Interventions to improve medication adherence are effective, but resource intensive. Interventions must be targeted to those who will potentially benefit most. We examined what heterogeneity exists in the value of adherence based on levels of comorbidity, and the changes in spending on medical services that followed changes in adherence behavior.

Retrospective cohort study examining medical spending for 2 years (April 1, 2011, to March 31, 2013) in commercial insurance beneficiaries.

 Multivariable linear modeling was used to adjust for differences in patient characteristics. Analyses were performed at the patient/condition level in 2 cohorts: adherent at baseline and nonadherent at baseline.

We evaluated 857,041 patients, representing 1,264,797 patient therapies consisting of 40% high cholesterol, 48% hypertension, and 12% diabetes. Among those with 3 or more conditions, annual savings associated with becoming adherent were $5341, $4423, and $2081 for patients with at least diabetes, hypertension, and high cholesterol, respectively. The increased costs for patients in this group who became nonadherent were $4653, $7946, and $4008, respectively. Depending on the condition and the direction of behavior change, savings were 2 to 7 times greater than the value for individuals with fewer than 3 conditions. In most cases, the value of preventing nonadherence (ie, persistence) was greater than the value of moving people who are nonadherent to an adherent state.

There is important heterogeneity in the impact of medication adherence on medical spending. Clinicians and policy makers should consider this when promoting the change of adherence behavior.

		Adherence to medication in patients with chronic disease leads to improved health outcomes and lower costs. Yet, physicians, health systems, and health plans continue to face significant challenges in promoting adherence among patients. 

		There are considerable differences in the value created by improving adherence behavior and preventing nonadherence, with respect to the baseline level of comorbidity and the direction of behavioral change (ie, adherent to nonadherent or vice versa). 

		Clinicians and policy makers determining how to cost-effectively deploy adherence promotion programs—particularly in the setting of providers at risk for medical service spending&mdash;should consider these findings in setting population health management priorities.

There is broad consensus that improving adherence to essential medications is central to better management of chronic conditions,

 and there is an extensive body of literature that consistently demonstrates a strong relationship among better adherence, improved outcomes, and reduced medical costs.

 The focus of researchers has shifted to developing a better understanding of how to best target and deliver interventions to improve adherence behavior.

The principal challenge is that medication adherence is quite personal, and there are numerous reasons why patients fail to adhere, ranging from the complexity of therapy

 and to understanding the rationale for therapy and its appropriate administration.

 No single intervention has proven effective at addressing all of the barriers experienced by patients.

As a result, payers and risk-bearing providers increasingly seek evidence concerning the clinical benefits and cost-effectiveness of targeted interventions to improve adherence and health outcomes, and, thus, reduce costs. Due to concern that the clinical and cost benefits may not be accrued by those making the investment, churn in insurance membership can influence decisions about investing in interventions to improve adherence. Studies that help characterize the value of improving adherence within patient subpopulations are essential, particularly in the short term, so payers and risk-bearing providers can be confident that investments to improve adherence will provide a positive return on their investment.

In this study, we used a de-identified longitudinal data set to assess the effect of adherence on healthcare costs, and then characterized the value of adherence in a 1-year follow-up period. We assessed the value of adherence by several key characteristics. We studied how patient comorbidity is associated with the value of adherence to better understand how targeting patients with multiple conditions might influence both health outcomes and the return on investment in adherence promotion programs. We also explored the differences in changes in medical spending between individuals who remain adherent and those who become adherent. There has been no previous work, to our knowledge, examining this latter question; however, such evidence is critical for providers, pharmacies, and pharmacy benefits managers to develop and target interventions, and for payers and risk-bearing providers to make cost-effective investments in care improvement.

Using a nationally representative de-identified medical claims data set with claims for 24 consecutive months, we examined the impact of change in medication adherence and change in the cost of medical care for 2 groups of patients: a) those who were nonadherent in the baseline year and became adherent in the second year compared with those who remained nonadherent, and b) those who were adherent in the baseline year and became nonadherent in the second year compared with those who remained adherent. Our study was limited to patients with at least 1 of 3 diseases: diabetes, hypertension, and high cholesterol (

). To gain additional insights into the relationship between change in adherence and change in medical spending, we stratified our analyses by the number of conditions included in the patient’s claims in the baseline year. We also conducted a subanalysis examining whether there was a synergistic impact on spend when the patient is experiencing both hypertension and diabetes.

The study sample was drawn from a de-identified data set obtained from a national data aggregator, consisting of medical and pharmacy claims from more than 10 million commercially insured patients. No Medicare or Medicaid beneficiaries were included in the sample. The baseline period for analyses was April 1, 2011, to March 31, 2012; the follow-up period was April 1, 2012, to March 31, 2013. To be included in the cohort, we required that patients with a pharmacy or medical claim in the baseline period have benefits eligibility during the entire follow-up period and to have at least 1 of the candidate conditions—diabetes, hypertension, or hypercholesterolemia&mdash;during the baseline period. To insure that we were as sensitive as possible in case identification, we used both 

International Classification of Diseases, Ninth Revision, Clinical Modification

 codes and medication profiles to identify patients with 1 of the 3 conditions (see 

The exposure of interest in this study was change in adherence status. For this purpose, adherence was measured using the medication possession ratio (MPR) calculated at the therapeutic class and averaged across the condition (eAppendix).

 MPR is a commonly used outcome metric designed to measure medication adherence. It is a proportion consisting of the total days’ supply of medication on hand divided by the elapsed time between the first fill of medication to the end of the year. Supply is prorated if the patient fills before the end of the year but won’t exhaust the fill until after year’s end. For our purposes, fill data time periods ran March to March, corresponding with eligibility data availability. Baseline MPR values spanned April 1, 2011, through March 31, 2012, and follow-up was measured between April 1, 2012, and March 31, 2013.

