Medication Adherence as a Measure of the Quality of Care Provided by Physicians

February 11, 2019

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):78-83Takeaway 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.

RESULTS

In Table 2, we report differences in patient characteristics, aggregated to the physician-year level. The table shows average medication adherence for patients with diabetes and patients with CHF, patient outcomes, number of patients per physician-year, and mean total annual medical expenditures. Our measure of medication adherence is higher in the diabetes sample than the CHF sample, with a mean of 40.2% PDC (interquartile range [IQR], 30.8%-50.0%) for patients with diabetes compared with 36.2% for patients with CHF (IQR, 27.3%-44.7%). The private-sector patients considered by Seabury et al8 showed similar results in that adherence was higher for patients with diabetes than those with CHF, but the average adherence levels were much higher in the private-sector data, perhaps reflecting the sicker nature of patients in Medicare. The average medical expenditures per patient-year are high: $39,954 for patients with diabetes and $54,723 for patients with CHF. Complication rates and spending levels in this analysis are higher than those seen in many other studies because this sample is composed of patients who saw a physician at least once in the year, restricted to physicians who see a relatively large number of patients with diabetes or CHF.

The average physician-year in the data covered 52 patients in the diabetes sample and 34 patients in the CHF sample. These figures are skewed by a subset of physicians who see a large number of patients; the physician in the 90th percentile saw 103 patients with diabetes and 65 with CHF in a year.

Table 3 compares the differences in health outcomes with average adherence across physician-years in the diabetes sample. The top panel reports the unadjusted association between outcomes and adherence, whereas the bottom panel reports the association between outcomes and adherence adjusted for patient characteristics, including age, sex, and comorbidities. For each outcome, the complication rate is averaged across physicians with low, moderate, or high average medication adherence among their patients. The results show a clear relationship between the average adherence and outcomes of a physician’s patients with diabetes. In the top “unadjusted” panel, physicians whose patients have low adherence have 49.6 patients per 1000 with uncontrolled diabetes compared with 22.1 per 1000 among physicians with high medication adherence. A similar relationship, in terms of both direction and magnitude, holds for ED visits and short-term and long-term complications. The magnitude of the effect diminishes following adjustment for patient characteristics: 45.9 per 1000 patients of physicians with low average adherence have uncontrolled diabetes compared with 26.3 per 1000 patients of physicians with high average adherence. In all cases, the bootstrap tests found that groupwise differences were statistically significant (P <.01 in all cases).

Table 4 reports analogous results for the CHF sample. As with the diabetes sample, patients of physicians with better average adherence also experienced better average outcomes. The unadjusted CHF hospitalization rate of patients of the low-adherence physicians was 51.8% compared with 45.7% for patients of physicians with high average adherence, a difference of 6.1 percentage points or 11.8%. In terms of CHF-related ED visits, the rate was 16.4% for patients of physicians with low adherence compared with 13.5% for physicians with high average adherence, a 17.8% difference. These results are consistent in terms of direction and magnitude for hospitalizations and ED visits for the other conditions. Comparing the top and bottom panels indicates that the regression adjustment for age, gender, and comorbidities has very little effect on the magnitude of the difference across physicians with low-adherence or high-adherence patients. As in the diabetes sample, in all cases the differences are statistically significant at the 1% level (P <.01).

Table 5 compares the proportion of the variance in the regression models of each medication adherence measure and outcome that is explained by physician fixed effects or patients’ own comorbidity profiles. Physician fixed effects consistently account for a much larger portion of the explained variation than do individual comorbidities. In the diabetes sample, physician fixed effects account for about 15% to 20% of the explained variation in medication adherence and 3% to 5% in outcomes. This is considerably more than patient comorbidities, which generally account for less than 1% of the explained variation in outcomes. For the CHF sample, physician fixed effects account for somewhat less of the explained variation in adherence (about 15%-18%) but more of the explained variation in outcomes (about 8%-11%). However, patient comorbidities still account for very little of the explained variation, typically 2% or less. One possible explanation is that the patient comorbidity profile is a relatively weak predictor of patient health, although some comorbidities (eg, depression) are strongly indicative of worse outcomes. In any case, the analyses from Tables 3 through 5 suggest that there are unobserved characteristics of physicians that systematically predict medication adherence and, further, that unobserved characteristics are predictive of patient outcomes. Of course, which unobserved characteristics led to better patient adherence and which led to better patient outcomes remains unknown.

DISCUSSION

Our analysis finds a strong correlation between medication adherence and patient outcomes at the physician level. This suggests that measuring the medication adherence of a physician’s patients can provide a signal of provider performance as measured by patient outcomes. More effort should be given to developing physician-level and/or disease-specific quality indications based on adherence and to evaluating whether the use of these measures could improve care and outcomes for patients with chronic diseases.

Although the relationship between improved adherence and better outcomes is well established at the patient level, it has not been established that the same relationship holds for individual physicians.4 Recent work shows that adherence can be a similarly useful measure of health plan performance.8 Prior work also shows that physicians with different measured quality also vary systematically in their prescribing patterns. For example, patients with type 2 diabetes of physicians who routinely prescribe metformin as frontline therapy have better outcomes than those of physicians who prescribe sulfonylureas.13 However, in this study we explicitly documented a correlation between improved patient outcomes and improved adherence to medication in the case of both diabetes and CHF.

