Variations in the longitudinal efficiency of academic medical centers

Health Affairs

Volumn 23, Issue SUPPL., 2004, Pages

  Fisher, Elliott S ^a,b   Wennberg, David E ^c   Stukel, Thérèse A ^d   Gottlieb, Daniel J ^a  

^a DARTMOUTH MEDICAL SCHOOL   (United States)

^b White River Junction (VT) VA M   (United States)

^c MAINE MEDICAL CENTER   (United States)

^d INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARTICLE; CHRONIC DISEASE; CONSULTATION; CONTROLLED STUDY; DIAGNOSTIC TEST; HEALTH CARE COST; HEALTH CARE DELIVERY; HEALTH CARE QUALITY; HOSPITALIZATION; HUMAN; MEDICAL SERVICE; MEDICAL SPECIALIST; MEDICAL STAFF; PATIENT CARE; PHYSICIAN; UNIVERSITY HOSPITAL; COHORT ANALYSIS; ECONOMICS; ORGANIZATION AND MANAGEMENT; STANDARD; SURVIVAL; TREATMENT OUTCOME;

ACADEMIC MEDICAL CENTERS; AGED; AGED, 80 AND OVER; COHORT STUDIES; EFFICIENCY, ORGANIZATIONAL; HEALTH SERVICES ACCESSIBILITY; HUMANS; QUALITY OF HEALTH CARE; SURVIVAL ANALYSIS; TREATMENT OUTCOME;

EID: 15544369809     PISSN: 02782715     EISSN: None     Source Type: Journal    
DOI: 10.1377/hlthaff.var.19     Document Type: Article

References (34)

