Decision Support in Dermatology and Medicine: History and Recent Developments Art Papier, MD
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Decision Support in Dermatology and Medicine: History and Recent Developments Art Papier, MD This article is focused on diagnostic decision support tools and will provide a brief history of clinical decision support (CDS), examine the components of CDS and its associated terminology, and discuss recent developments in the use and application of CDS systems, particularly in the field of dermatology. For this article, we use CDS to mean an interactive system allowing input of patient-specific information and providing customized medical knowledge-based results via automated reasoning, for example, a set of rules and/or an underlying logic, and associations. Semin Cutan Med Surg 31:153-159 © 2012 Elsevier Inc. All rights reserved. KEYWORDS clinical decision support, informatics, knowledge-based systems, health informa- tion technology, diagnostic software, contact allergen database, VisualDX, Iliad s computing evolved, physicians and other researchers access to a wealth of medical knowledge. These tools range Abegan exploring ways in which computer technology from online books with electronic indices and text search could be used to manage medical information and optimize functionality to more sophisticated databases with complex health care delivery. The earliest efforts were focused on hos- mappings of terminology and information. Although a few pital information systems for recording and storing medical have broadly defined CDS as computer-based medical infor- data and for administrative support, and on decision support mation, most in the field would choose a narrower definition tools to assist in patient care decision making.1-10 This article suggesting the need to customize information to a specific is focused on decision support tools, specifically diagnostic patient or clinical scenario using Ͼ 1 variable.11 For this decision support tools, and will provide a brief history of article, we use CDS to mean an interactive system allowing clinical decision support (CDS), examine the components of input of patient-specific information and providing custom- CDS and its associated terminology, and discuss recent de- ized medical knowledge-based results via automated reason- velopments in the use and application of CDS systems, par- ing, for example, a set of rules and/or an underlying logic, and ticularly in the field of dermatology. associations. In other words, CDS is the physician interacting with the computer source in real time to assist with thinking Diagnostic Decision Support and decision making on a specific patient case. As in aviation, in medicine, human error can lead to injury and death. Sim- Much like the term “evidence-based medicine,” the term ilar to pilot resource management systems used to reduce “clinical decision support” has, to some degree, become a pilot error,12 CDS systems have the potential to increase a buzzword and an industry cachet used broadly to describe almost any electronic medical information resource. There physician’s cognitive awareness, help them recognize their are many electronic clinical reference tools currently in use knowledge limitations, and assist with problem solving and that, by the strictest definition, would not fall into the cate- decision making in a specific patient context. gory of decision support systems but do facilitate efficient Several notable computerized systems designed to aid cli- nicians in the diagnosis of disease—“diagnostic expert sys- tems”—have emerged over the past 30 years. Starting in the Dermatology and Medical Informatics, University of Rochester College of 1980s, commercially released prototypes were being imple- Medicine, Rochester, NY. mented for medical education and patient care. Although Conflict of Interest Disclosures: The author is CMIO of Logical Images, Inc, varied in methodology (ie, data organization, logic structure) developer of VisualDx. and in subject matter (ie, specialized vs more general medi- Correspondence Author: Art Papier, MD, Dermatology and Medical Informat- ics, University of Rochester College of Medicine, 601 Elmwood Ave, Box cine), diagnostic expert systems all generate differential diag- 697, Rochester, NY 14642. E-mail: [email protected] noses by computing associations between patient-specific 1085-5629/12/$-see front matter © 2012 Elsevier Inc. All rights reserved. 