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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e

n engl j med 363;1 nejm.org july 1, 2010 45

special article

Regional Variations in Diagnostic Practices Yunjie Song, Ph.D., Jonathan Skinner, Ph.D., Julie Bynum, M.D., M.P.H.,

Jason Sutherland, Ph.D., John E. Wennberg, M.D., M.P.H., and Elliott S. Fisher, M.D., M.P.H.

From the Dartmouth Institute for Health Policy and Clinical Practice (Y.S., J. Skin- ner, J.B., J. Sutherland, J.E.W., E.S.F.), and the Departments of Medicine and Com- munity and Family Medicine ( J.B.), Dart- mouth Medical School; and the Depart- ment of Economics, Dartmouth College ( J. Skinner) — all in Hanover, NH; the National Bureau of Economic Research, Cambridge, MA ( J. Skinner), and the Vet- erans Affairs Outcomes Group, White River Junction, VT (E.S.F.). Address re- print requests to Dr. Fisher at the Dart- mouth Institute for Health Policy and Clinical Practice, Dartmouth College, 35 Centerra Pkwy., Suite 205, Lebanon, NH 03766, or at [email protected].

This article (10.1056/NEJMsa0910881) was published on May 12, 2010, and updated on June 8, 2010, at NEJM.org.

A b s t r a c t

Background

Current methods of risk adjustment rely on diagnoses recorded in clinical and ad- ministrative records. Differences among providers in diagnostic practices could lead to bias.

Methods

We used Medicare claims data from 1999 through 2006 to measure trends in diag- nostic practices for Medicare beneficiaries. Regions were grouped into five quin- tiles according to the intensity of hospital and physician services that beneficiaries in the region received. We compared trends with respect to diagnoses, laboratory test- ing, imaging, and the assignment of Hierarchical Condition Categories (HCCs) among beneficiaries who moved to regions with a higher or lower intensity of practice.

Results

Beneficiaries within each quintile who moved during the study period to regions with a higher or lower intensity of practice had similar numbers of diagnoses and similar HCC risk scores (as derived from HCC coding algorithms) before their move. The number of diagnoses and the HCC measures increased as the cohort aged, but they increased to a greater extent among beneficiaries who moved to re- gions with a higher intensity of practice than among those who moved to regions with the same or lower intensity of practice. For example, among beneficiaries who lived initially in regions in the lowest quintile, there was a greater increase in the average number of diagnoses among those who moved to regions in a higher quin- tile than among those who moved to regions within the lowest quintile (increase of 100.8%; 95% confidence interval [CI], 89.6 to 112.1; vs. increase of 61.7%; 95% CI, 55.8 to 67.4). Moving to each higher quintile of intensity was associated with an additional 5.9% increase (95% CI, 5.2 to 6.7) in HCC scores, and results were simi- lar with respect to laboratory testing and imaging.

Conclusions

Substantial differences in diagnostic practices that are unlikely to be related to pa- tient characteristics are observed across U.S. regions. The use of clinical or claims- based diagnoses in risk adjustment may introduce important biases in comparative- effectiveness studies, public reporting, and payment reforms.

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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e

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Risk adjustment is an essential ele-ment of comparative-effectiveness studies, measurements of health care performance, and payment programs and is destined to become even more important as health care reform pro- ceeds. Observational studies comparing the out- comes of various approaches to treatment1 or the performance of specific providers often adjust for patients’ preexisting diagnoses.2 The Medicare payment systems for institutional providers and health plans include payment adjustments that take into account the beneficiaries’ health or functional status.3,4 As payers move toward more bundled and value-based payment systems, incen- tives to avoid providing care for patients who are difficult to treat or patients for whom the cost of treatment is high will only increase.5 Inadequate risk adjustment could thus lead to flawed infer- ences, the “dumping” of high-risk patients, and distortions in insurance markets.

Risk adjustment is only as good as the infor- mation on which it is based. Current risk-adjust- ment methods depend on the diagnoses that are recorded by physicians in medical records or reg- istries or are coded by medical-records personnel and billing staff in hospital discharge abstracts and physician claims. Concern about the accuracy of the underlying data for risk adjustment has focused largely on the problem of upcoding — that is, recording conditions on submitted claims data in such a way that risk scores, and thus pay- ments, are higher.6 Differences in diagnostic practice have received much less attention. Stud- ies have highlighted the ways in which the inter- pretation of pathological and radiologic exami- nations varies among physicians and the ways in which these differences affect the proportion of test results that are identified as abnormal.7-10 There is also variation across practices11 and re- gions,12 among physicians treating patients with similar conditions, in the propensity to order di- agnostic tests or refer patients to subspecialists. If physicians have substantial and systematic differences in their diagnostic practices that are unrelated to the underlying health of their patients but are related to institutional or regional practice patterns, biases in risk adjustment will result.

