ABSTRACT
BACKGROUND
The relationship between practice intensity and the quality and outcomes of care has not been studied.
OBJECTIVE
To examine the relationship between primary care physicians’ costliness both for defined episodes of care and for defined patients and the quality and outcomes of care delivered to Medicare beneficiaries.
STUDY DESIGN
Cross sectional analysis of physician survey data linked to Medicare claims. Physician costliness measures were calculated by comparing the episode specific and overall costs of care for their patients with the care delivered by other physicians.
PARTICIPANTS
We studied physicians participating in the 2004–2005 Community Tracking Study Physician Survey linked with administrative claims from the Medicare program for the years 2004–2006.
MAIN MEASURES
Proportion of eligible beneficiaries receiving each of seven preventive services and rates of preventable admissions for acute and chronic conditions.
KEY RESULTS
The 2,211 primary care physician respondents included 937 internists and 1,274 family or general physicians who were linked to more than 250,000 Medicare enrollees. Patients treated by more costly physicians (whether measured by the overall costliness index or the episode-level index) were more likely to receive recommended preventive services, but were also more likely to experience preventable admissions. For instance, physicians in the lowest quartile of costliness performed appropriate monitoring for hemoglobin A1C for diabetics 72.8 % of the time, as compared with 81.9 % for physicians in the highest quartile of costliness (p < 0.01). In contrast, patients treated by the physicians in the lowest quartile of episode costliness were admitted at a rate of 1.8/100 for both acute and chronic Prevention Quality Indicators (PQIs), as compared with 2.9/100 for both acute and chronic PQIs for those treated by physicians in the highest quartile of costliness (p < 0.001).
CONCLUSIONS
Physician practice patterns are associated with the quality of preventive services delivered to Medicare patients. Ongoing efforts to influence physician practice patterns may have differential effects on different aspects of quality.
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REFERENCES
Davies S, McDonald KM, Schmidt E, Schultz E, Geppert J, Romano PS. Expanding the uses of AHRQ’s prevention quality indicators: validity from the clinician perspective. Med Care. 2011;49:679–85.
Chernew ME, Sabik L, Chandra A, Newhouse JP. Ensuring the fiscal sustainability of health care reform. N Engl J Med. 2010;362:1–3.
Choosing Wisely. 2013. (Accessed 18 December, 2013, at http://www.choosingwisely.org/.)
Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in medicare spending. Part 2: health outcomes and satisfaction with care. Ann Intern Med. 2003;138:288–99.
Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in medicare spending. Part 1: the content, quality, and accessibility of care. Ann Intern Med. 2003;138:273–88.
Baicker K, Chandra A. Medicare spending, the physician workforce, and beneficiaries’ quality of care. Health Aff (Millwood). 2004; Suppl Web Exclusives:W4-184-97.
Hadley J, Reschovsky JD. Medicare spending, mortality rates, and quality of care. Int J Health Care Finance Econ. 2012;12:87–105.
Kaestner R, Silber JH. Evidence on the efficacy of inpatient spending on Medicare patients. Milkbank Q. 2010;88:560–94.
Stukel TA, Fisher ES, Alter DA, et al. Association of hospital spending intensity with mortality and readmission rates in Ontario hospitals. J Am Med Assoc. 2012;307:1037–45.
CTS Physician Surveys and the HSC 2008 Health Tracking Physician Survey. 2013. (Accessed 18 December, 2013, at http://www.hschange.org/index.cgi?data=04.)
Risk Adjustment. 2013. (Accessed 18 December, 2013, at http://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk-Adjustors.html.)
Pham HH, Schrag D, Hargraves JL, Bach PB. Delivery of preventive services to older adults by primary care physicians. J Am Med Assoc. 2005;294:473–81.
Agency for Healthcare Research and Quality. AHRQ Quality Indicators Quality Indicator User Guide: Prevention Quality Indicators (PQI) Composite Measures Version 4.3: Agency for Healthcare Research and Quality; 2011 August.
Landon BE, Reschovsky JD, O’Malley AJ, Pham HH, Hadley J. The relationship between physician compensation strategies and the intensity of care delivered to Medicare beneficiaries. Health Serv Res. 2011;46:1863–82.
Ellis P, Sandy LG, Larson AJ, Stevens SL. Wide variation in episode costs within a commercially insured population highlights potential to improve the efficiency of care. Health Aff (Millwood). 2012;31:2084–93.
Jha AK, Orav EJ, Dobson A, Book RA, Epstein AM. Measuring efficiency: the association of hospital costs and quality of care. Health Aff (Millwood). 2009;28:897–906.
Mangione CM, Gerzoff RB, Williamson DF, et al. The association between quality of care and the intensity of diabetes disease management programs. Ann Intern Med. 2006;145:107–16.
Yasaitis L, Fisher ES, Skinner JS, Chandra A. Hospital quality and intensity of spending: is there an association? Health Aff (Millwood). 2009;28:w566–72.
Baicker K, Chandra A. Medicare spending, the physician workforce, and beneficiaries’ quality of care. Health Aff. 2004;Jan-Jun:W4-184-97.
Werner RM, Greenfield S, Fung C, Turner BJ. Measuring quality of care in patients with multiple clinical conditions: summary of a conference conducted by the Society of General Internal Medicine. J Gen Intern Med. 2007;22:1206–11.
Min LC, Wenger NS, Fung C, et al. Multimorbidity is associated with better quality of care among vulnerable elders. Med Care. 2007;45:480–8.
Min LC, Reuben DB, MacLean CH, et al. Predictors of overall quality of care provided to vulnerable older people. J Am Geriatr Soc. 2005;53:1705–11.
O’Malley AJ, Marcantonio ER, Murkofsky RL, Caudry DJ, Buchanan JL. Deriving a model of the necessity to hospitalize nursing home residents. Res Aging. 2007;29:606–25.
Newhouse JP, Garber AM. Geographic variation in health care spending in the United States insights from an institute of medicine reportgeographic variation in US health care spendingviewpoint. JAMA. 2013;310:1227–8.
Rosen A, Liebman E, Aizcorbe A, Cutler D. Comparing commercial systems for characterizing episodes of care: Bureau of Economic Analysis; 2012.
MaCurdy T, Kerwin J, Gibbs J, et al. Evaluating the functionality of the symmetry ETG and Medstat MEG software in forming episodes of care using medicare data. Burlingame: Acumen, LLC; 2008.
Acknowledgements
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The authors thank Cynthia Saiontz-Martinez for expert statistical programming and Johan Hong for editorial assistance.
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This work was supported by a grant from the National Institutes of Aging (1R01AG027312).
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The authors have no pertinent conflicts of interest to disclose.
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Landon, B.E., O’Malley, A.J., McKellar, M.R. et al. Higher Practice Intensity Is Associated with Higher Quality of Care but More Avoidable Admissions for Medicare Beneficiaries. J GEN INTERN MED 29, 1188–1194 (2014). https://doi.org/10.1007/s11606-014-2840-y
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DOI: https://doi.org/10.1007/s11606-014-2840-y