Appl Clin Inform 2019; 10(04): 719-728
DOI: 10.1055/s-0039-1696668
Special Topic: Visual Analytics in Healthcare
Georg Thieme Verlag KG Stuttgart · New York

Decision-Centered Design of Patient Information Visualizations to Support Chronic Pain Care

Christopher A. Harle
1  Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, United States
,
Julie DiIulio
2  Applied Decision Science, LLC, Dayton, Ohio, United States
,
Sarah M. Downs
1  Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, United States
,
Elizabeth C. Danielson
1  Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, United States
,
Shilo Anders
3  Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Robert L. Cook
4  Department of Epidemiology, University of Florida, Gainesville, Florida, United States
,
Robert W. Hurley
5  Department of Anesthesiology, Wake Forest University School of Medicine, Wake Forest University, Winston-Salem, North Carolina, United States
,
Burke W. Mamlin
6  Regenstrief Institute, Indianapolis, Indiana, United States
,
Laura G. Militello
2  Applied Decision Science, LLC, Dayton, Ohio, United States
› Author Affiliations
Funding This study was funded by the U.S. Department of Health and Human Services Agency for Healthcare Research and Quality R01HS023306.
Further Information

Publication History

15 April 2019

24 July 2019

Publication Date:
25 September 2019 (online)

Abstract

Background For complex patients with chronic conditions, electronic health records (EHRs) contain large amounts of relevant historical patient data. To use this information effectively, clinicians may benefit from visual information displays that organize and help them make sense of information on past and current treatments, outcomes, and new treatment options. Unfortunately, few clinical decision support tools are designed to support clinical sensemaking.

Objective The objective of this study was to describe a decision-centered design process, and resultant interactive patient information displays, to support key clinical decision requirements in chronic noncancer pain care.

Methods To identify key clinical decision requirements, we conducted critical decision method interviews with 10 adult primary care clinicians. Next, to identify key information needs and decision support design seeds, we conducted a half-day multidisciplinary design workshop. Finally, we designed an interactive prototype to support the key clinical decision requirements and information needs uncovered during the previous research activities.

Results The resulting Chronic Pain Treatment Tracker prototype summarizes the current treatment plan, past treatment history, potential future treatments, and treatment options to be cautious about. Clinicians can access additional details about each treatment, current or past, through modal views. Additional decision support for potential future treatments and treatments to be cautious about is also provided through modal views.

Conclusion This study designed the Chronic Pain Treatment Tracker, a novel approach to decision support that presents clinicians with the information they need in a structure that promotes quick uptake, understanding, and action.

Protection of Human and Animal Subjects

This study was approved by the Indiana University Institutional Review Board (IRB).