used in the research (https://github.com/recordlinkagerep/missingdataproject). This is helpful in reproducing their results using their datasets or applying to other similar data sets. <ref name = "2014, Ong et al.">Ong, 2014. Improving record linkage performance in the presence of missing linkage data http://www.j-biomed-inform.com/article/S1532-0464(14)00019-7/pdf<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Improving_record_linkage_performance_in_the_presence_of_missing_linkage_dataImproving record linkage performance in the presence of missing linkage data2015-10-21T22:41:43Z<p>Ooabiri: </p>
<hr />
<div>Article by:Ong, T. C., Mannino, M. V., Schilling, L. M., & Kahn, M. G. (2014)<br />
<br />
<br />
<br />
== Introduction ==<br />
In the absence of accurate and universal patient identifiers, record linkage methods use non-unique fields to link two or more records belonging to the same individual. These quasi-identifiers (e.g. date of birth, lastname, place of birth, etc) are fields that when combined together may uniquely identify an individual. In the healthcare setting, such identifiers may be missing due to a multitude of reasons, making record linkage very difficult. In relational databases used to store medical records, two or more records are linked using a primary key that must be unique and should not be missing but missing data are the fact of life in healthcare research. <br />
<br />
== Background ==<br />
There are two main approaches to matching two or more records using identifiers: deterministic and probabilistic. Deterministic methods link records based on exact agreement/disagreement of a combination of quasi-identifiers. Deterministic approaches are unable to match records with typographical or phonetic errors. Probabilistic methods calculate a likelihood score to determine if two records refer to the same person. The most common method is the Fellegi-Sunter (FS) method which considers each pair of quasi-identifier in a record pair to be either match or un-match based on the assigned matching weights. <br />
<br />
== Method ==<br />
The authors used the open-source Fine-grained record Linkage (FRIL) software that extends the distance algorithms and FS scoring methods to develop three methods (Weight Redistribution, Distance Imputation and Linkage Expansion). Weight Redistribution removes fields with missing data sets and redistributes the weights based on the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in linkage field. To test this methodology, the authors created two paired datasets initially containing 5000 records each that contains 9 fields with simulated values with varying corruption rates.<br />
<br />
== Results ==<br />
The method developed in this research did better than previous methods of record linkage. This study had a sensitivity ranging from .895 to .992 and Positive predictive values (PPV) ranging from 0.865 to 1.00 in data sets with low corruption rate. The authors also found increased corruption rates lead to decreased sensitivity in all methods. <br />
<br />
== Conclusion==<br />
Depending on the performance goal of the record linkage process, the three new methods responded well to big data sets with the missing values in some fields but none has 100% sensitivity with 100% specificity.<br />
<br />
== Remarks about the article ==<br />
The methodology used in this study address a lot of the issues from the previously reviewed similar studies. First it can be used with missing values in some fields. Second their methods are hybrid of deterministic and probabilistic methods. The authors gave access to the source codes, datasets, and the documentation used in the research (https://github.com/recordlinkagerep/missingdataproject). This is helpful in reproducing their results using their datasets or applying to other similar data sets.<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Improving_record_linkage_performance_in_the_presence_of_missing_linkage_dataImproving record linkage performance in the presence of missing linkage data2015-10-21T22:41:27Z<p>Ooabiri: </p>
<hr />
<div>Article by:Ong, T. C., Mannino, M. V., Schilling, L. M., & Kahn, M. G. (2014)<br />
<br />
== Introduction ==<br />
In the absence of accurate and universal patient identifiers, record linkage methods use non-unique fields to link two or more records belonging to the same individual. These quasi-identifiers (e.g. date of birth, lastname, place of birth, etc) are fields that when combined together may uniquely identify an individual. In the healthcare setting, such identifiers may be missing due to a multitude of reasons, making record linkage very difficult. In relational databases used to store medical records, two or more records are linked using a primary key that must be unique and should not be missing but missing data are the fact of life in healthcare research. <br />
<br />
== Background ==<br />
There are two main approaches to matching two or more records using identifiers: deterministic and probabilistic. Deterministic methods link records based on exact agreement/disagreement of a combination of quasi-identifiers. Deterministic approaches are unable to match records with typographical or phonetic errors. Probabilistic methods calculate a likelihood score to determine if two records refer to the same person. The most common method is the Fellegi-Sunter (FS) method which considers each pair of quasi-identifier in a record pair to be either match or un-match based on the assigned matching weights. <br />
<br />
== Method ==<br />
