Health Affairs, 25, no. 2 (2006): 521-531 doi: 10.1377/hlthaff.25.2.521 © 2006 by Project HOPE	New Online   Senate Health Reform Bill   Rewarding Providers   Public Option Policy Brief   Health Reform & Abortion   Delivery System Reform

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DataWatch

End-Of-Life Care At Academic Medical Centers: Implications For Future Workforce Requirements

Abstract

 
The expansion of U.S. physician workforce training has been justified on the basis of population growth, technological innovation, and economic expansion. Our analyses found threefold differences in physician full-time-equivalent (FTE) inputs for Medicare cohorts cared for at academic medical centers (AMCs); AMC inputs were highly correlated with the number of physician FTEs per Medicare beneficiary in AMC regions. Given the apparent inefficiency of current physician practices, the supply pipeline is sufficient to meet future needs through 2020, with adoption of the workforce deployment patterns now seen among AMCs and regions dominated by large group practices.

But how many more physicians are required to improve the future health and well-being of the population? Previous research has shown a complex relationship between physician supply, medical care use, and health outcomes. Physician supply and utilization rates, for example, are known to vary across regions, and these variations cannot be explained by differences in population age structure or health status.² Furthermore, both cross-sectional and cohort studies have failed to show an enduring link between greater supply and improved population health.³ Indeed, regions and states with more medical specialists and general internists appear to have lower quality of care as measured by mortality and common performance measures endorsed by the National Committee for Quality Assurance (NCQA).⁴ In light of these facts, we argue in this paper that regions and academic medical centers (AMCs) with low physician labor input rates should be viewed as useful benchmarks of overall efficiency in the allocation of physician labor.⁵ These benchmarks, in turn, can shed light on the increment in the current U.S. supply of physicians required to meet the needs of tomorrow’s elderly populations.

Study Data And Methods

Top Study Data And Methods Results Discussion NOTES

 
Although there is value in measuring variation in physician labor inputs across entire populations, such analyses could be criticized for possible differences in health status and adequacy of medical care. Instead, we first examined physician inputs to a more restricted but standardized population: Medicare patients living in Hospital Referral Regions (HRRs) and those who receive most of their care from AMCs, arguably sites providing the highest quality of care. For AMCs, we report physician full-time-equivalents (FTEs) during patients’ last six months of life, which greatly reduces the possibility that the differences in physician deployment relate to differences in illness or inadequacies in the quality of care. To establish benchmarks for efficient physician workforce allocation to this elderly population, we also examined the entire eligible Medicare populations living in HRRs.

Medicare study populations. We studied patients enrolled in both Parts A and B of fee-for-service (FFS) Medicare. The regional comparisons are among the 306 HRRs featured in the Dartmouth Atlas of Health Care for the resident population during 2000.⁶ The population served by AMCs consists of beneficiaries who died during 1999–2001 and who (1) received the majority of their hospitalized days during their last two years in an AMC and (2) were admitted for one or more of twelve chronic disease conditions that have a high probability of hospital death.⁷ National Medicare claims files that account for physician services are limited to a 20 percent beneficiary sample; our study cohort consisted of this sample. We identified 108 major AMCs integrated with medical schools; of these, our sample was restricted to the 79 AMCs with at least 100 chronically ill decedents during the study period.⁸ The percentage of hospitalized days in the last twenty-four months of life that occurred at the designated AMCs for these seventy-nine hospitals ranged from 77 percent to 94 percent of medical admissions, with a median of 87 percent. For each of these seventy-nine patient cohorts, we measured the total Medicare physician use over the last twenty-four months of life.

Measuring physician labor inputs. Physician inputs to populations who receive most of their inpatient care at a given hospital cannot be measured using traditional physician supply data such as the American Medical Association (AMA) Physician Masterfile.⁹ To develop such estimates, we used Medicare claims from 20 percent Part B and Outpatient Files to identify physician services provided to each region or each cohort assigned to a given hospital.¹⁰ Current Procedural Terminology (CPT) codes for each claim were linked to Work Relative Value Units (WRVUs), a standardized method of estimating physician labor for each medical service. We summed WRVUs by specialty for each region or hospital and then divided the totals by the specialty-specific WRVUs per FTE physician as measured in two large surveys of medical clinics.¹¹ Physician categories were primary care, medical specialists, surgical specialists, and hospital-based specialists.¹² The total was the sum of these categories in addition to psychiatry. Anesthesiology reimbursements are not based on WRVUs, so their labor could not be included.