1. See L.M. Nichols et al., "Are Market Forces Strong Enough to Deliver Efficient Health Care Systems? Confidence Is Waning," Health Affairs 23, no. 2 (2004): 8-21; and J.C. Robinson, "Hospital Tiers in Health Insurance: Balancing Consumer Choice with Financial Incentives," Health Affairs, 19 March 2003, content .healthaffairs.org/cgi/content/abstract/hlthaff.w3.135 (28 July 2004).
2. See L.M. Nichols et al., "Are Market Forces Strong Enough to Deliver Efficient Health Care Systems? Confidence Is Waning," Health Affairs 23, no. 2 (2004): 8-21; and J.C. Robinson, "Hospital Tiers in Health Insurance: Balancing Consumer Choice with Financial Incentives," Health Affairs, 19 March 2003, content .healthaffairs.org/cgi/content/abstract/hlthaff.w3.135 (28 July 2004).
3. See E.S. Fisher et al., "Hospital Readmission Rates for Cohorts of Medicare Beneficiaries in Boston and New Haven," New England Journal of Medicine 331, no. 15 (1994): 989-995.
4. See J.E. Wennberg et al., "Use of Hospitals, Physician Visits, and Hospice Care during the Last Six Months of Life among Cohorts Loyal to Highly Respected Hospitals in the United States," British Medical Journal 328, no. 7440 (2004): 607.
5. To our knowledge, the term longitudinal efficiency was introduced by Arnold Milstein, Pacific Business Group on Health, in testimony before the U.S. Senate Committee on Health, Education, Labor, and Pensions, 28 January 2004. Milstein refers to the need for a focus on the longitudinal experience of defined populations, because differences in unit price ignore potential differences in volume or quality that can influence the aggregate costs (and outcomes) of care for a population.
6. E.S. Fisher et al., "The Implications of Regional Variations in Medicare Spending, Part 1: The Content, Quality, and Accessibility of Care," Annals of Internal Medicine 138, no. 4 (2003): 273-287, and "The Implications of Regional Variations in Medicare Spending, Part 2: Health Outcomes and Satisfaction with Care," Annals of Internal Medicine 138, no. 4 (2003): 288-298.
7. E.S. Fisher et al., "The Implications of Regional Variations in Medicare Spending, Part 1: The Content, Quality, and Accessibility of Care," Annals of Internal Medicine 138, no. 4 (2003): 273-287, and "The Implications of Regional Variations in Medicare Spending, Part 2: Health Outcomes and Satisfaction with Care," Annals of Internal Medicine 138, no. 4 (2003): 288-298.
8. The original study used two different indices of local practice intensity to assign the cohorts to quintiles of price and illness-adjusted Medicare spending, one based on differences in practice intensity during the last six months of life (the End-of-Life Expenditure Index, or EOL-EI) and one based on differences in average practice intensity during the first six months after an acute episode of illness. Both measures were calculated on samples that were distinct from the study cohorts. Both were uncorrelated with differences in illness levels among Medicare enrollees across regions but highly correlated with differences in total per capita Medicare spending. We use the EOL-EI in the current study. For a detailed description of the methods, see ibid. and an appendix at www.annals.org/cgi/data/138/4/288/DC1/1 (28 July 2004, available only to subscribers of Annals of Internal Medicine).
9. The hip fracture and colorectal cancer cohorts were defined based upon an initial hospitalization identified in the MEDPAR hospital discharge files that occurred in the full three-year period 1 January 1993-31 December 1995 using appropriate International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes. The acute myocardial infarction (AMI) cohort comprised Medicare enrollees in the Cooperative Cardiovascular Project with completed medical record reviews whose confirmed AMI occurred between February 1994 and November 1995.
10. COTH membership was ascertained from the American Hospital Association (AHA) annual survey. The cohorts were defined using Medicare hospital claims records (MEDPAR file), and the linkage to the AHA file was based on the Medicare provider number recorded on the claims.
11. The standardized national price for hospital services was based upon the diagnosis-related group (DRG) for each discharge and the relative value units (RVUs) for each physician service. Details are provided in the appendix to the original study, www.annals.org/cgi/data/138/4/288/DC1/1.
12. J.E. Wennberg and M.M. Cooper, eds., The Quality of Medical Care in the United States: A Report on the Medicare Program, The Dartmouth Atlas of Health Care in the United States 1999 (Chicago: AHA Press, 1999). Data on resources, spending, and intensity measures for each of the 306 U.S. HRRs are available at Dartmouth Atlas of Health Care, "Is More Actually Better?" www.dartmouthadas.org/annals/fisher03.php (28 July 2004).
13. J.E. Wennberg and M.M. Cooper, eds., The Quality of Medical Care in the United States: A Report on the Medicare Program, The Dartmouth Atlas of Health Care in the United States 1999 (Chicago: AHA Press, 1999). Data on resources, spending, and intensity measures for each of the 306 U.S. HRRs are available at Dartmouth Atlas of Health Care, "Is More Actually Better?" www.dartmouthadas.org/annals/fisher03.php (28 July 2004).
14. A list of the ten largest hospitals in each intensity group is provided as Appendix Table 1 on the Health Affairs Web site, content.healthaffairs.org/ cgi/content/full/hlthaff.var.19/DC2.
15. In the lower-intensity regions, only 10 percent of the hospital beds are in major teaching hospitals, compared with 31 percent in the higher-intensity regions. This difference provides a second motivation for the current study: whether the higher-intensity practice pattern in high-cost regions was largely attributable to the predominance of teaching hospitals (or, asked another way, might teaching hospitals in the lower-intensity regions be just like teaching hospitals in the higher-intensity regions?).
16. Loyalty was measured as the average percentage of hospital days experienced by each patient that were provided by the index hospital; it was more than 95 percent for the first six months, 60-68 percent for the period from two to five years, and more than 50 percent for years two through five. The average over five years was more than 80 percent for each cohort. As a sensitivity analysis, we repeated all utilization analyses excluding patients who received any care outside their index hospital. The results were essentially unchanged, with much higher utilization rates observed in the higher-intensity hospitals, which indicates that the major findings of the study - marked hospital-specific differences in use - are not sensitive to differences in loyalty across the hospitals included in the current study.
