AI systems in clinical practice

Decision support systems
Web-based diagnosis reminder and knowledge mobilising system

developed by clinical domains keywords
Developed and delivered by Isabel Healthcare Inc in the USA and Isabel Healthcare Ltd in UK/Europe/India. Isabel covers all ages (neonates to geriatrics) and all major specialties and sub-specialties in Internal Medicine, Surgery, Gynecology & Obstetrics, Pediatrics, Geriatrics, Oncology, Toxicology and Bioterrorism. Clinical decision support system, diagnosis decision support system, differential diagnosis, diagnosis reminder system, knowledge mobilizing system, electronic health records [EHR] electronic medical records [EMR], medical error, misdiagnosis, patient safety, quality of care, internet, WWW, PDA.
location commissioned status
Global list of clients in USA, UK / Europe and India include hospital institutions, libraries, medical schools and resident programs. 20,000 registered individual users (May 2006). Pediatric system and pediatric sub- specialties released June 2001. Adult system released January 2005. Bioterrorism diagnosis reminder system released June 2005.

Isabel is a commercial product sold on an annual subscription basis. Isabel is web-based and sold to individual users, family practices, group facilities, medical schools, hospital institutions and interfaced / bundled with EMR systems.

isabel logo Isabel is a web-based diagnosis decision support system created in 2001 by physicians to offer diagnosis decision support at the point of care. Isabel has been extensively validated and been shown to enhance clinician’s cognitive skills and thereby improve patient safety and the quality of patient care. Isabel now covers all ages (neonates to geriatrics) and all major specialties and sub-specialties in Internal Medicine, Surgery, Gynecology & Obstetrics, Pediatrics, Geriatrics, Oncology, Toxicology and Bioterrorism. Isabel is fast and easy to use & gives the clinician an instant list of likely diagnoses for a given set of clinical features (symptoms, signs, results of tests and investigations etc). Following on from history taking and clinical examination Isabel assists the provider [“learned intermediary”] by reconciling [concept matching] patient data sets with data sets as described in established medical literature (textbooks and journals). Isabel also has the ability to suggest causative drugs for clinical features entered and allows clinicians to follow their hunches by related diagnoses and restricting searches to specific body systems. Isabel has been interfaced with electronic medical record systems [EMR]. Isabel is able to extract pre-assigned data from an EMR - on a single click delivers diagnoses and knowledge to the EMR user [no data entry into Isabel required]. Isabel not only assists in making the right diagnosis but helps answer clinical questions with up to date knowledge from textbooks and journals

Isabel uses Autonomy’s natural language processing software as opposed to standard key word searches. Isabel consists of a proprietary database of medical content and a tutored taxonomy of over 11,000 diagnoses and 4,000 drugs and heuristics. Each diagnosis / drug entity has a kernel of knowledge and heuristics (age, region, gender, pregnancy). So, when a clinician enters clinical features into Isabel he/she is given a list of likely diagnoses or causative drugs for consideration. The pattern of the clinical features entered is concept-matched with kernels of knowledge and the best matched kernels of knowledge (diagnoses) are returned for consideration. Autonomy technology is based on advanced pattern-matching techniques (non-linear adaptive digital signal processing) rooted in the theories of Bayesian Inference and Claude Shannon's Principles of Information. These enable identification of the patterns that naturally occur in text, based on the usage and frequency of words or terms that correspond to specific concepts. Based on the preponderance of one pattern over another in a piece of unstructured information, Autonomy technology enables computers to understand that there is a particular probability that a document in question is about a specific subject. In this way, Autonomy technology is able to extract a document's digital essence, encode the unique "signature" of the key concepts, then enable a host of operations to be performed on that text, automatically.

Isabel starts at an earlier point in the clinical journey with clinical features. Isabel helps the clinician reach a diagnosis first, and using the same proprietary technology mobilizes medical up to date knowledge from textbooks and journals.

Ramnarayan P, Roberts GC, Coren M et al. Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study. BMC Med Inform Decis Mak. 2006 Apr 28;6(1):22

[PubMed]   []