We conducted a study to determine the mag- nitude of the differences in diagnostic practices across U.S. regions, using changes in Medicare beneficiaries’ place of residence as a natural ex- periment.

M e t h o d s

Design Overview

Previous research has documented substantial regional differences in the intensity of health care services provided to Medicare beneficiaries — differences that are independent of the bene- ficiaries’ health or socioeconomic status.13 Be- cause beneficiaries are unlikely to be aware of the relative intensity of practice in their current region of residence or that of the region to which they move, we hypothesized that the average health status of beneficiaries who move to re- gions with a higher intensity of practice is simi- lar to the average health status of beneficiaries from the same region who move to lower-inten- sity regions. We tested this hypothesis by com- paring beneficiaries with respect to the number of diagnoses they had received and their use of health services before their move. We then fol- lowed the beneficiaries for 3 years after their move, stratifying them according to the intensity of practice in the region to which they moved. We hypothesized that those moving to higher- intensity regions would undergo more diagnostic tests and imaging services, would receive more diagnoses, and would thus have higher risk scores over time than those moving to lower-intensity regions.

Geographic Regions and the Definition of Intensity

We used a previously derived Medicare spending measure, the End-of-Life Expenditure Index, to define the local intensity of practice. This mea- sure is calculated as the average spending (ac- cording to standardized national prices) on hos- pital and physician services provided to Medicare enrollees 65 years of age or older who were in their last 6 months of life, adjusted for age, sex, and race. The End-of-Life Expenditure Index re- flects the component of local Medicare spending that is most closely associated with physician practice, rather than with local differences in the prevalence and severity of illnesses or in prices.14 In previous articles, we have shown that the large differences that exist across U.S. regions in health care spending at the end of life are unrelated to differences in case mix14 or patients’ preferences regarding their care.15 We calculated the End-of- Life Expenditure Index for each of the 306 U.S. Hospital Referral Regions (HRRs) for the period

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R egiona l Va r i ations in Di agnos tic Pr ac tices

n engl j med 363;1 nejm.org july 1, 2010 47

from 2001 through 2003 and grouped HRRs into quintiles of increasing intensity of practice.

Study Population

Figure 1 shows the way in which we defined the study population. We used Medicare enrollment and claims files to identify all persons who were enrolled in Medicare Part A and Part B and who were at least 65 years of age as of January 1, 1999, resided in 1 of the 50 U.S. states or Washington, DC, and changed their place of residence in 2001, 2002, or 2003. A total of 255,264 beneficiaries were identified. To ensure that we had complete follow-up and that the exposure to the health system in each region was consistent over the

entire 3-year period after the beneficiary moved, we excluded beneficiaries who died, who enrolled in a health maintenance organization (HMO) or lost their Part B coverage, or who moved more than once. We also excluded beneficiaries who received more than 10% of their claims from out- side their place of residence (“snowbirds”); how- ever, including these snowbirds in the analysis yielded similar results (see Tables 1 through 3 in the Supplementary Appendix, available with the full text of this article at NEJM.org).

Medicare beneficiaries were further stratified according to the quintile of the intensity of ser- vices provided in the region in which they origi- nally resided and the quintile of the intensity of

20% Sample, fee-for-service beneficiaries, 65 yr of age or older between Jan. 1, 1999, and Jan. 1, 2001

2001: 4,668,760 2002: 4,802,757 2003: 4,878,912

5,589,743 Unique beneficiaries were identified

255,264 Moved 2001: 90,347 2002: 80,661 2003: 84,256

5,334,479 Did not move

1,115,689 Died during 3-yr period

1,287,471 Enrolled in HMO or lost Part B cover- age in 3-yr period

116,796 Remained in cohort

49,431 Died during 3 yr after move

89,037 Moved twice, enrolled in HMO, or lost Part B coverage

51,914 Were included in the analysis

2,931,319 Were included in the analysis

64,882 Had ≥10% claims from outside their residence region of HRR (”snow- birds”)

Figure 1. Selection of the Study Population.

