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The American Journal of Managed Care February 2019
Impact of Hepatitis C Virus and Insurance Coverage on Mortality
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A. Mark Fendrick, MD; and Darrell George, BA
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Medication Adherence as a Measure of the Quality of Care Provided by Physicians
Seth A. Seabury, PhD; J. Samantha Dougherty, PhD; and Jeff Sullivan, MS
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Rebecca A. Gourevitch, MS; Ateev Mehrotra, MD, MPH; Grace Galvin, MPH; Avery C. Plough, BA; and Neel T. Shah, MD, MPP
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Cheryl L. Damberg, PhD; Marissa Silverman, MSPH; Lane Burgette, PhD; Mary E. Vaiana, PhD; and M. Susan Ridgely, JD
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Thomas E. Kottke, MD, MSPH; Jason M. Gallagher, MBA; Marcia Lowry, MS; Pawan D. Patel, MD; Sachin Rauri, MS; Juliana O. Tillema, MPA; Jeanette Y. Ziegenfuss, PhD; Nicolaas P. Pronk, PhD, MA; and Susan M. Knudson, MA
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Felix Sebastian Wicke, Dr Med; Anastasiya Glushan, BSc; Ingrid Schubert, Dr Rer Soc; Ingrid Köster, Dipl-Stat; Robert Lübeck, Dr Med; Marc Hammer, MPH; Martin Beyer, MSocSc; and Kateryna Karimova, MSc
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Medication Adherence as a Measure of the Quality of Care Provided by Physicians

Seth A. Seabury, PhD; J. Samantha Dougherty, PhD; and Jeff Sullivan, MS
Physician-level medication adherence is a strong predictor of patient health and should be considered as a measure of physician quality.
ABSTRACT

Objectives: To assess the extent to which medication adherence in congestive heart failure (CHF) and diabetes may serve as a measure of physician-level quality.

Study Design: A retrospective analysis of Medicare data from 2007 to 2009, including parts A (inpatient), B (outpatient), and D (pharmacy).

Methods: For each disease, we assessed the correlation between medication adherence and health outcomes at the physician level. We controlled for selection bias by first regressing patient-level outcomes on a set of covariates including comorbid conditions, demographic attributes, and physician fixed effects. We then classified physicians into 3 levels of average patient medication adherence—low, medium, and high—and compared health outcomes across these groups.

Results: There is a clear relationship between average medication adherence and patient health outcomes as measured at the physician level. Within the diabetes sample, among physicians with high average adherence and controlling for patient characteristics, 26.3 per 1000 patients had uncontrolled diabetes compared with 45.9 per 1000 patients among physicians with low average adherence. Within the CHF sample, also controlling for patient characteristics, the average rate of CHF emergency care usage among patients seen by physicians with low average adherence was 16.3% compared with 13.5% for doctors with high average adherence.

Conclusions: This study’s results establish a physician-level correlation between improved medication adherence and improved health outcomes in the Medicare population. Our findings suggest that medication adherence could be a useful measure of physician quality, at least for chronic conditions for which prescription medications are an important component of treatment.

Am J Manag Care. 2019;25(2):78-83
Takeaway Points
  • In both the diabetes and congestive heart failure disease spaces, average levels of patient medication adherence (calculated at the physician level) are significant predictors of patient health.
  • Average rates of hospitalization, emergency care, and comorbidities are lower among patients treated by physicians with high average adherence rates; this correlation persists after controlling for individual patient characteristics.
  • The utility of average medication adherence as a potential measure of physician quality should be examined.
Measuring the quality of care offered by healthcare providers is an important tool for effectively promoting value in the US healthcare system. New quality metrics are constantly being developed and refined, and they are increasingly used in performance-based contracting. From the standpoint of providing incentives to increase the value of care, ideally these measures would directly observe the incremental value of care—in economic terms, the “marginal product of care”—and link reimbursement to the difference between incremental value and incremental cost. However, this direct effect is not easy to measure. Quality metrics sometimes attempt to do so by capturing various elements of healthcare delivery, including process, outcomes, and patient satisfaction.1

More recently, quality measures have been developed to evaluate the prescribing patterns, use, and consequences of prescription medications. Some of this growth reflects the increasing availability of medications to treat and prevent chronic illnesses and the growing consensus that adherence is an important part of disease management. Study results routinely show that proper adherence can lead to lower net healthcare costs, particularly for patients with cardiovascular disease.2-7 Recent work also demonstrates that some health plans are systematically associated with higher medication adherence and better patient outcomes.8 This evidence coincides with the growing use of adherence measures in pay-for-performance systems such as the Medicare Advantage Star Rating System.

