Medical College of Wisconsin
CTSICores SearchResearch InformaticsREDCap

Decision-Centered Design of Patient Information Visualizations to Support Chronic Pain Care. Appl Clin Inform 2019 Aug;10(4):719-728

Date

09/27/2019

Pubmed ID

31556075

Pubmed Central ID

PMC6760988

DOI

10.1055/s-0039-1696668

Scopus ID

2-s2.0-85072653894 (requires institutional sign-in at Scopus site)   12 Citations

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.

Author List

Harle CA, DiIulio J, Downs SM, Danielson EC, Anders S, Cook RL, Hurley RW, Mamlin BW, Militello LG

Author

Robert W. Hurley MD, PhD Adjunct Professor of Anesthesiology and CTSI in the Anesthesiology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Chronic Pain
Clinical Decision-Making
Electronic Health Records
Humans
User-Computer Interface