We created 2 cohorts based on prescription-filling behavior within the condition in the baseline period: a) patients adherent (MPR ≥0.80) to medication at the condition level, and b) patients not adherent (MPR <0.80). Adherence behavior was considered only at the condition level, with the weighted average of MPR considered when there were multiple medications taken. To gain additional insights into how the change in adherence status might affect spending on medical services, we also conducted a stratified analysis, splitting our sample into patients with a Charlson Comorbidity Index score greater than or equal to 3 and those with a score of less than 3.

Finally, we conducted a descriptive analysis to examine whether the presence of comorbid hypertension modifies the relationship between change in adherence status and medical service utilization when diabetes is present. For this purpose, we compared the adjusted medical spend of 3 cohorts: a) patients with hypertension only, b) patients with diabetes only, and c) patients with both hypertension and diabetes. We evaluated the presence of effect modification by comparing the expected additive medical costs (ie, the spending of patients with hypertension plus the spending of patients with diabetes) with the actual observed spending of patients with diabetes and hypertension.

The outcome modeled in these analyses was the spending on health services during the follow-up period. We captured allowed charges for all inpatient and outpatient services, regardless of whether they were paid by the insurer or patient. As our primary outcome was concerned with the impact of medication adherence on medical service utilization, we did not include pharmacy costs in our outcome measure.

In the multivariable regression analyses of the post period medical spending, we adjusted for medical spending in the baseline period, age, gender, comorbidity (using the Charlson Comorbidity Index

), Census region, initiator/continuer medication use status, and preventive health service use. Initiator or continuer therapy utilization status was defined at the patient/condition level, where patients without a fill for a medication within the condition classification in the 6 months prior to the baseline period were considered initiators. Preventive health service use was included to adjust for "healthy user" bias,

 and was defined as having a claim for 1 of 52 preventive services during the baseline period.

 In this way, we controlled for the healthy user bias in 2 ways: a) by using each patient as his or her own historical control, and b) by adjusting for preventive service use.

We constructed generalized linear models to describe the total spending on health services during the follow-up period (ie, April 1, 2012, to March 31, 2013). Our unit of analyses was patient/therapy combination; thus, it was possible in our preliminary analyses for patients with multiple conditions to be considered in more than 1 disease cohort. For instance, a patient with hypertension and high cholesterol would be included in both our hypertension and high cholesterol results. In our descriptive analyses examining the additive effect of comorbidity, patients are included in only 1 cohort. Thus, the hypertension group includes only patients with hypertension, while the diabetic/hypertensive group includes individuals with both conditions.

The impact of change in adherence status was estimated by comparing the adjusted spend of those who maintained their adherence status with those whose status changed. For instance, patients who maintained adherence at baseline and follow-up were compared with those who became nonadherent at follow-up. This difference (ie, increase in spending) was considered the impact on medical service spending of becoming nonadherent. Similarly, patients who newly achieved adherence at follow-up were compared with those who were nonadherent at baseline and during follow-up. This difference (ie, reduction in spending) was considered to be the impact on medical service spending of becoming adherent.

In modeling medical spending, the use of non-Gaussian distributions is common due to the skewed nature of healthcare utilization on a population basis. However, these cautions are typically the result of a large proportion of community members with no medical spending.

 In the sample we evaluated, all patients were service utilizers, and we were modeling the change in medical spending among these patients between the 2 years. We examined this assumption empirically by testing the distribution of the change in spending in the follow-up year, and we found it to reflect a normal distribution. Therefore, we determined that ordinary least squares regression would be the most transparent and least biased approach to analysis.

All analyses were conducted using SAS Enterprise Guide 5.1 (SAS Institute, Cary, North Carolina). For this investigation, we used de-identified medical and pharmacy claims data from an external vendor; therefore, no institutional review board review for research on human subjects was required.

. The 4 cohorts analyzed are described in Table 1. The combined sample of patients with at least 1 of the candidate conditions totaled 857,041 distinct patients and 1,264,797 patient therapies. Of these, 61% had only 1 condition: high cholesterol (23.7%), hypertension (34.1%), or diabetes (3.5%). The balance (39%) had multiple conditions: high cholesterol/hypertension (24.6%), diabetes/hypertension (2.8%), diabetes/high cholesterol (2.3%), or all 3 conditions (8.9%). Nonadherent patients who became adherent were more likely to be male, be a medication continuer, and have hypertension; on average, they were older and had higher baseline medical service cost than those who remained nonadherent. Adherent patients who became nonadherent were more likely to be female, live in the South, have point-of-service coverage, engage in fewer health-seeking behaviors, have high cholesterol, and be a medication initiator; on average, they were younger and had higher baseline medical service costs than those who remained adherent.

The results of the multivariable analyses are presented in the 

. The number of conditions a patient had in the baseline period dramatically influenced the magnitude of change in spending when adherence behavior changed (

). Patients having 3 or more conditions at baseline had up to 7 times greater savings (or increased spend) than patients with 1 or 2 conditions. Specifically, patients with 1 to 2 conditions who became adherent had a modest reduction in spending ranging from a reduction of $757 per year (diabetes) to an increase of $365 per year (high cholesterol). In contrast, the group that had 3 or more conditions had savings ranging from $2081 for high cholesterol to $5341 for diabetes. Among the adherent patients who became nonadherent, those with 1 or 2 conditions had increased spend ranging from $1045 for patients with high cholesterol to $1706 for those with hypertension; those with 3 or more conditions had increase spend ranging of $4008 for patients with high cholesterol to $7946 for those with hypertension. With the exception of high cholesterol in the overall sample, and hypertension among those with 1 to 2 conditions (both in the nonadherent to adherent cohort), all of these differences were statistically significant at the 

It is notable in the Figure that the increase in spend among patients who were adherent and became nonadherent was larger than the decrease in spending for patients who were nonadherent and became adherent. After adjustment, patients with diabetes who became adherent spent $2495 less on health services in the year they became adherent than those who remained nonadherent. In contrast, patients with diabetes who were nonadherent after being adherent had an increased spend of $2763. This relationship was more pronounced in patients with hypertension and high cholesterol. Among patients who were nonadherent who became adherent and had hypertension, the spending was $766 less per year than those who remained nonadherent, while those with high cholesterol spent $26 less. However, among those who were adherent who became nonadherent, patients with hypertension spent $2663 more and patients with high cholesterol spent $1526 more.