17. See Appendix Table 2 at content.healthaffairs.org/cgi/content/full/ hlthaff.var.19/DC2.
18. The survival models were run on the entire hip fracture, colorectal cancer, and AMI cohorts from the original study (see www.annals.org/issues/ v138n4/toc.html), with interaction terms for the categories of teaching hospital and HRR-level end-of-life intensity index measured as a continuous variable. The estimates of the effect of a 10 percent increase in practice intensity on mortality were as follows: hip fracture, 1.003 (95 percent confidence interval: 0.999, 1.007); colorectal cancer, 1.007 (95 percent CI: 1.000, 1.013); and AMI, 1.012 (95 percent CI: 1.005, 1.020).
19. Although it is reasonable to imagine that patients' preferences may differ between Miami and Minneapolis, it seems less likely that patients receiving care in major U.S. AMCs in the same city would choose hospitals on the basis of their preferences - and if they did, one might expect those with strong preferences for "more intensive" care to choose the hospital with a stronger reputation for high-technology care. Longitudinal analyses in Boston showed that patients cared for at Massachusetts General Hospital had hospital utilization rates that were 25 percent lower than those cared for at Boston University Medical Center. Fisher et al., "Hospital Readmission Rates."
20. R.S. Pritchard et al., "Influence of Patient Preferences and Local Health System Characteristics on the Place of Death: SUPPORT Investigators, Study to Understand Prognoses and Preferences for Risks and Outcomes of Treatment," Journal of the American Geriatrics Society 46, no. 10 (1998): 1242-1250.
21. Medicare uses the ratio of interns and residents to beds to determine the level of supplemental payments for the indirect costs of medical education, which are presumed to include greater intensity of testing. L. Koenig et al., "Estimating the Mission-Related Costs of Teaching Hospitals," Health Affairs 22, no. 6 (2003): 112-122.
22. In our data, the ratio of interns and residents to beds was either unassociated (AMI, hip fracture cohorts) or negatively associated (colorectal cancer cohort, r = -.30, p < .001) with the intensity of physician services provided during the first six months of follow-up.
23. The notion that supply and utilization are associated is not new; numerous studies have documented the association between the local supply of resources and utilization. Early examples include Roemer's now classic observation of a strong association between the local bed supply and hospitalization rates. Since that time, studies have consistently revealed an association between specific resources and utilization, including physician supply and the frequency of physician visits; the local hospital bed supply and admission rates for discretionary (but not for emergent or nondiscretionary) conditions; and the use and availability of ICU beds.
24. A more formal way to look at these relationships is through linear regression. These two factors explain 43 percent of the regional variation in intensity.
25. This could explain why increases in the per capita supply of physicians have a greater impact in high-than in low-bedded areas: It is easier for the additional physician in a high-bedded area to provide patient care in the inpatient setting, where more can be done more quickly and - from the physician perspective - more efficiently.
26. See D.E. Singer et al., "Rationing Intensive Care - Physician Responses to a Resource Shortage," New England Journal of Medicine 309, no. 19 (1983): 1155-1160; and M.J. Strauss et al., "Rationing of Intensive Care Unit Services: An Everyday Occurrence," Journal of the American Medical Association 255, no. 9 (1986): 1143-1146.
27. See D.E. Singer et al., "Rationing Intensive Care - Physician Responses to a Resource Shortage," New England Journal of Medicine 309, no. 19 (1983): 1155-1160; and M.J. Strauss et al., "Rationing of Intensive Care Unit Services: An Everyday Occurrence," Journal of the American Medical Association 255, no. 9 (1986): 1143-1146.
28. The Medicare data can be used to estimate the actual inputs of hospital beds and nurse and physician full-time equivalents (FTEs). See J.E. Wennberg et al., "Use of Medicare Claims Data to Monitor Provider-Specific Performance among Patients with Severe Chronic Illness," Health Affairs, 7 October 2004, content.healthaffairs.org/cgi/content/abstract/hlthaff.var.5.
29. The regional analyses showed that for four of six acute care measures and three of four measures of the quality of preventive care, higher-intensity regions provided significantly lower quality of care. This is also consistent with recent analyses that find that states with higher per capita Medicare spending - and more specialist physicians - have lower levels of quality for Medicare enrollees. K. Baicker and A. Chandra, "Medicare Spending, the Physician Workforce, and Beneficiaries' Quality of Care," Health Affairs, 7 April 2004, content.healthaffairs.org/cgi/content/abstract/hlthaff.w4.184 (28 July 2004).
30. Evidence suggests that the chronic care model developed by Ed Wagner and colleagues, when implemented, can lead to improvements in both the process and outcome of care. T Bodenheimer, E.H. Wagner, and K. Grumbach, "Improving Primary Care for Patients with Chronic Illness: The Chronic Care Model, Part 2," Journal of the American Medical Association 288, no. 15 (2002): 1909-1914.
31. M.C. Creditor, "Hazards of Hospitalization of the Elderly," Annals of Internal Medicine 118, no. 3 (1993): 219-223.
32. E.S. Fisher and H.G. Welch, "Avoiding the Unintended Consequences of Growth in Medical Care: How Might More Be Worse?" Journal of the American Medical Association 281, no. 5 (1999): 446-453.
33. An early proposal by Pete Welch and Mark Miller focused on fostering integration at the medical staff level for the physician services provided during a single inpatient stay, whereas we suggest a longer time window. Their analysis also indicated that physicians have few hospital affiliations (averaging about 1.5 per physician) but admit almost all of their patients to a single hospital (averaging 90 percent). See W.P. Welch and M.E. Miller, "Proposals to Control High-Cost Hospital Medical Staffs," Health Affairs 13, no. 4 (1994): 42-57.
34. A possible approach to implementing a collaborative effort of AMCs, the National Institutes of Health (NIH), and the Agency for Healthcare Research and Quality (AHRQ) to address these issues is discussed in J.E. Wennberg, E.S. Fisher, and J.S. Skinner, "Geography and the Debate over Medicare Reform," Health Affairs, 13 February 2002, content.healthaffairs.org/cgi/ content/abstract/hlthaff.w2.96 (28 July 2004).