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      Uptake and impact of a clinical diagnostic decision support tool at an academic medical center

      , ,
      Diagnosis
      Walter de Gruyter GmbH

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          Abstract

          Use of differential diagnosis (DDX) generators may reduce the incidence of misdiagnosis-related harm, but there is a paucity of research examining the use and impact of such systems in real-world settings.

          In September 2012, the DDX generator VisualDx was made available across our entire academic healthcare system. We examined the use of VisualDx by month for the 18 months following its introduction. In addition, we compared the number of inpatient dermatology consults requested per month at the flagship hospital of our healthcare system for the 12 months before versus 18 months after VisualDx introduction.

          Across our entire academic healthcare system, there were a median of 474 (interquartile range 390–544) unique VisualDx sessions per month. VisualDx was accessed most frequently through mobile devices (35%) and the inpatient electronic health record (34%). Prior to VisualDx introduction, there was a non-significant increase in the number of inpatient dermatology consultations requested per month at the flagship hospital of our healthcare system (1.0 per month, 95% CI –2.5–4.6, p=0.54), which remained 1.0 consults per month (95% CI –0.9–2.9, p=0.27) following its introduction (p=0.99 comparing post- versus pre-introduction rates).

          The DDX generator VisualDx was regularly used, primarily on mobile devices and inpatient workstations, and was not associated with a change in inpatient dermatology consultation requests. Given the interest in DDX generators, it will be important to evaluate further the impact of such tools on the quality and value of care delivered.

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          Differential diagnosis generators: an evaluation of currently available computer programs.

          Differential diagnosis (DDX) generators are computer programs that generate a DDX based on various clinical data. We identified evaluation criteria through consensus, applied these criteria to describe the features of DDX generators, and tested performance using cases from the New England Journal of Medicine (NEJM©) and the Medical Knowledge Self Assessment Program (MKSAP©). We first identified evaluation criteria by consensus. Then we performed Google® and Pubmed searches to identify DDX generators. To be included, DDX generators had to do the following: generate a list of potential diagnoses rather than text or article references; rank or indicate critical diagnoses that need to be considered or eliminated; accept at least two signs, symptoms or disease characteristics; provide the ability to compare the clinical presentations of diagnoses; and provide diagnoses in general medicine. The evaluation criteria were then applied to the included DDX generators. Lastly, the performance of the DDX generators was tested with findings from 20 test cases. Each case performance was scored one through five, with a score of five indicating presence of the exact diagnosis. Mean scores and confidence intervals were calculated. Twenty three programs were initially identified and four met the inclusion criteria. These four programs were evaluated using the consensus criteria, which included the following: input method; mobile access; filtering and refinement; lab values, medications, and geography as diagnostic factors; evidence based medicine (EBM) content; references; and drug information content source. The mean scores (95% Confidence Interval) from performance testing on a five-point scale were Isabel© 3.45 (2.53, 4.37), DxPlain® 3.45 (2.63-4.27), Diagnosis Pro® 2.65 (1.75-3.55) and PEPID™ 1.70 (0.71-2.69). The number of exact matches paralleled the mean score finding. Consensus criteria for DDX generator evaluation were developed. Application of these criteria as well as performance testing supports the use of DxPlain® and Isabel© over the other currently available DDX generators.
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            Performance of a Web-Based Clinical Diagnosis Support System for Internists

            BACKGROUND Clinical decision support systems can improve medical diagnosis and reduce diagnostic errors. Older systems, however, were cumbersome to use and had limited success in identifying the correct diagnosis in complicated cases. OBJECTIVE To measure the sensitivity and speed of “Isabel” (Isabel Healthcare Inc., USA), a new web-based clinical decision support system designed to suggest the correct diagnosis in complex medical cases involving adults. METHODS We tested 50 consecutive Internal Medicine case records published in the New England Journal of Medicine. We first either manually entered 3 to 6 key clinical findings from the case (recommended approach) or pasted in the entire case history. The investigator entering key words was aware of the correct diagnosis. We then determined how often the correct diagnosis was suggested in the list of 30 differential diagnoses generated by the clinical decision support system. We also evaluated the speed of data entry and results recovery. RESULTS The 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. CONCLUSIONS The Isabel clinical decision support system quickly suggested the correct diagnosis in almost all of these complex cases, particularly with key finding entry. The system performed well in this experimental setting and merits evaluation in more natural settings and clinical practice.
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              The dermatology workforce shortage.

              While many dermatology workforce projections over the past two decades forecasted an impending oversupply, more recent reports have begun to suggest a shortage of dermatologic services. Anonymous surveys administered to practicing dermatologists and to recent training graduates were examined for surrogate indicators of the supply and demand for dermatologic services. The mean wait time for new patient appointments with dermatologists was 36 calendar days, but ranged widely based on location (means ranged from 9-120 days by state). About half (49%) of practicing dermatologists feel that they need more dermatologists in their communities, while only 20% describe the local supply as too high. The reported need for medical and general dermatologists is far more acute than for dermatologic subspecialists. Many practices (33%) are looking for new associates, and not surprisingly, most new graduates entering the workforce over the past 4 years (86-93%) do not describe any difficulty finding desirable positions. Fewer than 10% of recent graduates are dissatisfied with their current jobs. Based on survey data examining wait times, physician perception, use of physician extenders, searches for new employees, and experience of recent graduates entering the workforce, it appears there is an inadequate supply of dermatologists to meet the demand for services.
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                Author and article information

                Journal
                Diagnosis
                Walter de Gruyter GmbH
                2194-802X
                2194-8011
                June 1 2015
                June 1 2015
                : 2
                : 2
                : 123-127
                Article
                10.1515/dx-2014-0058
                566c915d-e8a9-49e3-8bbb-6cb2b744ce03
                © 2015

                http://creativecommons.org/licenses/by-nc-nd/3.0/

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