Journal of General Internal Medicine

, Volume 22, Issue 12, pp 1648–1655 | Cite as

Reasons for Not Intensifying Medications: Differentiating “Clinical Inertia” from Appropriate Care

  • Monika M. Safford
  • Richard Shewchuk
  • Haiyan Qu
  • Jessica H. Williams
  • Carlos A. Estrada
  • Fernando Ovalle
  • Jeroan J. Allison
Original Article



“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.


clinical 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

None disclosed.


  1. 1.
    Berlowitz DR, Ash AS, Hickey EC, et al. Inadequate management of blood pressure in a hypertensive population. N Engl J Med. 1998;339(27):1957–63.PubMedCrossRefGoogle Scholar
  2. 2.
    Borzecki AM, Wong AT, Hickey EC, Ash AS, Berlowitz DR. Hypertension control: how well are we doing? Arch Intern Med. 2003;163(22):2705–11.PubMedCrossRefGoogle Scholar
  3. 3.
    Grant RW, Cagliero E, Dubey AK, et al. Clinical inertia in the management of Type 2 diabetes metabolic risk factors. Diabet Med. 2004;21(2):150–55.PubMedCrossRefGoogle Scholar
  4. 4.
    National Institutes of Health N. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.
  5. 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
  6. 6.
    O’Connor PJ. Overcome clinical inertia to control systolic blood pressure. Arch Intern Med. 2003;163(22):2677–78.PubMedCrossRefGoogle Scholar
  7. 7.
    Wright JT, Jr., Dunn JK, Cutler JA, et al. Outcomes in hypertensive black and nonblack patients treated with chlorthalidone, amlodipine, and lisinopril. JAMA. 2005;293(13):1595–608.PubMedCrossRefGoogle Scholar
  8. 8.
    Rodondi N, Peng T, Karter AJ, et al. Therapy modifications in response to poorly controlled hypertension, dyslipidemia, and diabetes mellitus. Ann Intern Med. 2006;144(7):475–84.PubMedGoogle Scholar
  9. 9.
    O’Connor P. Commentary—improving diabetes care by combating clinical inertia. Health Serv Res. 2005;40(6 Pt 1):1854–61.PubMedCrossRefGoogle Scholar
  10. 10.
    Shewchuk R, O’Connor SJ. Using cognitive concept mapping to understand what health care means to the elderly: an illustrative approach for planning and marketing. Health Market Q. 2002;20(2):69–88.CrossRefGoogle Scholar
  11. 11.
    Levine DA, Saag KG, Casebeer LL, Colon-Emeric C, Lyles KW, Shewchuk RM. Using a modified nominal group technique to elicit director of nursing input for an osteoporosis intervention. Journal of the American Medical Directors Association 2006;7(7):420–5.PubMedCrossRefGoogle Scholar
  12. 12.
    Schiffman S, Reynolds M, Young F. Introduction to Multidimensional Scaling. New York: Academic Press; 1981.Google Scholar
  13. 13.
    Aldenderfer M, Blashfield R. Cluster Analysis. Beverly Hills, CA: Sage Publications; 1984.Google Scholar
  14. 14.
    Kruskall J, Wish M. Multi-Dimensional Scaling. Newbury Park, NJ: Sage Publications; 1990.Google Scholar
  15. 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. 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
  17. 17.
    Pickering TG. White coat hypertension: time for action. Circulation. 1998;98(18):1834–36.PubMedGoogle Scholar
  18. 18.
    Kerr EA, Smith DM, Hogan MM, et al. Building a better quality measure: are some patients with ‘poor quality’ actually getting good care? Med Care. 2003;41(10):1173–82.PubMedCrossRefGoogle Scholar
  19. 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
  20. 20.
    Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294(6):716–24.PubMedCrossRefGoogle Scholar
  21. 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. 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 Accessed August 8, 2007.
  23. 23.
    Casalino LP, Alexander GC, Jin L, Konetzka RT. General internists’ views on pay-for-performance and public reporting of quality scores: a national survey. Health Aff (Millwood). 2007;26(2):492–9.CrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2007

Authors and Affiliations

  • Monika M. Safford
    • 1
    • 2
  • Richard Shewchuk
    • 1
  • Haiyan Qu
    • 1
  • Jessica H. Williams
    • 1
  • Carlos A. Estrada
    • 1
    • 2
  • Fernando Ovalle
    • 1
  • Jeroan J. Allison
    • 1
  1. 1.University of Alabama at BirminghamBirminghamUSA
  2. 2.Deep South Center on Effectiveness at the Birmingham VA Medical CenterBirminghamUSA

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