153 http://dx.doi.org/10.1016/j.sder.2012.06.005 154 A. Papier clinical findings (eg, patient signs and symptoms entered by Table 1 Clinical Diagnosis Decision Support Systems the clinician during the patient encounter) and diseases. Be- Iliad sides generating differential diagnoses, the systems for the Developed in early 1980s at Utah School of Medicine most part also provide general information on the diseases Uses Bayesian logic to rank diagnostic probability covered (etiology, pathology, predominating signs and Covers >1,500 internal medicine diagnoses symptoms, and therapy recommendations). This informa- No longer commercially available tion is typically available via differential listings or search by Quick Medical Reference disease, allowing for more efficient access to medical data Developed at University of Pittsburgh in 1980 than could be achieved by paging through textbooks or re- Uses non-Bayesian reasoning for internal medicine diagnosis searching journal articles. More than 5,000 clinical findings mapped to >750 One of the earliest diagnostic tools developed was Iliad diseases (University of Utah School of Medicine, Dept. of Medical No longer commercially available, last update in 2001 13 Informatics, Salt Lake City, UT). It was first created in the DXplain 1980s to assist physicians with internal medicine diagnosis. Developed in 1984 at Massachusetts General Hospital Iliad uses Bayesian reasoning to calculate probabilities of di- Relational database covering >2,400 diseases, with full agnoses in relation to patient findings entered by the physi- differentials for >5,000 clinical findings cian. DXplain (Massachusetts General Hospital Lab. of Com- Currently in use by hospitals and medical schools puter Science, Boston, MA), also released in the 1980s, PKC Advisor covers general medicine diagnosis.14 Using a modified form Developed by Dr. Lawrence Weed at the University of of Bayesian logic, the system ranks diagnostic possibilities Vermont Uses combinatorial logic to provide differential diagnosis from most likely to least likely on the basis of finding fre- for a variety of problem-based topics quency and importance in relation to diagnoses. DXplain is Commercially available to institutions presently used primarily as a clinical education tool but can Isabel also be used for clinical reference. Quick Medical Reference Launched in 2002 with support from UK National Health (University of Pittsburgh, PA; First DataBank, Inc., San Service and UK Department of Health Bruno, CA) is another diagnostic program for internal med- Search engine allowing natural language processing of icine that uses a non-Bayesian algorithm to assess associa- patient-specific queries tions between patient findings and individual diseases and Licenses/links to 100,000 documents produce a list of potential diagnoses.15 Isabel (Isabel Health- Commercially available to individuals and institutions care, Ltd., Haslemere, UK) is a diagnosis decision support VisualDx system released in 2002. It was initially designed for pediatric Released through a partnership with the University of Rochester in 2001 medicine but now covers all age-groups. As with the other First visual diagnostic decision support system systems, it provides a list of possible diagnoses based on Relational database covering >1,200 disease topics and physician entry of patient data. It is unique in that it uses containing >24,000 images, approximately 36,000 natural language processing, as will IBM’s (Armonk, NY) finding–diagnosis relationships, and >5,000 soon-to-be-released Watson project in health care. Like other medication–disease relationships systems, Isabel links to external knowledge sources related to Commercially available to individuals and institutions a disease (eg, journal article abstract). However it does not publish its own clinical content.16 There are others, too, such as PKC Advisor (PKC Corp., Burlington, VT), which uses combinatorial logic to provide differential diagnosis for a might use differing lists of symptoms, signs, diagnoses, and variety of problem-based topics, such as “chest pain” or “con- other terms. However, if digital systems are going to commu- stipation.” However, it does not publish its own clinical con- nicate accurately, there must be shared standardized terms to tent.16 First introduced in 2001, VisualDx is an image-rich ensure reliability and prevent error. In the field of dermatol- diagnostic CDS system that allows input of visual clues, ogy, one of the first large-scale efforts to standardize termi- symptoms, and patient history to assist physicians in diagno- nology was the British Association of Dermatologists (Lon- sis and management of diseases. A summary of CDS tool don, UK) lexicon project led by Dr Robert Chalmers. The characteristics is provided in Table 1. National Institute of Arthritis and Musculoskeletal and Skin Diseases (Bethesda, MD) also recognized a need to develop a Building Blocks of CDS dermatology lexicon and funded the Dermatology Lexicon Project in 2001.17 The American Academy of Dermatology CDS systems require certain foundational building blocks if (Schaumburg, IL) assumed responsibility of the Dermatology they are to work safely, be in the workflow,