Assessing the Burden of Treatment
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Current healthcare systems and guidelines are not designed to adapt to care for the large and growing number of patients with complex care needs and those with multimorbidity. Minimally disruptive medicine (MDM) is an approach to providing care for complex patients that advances patients’ goals in health and life while minimizing the burden of treatment. Measures of treatment burden assess the impact of healthcare workload on patient function and well-being. At least two of these measures are now available for use with patients living with chronic conditions. Here, we describe these measures and how they can be useful for clinicians, researchers, managers, and policymakers. Their work to improve the care of high-cost, high-use, complex patients using innovative patient-centered models such as MDM should be supported by periodic large-scale assessments of treatment burden.
KEY WORDStreatment burden multimorbidity quality measures chronic disease minimally disruptive medicine
We would like to thank the International Minimally Disruptive Medicine workgroup, especially David T. Eton and Viet Thi Tran, for their input on earlier versions of this manuscript. The workgroup members include Summer Allen, Kasey Boehmer, Juan Pablo Brito, Ian Hargraves, Katie Gallacher, Michael R. Gionfriddo, Aaron Leppin, Frances Mair, Marc R. Matthews, Carl May, Victor M. Montori, Elizabeth Rogers, Nilay Shah, Nathan Shippee, Kate Vickery, and Kathleen Yost.
Compliance with Ethical Standards
GSB and VMM were supported by CTSA grant numbers TL1 TR000137 and UL1 TR000135, respectively, from the National Center for Advancing Translational Science (NCATS), a component of the National Institutes of Health (NIH). ARQ is supported by an American Diabetes Association career development award (ADA 7–13-CD-08). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Conflict of Interest
The authors declare that they do not have a conflict of interest.
- 1.Wu S-Y, Green A. Projection of chronic illness prevalence and cost inflation. Santa Monica, CA: RAND Health. 2000;18.Google Scholar
- 4.Anderson G. Chronic Care: Making the Case for Ongoing Care. Princeton, NJ: Robert Wood Johnson Foundation; 2010. Available at: http://www.rwjf.org/content/dam/farm/reports/reports/2010/rwjf54583. Accessed Jun 12, 2017.
- 9.Tinetti ME, Fried TR, Boyd CM. Designing health care for the most common chronic condition—multimorbidity. JAMA. 2012;307(23):2493–2494.Google Scholar
- 12.Eton DT, Yost KJ, Lai JS, et al. Development and validation of the Patient Experience with Treatment and Self-management (PETS): a patient-reported measure of treatment burden. Qual Life Res. 2016.Google Scholar
- 21.U.S. Department of Health and Human Services. Multiple Chronic Conditions: A Strategic Framework Optimum Health and Quality of Life for Individuals with Multiple Chronic Conditions 2010; http://www.hhs.gov/sites/default/files/ash/initiatives/mcc/mcc_framework.pdf. Accessed Jun 12, 2017.
- 26.University of Southampton. Horizons: Understanding the impact of cancer diagnosis and treatment on everyday life. 2017; http://www.horizons-hub.org.uk/index.html. Accessed Jun 12, 2017.
- 29.Sidorkiewicz S, Tran V-T, Cousyn C, Perrodeau E, Ravaud P. Discordance between drug adherence as reported by patients and drug importance as assessed by physicians. Ann Fam Med. 2016;14(5):415–421.Google Scholar
- 32.Hodes R, Suzman R. Growing older in America: The health and retirement study. Bethesda: National Institute on Aging, National Institute of Health, US Department of Health and Human Services. 2007.Google Scholar
- 33.Montaquila J, Freedman V, Edwards B, Kasper J. National Health and Aging Trends Study Round 1 Sample Design and Selection. NHATS Technical Paper# 1. Baltimore: Johns Hopkins University School of Public Health. 2012.Google Scholar