Appl Clin Inform 2018; 09(04): 905-913
DOI: 10.1055/s-0038-1676338
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Identifying, Analyzing, and Visualizing Diagnostic Paths for Patients with Nonspecific Abdominal Pain

Goutham Rao
1   Department of Family Medicine and Community Health, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio, United States
,
Katherine Kirley
2   American Medical Association, Chicago, Illinois, United States
,
Paul Epner
3   Society to Improve Diagnosis in Medicine, Evanston, Illinois, United States
,
Yiye Zhang
4   Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States
,
Victoria Bauer
5   Ambulatory Primary Care Innovations Group, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Rema Padman
6   Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
,
Ying Zhou
5   Ambulatory Primary Care Innovations Group, NorthShore University HealthSystem, Evanston, Illinois, United States
,
Anthony Solomonides
5   Ambulatory Primary Care Innovations Group, NorthShore University HealthSystem, Evanston, Illinois, United States
› Author Affiliations
Further Information

Publication History

25 July 2018

25 October 2018

Publication Date:
19 December 2018 (online)

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.

Protection of Human and Animal Subjects

This study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed and approved by the Institutional Review Board of the NorthShore University HealthSystem.


Supplementary Material