MANAGING CHRONIC PAIN IN THE MIDST OF AN OPIOID EPIDEMIC
FUNDED BY: Agency for Healthcare Research and Quality (AHRQ)
PRIME CONTRACTOR: Indiana University
PROBLEM: At the time of this project, America was in the midst of an opioid crisis. Chronic pain affected 100 million Americans at a cost of $600 billion in healthcare and productivity annually. Prescription painkillers were widely misused and abused, leading to 475,000 emergency visits and 15,000 deaths annually. There was a research gap in understanding how providers make sense of chronic pain and how electronic health records help and hinder the treatment of chronic pain. The aim of this project was to design a decision support tool to increase safe and effective primary care for patients with chronic pain.
APPROACH: Applied Decision Science led the cognitive task analysis and design activities for the project. We provided a cognitive task analysis workshop to train the project team on conducting critical decision method interviews with clinicians. To demonstrate the method, we led the initial interviews and helped the team refine the interview guide. In the next phase of the project, we qualitatively analyzed 89 clinician interviews and patient-visit transcripts. We helped develop codebooks and participated in coding sessions where key themes were identified and later analyzed. To move from research findings to design applications, we helped facilitate a multidisciplinary design workshop with researchers and end-users. The findings were distilled into key decision requirements to be supported by design. This decision-centered approach is what drives our design. We focused on supporting the most difficult decisions and demands clinicians face when treating patients with chronic pain. We then used an iterative, user-centered design process to develop an exploratory prototype.
IMPACT: The resulting design, the Chronic Pain Treatment Tracker, is an interactive prototype that helps clinicians manage patients with chronic pain. It is designed to help organize and make sense of key information that is often difficult to find and integrate. Specifically, it supports understanding current and past treatment plans and identifying treatment options. The Chronic Pain Treatment Tracker is a novel approach to decision support because it focuses more on sensemaking and less on algorithmic-based guidelines that are pushed to providers. This approach is an important contribution to clinical-decision support. As the volume of data in electronic health records continues to grow, clinicians increasingly need well-designed tools to help them navigate patient history, sift through relevant contextual information, and identify the most promising treatments. This is particularly true for complex conditions such as chronic pain.
Diiulio, J., Militello, L.G., Andraka-Christou, B.T., Cook, R.L., Hurley, R.W., Downs, S.M., Anders S., Mamlin, B.W., Danielson, E.C., and Harle, C.A. (2020). Factors That Influence Changes to Existing Chronic Pain Management Plans. Journal of the American Board of Family Medicine. 33:42-50.
Militello, L.G., Hurley, R.W., Cook, R.L., Danielson, E., Diiulio, J., Downs, S.M., Anders, S., Harle, C.A. (2020). Primary care clinicians’ beliefs and strategies for managing chronic pain in an era of a national opioid epidemic. Journal of General Internal Medicine. Advance online publication. doi: 10.1007/s11606-020-06178-2.
Militello, L. G., Anders, S., Downs, S. M., Diiulio, J., Danielson, E. C., Hurley, R. W., & Harle, C. A. (2018). Understanding how primary care clinicians make sense of chronic pain. Cognition, Technology & Work, 20(4), 575-584.
Harle, C., Anders, S., Militello, L., Downs, S., Danielson, E., Mamlin, B., … & Hurley, R. (2017). (300) Developing clinical decision support for chronic pain by understanding clinician information needs during primary care visits. The Journal of Pain, 18(4), S50.
Harle, C. A., DiIulio, J., Downs, S. M., Danielson, E. C., Anders, S., Cook, R. L., … & Militello, L. G. (2019). Decision-Centered Design of Patient Information Visualizations to Support Chronic Pain Care. Applied clinical informatics, 10(04), 719-728