, we present the results of the descriptive analyses examining the synergistic effect of hypertension and diabetes on medical service spending, and, here again, we see the dramatic impact of the presence of 3 or more conditions. Among patients with 2 conditions (ie, hypertension and diabetes only), there was modest impact above the expected contribution of the individual conditions alone. However, among those with 3 or more conditions, there were substantial differences between the expected (additive) spend and actual spend. For patients with 3 or more conditions who became adherent, the savings were $2418 greater than the expected. For patients with 3 or more conditions who became nonadherent, the increased spend was $1261 greater than the expected. In all cases, except for those patients with 1 to 2 conditions who became adherent, the difference between the additive (expected) and actual was significantly different at the 

In this study, we used a rigorous design to demonstrate that short-term changes in adherence have a meaningful and immediate impact on changes in healthcare spending. It is not necessarily surprising to learn that patients with multiple conditions have higher adherence-related savings potential than those with fewer conditions, but the magnitude of this difference demonstrates that the relationship is likely to be nonlinear. These results have key implications for targeting adherence interventions. In the Figure, we can clearly see that the change in spending associated with change in adherence behavior is much higher when the patient has 3 or more conditions. This would indicate that priority should be given to patients with multiple conditions when implementing adherence programs in resource-constrained settings.

Our findings have important implications in an era when we see new payment models moving risk for patient health status closer to physicians and health systems. If the adherence behavior and results from our analyses were applied to a sample of 100,000 members with each disease, the plan savings would range from $38 million for patients with high cholesterol to $63 million for those with diabetes. Given the increasingly limited budgets that are available for providing medical services, it is essential that spending by payers and providers implementing healthcare programs represent an efficient use of resources. A key finding of our study was that a disproportionate share of these savings came from patients with 3 or more conditions. In this cohort, 17% of the patients would have 3 or more conditions, yet 31% of the savings among patients with diabetes, 53% of savings for patients with high cholesterol, and 60% of savings for patients with hypertension would come from patients with greater comorbidity.

Similarly, the findings point to the importance of persistence in programs promoting adherence. In our example of 100,000 patients with each condition, we found that between 50% and 80% of our savings, depending on condition and level of comorbidity, come from keeping people adherent rather than promoting adherence among those who are nonadherent. This is a nuance that has not been previously reported in the literature, but has profound implications in the targeting of adherence resources. It may be easier and more efficient to prevent nonadherence in a patient who is currently adherent to their medication than it is to treat nonadherence in a patient who is nonadherent. It follows that there will be a high return on investment from programs promoting persistency and in implementation of targeting algorithms that identify patients who are currently adherent but at risk of nonadherence, so that a successful early intervention can be made.

Finally, the finding concerning the synergistic impact of comorbid hypertension and diabetes points to a particular case of the finding we made that surrounds the importance of 3 or more conditions. In this we see, not surprisingly, the importance of adherence in patients with diabetes regardless of the level of comorbidity or persistence status. The impact of comorbidity on the change in medical spend in patients with diabetes and hypertension is well in excess of that seen when we combine the impact of each condition standing on its own. This would indicate—as has long been recognized&mdash;that it is critical to take aggressive steps to ensure adherence among patients with diabetes, particularly among those with hypertension.

Taken together, our findings have important implications for insurers and provider groups, such as accountable care organizations, who are at risk for the cost of medical services. Although a number of studies have demonstrated convincingly that patients who are adherent to their medications incur lower costs, this study helps establish that “all adherence is not equal.” Changing a patient’s behavior is not a costless effort. This is particularly true in the field of adherence where the cost of interventions might range substantially based on the nature and comprehensiveness of the intervention. Our findings provide evidence that a carefully nuanced targeting program that tailors interventions to patients based upon their adherence history and comorbidities would result in greater benefit from these programs than would a program with a less focused approach.

Our study has several limitations. First, in a cohort study such as ours, it is impossible to control for all potential sources of confounding. Despite our efforts to address this in multivariable analyses, there are likely to be several unknown factors separate from adherence that might account for the impact we have identified. Second, the presence of the healthy user bias challenges the validity of many studies examining the value of medication adherence. Some authors have made recommendations to resolve this through the inclusion of covariates identifying patients who engage in good health practices; however, we found that more than 80% of our cohort members engaged in preventive health services, thus limiting the usefulness of this as a method to adjust for this bias. Nevertheless, our study design addresses this bias by stratifying on baseline adherence behavior and examining the impact of behavior change within the cohort members. In this manner, each patient acts as their own control, limiting the impact of the healthy user effect. Third, we did not consider the cost of medications in our estimation of the impact of the change in adherence behavior on the cost of medical care. It was not our intention to conduct a cost-benefit study of the impact of pharmacy care in these conditions; that study has been conducted previously, and it demonstrated a strong return on investment associated with pharmacy care.

 Our intention was to examine the impact of heterogeneity in change in adherence behavior on medical spend, and we believe that we have clearly demonstrated the potential importance of heterogeneity in response to those considering implementation of adherence programs. Additional studies of the cost-benefit of pharmaceutical interventions may be the subject of future investigations.

Fourth, in our analyses, we have focused exclusively on the financial impact of medication adherence on payers, ignoring the detrimental impact that medication nonadherence might have on the quality of life of the patient or their family. However, we conducted these analyses from the payer’s perspective—as it is the payer who makes coverage decisions in the United States&mdash;and there is considerable heterogeneity in the way that quality-of-life information is used by payers, as well as a multiplicity of quality-of-life measures used, even for the 3 conditions considered here. Thus, it is not clear how to properly measure quality of life related to medication adherence in a manner meaningful to a payer, or how meaningful the results might be for them. Therefore, we have taken the more conservative approach of considering only the financial impact, which might be considered the lower bound of potential impact of a change in adherence behavior. Finally, we have addressed the consequence of changes in adherence behavior and the importance of persistency, but we have not spoken to the long-term impact of the 40% of the cohort members who remained nonadherent. Finding methods to move these patients to healthy behaviors remains an essential and intractable public health challenge.

