Goutham Rao1, Katherine Kirley2, Paul Epner3, Yiye Zhang4, Victoria Bauer5, Rema Padman6, Ying Zhou5, Anthony Solomonides5. 1. Department of Family Medicine and Community Health, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio, United States. 2. American Medical Association, Chicago, Illinois, United States. 3. Society to Improve Diagnosis in Medicine, Evanston, Illinois, United States. 4. Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States. 5. Ambulatory Primary Care Innovations Group, NorthShore University HealthSystem, Evanston, Illinois, United States. 6. Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States.
Abstract
BACKGROUND: Diagnosis is complex, uncertain, and error-prone. Symptoms such as nonspecific abdominal pain are especially challenging. A diagnostic path consists of diagnostic steps taken from initial presentation until a diagnosis is obtained or the evaluation ends for other reasons. Analysis of diagnostic paths can reveal patterns associated with more timely and accurate diagnosis. Visual analytics can be used to enhance both analysis and comprehension of diagnostic paths. OBJECTIVE: This article applies process-mining methods to extract and visualize diagnostic paths from electronic health records (EHRs). METHODS: Patient features, actions taken (i.e., tests, referrals, etc.), and diagnoses obtained for 501 adult patients (half female, half ≥50 years of age) presenting with abdominal pain were extracted from an EHR database to construct diagnostic paths from a hospital system in suburban Chicago, Illinois, United States. A stable diagnosis was defined as the same diagnosis recorded twice in a 12-month period; a working diagnosis was recorded only once. Three different types of path visualizations were obtained. RESULTS: A stable diagnosis was obtained in 63 (13%) patients after 12 months. In 271 (54%) patients, a working diagnosis was obtained. Mean path duration was 145.3 days (standard deviation, 195.1 days). These 63 patients received 75 stable diagnoses. CONCLUSION: Structured EHR data can be used to construct diagnostic paths to gain insight into diagnostic practices for complaints such as abdominal pain. Georg Thieme Verlag KG Stuttgart · New York.
BACKGROUND: Diagnosis is complex, uncertain, and error-prone. Symptoms such as nonspecific abdominal pain are especially challenging. A diagnostic path consists of diagnostic steps taken from initial presentation until a diagnosis is obtained or the evaluation ends for other reasons. Analysis of diagnostic paths can reveal patterns associated with more timely and accurate diagnosis. Visual analytics can be used to enhance both analysis and comprehension of diagnostic paths. OBJECTIVE: This article applies process-mining methods to extract and visualize diagnostic paths from electronic health records (EHRs). METHODS:Patient features, actions taken (i.e., tests, referrals, etc.), and diagnoses obtained for 501 adult patients (half female, half ≥50 years of age) presenting with abdominal pain were extracted from an EHR database to construct diagnostic paths from a hospital system in suburban Chicago, Illinois, United States. A stable diagnosis was defined as the same diagnosis recorded twice in a 12-month period; a working diagnosis was recorded only once. Three different types of path visualizations were obtained. RESULTS: A stable diagnosis was obtained in 63 (13%) patients after 12 months. In 271 (54%) patients, a working diagnosis was obtained. Mean path duration was 145.3 days (standard deviation, 195.1 days). These 63 patients received 75 stable diagnoses. CONCLUSION: Structured EHR data can be used to construct diagnostic paths to gain insight into diagnostic practices for complaints such as abdominal pain. Georg Thieme Verlag KG Stuttgart · New York.
Authors: Lawrence M Lewis; Gerald A Banet; Michelle Blanda; Fredric M Hustey; Stephen W Meldon; Lowell W Gerson Journal: J Gerontol A Biol Sci Med Sci Date: 2005-08 Impact factor: 6.053
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