" ABSTRACT: BACKGROUND: Computerized decision support systems (DSS) have mainly focused on improving clinicians' diagnostic accuracy in unusual and challenging cases. However, since diagnostic omission errors may predominantly result from incomplete workup in routine clinical practice, the provision of appropriate patient- and context-specific reminders may result in greater impact on patient safety. In this experimental study, a mix of easy and difficult simulated cases were used to assess the impact of a novel diagnostic reminder system (ISABEL) on the quality of clinical decisions made by various grades of clinicians during acute assessment. METHODS: Subjects of different grades (consultants, registrars, senior house officers and medical students), assessed a balanced set of 24 simulated cases on a trial website. Subjects recorded their clinical decisions for the cases (differential diagnosis, test-ordering and treatment), before and after system consultation. A panel of two pediatric consultants independently provided gold standard responses for each case, against which subjects quality of decisions was measured. The primary outcome measure was change in the count of diagnostic errors of omission (DEO). A more sensitive assessment of the systems impact was achieved using specific quality scores; additional consultation time resulting from DSS use was also calculated. RESULTS: 76 subjects (18 consultants, 24 registrars, 19 senior house officers and 15 students) completed a total of 751 case episodes. The mean count of DEO fell from 5.5 to 5.0 across all subjects ...; no significant interaction was seen with subject grade. Mean diagnostic quality score increased after system consultation... ISABEL reminded subjects to consider at least one clinically important diagnosis in 1 in 8 case episodes, and prompted them to order an important test in 1 in 10 case episodes. Median extra time taken for DSS consultation was 1 min (IQR: 30 sec to 2 min). CONCLUSION: The provision of patient- and context-specific reminders has the potential to reduce diagnostic omissions across all subject grades for a range of cases. This study suggests a promising role for the use of future reminder-based DSS in the reduction of diagnostic error. "

Ramnarayan P, Tomlinson A, Kulkarni G, Rao A, Britto J. A Novel Diagnostic Aid (ISABEL): Development and Preliminary Evaluation of Clinical Performance. Medinfo. 2004;2004:1091-5.

[PubMed]   []

" Clinical diagnostic aids are relatively scarce, and are seldom used in routine clinical practice, even though the burden of diagnostic error may have serious adverse consequences. This may be due to difficulties in creating, maintaining and even using such expert systems. The current article describes a novel approach to the problem, where established medical content is used as the knowledge base for a pediatric diagnostic reminder tool called ISABEL. The inference engine utilizes advanced textual pattern-recognition algorithms to extract key concepts from textual description of diagnoses, and generates a list of diagnostic suggestions in response to clinical features entered in free text. Development was an iterative process, relying on sequential evaluation of clinical performance to provide the basis for improvement. The usage of the system over the past 2 years, as well as results of preliminary clinical performance evaluation are presented. These results are encouraging. The ISABEL model may be extended to cover other domains, including adult medicine. "

Ramnarayan P, Tomlinson A, Rao A, Coren M, Winrow A, Britto J. ISABEL: a web-based differential diagnostic aid for paediatrics: results from an initial performance evaluation. Arch Dis Child. 2003 May;88(5):408-13.

[PubMed]   [ADC Online]

"AIMS: To test the clinical accuracy of a web based differential diagnostic tool (ISABEL) for a set of case histories collected during a two stage evaluation. METHODS: Setting: acute paediatric units in two teaching and two district general hospitals in the southeast of England. ... Conclusion: ISABEL showed acceptable clinical accuracy in producing the final diagnosis for a variety of real as well as hypothetical case scenarios."
Ramnarayan P, Britto J. Paediatric clinical decision support systems. Arch Dis Child 2002 Nov;87(5):361-2.

[PubMed]   [ADC Online]

" A computerised clinical decision support system (CDSS) is "a computer based tool using explicit knowledge to generate patient specific advice or interpretation".1 Our use of computers has been driven not only by the increasing need to manage large amounts of information, but also by the imperative to make evidence based and cost effective decisions on a daily basis.2 Furthermore, there is accumulating evidence to prove that computer aided medical tools address the growing information needs of the busy clinician3 and improve healthcare processes as well as patient outcomes.4 In turn, this has led to the rapid proliferation of a variety of CDSS. This leading article summarises the past, present, and future of such systems, with special emphasis on their role in paediatrics. "

Review of Isabel
Thomas NJ. Web Report : Isabel Critical Care 2002, 7:99-100 (17 October 2002)

[]   [Critical Care Forum]  
" ... The paramount function of the website is its utility as a diagnostic aid. After receiving a patient age category and some clinical features, Isabel will output an instantaneous list of up to 15 differential diagnoses. This feature can be invaluable in a difficult-to-diagnose pediatric case and can serve as an excellent, readily available reference for faculty, fellows, pediatric house officers, and medical students. This ‘diagnostic tool’ has been found to include the correct diagnosis in 91–95% of cases, and is presently being validated in a large, multicenter, real-time trial. Since discovering this website, I have listed the main clinical characteristics of every medical admission to our PICU for the past month, and Isabel has included the correct diagnosis in every case. ... "
contact links
Isabel Healthcare Inc.
PO Box 8393
Virginia 20195-8393

T: +1-703-403-8377

In the UK:

Isabel Healthcare Ltd.
PO Box 244
GU27 1WU

T: +44(0)1428-644-886

 bullet  Isabel diagnosis reminder system : registration, demonstration ...  bullet  Autonomy decision support technology

Dr. Joseph Britto MD, Clinical Director, ISABEL Healthcare.

Entry on OpenClinical: 10 May 2004
Last main update: 11 May 2004; 26 January 2005; 05 June 2006
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