HMO denotes health maintenance organization, and HRR Hospital Referral Region.

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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e

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services provided in the region to which they moved. This classification resulted in 25 sub- groups, 1 for each possible combination of the quintiles before and after the move. For some analyses, we simply compared beneficiaries who moved to any HRR with a lower intensity of prac- tice, those who moved to a region within their original quintile of intensity, and those who moved to an HRR in any higher-intensity quin- tile. As a comparison group, we also included those who did not move outside their HRR.

Diagnostic Practices and Risk Scores

Our measures include both diagnostic practices (rates of diagnostic testing, imaging rates, and numbers of major chronic conditions) and Hier- archical Condition Categories (HCC) risk scores, which are currently used by Medicare for pro- gram payment. We measured rates of diagnostic testing and imaging services by first using the Berenson–Eggers Type of Service Codes (BETOS) to classify physician claims as laboratory tests or imaging services. We then counted the frequency of each type of claim for each beneficiary.

We counted the number of major chronic conditions that were documented in the Medicare physician and hospital claims data for each ben- eficiary during each of the 2 years before and the 3 years after a move. To reduce the likelihood of including “rule-out” diagnoses, we recorded a diagnosis as present if it was coded on an inpa- tient discharge abstract or on two physician claims submitted at least 7 days apart. We restricted this analysis to nine serious chronic conditions, on the basis of the work of Iezzoni and colleagues,16 as adapted for use in the 2008 Dartmouth Atlas of Health Care (see Table 7 in the Supplementary Appendix). We calculated HCC risk scores with the use of the HCC coding algorithms that are used by the Centers for Medicare and Medicaid Services (CMS) to adjust payments for Medicare Advantage plans.17

Statistical Analysis

Unless otherwise specified, all the data presented in the tables are simple counts, means, and pro- portions. We estimated the average effect of a move to a region that was one quintile higher in intensity, using regression models in which the dependent variable was the beneficiary’s final number of diagnoses or HCC risk score and the independent variables were age, race, sex, the

original number of diagnoses or HCC risk score, and the change in the number of quintiles up or down they moved, which could range from −4 to +4. We carried out sensitivity analyses in which we included and excluded snowbirds and strati- fied the moves across all five levels of intensity. Results were similar in each case. Finally, to fur- ther determine potential differences in health status at the time of the move, we compared 1-year and 3-year rates of death among beneficia- ries who moved to regions with a higher intensity or a lower intensity of practice. Confidence inter- vals were calculated with the use of the bootstrap method (Stata software).

R e s u l t s

Study Population

Table 1 shows the baseline characteristics of all the Medicare beneficiaries who were eligible for inclusion and who had complete follow-up, strat- ified according to the intensity of services pro- vided in their original HRR of residence and, if they moved, the intensity of services in the region to which they moved. Residents of higher-inten- sity regions generally had more office visits, un- derwent more diagnostic tests, had a higher num- ber of diagnoses, and had higher risk scores than did residents in lower-intensity regions. The key analysis, however, involved determining whether there was a difference in these factors when per- sons who originally resided in a region with a given quintile of intensity moved to a region with a higher or lower quintile of intensity or to an- other region with the same quintile of intensity. With few exceptions, notably in quintile 5, among the beneficiaries who moved, the number of di- agnoses, the risk scores, and the number of diag- nostic tests and imaging services were similar among persons within each quintile during the period before their move.

Diagnostic Practices and HCC Risk Scores

Table 2 shows the percent increases in laboratory tests, imaging services, and number of diagno- ses among beneficiaries who did not move and among those who moved to a new HRR, strati- fied according to the intensity of services in the region to which they moved. Among both those who moved and those who did not move, there was a strong secular trend toward increased rates of diagnostic testing and a greater prevalence of

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R egiona l Va r i ations in Di agnos tic Pr ac tices

n engl j med 363;1 nejm.org july 1, 2010 49

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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e

n engl j med 363;1 nejm.org july 1, 201050

diagnosed conditions. For example, among per- sons in quintile 2 who did not move, the number of diagnoses rose by 63.9% (95% confidence in- terval, [CI], 63.3 to 64.5); among those who moved to the less intensive quintile 1, the number of di- agnoses increased by 52.7% (95% CI, 40.5 to 64.9), whereas among those who moved to a more in- tensive region (quintiles 3 through 5), the num- ber of diagnoses increased by 91.3% (95% CI, 80.3 to 102.4). The results were similar for imag- ing and laboratory tests.