The authors used the open-source Fine-grained record Linkage (FRIL) software that extends the distance algorithms and FS scoring methods to develop three methods (Weight Redistribution, Distance Imputation and Linkage Expansion). Weight Redistribution removes fields with missing data sets and redistributes the weights based on the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in linkage field. To test this methodology, the authors created two paired datasets initially containing 5000 records each that contains 9 fields with simulated values with varying corruption rates.<br />
<br />
== Results ==<br />
The method developed in this research did better than previous methods of record linkage. This study had a sensitivity ranging from .895 to .992 and Positive predictive values (PPV) ranging from 0.865 to 1.00 in data sets with low corruption rate. The authors also found increased corruption rates lead to decreased sensitivity in all methods. <br />
<br />
== Conclusion==<br />
Depending on the performance goal of the record linkage process, the three new methods responded well to big data sets with the missing values in some fields but none has 100% sensitivity with 100% specificity.<br />
<br />
== Remarks about the article ==<br />
The methodology used in this study address a lot of the issues from the previously reviewed similar studies. First it can be used with missing values in some fields. Second their methods are hybrid of deterministic and probabilistic methods. The authors gave access to the source codes, datasets, and the documentation used in the research (https://github.com/recordlinkagerep/missingdataproject). This is helpful in reproducing their results using their datasets or applying to other similar data sets.<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]</div>Ooabirihttp://clinfowiki.org/wiki/index.php/Improving_record_linkage_performance_in_the_presence_of_missing_linkage_dataImproving record linkage performance in the presence of missing linkage data2015-10-21T22:38:28Z<p>Ooabiri: Created page with "== Introduction == In the absence of accurate and universal patient identifiers, record linkage methods use non-unique fields to link two or more records belonging to the same..."</p>
<hr />
<div>== Introduction ==<br />
In the absence of accurate and universal patient identifiers, record linkage methods use non-unique fields to link two or more records belonging to the same individual. These quasi-identifiers (e.g. date of birth, lastname, place of birth, etc) are fields that when combined together may uniquely identify an individual. In the healthcare setting, such identifiers may be missing due to a multitude of reasons, making record linkage very difficult. In relational databases used to store medical records, two or more records are linked using a primary key that must be unique and should not be missing but missing data are the fact of life in healthcare research. <br />
<br />
== Background ==<br />
There are two main approaches to matching two or more records using identifiers: deterministic and probabilistic. Deterministic methods link records based on exact agreement/disagreement of a combination of quasi-identifiers. Deterministic approaches are unable to match records with typographical or phonetic errors. Probabilistic methods calculate a likelihood score to determine if two records refer to the same person. The most common method is the Fellegi-Sunter (FS) method which considers each pair of quasi-identifier in a record pair to be either match or un-match based on the assigned matching weights. <br />
<br />
== Method ==<br />
The authors used the open-source Fine-grained record Linkage (FRIL) software that extends the distance algorithms and FS scoring methods to develop three methods (Weight Redistribution, Distance Imputation and Linkage Expansion). Weight Redistribution removes fields with missing data sets and redistributes the weights based on the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in linkage field. To test this methodology, the authors created two paired datasets initially containing 5000 records each that contains 9 fields with simulated values with varying corruption rates.<br />
<br />
== Results ==<br />
The method developed in this research did better than previous methods of record linkage. This study had a sensitivity ranging from .895 to .992 and Positive predictive values (PPV) ranging from 0.865 to 1.00 in data sets with low corruption rate. The authors also found increased corruption rates lead to decreased sensitivity in all methods. <br />
<br />
== Conclusion==<br />
Depending on the performance goal of the record linkage process, the three new methods responded well to big data sets with the missing values in some fields but none has 100% sensitivity with 100% specificity.<br />
<br />
== Remarks about the article ==<br />
The methodology used in this study address a lot of the issues from the previously reviewed similar studies. First it can be used with missing values in some fields. Second their methods are hybrid of deterministic and probabilistic methods. The authors gave access to the source codes, datasets, and the documentation used in the research (https://github.com/recordlinkagerep/missingdataproject). This is helpful in reproducing their results using their datasets or applying to other similar data sets.<br />
<br />
==Related Topics==<br />
[[Master patient index]]<br />
<br />
[[Performance of probabilistic method to detect duplicate individual case safety reports]]<br />
<br />
[[Matching identifiers in electronic health records: implications for duplicate records and patient safety]]<br />
<br />
==Reference==<br />
<references/><br />
<br />
[[Category: Reviews]]<br />
[[Category: Patient Matching Algorithm]]</div>Ooabiri