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 illnesses. Whether this is due to a higher disease burden 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 practice was associated with greater increases in diagnostic 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 diagnostic tests among individual physicians and office practices11
and variations across regions in physicians’ reports of their likelihood of ordering tests.12
Higher rates of diagnostic testing lead to an increased number of diagnoses of specific clinical conditions, such as prostate cancer, thyroid disease, or vascular disease.18–20
The biases introduced by greater diagnostic intensity have been well documented in the context of cancer.21,22
Our study builds on this earlier work.
This study has several limitations. We cannot be certain that the beneficiaries who were recorded as having received a new diagnosis actually 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 differences 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 practice 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 beneficiaries. Several previous studies involving specific clinical cohorts have shown no evidence of a survival benefit when care is provided in regions13,23
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 patients 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 beneficiaries who moved to regions with higher-intensity practices, this result should not be interpreted as implying that greater diagnostic intensity offers no benefits. Rather, it underscores the need for research to determine the specific clinical settings 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 hospitals.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 episodes of care could also be distorted. The differences are not likely to be trivial, on the basis of our analyses. By the end of the study, beneficiaries 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 estimates of the differences in diagnostic intensity across these regions. Under a public reporting or payment program that relied on the unmodified 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 Advantage 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 payment reforms and performance-measurement initiatives 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 inclined to perform more intensive screening and diagnostic testing, with clear effects on costs and uncertain effects on health outcomes. Alternatively, risk-adjustment models could fail to account 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 concerns only underscore the importance of continued efforts to advance the development of unbiased methods of risk adjustment as health care reform proceeds.
These challenges could become more manageable as comprehensive electronic health records are implemented. To help improve risk adjustment, such systems would need to incorporate both nonclinical factors that may predict a patient’s lack of adherence to clinical advice (e.g., 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 possible that measures of health risks reported by patients (e.g., smoking and exercise patterns) and functional status (physical, social, and role function) could be incorporated in risk-adjustment models to improve their performance.
The newly passed health care reform legislation 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.