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

Goutham Rao; Katherine Kirley; Paul Epner; Yiye Zhang; Victoria Bauer; Rema Padman; Ying Zhou; Anthony Solomonides

doi:10.1055/s-0038-1676338

Applied Clinical Informatics, Table of Contents

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

Authors

Goutham Rao

¹Department of Family Medicine and Community Health, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio, United States
Katherine Kirley

²American Medical Association, Chicago, Illinois, United States
Paul Epner

³Society to Improve Diagnosis in Medicine, Evanston, Illinois, United States
Yiye Zhang

⁴Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, United States
Victoria Bauer

⁵Ambulatory Primary Care Innovations Group, NorthShore University HealthSystem, Evanston, Illinois, United States
Rema Padman

⁶Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Ying Zhou

⁵Ambulatory Primary Care Innovations Group, NorthShore University HealthSystem, Evanston, Illinois, United States
Anthony Solomonides

⁵Ambulatory Primary Care Innovations Group, NorthShore University HealthSystem, Evanston, Illinois, 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.

Keywords

diagnosis - primary care - electronic health records - abdominal pain

Full Text

References

References
1 National Academies of Sciences, Engineering, and Medicine. Improving Diagnosis in Health Care. Washington, DC: The National Academies Press; 2015: 2
2 Saber Tehrani AS, Lee H, Mathews SC. , et al. 25-Year summary of US malpractice claims for diagnostic errors 1986-2010: an analysis from the National Practitioner Data Bank. BMJ Qual Saf 2013; 22 (08) 672-680
3 Kim AS, Sidney S, Klingman JG, Johnston SC. Practice variation in neuroimaging to evaluate dizziness in the ED. Am J Emerg Med 2012; 30 (05) 665-672
4 Cherkin DC, Deyo RA, Wheeler K, Ciol MA. Physician variation in diagnostic testing for low back pain. Who you see is what you get. Arthritis Rheum 1994; 37 (01) 15-22
5 Marewski JN, Gigerenzer G. Heuristic decision making in medicine. Dialogues Clin Neurosci 2012; 14 (01) 77-89
6 Pines JM, Isserman JA, Szyld D, Dean AJ, McCusker CM, Hollander JE. The effect of physician risk tolerance and the presence of an observation unit on decision making for ED patients with chest pain. Am J Emerg Med 2010; 28 (07) 771-779
7 Graber ML, Franklin N, Gordon R. Diagnostic error in internal medicine. Arch Intern Med 2005; 165 (13) 1493-1499
8 Ely JW, Kaldjian LC, D'Alessandro DM. Diagnostic errors in primary care: lessons learned. J Am Board Fam Med 2012; 25 (01) 87-97
9 Song Y, Skinner J, Bynum J, Sutherland J, Wennberg JE, Fisher ES. Regional variations in diagnostic practices. N Engl J Med 2010; 363 (01) 45-53
10 Goldman RD, Scolnik D, Chauvin-Kimoff L. , et al; Fever in Infants Group Research, Pediatric Emergency Research of Canada. Practice variations in the treatment of febrile infants among pediatric emergency physicians. Pediatrics 2009; 124 (02) 439-445
11 Adelman A. Abdominal pain in the primary care setting. J Fam Pract 1987; 25 (01) 27-32
12 Kerber KA, Newman-Toker DE. Misdiagnosing dizzy patients: common pitfalls in clinical practice. Neurol Clin 2015; 33 (03) 565-575 , viii
13 Newman-Toker DE. Missed stroke in acute vertigo and dizziness: it is time for action, not debate. Ann Neurol 2016; 79 (01) 27-31
14 Rao G, Epner P, Bauer V, Solomonides A, Newman-Toker DE. Identifying and analyzing diagnostic paths: a new approach for studying diagnostic practices. Diagnosis (Berl) 2017; 4 (02) 67-72
15 Zhang Y, Padman R, Epner P, Bauer V, Solomonides A, Rao G. Identifying diagnostic paths for undifferentiated abdominal pain from electronic health record data. AMIA Jt Summits Transl Sci Proc 2018; 2017: 290-299
16 Woodwell DA, Cherry DK. National Ambulatory Medical Care Survey: 2002 summary. Adv Data 2004; (346) 1-44
17 Brekke M, Eilertsen RK. Acute abdominal pain in general practice: tentative diagnoses and handling. A descriptive study. Scand J Prim Health Care 2009; 27 (03) 137-140
18 van den Heuvel-Janssen HA, Borghouts JA, Muris JW, Koes BW, Bouter LM, Knottnerus JA. Chronic non-specific abdominal complaints in general practice: a prospective study on management, patient health status and course of complaints. BMC Fam Pract 2006; 7: 12
19 Klinkman MS. Episodes of care for abdominal pain in a primary care practice. Arch Fam Med 1996; 5 (05) 279-285
20 Lewis LM, Banet GA, Blanda M, Hustey FM, Meldon SW, Gerson LW. Etiology and clinical course of abdominal pain in senior patients: a prospective, multicenter study. J Gerontol A Biol Sci Med Sci 2005; 60 (08) 1071-1076
21 van der Weijden T, van Bokhoven MA, Dinant GJ, van Hasselt CM, Grol RP. Understanding laboratory testing in diagnostic uncertainty: a qualitative study in general practice. Br J Gen Pract 2002; 52 (485) 974-980
22 Gans SL, Pols MA, Stoker J, Boermeester MA. ; expert steering group. Guideline for the diagnostic pathway in patients with acute abdominal pain. Dig Surg 2015; 32 (01) 23-31
23 Caban JJ, Gotz D. Visual analytics in healthcare--opportunities and research challenges. J Am Med Inform Assoc 2015; 22 (02) 260-262
24 Zhang Y, Padman R, Patel N. Paving the COWpath: learning and visualizing clinical pathways from electronic health record data. J Biomed Inform 2015; 58: 186-197
25 Perer A, Wang F, Hu J. Mining and exploring care pathways from electronic medical records with visual analytics. J Biomed Inform 2015; 56: 369-378
26 Gotz D, Wang F, Perer A. A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data. J Biomed Inform 2014; 48: 148-159

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