There is also a growing effort to increase quality monitoring for individual physicians. As part of the 2010 Affordable Care Act (ACA), CMS reports information on individual physician quality through the Physician Compare Initiative.9 More recently, the Medicare Access and CHIP Reauthorization Act of 2015 created the Quality Payment Program, through which some participating physicians receive reimbursement modifications based on reported quality metrics, including some based on adherence. However, despite the use of these metrics, it is not well established whether adherence varies systematically across physicians or correlates with the quality of medical care provided. Although the association between adherence and outcomes has been established generally, it is possible that at the individual physician level the signal to noise ratio is too weak to make it a useful reimbursement tool. More work is needed to understand if some physicians are better than others in terms of promoting medication adherence and, if so, what this means for patient outcomes.

This study compares average patient medication adherence with select outcomes-based quality measures at the physician level using Medicare claims data. We do this for all patients in a nationally representative sample of Medicare data who have diabetes or congestive heart failure (CHF), 2 expensive chronic conditions that require extensive medication management to treat. We examine whether there are systematic differences in patient adherence across physicians and how those differences are associated with improved patient outcomes. Although this analysis cannot indicate a causal relationship between physician behavior and medication adherence and patient outcomes, it can evaluate whether any association exists between systematic physician-level differences in medication adherence and patient outcomes. Establishing that such a relationship exists is a necessary first step toward validating the use of physician-level measures of medication adherence in incentive-based reimbursement schemes.

DATA AND METHODS

This study uses linked Medicare parts A (inpatient), B (outpatient), and D (pharmacy) claims data from a nationally representative, randomly selected 20% sample of Medicare beneficiaries 65 years and older with fee-for-service coverage of Medicare parts A, B, and D. Additionally, beneficiary enrollment, demographic information, and vital status come from the Medicare Denominator File. The inpatient data provide information on all hospital stays, including length of stay, the diagnosis-related group associated with the stay, and up to 10 individual procedure codes and diagnostic codes. The outpatient data include information on outpatient hospital visits, home hospice care, and the use of durable medical equipment. It also includes all claims submitted by physicians and other health providers, including physician office visits. The Part D prescription drug information provides information on prescription drug events, including the National Drug Code, number of days supplied, and date of service. These Part D data are linked with individual beneficiaries’ vital status from the Denominator File, which contains geographic identifiers, date of birth, date of death, gender, race, and age. Because Part D did not begin until 2006, we restrict our analysis to patients from 2007 through 2009. Although these data are older, they provide a useful perspective because they are based on a period prior to the passage of the ACA, when physician prescribing patterns were likely unaffected by the recent drive toward improved quality metrics.10-12

We provide a brief summary of our analytical methods in this paragraph. Complete details are provided in the technical eAppendix (available at ajmc.com). We assess the relationship between adherence and several measures of patient health (including rates of hospitalization, emergency department [ED] care, and comorbidities), all calculated at the physician level. The full set of outcomes considered is listed in Table 1. To accomplish this, we first used claims data to generate a measure of patient-level adherence, the proportion of days covered (PDC). We next executed a series of patient-level regressions, in which 2 classes of outcomes (medication adherence and quality measures based on patient health) were modeled as a function of patient characteristics (including age, gender, and comorbid conditions) and physician fixed effects. We then predicted physician-level adherence and quality values from these regressions, calculated at the population mean of those patients. This produced measures of physician-level adherence and quality that controlled for observable differences in physician patient populations. Next, we regressed the generated physician-level quality measures on the generated physician-level measures of medication adherence. Because we employed a 2-stage model, we used bootstrap methods to estimate standard errors. Finally, we examined the relative extent to which physician fixed effects and individual patient comorbidity profiles predict patient health outcomes, using analysis of variance.


 
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