Reasons for Not Intensifying Medications: Differentiating “Clinical Inertia” from Appropriate Care
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“Clinical inertia” has been defined as inaction by physicians caring for patients with uncontrolled risk factors such as blood pressure. Some have proposed that it accounts for up to 80% of cardiovascular events, potentially an important quality problem. However, reasons for so-called clinical inertia are poorly understood.
To derive an empiric conceptual model of clinical inertia as a subset of all clinical inactions from the physician perspective.
We used Nominal Group panels of practicing physicians to identify reasons why they do not intensify medications when seeing an established patient with uncontrolled blood pressure.
Measurements and Main Results
We stopped at 2 groups (N = 6 and 7, respectively) because of the high degree of agreement on reasons for not intensifying, indicating saturation. A third group of clinicians (N = 9) independently sorted the reasons generated by the Nominal Groups. Using multidimensional scaling and hierarchical cluster analysis, we translated the sorting results into a cognitive map that represents an empirically derived model of clinical inaction from the physician’s perspective. The model shows that much inaction may in fact be clinically appropriate care.
Many reasons offered by physicians for not intensifying medications suggest that low rates of intensification do not necessarily reflect poor quality of care. The empirically derived model of clinical inaction can be used as a guide to construct performance measures for monitoring clinical inertia that better focus on true quality problems.
KEY WORDSclinical inertia primary care conceptual model
We thank Nelda Wray, MD, MPH for her helpful comments on an early draft of the manuscript. This work was made possible by support from NIDDK R18DK65001-01A2 (supported all authors, Allison, PI) and VA HSR&D IIR04-266 (supported Safford and Allison, Safford, PI).
Conflict of Interest Disclosure
- 4.National Institutes of Health N. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. http://www.nhlbi.nih.gov/guidelines/hypertension/jncintro.htm.
- 5.O’Connor P, Sperl-Hillen J, Johnson P, Rush W, Biltz G. Clinical inertia and outpatient medical errors. In: Advances in Patient Safety: From Research to Implementation, Volume 2: Concepts and Methodology. Vol 2 (of 4). Rockville, MD: Agency for Healthcare Research and Quality; 2005:293–308.Google Scholar
- 12.Schiffman S, Reynolds M, Young F. Introduction to Multidimensional Scaling. New York: Academic Press; 1981.Google Scholar
- 13.Aldenderfer M, Blashfield R. Cluster Analysis. Beverly Hills, CA: Sage Publications; 1984.Google Scholar
- 14.Kruskall J, Wish M. Multi-Dimensional Scaling. Newbury Park, NJ: Sage Publications; 1990.Google Scholar
- 15.Speece D. Methodological issues in cluster analysis: how clusters become real. In: Learning disabilities: Theoretical research issues. Hillsdale, NJ: Erlbaum; 1990:210–213.Google Scholar
- 16.Joseph F, Hair J, Anderson RE, Tatham RL, Black WC. Multivariate Data Analysis. 5th ed. Upper Saddle River, NJ: Prentice-Hall, Inc.; 1998.Google Scholar
- 19.Goodwin JS. Embracing complexity: a consideration of hypertension in the very old. J Gerontol Ser A Biol Sci Med Sci. 2003;58(7):653–8.Google Scholar
- 21.Safford MM, Allison JJ, Kiefe CI. Patient complexity: more than comorbidity. The vector model of complexity. J Gen Intern Med. 2007;22(s9).Google Scholar
- 22.Bodenheimer T, May JH, Berenson RA, Coughlan J. Can Money Buy Quality? Physician Response to Pay for Performance. Center for Studying Health System Change; 2005. Available at http://www.hschange.org/CONTENT/807/. Accessed August 8, 2007.