Medication adherence is a critical health problem worldwide. Resolving it requires careful targeting of effective programs, and one element of that targeting criteria must be the value of adherence to the patient and payer

 What we have demonstrated will vary with the direction of adherence behavior change and the comorbid conditions; thus, thoughtful policy makers, investigators, and clinicians should take these factors into consideration when implementing their adherence programs.

 Division of Enterprise Research and Analytic Development, CVS Health (SMK, RLP, CG, OSM, TB, WHS), Woonsocket, RI.

 All authors are employees of CVS Health, a pharmacy company and PBM with an interest in better adherence in general. All work was performed as part of their employment with CVS Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of CVS Health. Plan member privacy is important to us. Our employees are trained regarding the appropriate way to handle members’ private health information. These analyses were conducted using data de-identified in accordance with HIPAA regulations and CVS Health business associate agreements. The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (SMK, CG); acquisition of data (RLP); analysis and interpretation of data (SMK, CG, RLP); drafting of the manuscript (SMK); critical revision of the manuscript for important intellectual content (SMK, CG, OSM, WHS, TB); statistical analysis (SMK, CG, RLP); administrative, technical, or logistic support (SMK, OSM, CG); and supervision (SMK, CG, OSM).

 Charmaine Girdish, MPH, CVS Health, 9501 E Shea Blvd, Scottsdale, AZ 85260. E-mail: Charmaine.Girdish@CVSHealth.com.

1. Cutler DM, Everett W. Thinking outside the pillbox—medication adherence as a priority for health care reform. 

2010;362(17):1553-1555. doi: 10.1056/NEJMp1002305.

2. Thinking outside the pillbox: a system-wide approach to improving patient medication adherence for chronic disease. New England Healthcare Institute website. http://www.nehi.net/writable/publication_files/file/pa_issue_brief_final.pdf. Published August 2009 Accessed July 19, 2016.

Articles

WEB EXCLUSIVE

The authors found that comorbidity burden and the direction of behavioral change influence the relationship between adherence and medical spend. This could affect the cost-benefit considerations of medication adherence programs.

Association Among Change in Medical Costs, Level of Comorbidity, and Change in Adherence Behavior

Current alternative payment models (APMs) that move away from traditional fee-for-service payment often have explicit goals to reduce utilization in episodic settings, such as emergency departments (ED). We apply the new HHS payment reform taxonomy to illustrate a pathway to success for EDs in APMs. Despite the unique challenges faced by EDs, a variety of category 2 and 3 APMs may be applicable to EDs in the short- and long term to improve efficiency and value. Full and partially capitated models create incentives for longitudinal and episodic ED providers and payers to unite to create interventions to reduce costs. However, prospective attribution remains a challenge for EDs because of exogenous demand, which makes it important for EDs to be one of the components of capitated payment along with longitudinal providers who can exert greater control on overall care demands. The goal of payment and delivery reforms in ED care is to improve population health across the continuum of acute and longitudinal care. In order to deliver cost-conscious care, ED providers will need additional resources, expanded information, and new processes and metrics to facilitate cost-conscious decisions. Improved availability of electronic information across settings, evidence generated from developing and testing acute care—specific payment models, and engaging acute care providers directly in reform efforts will help meet these goals.

	Emergency care is a vital part of the US health system, providing a safety net, care in public health emergencies, and convenient around-the-clock care. Many reforms attempt to reduce the demand of acute services, but few attempt to improve the efficiency of the acute care system through payment reform and delivery innovation. This paper:

		Provides a path to transition acute care away from fee-for-service.

		Describes how payment reform can support specific acute care delivery innovations.

		Details practical next steps for acute care physicians, policy makers, and hospitals.

Hospital-based emergency departments (EDs) play a central role in US healthcare delivery, with 136 million visits in 2011.

 For decades, increases in ED visit rates have outpaced population growth, and led to overcrowding, when coupled with rising intensity of care and more ED-based imaging, laboratory tests, and treatments.

 ED care is often fragmented and more costly than other settings; as a result, EDs are a target for reforms.

The Affordable Care Act and, more recently, the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA), both promote the adoption of alternative payment models (APMs) to improve patient outcomes and reduce costs.

 Yet, how APMs will apply to EDs is unclear.

 Accountable care organizations and other risk-bearing entities primarily focus on reducing ED visits rather than engaging with EDs to improve care delivery across settings.

However, implementing APMs in EDs will have several challenges: 1) EDs do not control the demand for ED care; rather, care demands come from surrounding community needs

; 2) most of today’s EDs are not designed to follow patients longitudinally, making it difficult to track overall patient costs; 3) EDs must comply with the Emergency Medical Treatment and Labor Act (EMTALA) and treat patients regardless of their ability to pay, and as a result, EDs serve as the safety net; 4) EDs are indispensable resources during public health emergencies; therefore, if APMs impact ED viability, additional resources will be necessary to provide a safety net and respond to disasters, and 5) because ED standards of care often involve liberal diagnostic testing and admission to rule out life-threatening conditions,

 changing care patterns via payment reform may increase misdiagnoses.

Potential Alternative Payment and Delivery Models for EDs

Despite these challenges, several APMs are potentially applicable to EDs. In Table 1,

 descriptions of APMs from categories 1 through 4 are presented in the new HHS payment taxonomy framework.

Category 1 starts with traditional fee-for-service (FFS), and each higher-level category shifts to person- or population-level payments, increasing financial risk and accountability, and aggregating payments across providers.