Standardized FTEs. It would not be surprising if the mix of patients with terminal chronic conditions differed across hospitals and regions. To provide hospital-specific rates that were standardized for case-mix, estimates for FTEs per 1,000 patients were adjusted using a fixed-effects linear regression model. A separate model was run for each specialty and aggregation of specialties (primary care and medical, surgical, and hospital-based specialties). The dependent variable was specialty-specific FTEs per decedent. Independent variables included the patient’s age (67–69, 70–74, 75–79, 80–84, and 85–99), sex, race (nonblack and black), and a dummy variable for each of the twelve chronic conditions. To calculate standardized hospital-specific rates, we included a dummy variable for each of the hospitals. Regression coefficients were centered to the national rate to represent average FTEs per 1,000 or 10,000 beneficiaries by hospital or region.¹³ Regional rates were calculated for the sixty HRRs with one of the above AMCs and adjusted for age, sex, and race differences using the indirect method. End-of-life cohort members were excluded from the regional rates.

Results

Top Study Data And Methods Results Discussion NOTES

 
Labor inputs. The amount of physician labor used to care for chronically ill Medicare beneficiaries at the end of life differed widely among the AMCs in this study (Exhibit 1). The total number of standardized physician FTEs per 1,000 decedents varied from 6.0 to 28.3, with a median of 11.2 and an interquartile range (25th–75th ) of 9.3 to 13.7. A similar degree of variation was observed for primary care physicians (range, 1.5–8.8; median, 3.7; and interquartile range, 2.9–5.0) and medical specialists (range, 2.4–15.0; median, 4.9; and interquartile range, 4.0–6.3). During the last six months of life, surgical and hospital-based specialists contributed a relatively smaller proportion of the total physician labor (median, 1.0 and 1.1 each), but the variation of standardized FTEs per 1,000 beneficiaries was similarly high across the hospitals.¹⁴

Input rates were also correlated between specific medical specialists. For example, labor inputs for cardiologists correlated with those of hematologist/oncologists (Pearson’s correlation, 0.56), pulmonologists (0.64), and neurologists (0.70).

These results show that there are wide differences in physician effort in caring for cohorts of similar patients. The patterns of labor input during patients’ last six months of life were closely correlated with the overall level of effort toward the entire FFS Medicare population living in the same region (end-of-life cohort members were excluded from these regional rates) (Exhibit 3). Los Angeles, California; Manhattan, New York; and New Brunswick, New Jersey, had very high labor inputs for both AMCs’ end-of-life cohorts and the region’s Medicare population. Minneapolis and Rochester, Minnesota (home of the Mayo Clinic), had low regional and AMC physician labor effort. This correlation suggests that the variation observed across regions cannot be explained by differences in health status, adequacy of care, or patients’ preferences but is linked to idiosyncratic patterns of labor input found in both AMCs and their associated regions.

View larger version (27K): [in this window] [in a new window]   EXHIBIT 3 Physician Labor Inputs For All Medicare Beneficiaries In HRRs Versus End-Of-Life Cohorts Loyal To AMCs, 1999–2001

Top Study Data And Methods Results Discussion NOTES

Additional needs on the horizon. Further arguments have been made that technological advances and economic expansion will require more physicians. It is hard to understand how differences in technology could explain the variations observed among AMCs. Indeed, the relatively low labor input associated with the Mayo Clinic suggests that higher levels of physician effort in other centers may be associated with a misapplication of medical technology.

With respect to economic development, although it was not the primary focus of this paper, we found little correlation between the median household income of the AMC regions and their labor input rates.¹⁷

Accounting for geographic variation. Although this study is limited to differences in physician FTEs for the elderly, the magnitude of variation, two- to threefold, is similar to that observed in the regional per capita physician supply, when controlling for population differences. These differences, unexplained by any reason salient to the health and well-being of populations, dwarf the requirements in physicians attributed to temporal trends in population growth, physician utilization rates, or economic expansion. Indeed, the percentage differences in future supply and "requirements" or "needs" projected by COGME’s Sixteenth Report are less than the percentage differences in physician labor input noted between Massachusetts General Hospital in Boston and Strong Memorial Hospital in Rochester.

A common explanation offered for geographic variation in resource allocation and use is differences in health status or in patients’ care preferences. For this reason, we have studied variation across cohorts with similar illness severity. Nor does it seem plausible that patients choose their hospitals or the regions they live in based on the amount of care they wish to receive in their last six months of life.