The percent increase in HCC risk scores, strati- fied according to the original quintile of inten- sity and the quintile of intensity of the region to which the beneficiary moved, is shown in Figure 2. Moving to a region with a higher intensity of practice was consistently associated with a great-

er increase in risk scores. Similar patterns were observed in each of the five quintiles.

We also estimated the average effect on the number of diagnoses of moving to a region that was one quintile of intensity higher. Receiving care in a quintile of intensity that was one step higher was associated with a 5.9% increase in the HCC risk score (95% CI, 5.2 to 6.7). We re- stricted the analysis to beneficiaries who moved within each of the nine major U.S. census re- gions and found the same pattern of greater increases in the number of diagnoses and in risk scores among those who moved to higher-inten- sity regions (see Tables 8 through 10 in the Supplementary Appendix). Finally, we compared 1-year and 3-year rates of death after the move, adjusting for age, sex, and race. The relative risk

Table 2. Percent Increase in the Number of Major Diagnoses, Diagnostic Tests, and Imaging Services among Medicare Beneficiaries, According to the Intensity of Practice in the Hospital Referral Region (HRR) in Which They Lived.

Beneficiary No. of

Beneficiaries Increase in Diagnoses

per Person Increase in Imaging Services

per Person Increase in Laboratory

Tests per Person

percent (95 percent confidence interval)

Resident of quintile 1, region with lowest intensity of practice

Did not change HRR of residence 600,490 65.6 (64.9 to 66.3) 43.9 (43.2 to 44.5) 31.6 (31.1 to 32.1)

Moved within quintile 1 7,984 61.7 (55.8 to 67.4) 24.1 (19.1 to 29.0) 23.7 (19.1 to 28.2)

Moved to higher-intensity region 3,811 100.8 (89.6 to 112.1) 66.1 (57.5 to 74.7) 74.2 (64.7 to 83.6)

Resident of quintile 2

Did not change HRR of residence 583,865 63.9 (63.3 to 64.5) 49.6 (48.9 to 50.3) 37.4 (36.9 to 37.9)

Moved to lower-intensity region 1,589 52.7 (40.5 to 64.9) 26.4 (15.5 to 37.4) 6.0 (−2.9 to 14.9)

Moved within quintile 2 2,302 62.6 (53.4 to 72.6) 26.6 (17.8 to 35.3) 36.4 (27.1 to 45.7)

Moved to higher-intensity region 3,206 91.3 (80.3 to 102.4) 49.7 (41.9 to 57.5) 59.0 (50.1 to 67.9)

Resident of quintile 3

Did not change HRR of residence 593,265 62.6 (62.0 to 63.2) 47.8 (47.1 to 48.5) 36.0 (35.5 to 36.4)

Moved to lower-intensity region 2,205 65.4 (54.1 to 76.7) 35.3 (25.0 to 45.5) 33.5 (24.9 to 42.1)

Moved within quintile 3 2,862 60.0 (50.4 to 68.7) 28.7 (20.9 to 36.6) 36.7 (29.3 to 44.1)

Moved to higher-intensity region 2,448 84.6 (72.6 to 96.7) 62.0 (50.8 to 73.2) 76.3 (64.5 to 88.2)

Resident of quintile 4

Did not change HRR of residence 624,235 62.5 (61.9 to 63.1) 49.7 (49.1 to 50.3) 37.6 (37.1 to 38.0)

Moved to lower-intensity region 4,509 61.8 (54.4 to 69.2) 25.9 (20.1 to 31.6) 15.5 (10.3 to 20.7)

Moved within quintile 4 5,188 68.4 (62.4 to 74.4) 33.1 (27.2 to 39.1) 40.2 (34.7 to 45.6)

Moved to higher-intensity region 1,211 84.3 (70.0 to 98.6) 50.2 (35.8 to 64.5) 48.8 (37.4 to 60.1)

Resident of quintile 5, region with highest intensity of practice

Did not change HRR of residence 529,464 54.8 (54.3 to 55.3) 50.4 (49.7 to 51.1) 34.1 (33.6 to 34.6)

Moved to lower-intensity region 5,485 42.6 (37.0 to 48.3) 16.6 (11.4 to 21.9) 6.6 (2.2 to 11.0)

Moved within quintile 5 9,114 58.5 (54.4 to 62.5) 38.6 (33.9 to 43.4) 32.8 (29.2 to 36.4)

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of death at 1 year among beneficiaries who moved to a region with a higher intensity of practice and among those who moved to a re- gion with a lower intensity of practice, was identical: 0.94 (95% CI, 0.90 to 0.98). At 3 years, there was no evidence of a survival benefit among those who moved to higher-intensity re- gions as compared with those who moved to lower-intensity regions (Table 11 in the Supple- mentary Appendix).

