Difference between revisions of "Isabel"
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Diagnostic error is a significant problem in acute specialties such as emergency medicine and primary care. Studies show that errors of omission far outnumber errors of commission. Errors of omission are caused by cognitive biases intrinsic to diagnostic decision making; examples are premature closure, confirmation bias and faulty context generation. Isabel aims to provide clinically important alternative diagnoses for consideration; it is not a diagnostic oracle. In this sense, it is most useful in acute environments such as emergency departments, critical care units and family practice, although it can be used in any setting. | Diagnostic error is a significant problem in acute specialties such as emergency medicine and primary care. Studies show that errors of omission far outnumber errors of commission. Errors of omission are caused by cognitive biases intrinsic to diagnostic decision making; examples are premature closure, confirmation bias and faulty context generation. Isabel aims to provide clinically important alternative diagnoses for consideration; it is not a diagnostic oracle. In this sense, it is most useful in acute environments such as emergency departments, critical care units and family practice, although it can be used in any setting. | ||
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Using the same proprietary technology which powers the diagnosis reminder system, Isabel also mobilizes knowledge to help the clinician find relevant and diagnosis specific answers to clinical questions more easily and quickly at the point of care. | Using the same proprietary technology which powers the diagnosis reminder system, Isabel also mobilizes knowledge to help the clinician find relevant and diagnosis specific answers to clinical questions more easily and quickly at the point of care. | ||
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Isabel uniquely adds intelligence to the electronic medical record (EMR) by processing extracted relevant clinical information thereby automatically providing the clinician with diagnosis support instantly with no additional data entry. To date, Isabel interfaces with [[NextGen]], PatientKeeper and A4 Health Systems, along with a number of hospital-based [[EMR]] vendors including [[Cerner Millennium|Cerner]]. | Isabel uniquely adds intelligence to the electronic medical record (EMR) by processing extracted relevant clinical information thereby automatically providing the clinician with diagnosis support instantly with no additional data entry. To date, Isabel interfaces with [[NextGen]], PatientKeeper and A4 Health Systems, along with a number of hospital-based [[EMR]] vendors including [[Cerner Millennium|Cerner]]. | ||
| − | + | Isabel is available as an integrated moduled for the [http://www.clinfowiki.org/wiki/index.php/T_SystemEV T SystemEV] Emergency Department Information System.(4) | |
| − | + | While Isabel is available on a subscription basis to medical professionals and centers, in late 2012, the company announced the introduction of a free online diagnostic service for patients. In an attempt to assuage the conflict between providers and patients bring their own Internet health research to clinical visits, Isabel is offering patients a symptom checker to engage patients in medical decision making.(8) | |
| − | + | ==Effectiveness Studies | |
| − | + | Isabel has been studied in a number of settings and is remarkably accurate. [http://www.ncbi.nlm.nih.gov/pubmed/17238463] | |
| − | + | Recent evaluation of its impact on subjects' decision making in BMC Medical Informatics and Decision Making: [http://www.biomedcentral.com/1472-6947/6/22/abstract] | |
| − | + | A study as St. Mary's Hospital in London showed that by using the application, an improvement of the time taken to process case simulation in an acute care setting. This system has been used to measure the impact of diagnostic support tools on clinical decision making and also tested for developing a quality score that may become a key outcome measure in larger studies to fully assess the value of diagnostic decision aids.(1) | |
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| + | It has been studied as a support tool for Internal Medicine diagnosis. In one study in the VA Medical Center (USA), this clinical decision support system suggested the correct diagnosis in 48 of 50 cases (96%) with key findings entry, and in 37 of the 50 cases (74%) if the entire case history was pasted in. Pasting took seconds, manual entry less than a minute, and results were provided within 2-3 seconds with either approach. It was concluded that Isabel clinical decision support system quickly suggested the correct diagnosis in almost all of these complex cases.(2) | ||
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| + | Recently, a multi-center study looked for its validation as a diagnostic reminder system in emergency medicine and found out that the diagnostic system displayed the final discharge diagnosis in 95% of inpatients and 90% of "must-not-miss" diagnoses suggested by the expert panel. The discharge diagnosis appeared within the first 10 suggestions in 78% of cases. This study concludes that Isabel diagnostic aid has been shown to be of potential use in reminding junior doctors of key diagnoses in the emergency department. (3) | ||
== History == | == History == | ||
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# Validation of a diagnostic reminder system in emergency medicine: a multi-centre study; Ramnarayan P, Cronje N, Brown R, Negus R, Coode B, Moss P, Hassan T, Hamer W, Britto J;Emerg Med J. 2007 Sep;24(9):619-24. | # Validation of a diagnostic reminder system in emergency medicine: a multi-centre study; Ramnarayan P, Cronje N, Brown R, Negus R, Coode B, Moss P, Hassan T, Hamer W, Britto J;Emerg Med J. 2007 Sep;24(9):619-24. | ||
# http://www.tsystem.com/Products/T-SystemEV/Product-Overview/T-SystemEV-Diagnosis-Decision-Support | # http://www.tsystem.com/Products/T-SystemEV/Product-Overview/T-SystemEV-Diagnosis-Decision-Support | ||
| − | # | + | # 8. http://www.informationweek.com/healthcare/patient/isabel-opens-online-diagnostic-system-to/240144314 |
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[[Category: EHR]] | [[Category: EHR]] | ||
[[Category: UT-SHIS SP09]] | [[Category: UT-SHIS SP09]] | ||
Revision as of 19:42, 7 September 2013
Isabel is a diagnostic reminder system that has been in existence since 2001. Initially developed for pediatrics, it now covers all specialties and age groups.