Study limitations. Our study had several limitations. First, adjustments for illness using claims data miss many details important for prognosis that ordinarily require chart review. However, by studying labor inputs during the last six months of life, we narrowed possible differences in health status. Second, reliance on FFS claims omits the approximately 17 percent of beneficiaries enrolled in Medicare+ Choice (M+C, now Medicare Advantage) plans, and the percentage enrolled will vary across regions and AMCs. We expect that patients enrolled in such plans would generally experience fewer physician inputs than similarly ill patients in FFS. Third, we used surveys for estimating physician WRVUs per FTE that included a mixture of academic and nonacademic group practices; this would not have affected the relative differences we observed across centers and regions. We also note that we did not measure resource inputs of other domains, such as nonphysician labor or hospice care, and this limits the explanation of the differences observed. Although we observed that AMCs with high ratios of specialist FTEs generally also had high ratios of primary care FTEs, we do not know if this relationship holds true for both general internists and family physicians. Furthermore, the professional roles of primary care physicians at AMCs might be different from those serving broader populations.¹⁸

Policy implications. Calls for increasing the supply of physicians should not ignore the evidence that health outcomes among Medicare populations are not likely to be improved by increasing physician labor input beyond that now seen among low-rate AMCs or HRRs. Similar findings for other patient populations have been amply documented.¹⁹ Although acceptable medical care may be consistent with very different workforce levels, the primary difference between high- and low-supply areas appears to be the volume and cost of care delivered, with an apparent, albeit small, negative effect of high physician supply on the quality and outcomes of care and patients’ perceptions of barriers to access. From the point of view of gains in population health, the low-resource-input regions appear to be more efficient in improving health.

We note that several low-input regions are served by well-known AMCs with strong reputations for high-quality care. We also note that in several of the low-input regions, much of the care is provided by large, multispecialty group practices (for example, the Mayo Clinic) or integrated delivery systems (for example, Intermountain Health Care). The association between large group practices and efficient use of physician workforce has been documented for prepaid group practices such as Kaiser Permanente.²⁰ Our study suggests that even in FFS environments, group practices use fewer physicians per capita than is true in small-group or solo practices (the dominant modes in most U.S. regions). The "need" for physicians in 2020 may thus depend more on how we change the organization and financing of health care than on Americans’ health care needs. Instead of financing further growth in our medical education system, resources might be better directed to reorganizing delivery systems to models of FFS and prepaid group practice that have already demonstrated that they can deliver good care at relatively low costs.

Editor's Notes

 
David Goodman (david.goodman{at}dartmouth.edu) is a professor of pediatrics and of community and family medicine at the Center for the Evaluative Clinical Sciences (CECS), Dartmouth Medical School, in Hanover, New Hampshire. Thérèse Stukel is a senior scientist and vice president for research at the Institute for Clinical Evaluative Sciences in Toronto, Ontario. Chiang-hua Chang is a research associate at the CECS. John Wennberg is director of the CECS and the Peggy Y. Thomson Professor for the Evaluative Clinical Sciences at Dartmouth Medical School.

This research was funded in part by National Institute on Aging (NIA) Grant no. P01 AG019783 and the Robert Wood Johnson Foundation.

NOTES

Top Study Data And Methods Results Discussion NOTES

 