Category 2 links FFS payments with quality. In practice, FFS would continue, but EDs could receive additional payments for reaching benchmarks (ie, better patient experience, improved care); or for value-creating activities, such as care coordination and preventing admissions; or for specific services, such as patient call-backs. Paying directly for ED care coordination can improve care and reduce costs. For example, the ED physicians at Upper Chesapeake Hospital in Maryland call back certain patients after ED discharge to ensure care plans are executed properly. The program successfully identifies short-term problems, improves patient experience, and pushes the ED to think beyond episodic care to a more longitudinal approach. ED physicians receive $20 per patient for this service.

 Category 3 consists of shared savings and risk models, where groups of providers and facilities would receive FFS payments, but also share in the upside and downside relative to a pre-specified budget. With target costs in mind, ED providers can establish frequent-user programs, which reduce costs through personalized care plans for patients with complex and psychosocial needs. For example, the home visit program at the University of Colorado, called Bridges to Care, enrolls high-cost ED patients in a health-coaching program facilitated by local federally qualified health centers. Patients are connected to a nurse practitioner, who makes home visits to identify care barriers and designs individualized interventions. In this model, the ED has an incentive to connect patients to value-producing activities that reduce costs and result in shared savings.

 However, because ED investments are required to implement these programs, it is important to ensure ED providers benefit from shared savings.

Bundled payments for episodic illness are another model in category 3. For example, ED providers can be paid a fixed amount for minor conditions, where monitoring for short-term relapses and complications are important (eg, asthma exacerbations, cellulitis). EDs can focus on bringing these cases to a close by scheduling ED or clinic follow-up for reevaluation. For higher-acuity conditions, where extended observation may be needed (eg, transient ischemic attack, chest pain), ED-based observation care improves care efficiency by reducing avoidable hospital admissions.

 For example, Arkansas’s Health Care Payment Improvement Initiative established acute care—specific retrospective bundles across Medicaid and its 3 largest commercial payers. The asthma bundle is triggered by a hospital visit (ED or inpatient) and includes all related facility services, inpatient professional services, ED visits, observations, labs and diagnostics, outpatient costs (eg. counseling), medications, and select costs for relevant care for 30 days.

At the end of the spectrum, category 4 includes population-based payments, such as capitation and global budgets. Here, ED providers, or their facilities, would receive a fixed payment based on prior utilization, the local population, or total expected costs for a period of time. Population-based payments provide the greatest incentive to build systems to reduce inefficient care and to prevent acute care visits entirely through disruptive innovations (ie, telemedicine), as well as to shift low-acuity care into other settings (ie, urgent care centers). The Kaiser On-Call program provides members with 24/7 access to nurses and physicians to facilitate decisions regarding care setting.

 Through Kaiser Permanente California’s unique integrated structure and contractual relationships, ED physicians are paid a market-competitive base salary with incentives based on quality and patient experience measures. There are no financial incentives for providing unneeded services and no financial gain for withholding clinically necessary care.

Full and partially capitated models create incentives for longitudinal and episodic ED providers to collaborate and reduce costs across the continuum. However, prospective attribution remains a challenge for EDs because they fall under EMTALA and do not directly control much of their demand. Therefore, EDs should ideally be a component of a capitated payment rather than the risk-bearing entity.

The Reform Roadmap to Delivering Integrated Acute Care

MACRA has provided opportunities through the Physician-Focused Payment Model Technical Advisory Committee for the development of specialty APMs. As such, ED physicians are a critical stakeholder to enable successful implementation of ED APMs and provide guidance to CMS on how APMs should be applied to acute care. One key area of need is related to the major gaps in ED quality measures. Most focus on discrete care processes for limited conditions or measure only 1 domain of quality, such as care timeliness (eg, ED boarding time).

 With ED physician input, valid and reliable quality measures can be developed to ensure desired outcomes are achieved and care is not stinted.

Measures should be developed with 3 key principles in mind. First, guidelines and pathways for specific complaints should be adhered to.

 Second, resource utilization should be balanced with measures of misdiagnoses and diagnostic yield.

 Third, information sharing is a necessary component to cost-effective ED care (ie, interoperability of information across settings). Through the American College of Emergency Physician’s Clinical Emergency Data Registry, an expanding list of quality metrics developed by ED physicians will be useful to monitor quality or for use in APMs.

 ED payment and delivery models should also be tested, prior to scaling, for safety and unintended consequences. The goal is for patients to continue to receive high-quality acute and emergency care, and that access is not reduced by implicit incentives in APMs. Table 2 provides a roadmap to integrate acute care super-imposed on the HHS payment taxonomy framework. This illustrates how EDs can move from category 2 to 4 through payment and delivery reforms, and describes potential barriers and specific information-sharing requirements.

Ultimately, the goal of payment and delivery reforms in emergency care is about collaborating with the ED to improve population health across the continuum of care. Transitioning from FFS to APMs needs to occur gradually; ideally, it should be disruptive, but not destructive, to the fundamental functions of EDs. FFS with links to quality may be the most viable short-term model to help EDs prepare for APMs. As this transition progresses across care settings, EDs, health systems, and the government will also need to monitor how potential volume reductions in ED visits impact the long-term financial sustainability of EDs, given high fixed costs.

 The Brookings Institution (JMP), Washington, DC; George Washington University (JMP), Washington, DC; Duke University (FM, MG, MM), Durham, NC; University of Colorado (JW), Denver, CO.

 Dr Richard Merkin and The Merkin Family Foundation.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (MG, FM, MM, JMP); acquisition of data (MG, FM); analysis and interpretation of data (MG, FM, MM, JMP, JW); drafting of the manuscript (MG, FM, MM, JMP, JW); critical revision of the manuscript for important intellectual content (MG, FM, MM, JMP, JW); obtaining funding (MM); administrative, technical, or logistic support (MG, FM, MM); and supervision (MM, JMP).