Beneficiaries who moved to quintile 5 regions had risk scores that were, on average, 19% higher than those of beneficiaries who moved to quin- tile 1 regions (see Table 6 in the Supplementary Appendix). Adjusting for such an increase in HCC scores would reduce the 1-year rate of death by 15%, as compared with the unadjusted rate of death.

D i s c u s s i o n

Among all Medicare beneficiaries, residence in regions of the United States that have a higher intensity of services is associated with a higher reported prevalence of common chronic illness- es. Whether this is due to a higher disease bur- den or to differences in diagnostic practices in high-intensity regions (or both) was unknown. To address this question, we followed Medicare beneficiaries for 2 years before and 3 years after a move and found that a move to a region with a higher intensity of practice as compared with a move to a region with a lower intensity of prac- tice was associated with greater increases in di- agnostic testing, the number of recorded chronic conditions, and HCC risk scores, with no apparent survival benefit.

This study extends previous research on variations in diagnostic practices. Earlier studies have documented variations in the use of diag- nostic tests among individual physicians and office practices11 and variations across regions in physicians’ reports of their likelihood of or- dering tests.12 Higher rates of diagnostic testing lead to an increased number of diagnoses of spe- cific clinical conditions, such as prostate cancer, thyroid disease, or vascular disease.18-20 The bi- ases introduced by greater diagnostic intensity have been well documented in the context of can- cer.21,22 Our study builds on this earlier work.

This study has several limitations. We cannot be certain that the beneficiaries who were re-

0 15 30 45 60

Percent Increase in HCC Scores

Quintile 1

Moved to 1

Moved to 2

Moved to 3

Moved to 4

Moved to 5

Quintile 2

Moved to 1

Moved to 2

Moved to 3

Moved to 4

Moved to 5

Quintile 3

Moved to 1

Moved to 2

Moved to 3

Moved to 4

Moved to 5

Quintile 4

Moved to 1

Moved to 2

Moved to 3

Moved to 4

Moved to 5

Quintile 5

Moved to 1

Moved to 2

Moved to 3

Moved to 4

Moved to 5

Figure 2. Percent Increase in Hierarchical Condition Category (HCC) Scores among Medicare Beneficiaries Who Changed Their Place of Residence.

Higher quintiles indicate a higher intensity of practice. The results were stratified according to the intensity of practice in the Hospital Referral Region in which the beneficiary lived before the move and the one in which he or she lived after the move. Horizontal I bars indicate 95% confidence intervals.

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corded as having received a new diagnosis actu- ally had the disease. Given the increased rates of imaging and laboratory testing, however, at least some of the additional diagnoses are likely to have indicated newly detected conditions. We also cannot rule out the possibility that there were dif- ferences in the beneficiaries’ underlying health status at the time of the move. However, the similarities in the number of diagnoses and in rates of testing before the move, as well as in the relative risk of death 1 year after the move, are consistent with the view that the underlying health status of the beneficiaries was similar for those who moved to regions with more intensive prac- tice and those who moved to regions with less intensive practice.

Our study has not adequately assessed the effect of the regional differences in diagnostic intensity on the health outcomes for beneficia- ries. Several previous studies involving specific clinical cohorts have shown no evidence of a sur- vival benefit when care is provided in regions13,23 or hospitals24 with a higher intensity of services. One study showed that there was a small benefit with respect to survival when the intensity of life- sustaining treatments was greater,25 and a study involving six hospitals showed that among pa- tients with heart failure, the rate of death was lower in the hospitals that used more resources in caring for patients than in those that used less.2 The analyses in all these studies adjusted the data for patients’ diagnoses as coded from provider data and thus risked overadjustment in higher- intensity regions and hospitals (and conversely, underadjustment in lower-intensity regions or hospitals). Although our study did not show a significantly higher rate of survival among bene- ficiaries who moved to regions with higher-inten- sity practices, this result should not be interpreted as implying that greater diagnostic intensity of- fers no benefits. Rather, it underscores the need for research to determine the specific clinical set- tings in which greater diagnostic intensity does — or does not — confer a benefit.