1. Association of American Medical Colleges, "Workforce: Position Statement," http://www.aamc.org/workforce (accessed 1 July 2005).See also Council on Graduate Medical Education, Physician Workforce Policy Guidelines for the United States, 2000–2020, Sixteenth Report, January 2005, http://www.cogme.gov/report16.htm (accessed 1 July 2005).
2. J. Wennberg and M. Cooper, eds., The Dartmouth Atlas of Health Care—1998, 2d ed. (Chicago: American Hospital Association, 1997); and D.C. Goodman et al., "Are Neonatal Intensive Care Resources Located According to Need? Regional Variation in Neonatologists, Beds, and Low Birth Weight Newborns," Pediatrics 108, no. 2 (2001): 426–431.[Abstract/Free Full Text]
3. See, for example, A.L. Cochrane, A.S. St. Leger, and F. Moore, "Health Service ‘Input’ and Mortality ‘Output’ in Developed Countries," Journal of Epidemiology and Community Health 51, no. 4 (1997): 344–348[Free Full Text]; R.L. Vogel and R.J. Ackermann, "Is Primary Care Physician Supply Correlated with Health Outcomes?" International Journal of Health Services 28, no. 1 (1998): 183–196; and H. Krakauer et al., "Physician Impact on Hospital Admission and on Mortality Rates in the Medicare Population," Health Services Research 31, no. 2 (1996): 191–211.[Web of Science][Medline]
4. K. Baicker and A. Chandra, "Medicare Spending, the Physician Workforce, and Beneficiaries’ Quality of Care," Health Affairs 23 (2004): w184–w197 (published online 7 April 2004; 10.1377/hlthaff.w4.184).
5. E.S. Fisher et al., "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[Abstract/Free Full Text]; 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[Abstract/Free Full Text]; and E.S. Fisher et al., "Variations in the Longitudinal Efficiency of Academic Medical Centers," Health Affairs 23 (2004): var19–var32 (published online 7 October 2004; 10.1377/hlthaff.var.19).
6. Wennberg and Cooper, eds., Dartmouth Atlas of Health Care.
7. L.I. Iezzoni et al., "Chronic Conditions and Risk of In-Hospital Death," Health Services Research 29, no. 4 (1994): 435–460.[Web of Science][Medline]
8. The data for the identification of AMCs were provided by Ed Salsberg of the AAMC. Our primary group of AMCs were those integrated with medical schools (N = 79). These were members of the Council of Teaching Hospitals (COTH) (N = 117 classified as integrated), had more than fifty residents (N = 324), and were classified by the Centers for Medicare and Medicaid Services (CMS) as major teaching hospitals (N = 300). We also studied independent AMCs (N = 246), defined as those hospitals with at least fifty residents (N = 324) that either were members of COTH (N = 163 classified as independent) or were classified by the CMS as major teaching hospitals (N = 300).
9. L.M Baldwin et al., "Linking Physician Characteristics and Medicare Claims Data: Issues in Data Availability, Quality, and Measurement," Medical Care 40, no. 8 Supp. (2002): iv-82–iv-95.
10. The Outpatient File is particularly important for capturing physician services that are billed through facility claims and are not, therefore, represented in the Part B file. This includes services provided at community health centers and rural health clinics.
11. The primary source was Medical Group Management Association, Physician Compensation and Productivity Survey: 2002 Report Based on 2001 Data (Englewood, Colo.: MGMA, 2003). A secondary survey was from RSM McGladrey Inc. in Bloomington, Minnesota.
12. The specialties were categorized as follows. Primary care: family practice and general internal medicine; medical specialties: allergy/immunology, cardiology, critical care medicine, dermatology, emergency medicine, endocrinology, gerontology, gastroenterology, hematology/oncology, infectious disease, nephrology, neurology, pulmonary, radiation oncology, rehabilitation medicine, and rheumatology; surgical specialties: cardiovascular surgery, general surgery, neurosurgery, obstetrics-gynecology, ophthalmology, orthopedics, otolaryngology, plastic and reconstructive surgery, urology, and vascular surgery; and hospital-based specialties: pathology and radiology.
13. For additional details on the statistical methods used, 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): 607–611.[Abstract/Free Full Text]
14. This study presents the results from AMCs integrated with medical schools (as reported to COTH), although end-of-life physician labor inputs were also calculated for AMCs considered to be "independent." Regression adjustment models include all AMCs. Labor inputs were slightly higher for independent compared to integrated AMCs, with a median total physician FTEs of 13.3 per 1,000 decedents. We also calculated the end-of-life labor input for the entire U.S. Medicare cohort. The rate was 11.7 FTEs per 1,000 decedents.
15. U.S. Census Bureau, "U.S. Interim Projections by Age, Sex, Race, and Hispanic Origin," 18 March 2004, http://www.census.gov/ipc/www/usinterimproj (accessed 19 November 2004).
16. COGME, Physician Workforce Policy Guidelines, 16.
17. The R² for the sixty HRRs’ median household income versus the AMCs’ total physician FTEs per Medicare cohort patient was 0.08; the R² for HRRs’ median household income versus their total physician FTEs per Medicare beneficiary was 0.02.
18. B. Starfield et al., "The Effects of Specialist Supply on Populations’ Health: Assessing the Evidence," Health Affairs 24 (2005): w97–w107 (published online 15 March 2005; 10.1377/hlthaff.w5.97).
19. For example, see D.C. Goodman et al., "The Relation between the Availability of Neonatal Intensive Care and Neonatal Mortality," New England Journal of Medicine 346, no. 20 (2002): 1538–1544.[Abstract/Free Full Text]
20. J.P. Weiner, "Prepaid Group Practice Staffing and U.S. Physician Supply: Lessons for Workforce Policy," Health Affairs 23 (2004): w43–w59 (published online 4 February 2004; 10.1377/hlthaff.w4.43); and D.C. Goodman et al., "Benchmarking the U.S. Physician Workforce: An Alternative to Needs-based or Demand-based Planning," Journal of the American Medical Association 276, no. 22 (1996): 1811–1817.[Abstract/Free Full Text]

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