 Jesse M. Pines, MD, George Washington University, 2100 Pennsylvania Ave NW, Rm 314, Washington, DC 20037. E-mail: pinesj@gwu.edu.

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COMMENTARY

The authors apply HHS’s payment taxonomy framework to acute unscheduled care and describe how payment reform supports delivery innovation.

Aligning Payment Reform and Delivery Innovation in Emergency Care

 To evaluate the impact of moving to a patient-centered medical home (PCMH) model in safety net clinics in a managed Medicaid plan.

 Quasi-experimental, difference-in-differences design.

 The study examined whether the PCMH model reduced emergency department (ED) use and whether the growth in the seniors and people with disabilities (SPDs) population crowds out lower-cost populations. The study compared 7 PCMH safety net clinics (22,870 members) in late 2011 in the greater Los Angeles area with 110 general safety net clinics (143,530 members) between January 2011 and December 2013. During the time from 2011 to 2012, California began transitioning SPDs from fee-for-service Medicaid into managed care systems under a federal waiver.

 Among clinics with less than 10% SPD membership, a PCMH model was associated with more office visits and less ED use. In particular, PCMH clinics—relative to non-PCMH clinics&mdash;reduced ED visits by an average of 70 visits per 1000 members per year (PTMPY) and reduced avoidable ED visits by 20 visits PTMPY. Neither the change in office visits nor ED visits was evident in clinics with SPD membership greater than 10%.

 Adopting a PCMH model in safety net practices can effectively reduce ED use and increase the use of office visits among Medicaid patients. However, the beneficial effects of the PCMH model can be muted by a sudden influx of high-need users.    

This study shows that implementing the patient-centered medical home (PCMH) model in safety net clinics can have a meaningful impact on reducing the use of emergency departments (EDs). However, the extent to which a PCMH can successfully reduce ED visits may depend on the capacity of clinics to increase access to primary care. We found that the effects of PCMHs on reducing ED visits were smaller in clinics that experienced a greater increase in seniors and individuals with disabilities.Safety net clinics play a pivotal role in delivering both primary and specialty care to millions of low-income Californians.

 These clinics comprise licensed primary care clinics, clinics operated by government entities (eg, counties and cities), and clinics operated by federally recognized Indian tribes or tribal organizations. Safety net clinics provide care to medically underserved populations, regardless of their ability to pay. In 2011, the State of California authorized a section 1115 Medicaid waiver that mandated enrollment of seniors and people with disabilities (SPD) in managed Medicaid plans, which, in turn, led to an influx of patients with chronic conditions into safety net clinics.

The California Medicaid population is associated with frequent hospital admissions and heavy reliance on the emergency department (ED).

 Medicaid provides insurance to underserved, minority, and low-income patients—the populations most susceptible to fragmented and uncoordinated care. In light of this knowledge, a Los Angeles local health plan initiated a pilot program to transform selected safety net clinics into patient-centered medical homes (PCMHs) in the hopes of improving patient care and alleviating the impact of the SPD influx. Specifically, the intervention of this program focused on providing implementation services on: 1) on-site and virtual technical assistance on topics like optimizing team-based care, patient experience, population health management, care coordination, and patient access to care; 2) workflow analysis and process improvement support; 3) access to subject matter experts on key topics like care coordination; 4) provision of customized coaching training; and 5) administration of the PCMH Assessment.

In a PCMH, each patient has an ongoing relationship with a primary care physician who leads a team that takes collective responsibility for the patient’s care. A PCMH model emphasizes enhanced care through open scheduling, expanded hours, and communication among patients, providers, and staff. Care is facilitated by disease registries, health information technology (IT), the exchange of health information among providers, and other means to ensure that patients receive proper care in a culturally and linguistically appropriate manner.

 PCMH pilot programs in integrated delivery systems and multi-payer—sponsored initiatives have shown promise in improving the quality of patient care, reducing hospitalization and ED visits, and lowering Medicaid costs.

 More than half of US states have implemented a variety of payment policy changes and other reforms to Medicaid to help primary care providers function as PCMHs.

 Thus far, among those states, Colorado, Minnesota, New York, North Carolina, Oklahoma, South Carolina, and Vermont have reported fewer ED visits, hospitalizations, and costs.

Our research focuses on the impact of PCMHs on a previously untested population: safety net clinics serving the greater Los Angeles area. Los Angles is the largest urban area in California, with approximately half of its population of Hispanic descent. The transition to the PCMH model coincided with the state-mandated switch of SPDs from fee-for-service to a managed Medicaid plan (MMP). This switch created a potential complication: the new, high-use SPD members—with their demand on health services being much higher than regular Medicaid members&mdash;could crowd out or delay routine medical services for all other Medicaid recipients at safety net clinics.

 Therefore, our second research question asks whether the PCMH model was less effective in clinics that experienced a larger influx of heavy users.

This study was based on a local MMP that initiated a PCMH pilot project. Participation in this initiative was voluntary to safety net clinics. The eligibility criteria included: 1) being located within Los Angeles county, 2) being a safety net provider, 3) being part of contracted provider network, and 4) not participating in another externally led PCMH program. The detailed selection criteria can be found in eAppendix A (

Among 11 PCMH-certified safety net clinics in the greater Los Angeles area in early 2012, 7 clinics were included in the study because they contracted with at least 300 of the plan’s members.

 The comparison group was 110 safety net clinics with at least 300 of the plan’s members; these clinics were located in Los Angeles County, but prior to 2013, were not recognized as PCMHs by the National Committee for Quality Assurance (NCQA), Utilization Review Accreditation Committee, and Joint Commission.

We focused on the non-SPD population younger than 65 years old, given that the SPD population did not complete the transition to an MMP until mid-2012. In addition, we excluded patients who switched between the PCMH and comparison group during the study. Lastly, we required a 10-month minimum Medicaid eligibility during each study year for both groups to ensure sufficient exposure or interaction between patients and their primary care physicians. The study cohort derivation flow is provided in eAppendix B.

