Theoretical Medicine and Bioethics

, Volume 33, Issue 4, pp 263–277 | Cite as

Improving the quality of medical care: the normativity of evidence-based performance standards



Poor quality medical care is sometimes attributed to physicians’ unwillingness to act on evidence about what works best. Evidence-based performance standards (EBPSs) are one response to this problem, and they are increasingly employed by health care regulators and payers. Evidence in this instance is judged according to the precepts of evidence-based medicine (EBM); it is probabilistic, and the randomized controlled trial (RCT) is the gold standard. This means that EBPSs suffer all the infirmities of EBM generally—well rehearsed problems with the external validity of research findings as well as the inferential leap from study results in the aggregate to individual patient care. These theoretical weaknesses promise to have a practical impact on the care of patients. To avoid this, EBPSs should be understood as guidelines indicative of average effectiveness rather than standards to be applied in every case.


Quality of care Evidence-based performance standards Evidence-based medicine Probablism Average treatment effect Heterogeneity of treatment effect n + 1 trials Health policy 


  1. 1.
    Goodman, K.W. 2003. Ethics and evidence-based medicine: Fallibility and responsibility in clinical science. Cambridge: Cambridge University Press.Google Scholar
  2. 2.
    Timmermans, S., and M. Berg. 2003. The gold standard: The challenge of evidence-based medicine and standardization in health care. Philadelphia: Temple University Press.Google Scholar
  3. 3.
    Bodenheimer, T., and K. Grumbach. 2009. Understanding health policy: A clinical approach, 5th ed. New York: McGraw-Hill.Google Scholar
  4. 4.
    Institute of Medicine. 2000. To err is human: Building a safer health care system. Washington, D.C.: National Academies Press.Google Scholar
  5. 5.
    Chassin, M.R., and J.M. Loeb. 2011. The ongoing quality improvement journey. Health Affairs 30(4): 558–567.CrossRefGoogle Scholar
  6. 6.
    Marks, H.M. 1997. The progress of experiment: Science and therapeutic reform in the United States, 1900–1990. New York: Cambridge University Press.Google Scholar
  7. 7.
    Institute of Medicine. 2001. Crossing the quality chasm: A new health system for the 21st century. Washington, D.C.: National Academies Press.Google Scholar
  8. 8.
    Qaseem, A., V. Snow, D.K. Owens, and P. Shekelle. 2010. The development of clinical practice guidelines and guidance statements of the American College of Physicians: Summary of methods. Annals of Internal Medicine 153(3): 194–197.Google Scholar
  9. 9.
    American College of Physicians. 2011. Guideline process. Accessed 7/31/2011.
  10. 10.
    Oxford Centre for Evidence-Based Medicine. 2001. Levels of evidence.[1].htm. Accessed 8/21/11.
  11. 11.
    Werner, R.M., J.T. Kolstad, E.A. Stuart, and D. Polsky. 2011. The effect of pay-for-performance in hospitals: Lessons for quality improvement. Health Affairs 30(4): 690–698.CrossRefGoogle Scholar
  12. 12.
    Tanenbaum, S.J. 2009. Pay-for-performance in medicare: Evidentiary irony and the politics of value. Journal of Health Politics, Policy and Law 34(5): 717–746.CrossRefGoogle Scholar
  13. 13.
    Schon, D.A. 1983. The reflective practitioner: How professionals think in action. London: Temple Smith.Google Scholar
  14. 14.
    Klein, G.A. 1999. Sources of power: How people make decisions. Cambridge, MA: MIT Press.Google Scholar
  15. 15.
    Goldenberg, M.J. 2006. On evidence and evidence-based medicine: Lessons from the philosophy of science. Social Science and Medicine 62: 2621–2632.CrossRefGoogle Scholar
  16. 16.
    Hacking, I. 1990. The taming of chance. New York: Cambridge University Press.Google Scholar
  17. 17.
    Marks, H.M. 2009. What does evidence do? Histories of therapeutic research. In Harmonizing drugs: Standards in 20th century pharmaceutical history, ed. C. Bonah, C. Masutti, A. Rasmussen, et al. Paris: Editions Glyphe.Google Scholar