Our findings nonetheless have implications for health care reform. Comparative-effectiveness studies could be biased by the well-documented differences in diagnostic intensity across hospi- tals.26 Under public reporting programs, patients may be subject to harm to the extent that their own choices or their physicians’ referrals are based on biased risk-adjusted quality measures.

Capitation systems and bundled payments for epi- sodes of care could also be distorted. The differ- ences are not likely to be trivial, on the basis of our analyses. By the end of the study, beneficia- ries who had moved to quintile 5 regions (those with the highest intensity of practice) had risk scores that were, on average, 19% higher than those of beneficiaries who had moved to quintile 1 regions (those with the lowest intensity of practice). Since patients had similar baseline health status, these differences are plausible es- timates of the differences in diagnostic intensity across these regions. Under a public reporting or payment program that relied on the unmodi- fied HCC scores, capitated reimbursement rates would be as much as 19% higher in the high- intensity regions solely because of bias related to diagnostic practice, particularly since the CMS has been relying to a greater extent on HCC scores in adjusting payments to Medicare Advan- tage plans.27 In addition, our results suggest that when HCC scores are overestimated by 19%, risk-adjusted rates of death would appear to be 15% lower.

We recognize that biases related to diagnostic intensity are not the only challenge confronting risk adjustment. A major concern about both pay- ment reforms and performance-measurement ini- tiatives is their potential for adversely affecting behavior. For example, if providers are more highly compensated for treating patients with more diagnoses, they could conceivably be in- clined to perform more intensive screening and diagnostic testing, with clear effects on costs and uncertain effects on health outcomes. Alterna- tively, risk-adjustment models could fail to ac- count for the difficulty of caring for truly high- risk patients or those whose care is made more difficult owing to challenges such as language barriers, poor health literacy, or lack of social support, encouraging some providers to avoid or stop providing care for such patients. Such con- cerns only underscore the importance of contin- ued efforts to advance the development of unbi- ased methods of risk adjustment as health care reform proceeds.

These challenges could become more manage- able as comprehensive electronic health records are implemented. To help improve risk adjust- ment, such systems would need to incorporate both nonclinical factors that may predict a pa- tient’s lack of adherence to clinical advice (e.g.,

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homelessness or poverty) and clinical data that are less subject to bias that is due to differences in diagnostic practices. Examples of such data include stage and grade in the case of patients with cancer and ejection fraction in the case of those with congestive heart failure. It is also pos- sible that measures of health risks reported by patients (e.g., smoking and exercise patterns) and functional status (physical, social, and role func- tion) could be incorporated in risk-adjustment models to improve their performance.

The newly passed health care reform legisla- tion includes substantial increases in funding for comparative-effectiveness research programs and establishes major initiatives that will move Medicare and Medicaid toward bundled payment systems. Our findings underscore the need for additional efforts to advance risk-adjustment methods as reform proceeds.

Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.

References

Birkmeyer JD, Stukel TA, Siewers AE, 1. Goodney PP, Wennberg DE, Lucas FL. Surgeon volume and operative mortality in the United States. N Engl J Med 2003;349:2117-27.

Ong MK, Mangione CM, Romano PS, 2. et al. Looking forward, looking back: as- sessing variations in hospital resource use and outcomes for elderly patients with heart failure. Circ Cardiovasc Qual Outcomes 2009;2:548-57.

Medicare payment basics: hospital 3. acute inpatient services payment system. Washington, DC: Medicare Payment Advi- sory Commission, 2008.

Skilled nursing facilities prospective 4. payment system: overview. Baltimore: Cen- ters for Medicare and Medicaid Services, 2009. (Accessed May 7, 2010, at http://www .cms.hhs.gov/SNFPPS/01_overview.asp.)

Fisher ES. Paying for performance — 5. risks and recommendations. N Engl J Med 2006;355:1845-7.

Silverman E, Skinner J. Medicare up-6. coding and hospital ownership. J Health Econ 2004;23:369-89.