We used a local MMP’s administrative claims data from January 1, 2011, through December 31, 2013, in the analysis. The study timeframe spanned pre- and post-PCMH transformation. Under an MMP, all members are required to select, or are otherwise assigned to, a primary care physician. The PCMH cohort included members served by a primary care provider affiliated with a PCMH clinic, and the non-PCMH cohort included those served by a primary care provider affiliated with non-PCMH clinics. We also included data on the members’ demographics (age, gender, race/ethnicity, and enrollment history); service dates; episodes (hospital admission and ED visit); 

 diagnosis codes; procedure codes; and pharmacy claims.

We identified comorbidities using the Medicaid Rx model, a pharmacy-based risk adjustment model to adjust capitated payments to health plans that enroll Medicaid beneficiaries.

Hospital admissions, ED visits, and office visits were measured using NCQA standard definitions.

 To more thoroughly examine the impact of PCMHs on ED visits, we also included avoidable ED visits, as defined by the California Department of Health Services Collaborative.

 All utilization rates are presented as per 1000 members per year (PTMPY).

Observable characteristics between the PCMH and non-PCMH groups were compared using the Kruskal-Wallis and 

 tests for continuous and categorical variables, respectively. For the main outcomes of interest, difference-in-differences (DID) analyses were conducted by fitting generalized estimating equations with negative binomial distributions and robust standard errors to account for heteroscedasticity and clustering of patients within practices.

 Independent variables included indicators for years 2012 and 2013 (with 2011 serving as a reference), members’ demographics and comorbidities, and interactions between time periods and PCMH/non-PCMH status. A 2-tailed 

 value <.05 was treated as significant in all statistical tests. All the data management and analyses were conducted using SAS version 9.3 (SAS Institute, Cary, North Carolina).

We met with leaders of 3 PCMH and 3 non-PCMH clinics (ie, chief executive officer, chief medical officer, and medical directors) and collected their feedback on PCMH models with in-depth, semi-structured interviews. The leaders were asked about clinic operations and their approaches to patient care, including the use of health IT, involving patients in decision making, disease management, measurement of quality, and access to care. The goal was to identify differences in attributes between the 2 cohorts, as well as to seek the opinion of clinic leaders on the plausibility of our study findings and the potential differences in clinic attributes that might explain the study results. The detailed survey questions are presented in eAppendix C. This study was approved by the Institutional Review Board at the University of Southern California. 

Across the 3 years of the study, on average, 23,662 members were in the PCMH group and 138,152 were in the non-PCMH group. The distributions of age categories, gender, and comorbidities in 2011 were similar between the 2 groups, and 70% of the study population was younger than 18 years old. However, the proportion of Hispanics was 6% to 7% higher in the non-PCMH group (

Similarly, we found little or no difference in observed patient characteristics by PCMH status in clinics with SPD membership less than or greater than 10%. The same distribution patterns in 2011 were evident in 2012 and 2013. 

DID analysis compared the difference in utilization between PCMH and non-PCMH clinics in the pre-PCMH and post-PCMH periods. The results, based on the adjusted regression analysis in 

, include the analyses on primary aim (ie, the impact of PCMH on all clinics) and the secondary aim (ie, the response of PCMH clinics to different proportions of SPD membership).

In the pre-PCMH period, PCMH clinics had 32 fewer ED visits and 22 fewer avoidable ED visits (all results are PTMPY) (Table 2). Following implementation of the PCMH model, the use of ED visits declined much faster in PCMH clinics than non-PCMH clinics, and as a result, by 2013, PCMH clinics had 70 fewer ED visits and 24 fewer avoidable ED visits. We found no evidence of differing trends in inpatient hospital care by PCMH status. In contrast to trends in ED visits, PCMH clinics experienced a more rapid increase in office visits. In the pre-PCMH period, PCMH clinics had 77 fewer office visits (Table 2). In 2013, however, this difference reversed, and PCMH clinics had 163 more office visits. Overall, the trends in use suggest that increased access to primary care in PCMH clinics might have resulted in less frequent use of the ED.

Parts B and C of Table 2 shows the trends in utilization stratified by the proportion of SPD membership. Our hypothesis was that the influx of SPDs would constrain the capacity of clinics and reduce the effects of PCMHs on access to care and use of ED visits. The results support the hypothesis, as we see a greater increase in office visits and larger declines in ED visits owing to PCMHs in clinics that experienced a smaller influx of SPDs (Table 2 and the 

The unadjusted DID analyses are presented in eAppendix D. The results are consistent with the adjusted analysis presented in Table 2 and the Figure: we found a large decrease in ED visits and an increase in office visits at PCMH clinics. Additionally, the effect of PCMH status on the decrease in ED visits is larger in clinics that experienced a smaller influx of SPDs.

PCMH clinics in the study had extended hours, weekend hours, and a helpline, whereas only 1 non-PCMH clinic had extended hours. PCMH clinics also tend to offer more disease management programs. One PCMH clinic stated that health IT was useful in informing and improving decisions. There were no conclusive findings on the attributes related to quality of care and patient engagement, however. A summary of the major themes identified during the interview is provided in eAppendix E.

Medicaid beneficiaries use the ED at an almost 2-fold higher rate than the privately insured.

 Safety net clinics, which stand on the frontlines to provide care for the majority of Medicaid and uninsured patients, can play an important role in reducing the use of emergency care. This study shows that implementing the PCMH model in safety net clinics can have a meaningful impact on reducing the use of EDs; however, the extent to which PCMHs can successfully reduce ED visits may depend on the capacity of clinics to increase access to primary care. We found that the effects of PCMHs on reducing ED visits were smaller in clinics that experienced a greater increase in SPDs. 

Previous studies on the impact of the PCMH model have been largely concentrated in integrated health systems and multi-payer models.

 A few Medicaid-only studies, including those from New York (Priority Community Healthcare Center program), North Carolina (Community Care), Oklahoma, and Vermont, have reported an improvement in ED visit rates ranging from 7.5% to 31%.