  18. 18.
    Wennberg, J.E. 2010. Tracking medicine: A researcher’s quest to understand health care. New York: Oxford University Press.Google Scholar
  19. 19.
    Tanenbaum, S.J. 1994. Knowing and acting in medical practice: The epistemological politics of outcomes research. Journal of Health Politics, Policy and Law 19(1): 24–44.CrossRefGoogle Scholar
  20. 20.
    Gray, B.H., M.K. Gusmano, and S.R. Collins. 2003. AHCPR and the changing politics of health services research. Health Affairs W3: 283–307.Google Scholar
  21. 21.
    Moyé, L.A. 2006. Statistical reasoning in medicine: The intuitive p-value primer, 2nd ed. New York: Springer.Google Scholar
  22. 22.
    Goodman, S. 1999. Probability at the bedside: The knowing of chances or the chances of knowing. Annals of Internal Medicine 130(7): 604–606.Google Scholar
  23. 23.
    Dans, A.L., L.F. Dans, G.H. Guyatt, and S. Richardson. 1998. Users’ guides to the medical literature: XIV. How to decide on the applicability of clinical trial results to your patient. Evidence-based medicine working group. JAMA 279(7): 545–549.CrossRefGoogle Scholar
  24. 24.
    Kravitz, R.L., N. Duan, and J. Braslow. 2004. Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Quarterly 82(4): 661–687.CrossRefGoogle Scholar
  25. 25.
    Agency for Health Care Research and Quality. 2010. Comparative effectiveness review methods: Clinical heterogeneity. Publication No. 10-EHC070-EF. Accessed 7/27/2012.
  26. 26.
    Kravitz, R.L., N. Duan, E.J. Niedzinski, M.C. Hay, S.K. Subramanian, and T.S. Weisner. 2008. What ever happened to N-of-1 trials? Insiders’ perspectives and a look to the future. Milbank Quarterly 86(4): 533–555.CrossRefGoogle Scholar
  27. 27.
    Boyd, C., J. Darer, C. Boult, et al. 2005. Clinical practice guidelines and the quality of care for older patients with multiple comorbid diseases: Implications for pay for performance. JAMA 294(6): 716–724.CrossRefGoogle Scholar
  28. 28.
    Pogach, L.M.A., A. Tawari, M. Maney, et al. 2007. Should mitigating comorbidities be considered in assessing healthcare plan performance in achieving optimal glycemic control? American Journal of Managed Care 13: 133–140.Google Scholar
  29. 29.
    Walter, L.C., N.P. Davidowitz, P.A. Heineken, and K.E. Covinsky. 2004. Pitfalls of converting practice guidelines into quality measures: Lessons learned from a VA performance measure. JAMA 291(20): 2466–2470.CrossRefGoogle Scholar
  30. 30.
    Sackett, D., R.B. Haynes, G.H. Guyatt, et al. 1991. Clinical epidemiology: A basic science of clinical medicine, 2nd ed. Boston: Little, Brown.Google Scholar
  31. 31.
    Brookes, S.T., L. Biddle, C. Paterson, G. Woolhead, and P. Dieppe. 2007. “Me’s me and you’s you”: Exploring patients’ perspectives of single patient (n-of-1) trial in the UK. Trials 8(10): 1–8. Scholar
  32. 32.
    Doran, T., C. Fullwood, H. Gravelle, D. Reeves, E. Kontopantelis, U. Hiroeh, and M. Roland. 2006. Pay-for-performance programs in family practices in the United Kingdom. New England Journal of Medicine 355: 375–384.CrossRefGoogle Scholar
  33. 33.
    Angell, M. 1993. The doctor as double agent. Kennedy Institute Ethics Journal 3: 279–286.CrossRefGoogle Scholar
  34. 34.
    Morone, J.A. 2011. Big ideas, broken institutions, and the wrath at the grass roots. Journal of Health Politics, Policy and Law 36(3): 375–386.CrossRefGoogle Scholar
  35. 35.
    Avorn, J. 2009. Debate about funding comparative-effectiveness research. New England Journal of Medicine 360(19): 1927–1929.CrossRefGoogle Scholar
  36. 36.
    Coelho, T. 2010. A patient advocate’s perspective on patient-centered comparative effectiveness research. Health Affairs 29(10): 1885–1890.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.College of Public HealthOhio State UniversityColumbusUSA

Personalised recommendations