Elmore JG, Wells CK, Lee CH, Howard 7. DH, Feinstein AR. Variability in radiolo- gists’ interpretations of mammograms. N Engl J Med 1994;331:1493-9.

Schnitt SJ, Connolly JL, Tavassoli FA, 8. et al. Interobserver reproducibility in the diagnosis of ductal proliferative breast le- sions using standardized criteria. Am J Surg Pathol 1992;16:1133-43.

Black WC, Welch HG. Advances in di-9. agnostic imaging and overestimations of disease prevalence and the benefits of therapy. N Engl J Med 1993;328:1237-43.

Urban BA, Fishman EK, Kuhlman JE, 10. Kawashima A, Hennessey JG, Siegelman SS. Detection of focal hepatic lesions with spiral CT: comparison of 4- and 8-mm in-

terscan spacing. AJR Am J Roentgenol 1993;160:783-5.

Stafford RS, Misra B. Variation in rou-11. tine electrocardiogram use in academic primary care practice. Arch Intern Med 2001;161:2351-5.

Sirovich B, Gallagher PM, Wennberg 12. DE, Fisher ES. Discretionary decision making by primary care physicians and the cost of U.S. health care. Health Aff (Millwood) 2008;27:813-23.

Fisher ES, Wennberg DE, Stukel TA, 13. Gottlieb DJ, Lucas FL, Pinder EL. The im- plications of regional variations in Medi- care spending. Part 2: health outcomes and satisfaction with care. Ann Intern Med 2003;138:288-98.

Idem14. . The implications of regional variations in Medicare spending. Part 1: the content, quality, and accessibility of care. Ann Intern Med 2003;138:273-87.

Barnato AE, Herndon MB, Anthony 15. DL, et al. Are regional variations in end- of-life care intensity explained by patient preferences? A study of the US Medicare population. Med Care 2007;45:386-93.

Iezzoni LI, Heeren T, Foley SM, Daley 16. J, Hughes J, Coffman GA. Chronic condi- tions and risk of in-hospital death. Health Serv Res 1994;29:435-60.

Pope GC Kautter J, Ellis RP, et al. Risk 17. adjustment of Medicare capitation pay- ments using the CMS-HCC model. Health Care Financ Rev 2004;25:119-41.

Ezzat S, Sarti DA, Cain DR, Braun-18. stein GD. Thyroid incidentalomas: preva- lence by palpation and ultrasonography. Arch Intern Med 1994;154:1838-40.

Lederle FA, Walker JM, Reinke DB. 19. Selective screening for abdominal aortic aneurysms with physical examination and ultrasound. Arch Intern Med 1988;148: 1753-6.

Lu-Yao G, Albertsen PC, Stanford JL, 20. Stukel TA, Walker-Corkery ES, Barry MJ. Natural experiment examining impact of aggressive screening and treatment on prostate cancer mortality in two fixed co- horts from Seattle area and Connecticut. BMJ 2002;325:740.

Feinstein AR, Sosin DM, Wells CK. 21. The Will Rogers phenomenon: stage mi- gration and new diagnostic techniques as a source of misleading statistics for sur- vival in cancer. N Engl J Med 1985;312: 1604-8.

Welch HG, Schwartz LM, Woloshin S. 22. Are increasing 5-year survival rates evi- dence of success against cancer? JAMA 2000;283:2975-8.

Skinner JS, Staiger DO, Fisher ES. Is 23. technological change in medicine always worth it? The case of acute myocardial in- farction. Health Aff (Millwood) 2006; 25(2):w34-w47.

Fisher ES, Wennberg DE, Stukel TA, 24. Gottlieb DJ. Variations in the longitudinal efficiency of academic medical centers. Health Aff (Millwood) 2004;Suppl Web Exclusives:VAR19-VAR32.

Barnato AE, Chang CC, Farrell MH, 25. Lave JR, Roberts MS, Angus DC. Is sur- vival better at hospitals with higher “end- of-life” treatment intensity? Med Care 2010;48:125-32.

Wennberg JE, Fisher ES, Goodman 26. DC, Skinner J. Tracking the care of patients with severe chronic illness: the Dartmouth atlas of health care 2008. Hanover, NH: The Dartmouth Atlas Project, 2008.

2006 Risk adjustment data basic 27. training for Medicare Advantage organi- zations: participant guide. Baltimore: Centers for Medicare and Medicaid Ser- vices, 2006. Copyright © 2010 Massachusetts Medical Society.

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