Isabel, diagnostic reminder system
Diagnostic error is a significant problem in acute specialties such as emergency medicine and primary care. Studies show that errors of omission far outnumber errors of commission. Errors of omission are caused by cognitive biases intrinsic to diagnostic decision making; examples are premature closure, confirmation bias and faulty context generation. Isabel aims to provide clinically important alternative diagnoses for consideration; it is not a diagnostic oracle. In this sense, it is most useful in acute environments such as emergency departments, critical care units and family practice, although it can be used in any setting.
Using the same proprietary technology which powers the diagnosis reminder system, Isabel also mobilizes knowledge to help the clinician find relevant and diagnosis specific answers to clinical questions more easily and quickly at the point of care.
Isabel uniquely adds intelligence to the electronic medical record (EMR) by processing extracted relevant clinical information thereby automatically providing the clinician with diagnosis support instantly with no additional data entry. To date, Isabel interfaces with NextGen, PatientKeeper and A4 Health Systems, along with a number of hospital-based EMR vendors including Cerner.
Isabel is available as an integrated moduled for the T SystemEV Emergency Department Information System.(4)
While Isabel is available on a subscription basis to medical professionals and centers, in late 2012, the company announced the introduction of a free online diagnostic service for patients. In an attempt to assuage the conflict between providers and patients bring their own Internet health research to clinical visits, Isabel is offering patients a symptom checker to engage patients in medical decision making.(8)
==Effectiveness Studies
Isabel has been studied in a number of settings and is remarkably accurate. [1]
Recent evaluation of its impact on subjects' decision making in BMC Medical Informatics and Decision Making: [2]
A study as St. Mary's Hospital in London showed that by using the application, an improvement of the time taken to process case simulation in an acute care setting. This system has been used to measure the impact of diagnostic support tools on clinical decision making and also tested for developing a quality score that may become a key outcome measure in larger studies to fully assess the value of diagnostic decision aids.(1)
It has been studied as a support tool for Internal Medicine diagnosis. In one study in the VA Medical Center (USA), this clinical decision support system suggested the correct diagnosis in 48 of 50 cases (96%) with key findings entry, and in 37 of the 50 cases (74%) if the entire case history was pasted in. Pasting took seconds, manual entry less than a minute, and results were provided within 2-3 seconds with either approach. It was concluded that Isabel clinical decision support system quickly suggested the correct diagnosis in almost all of these complex cases.(2)
Recently, a multi-center study looked for its validation as a diagnostic reminder system in emergency medicine and found out that the diagnostic system displayed the final discharge diagnosis in 95% of inpatients and 90% of "must-not-miss" diagnoses suggested by the expert panel. The discharge diagnosis appeared within the first 10 suggestions in 78% of cases. This study concludes that Isabel diagnostic aid has been shown to be of potential use in reminding junior doctors of key diagnoses in the emergency department. (3)
History
Isabel was founded in July 1999 by Jason Maude and Charlotte Maude together with Dr. Joseph Britto in honor of the Maude's infant daughter Isabel Maude and all patients whose lives have been impacted by a missed or delayed diagnosis. The founders of ISABEL wanted to create a system that would enable doctors to reduce the number of patients who become seriously ill as a result of an incorrect or delayed diagnosis. After two years of research and validation led by P. Ramnarayan MD, the pediatric version of the Isabel diagnostic tool was launched in June 2002. With support from the UK Department of Health, the UK National Health Service and other key medical institutions, Isabel quickly gained credibility and support from around the world. Isabel is a Web-based clinical decision support system which uses cutting edge technology from Autonomy an industry leader in Meaning Based Computing (MBC) coupled with proprietary Isabel algorithms to 'understand' vast amounts of medical knowledge. This enables the system to instantly provide the busy clinician with a safe checklist of likely diagnoses for a set of signs and symptoms entered in free text. The clinicians who 'Isabel' their patients at an early stage are able to offer a higher quality of care and reduce clinical risk by ensuring that important possible diagnoses have not been missed.
References
- http://publications.autonomy.com/pdfs/Autonomy/Case%20Studies/Pharmaceutical/
- http://www.isabelhealthcare.com
- http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1513379&blobtype=pdf
- Measuring the impact of diagnostic decision support on the quality of clinical decision making: development of a reliable and valid composite score; Ramnarayan P, Kapoor RR, Coren M, Nanduri V, Tomlinson AL, Taylor PM, Wyatt JC, Britto JF; J Am Med Inform Assoc. 2003 Nov-Dec;10(6):608-10.
- Performance of a web-based clinical diagnosis support system for internists;Graber ML, Mathew A;J Gen Intern Med. 2008 Jan;23 Suppl 1:85-7.
- Validation of a diagnostic reminder system in emergency medicine: a multi-centre study; Ramnarayan P, Cronje N, Brown R, Negus R, Coode B, Moss P, Hassan T, Hamer W, Britto J;Emerg Med J. 2007 Sep;24(9):619-24.
- http://www.tsystem.com/Products/T-SystemEV/Product-Overview/T-SystemEV-Diagnosis-Decision-Support
- 8. http://www.informationweek.com/healthcare/patient/isabel-opens-online-diagnostic-system-to/240144314
