Designing User-Centered Decision Support Tools for Chronic Pain in Primary Care (Indiana)

Project Final Report (PDF, 729.76 KB) Disclaimer

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Designing User-Centered Decision Support Tools for Chronic Pain in Primary Care - Final Report

Citation:
Harle C. Designing User-Centered Decision Support Tools for Chronic Pain in Primary Care - Final Report. (Prepared by Indiana University-Purdue University at Indianapolis under Grant No. R01 HS023306). Rockville, MD: Agency for Healthcare Research and Quality, 2020. (PDF, 729.76 KB)

The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
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By characterizing common patterns in information availability, information use, and care planning by primary care providers during patient visits for chronic pain, researchers created new electronic health record-based decision support tools to guide clinicians’ perceptions and judgments of noncancer pain to increase use of guideline-based patient assessment and treatment.

Project Details - Ended

Summary:

While millions of Americans suffer from chronic pain, negatively impacting healthcare costs and lost worker productivity, most providers receive little training in pain care. The use of decision support tools, such as computerized alerts or checklists within electronic health records (EHRs), has the potential to improve pain care. However, to be effective, decision support must be designed based on a detailed understanding of how clinicians use information to understand patients’ conditions (i.e., clinical sensemaking). Therefore, research is needed to characterize clinical work environments, information needs and use, and decisionmaking. Such research is especially relevant to chronic noncancer pain in primary care, where clinicians often report dissatisfaction and uncertainty when managing patients.

The overall goal of this project was to develop decision support tools that integrate with EHRs to increase the quality and effectiveness of chronic pain care.

The specific aims of the project are as follows:

  • Characterize primary care clinicians’ information use and decisionmaking patterns during patient visits for musculoskeletal pain to determine how they align with clinical practice guidelines 
  • Prototype and preliminarily evaluate new decision support designs to meet clinicians’ information needs and guide them toward guideline-based information use and care choices 

For the first aim, data were collected from medical records, visit audio recordings, and post-visit clinician interviews. These data were analyzed to cluster visits into similar sensemaking narratives and used to identify common missed opportunities in which decision support could have altered the use of clinical information or altered care plan choices to better align with guideline recommendations. The project team conducted a design workshop and usability testing to produce prototypes for guideline-based decision support systems that was integrated in EHRs.

This study demonstrated the value of a user-centered approach to designing clinical decision support systems for clinicians caring for patients with chronic pain. The user-centered design approach puts the human user first when creating new computer applications and the researchers analyzed how clinicians used patient information in their work to choose the safest and most effective medications and other treatments for pain.

Four design approaches were found to be key for EHR and clinical decision support developers to help clinicians be more efficient and guideline-concordant during chronic pain care: (1) making key information accessible in a single view, (2) organizing information in tables, (3) allowing users to interactively move between summary and detailed information, and (4) providing visual cues to help focus users’ attention. These four design approaches were used to create two prototype clinical decision support systems: Chronic Pain OneSheet and Chronic Pain Treatment Tracker.