 Our study, which features a heavily Hispanic population, is congruent with these studies, which is an exciting finding because being able to replicate the favorable findings of the PCMH model in populations with different cultures and lifestyles indicates that the standards for PCMH, as recognized by the NCQA, are effective beyond geographic and population boundaries.

According to Sommers et al, widespread inappropriate use of the ED among Medicaid beneficiaries can be attributed, in part, to unmet health needs and lack of access to appropriate primary care.

 Similarly, our interview results show that when PCMH clinics had improved access to care, the ED use was reduced. In regards to the impact of the adoption of health IT in PCMH clinics, the responses from the interview are in line with the findings from Adler-Milstein et al.

 Clinically focused health IT was widely adopted and applied in PCMH clinics, although the application of patient engagement function was still at a rudimentary stage.

In addition to DID results, the PCMH clinics tended to begin with lower ED rates than the non-PCMH clinics in the pre-implementation period. One possible explanation is that the cutoff dates of the pre- and post periods are somewhat arbitrary; some PCMH clinics began preparing for the PCMH model as early as the beginning of 2011. As a result, the impact of PCMHs on ED use might have started in 2011, rather than 2012 (after the official NCQA recognition). We also cannot rule out the possibility that clinics selected to participate might have had better infrastructure to serve their patients or had already implemented some PCMH elements before official recognition. The DID study design minimizes the impact from the difference in clinic infrastructure between PCMH and non-PCMH groups, as does the focus on the difference from pre-PCMH to post PCMH.

According to the 1115 waiver, a group of heavy health service users (ie, SPD) was transitioned from fee-for-service Medicaid to a MMP beginning in June 2011 and continuing through May 2012. With FFS, patients visit the doctor or hospital of their choice; switching from FFS to MMP redirects SPD members to seek service through a designated primary care provider. As a result, this transition created a unique situation for this study, such that we could examine how the PCMH model would respond to a sudden influx of a new population of high-need users. Although it is arguable that SPDs might have been served by the same physician before and after the transition, the eligibility data show that less than 4% of SPDs kept their prior affiliation, and 7% to 8% chose their own primary care provider. That said, the majority of SPDs were assigned to a new primary care provider through an auto-assignment algorithm that chooses physicians solely based on proximity to a clinic and the member’s age and primary language.

As expected, the PCMH clinics less affected by this transition had better results in reducing both ED and avoidable ED visits, whereas other PCMH clinics receiving more than 10% of SPDs had more consistent rates of ED use in the first year of the post-PCMH period and a minor dip in the second year. Our results suggest a potential crowding-out effect, where the introduction of a new population constrains resources that would otherwise be allocated to the existing non-SPD Medicaid beneficiaries.

Given that around 70% of our study population was younger than 18 years old, it would be difficult to generalize this finding to other Medicaid programs with a high proportion of adult members. In addition, this study only examined the outcomes from members of a local MMP, not a complete panel served by safety net clinics. Thus, the results may not be generalizable to the uninsured or another subset of populations.

Our results from a large urban Medicaid population suggest that a PCMH model in safety net practices can effectively reduce ED use and increase the use of office visits among Medicaid patients. We will continue to follow up with this PCMH pilot cohort to focus on the impact to the newly transitioned SPD members. The findings support the effectiveness of the PCMH model that avoids ED use, while also revealing the potential limitations of the PCMH model in response to a sudden influx of high-need healthcare users.

 LA Care Health Plan (L-HC, MT, Y-CL), Los Angeles, CA; Schaeffer Center for Health Policy and Economics, University of Southern California (L-HC, NS), Rancho Cucamonga, CA; Inland Empire Health Plan (JNS), Los Angeles, CA.

 At the time of the manuscript preparation, Dr Chu, Ms Lee, and Mr Tu were employed by LA Care Health Plan, which provided the data for this study. This study was developed based on a class project while L-HC was attending the PhD program. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (L-HC, Y-CL, JNS, NS, MT); acquisition of data (L-HC, Y-CL, JNS, NS, MT); analysis and interpretation of data (L-HC, JNS, NS, MT); drafting of the manuscript (L-HC, JNS, NS); critical revision of the manuscript for important intellectual content (L-HC, JNS, NS); statistical analysis (L-HC, Y-CL, NS); provision of patients or study materials (L-HC, NS); administrative, technical, or logistic support (L-HC, NS, MT); and supervision (L-HC, NS).

 Li-Hao Chu, PhD, LA Care Health Plan, 1055 West 7th St, 10th Fl, Los Angeles, CA 90017. E-mail: lihaochu@usc.edu.

1. Saviano EC. California’s safety-net clinics: a primer. California HealthCare Foundation website. http://www.chcf.org/publications/2009/03/californias-safetynet-clinics-a-primer. Published March 2009. Accessed July 2, 2014.

2. McConville S, Lee H. Emergency department care in California: who uses it and why? Public Policy Institute of California website. http://www.ppic.org/content/pubs/cacounts/CC_808SMCC.pdf. Published August 2008. Accessed July 2, 2014.

METHODS

Adopting a patient-centered medical home model in safety net practices can effectively reduce emergency department use and increase the use of office visits among Medicaid patients.

August 2016

Association Among Change in Medical Costs, Level of Comorbidity, and Change in Adherence Behavior

Aligning Payment Reform and Delivery Innovation in Emergency Care

The Impact of Patient-Centered Medical Homes on Safety Net Clinics

Variation in US Outpatient Antibiotic Prescribing Quality Measures According to Health Plan and Geography

Socioeconomic Disparities in Adoption of Personal Health Records Over Time

The Impact of Formulary Drug Exclusion Policies on Patients and Healthcare Costs

Choosing Wisely: Spine Imaging

Opinions on the Hospital Readmission Reduction Program: Results of a National Survey of Hospital Leaders

The Financial Impact of Team-Based Care on Primary Care

Association of Part D Coverage Gap With COPD Medication Adherence