Unfortunately, our data do not cast light on why some physicians have patients with better medication adherence. Evidence suggests that many physicians feel untrained or ill equipped to promote medication adherence.14 If that is true, it is possible that the systematic differences in medication adherence across physicians could reflect something intrinsic about the physician—patient relationship. Past work has found that good physician–patient relationships were important for promoting medication adherence.15-17 Other work has found that physician characteristics, such as job satisfaction and propensity to schedule follow-up visits, were associated with improved adherence.18 At the very least, the existing evidence is clear that there is an association between a physician’s engagement with patients and better medication adherence by patients. Future work should better establish the activities or qualities of physicians that promote medication adherence and use these to develop physician guidelines to help promote these more broadly. Additionally, there is likely correlation between average adherence at the physician level and average adherence at the pharmacy level; future work should investigate which of these levels is better able to influence adherence.

Our findings suggest that medication adherence measures have the potential to be a useful quality measurement tool, at least for chronic conditions for which prescription medications are an important component of treatment. There are many potential advantages to using medication adherence as a quality metric. For one, although medication use is imperfect, there are commonly accepted and objective metrics that can be constructed using pharmacy claims data. Also, medication use is much more common than many types of complications, so it can be more accurately measured and is potentially more informative than a performance metric based on outcomes.

Although more needs to be done to establish a causal result, the cost implications could be large. Seabury et al8 demonstrated that in addition to predicting outcomes, plan-level medication adherence is a strong signal of costs. In other words, plans whose patients had better adherence also had lower total average expenditures among their patients with diabetes and CHF. Although it is unclear how much of the difference between high- and low-adherence physicians is actually due to factors within a physician’s control, our findings suggest the potential for large savings if we can sufficiently train and incentivize physicians to help their patients better adhere to their medication regimens. In the United States in 2014, there were more than 330,000 ED admissions for hyperglycemia or hypoglycemia and about 1 million for CHF.19 Prior studies have estimated the cost of these admissions at around $2000 each.20,21 If improved adherence reduced the incidence of these events by even a small amount, the cost savings would be large. For example, a 5% reduction in each type of admission would yield an estimated cost savings of almost $130 million. This suggests a need for careful, rigorous evaluation of new initiatives such as the Quality Payment Program to better understand what impact, if any, incentivizing physicians to improve quality has on patient outcomes.

Limitations

This study has several important limitations. First, our sample was large, but it was limited to Medicare patients. Although we expect the relationship between adherence and outcomes to generalize to commercial patients, this should be demonstrated in future work. Also, the quality measures were limited by the fact that our data are claims based and lack information about disease severity. Some outcomes that we considered are relatively rare, and many physicians had no patients with these complications. Thus, although the relationship between adherence and outcomes is informative in the aggregate, patient outcomes may provide limited information about the performance of any single provider. Further studies might consider how closely average medication adherence predicts more clinically relevant measures of provider performance (eg, average glycated hemoglobin levels). Also, although we defined good adherence as having a PDC above 80%, the level chosen by CMS, it would be useful to evaluate the predictive ability of different threshold values.

Another limitation is that we only measured an association between average adherence and patient outcomes, and this relationship may not be causal. If healthier patients systematically select to certain physicians, it could overstate the correlation between adherence and outcomes due to what is known as the “healthy adherer” effect. For example, Dormuth et al22 found that patients with better adherence also display many other positive health habits, such as safer driving. Although it is not clear that patients systematically select to physicians according to unobserved health factors, if they do, it would significantly impact the interpretation of our findings. If the association that we estimated represents a true causal relationship, then we would expect that tying reimbursement to adherence could improve outcomes for patients by providing physicians with enhanced incentives for better performance. If, on the other hand, high-adherence patients self-select toward some physicians more than others, reimbursing physicians based on adherence would simply reward physicians for something they have limited impact on. This could even generate adverse incentives, if it promoted “cream skimming” and gave physicians incentives to avoid sick patients. We find some evidence to suggest that the selection across providers is relatively minor, at least according to noncardiovascular comorbid conditions. Nevertheless, this accentuates the importance of understanding the mechanisms through which physicians influence adherence, as this will help policy makers devise quality metrics and reimbursement schemes to promote the right incentives to providers and unlock value for patients.

An additional limitation is that it is difficult to precisely measure adherence to injectable medications in claims data, due to the lack of information on titration instructions. This adds some uncertainty to our findings for the diabetes population, but that uncertainty is partially offset by the parallel conclusions in the CHF population. Also, some patients are seen by more than 1 physician in a given year and thus may contribute to the adherence and outcome fixed effects for multiple physicians. We assume that no single patient could create significant correlation across physicians, based on the fact that we required a minimum of 10 patients per physician.

Lastly, we were unable to adjust for the effect of social determinants of health (SDH). Several studies have demonstrated that SDH and patient outcomes are closely related. However, the observational claim-based Medicare database provides very limited information on SDH, which may result in unmeasured confounding.23,24

CONCLUSIONS

We sought to assess the potential for utilization of physician-level measures of medication adherence as quality indicators. For both CHF and diabetes and controlling for other factors, we found that the patients of physicians with higher average rates of adherence have better health outcomes than do patients of physicians with low average rates of adherence. This suggests that medication adherence may be a useful indicator of quality of care at the physician level.Author Affiliations: Keck School of Medicine, University of Southern California (SAS), Los Angeles, CA; Pharmaceutical Research and Manufacturers of America (PhRMA) (JSD), Washington, DC; Precision Health Economics (JS), Boston, MA.

Source of Funding: PhRMA provided funding for this study.

Author Disclosures: Dr Seabury is a consultant for Precision Health Economics, a life-sciences consulting company. Mr Sullivan is an employee of and holds stock options in Precision Medicine Group, which consults for pharmaceutical companies. The remaining author reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (SAS, JSD); analysis and interpretation of data (JSD, JS); drafting of the manuscript (SAS); critical revision of the manuscript for important intellectual content (SAS, JSD, JS); and statistical analysis (SAS, JS).

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