 Primary care providers (PCPs) and clinical pharmacists have concerns about the adverse consequences of using medications inappropriately and generally support the notion of reducing unnecessary drugs. Despite this attitude, many factors impede clinicians’ ability to discontinue medication in clinical settings. We sought to develop a survey instrument that assesses PCPs' and pharmacists’ experiences, attitudes, and beliefs toward medication discontinuation.

 Survey development and psychometric assessment.

Based on a conceptual framework, we developed a questionnaire and surveyed a national sample of Department of Veterans Affairs PCPs with prescribing privileges, including physicians, nurse practitioners, physician assistants, and clinical pharmacy specialists. We randomly divided respondents into derivation and validation samples and used iterations of multi-trait analysis to assess the psychometric properties of the proposed measures. Multivariable regression models identified factors associated with the outcome of self-rated comfort with medication discontinuation.

Using established criteria for scale development, we identified 5 scales: Medication Characteristics, Current Patient Clinical Factors, Predictions of Future Health States, Patients’ Resources to Manage Their Own Health, and Education and Experience. Three of these dimensions predicted providers’ self-rated comfort with making decisions to discontinue medication (Current Patient Clinical Factors, Predictions of Future Health States, and Education and Experience).

 We developed a psychometrically sound instrument to measure prescribers’ attitudes toward, and experiences with, medication discontinuation. This survey will enable identification of perceived barriers to, and facilitators of, proactive discontinuation—an important step toward developing interventions that improve the quality and safety of care in medication use.

		We developed a psychometrically sound instrument to measure prescribers’ attitudes toward, and experiences with, medication discontinuation, an emerging area of importance in healthcare. 

		Using this survey instrument can identify provider attitudes, beliefs, and/or other specific decision-making considerations that are associated with reluctance to discontinue a medication. 

		Having identified these barriers, future interventions can be designed to improve the quality and safety of medication prescribing.

Use of 5 or more medicines, often considered polypharmacy, is associated with adverse drug events (ADEs).

 ADEs, in turn, lead to increased healthcare utilization, costs, and morbidity.

 Approximately 40% of adults 65 years or older are exposed to polypharmacy, with similar prevalence in Department of Veterans Affairs (VA) patients.

 One approach to prevent polypharmacy-related ADEs is to reduce medications that are outdated, not indicated, or of limited benefit relative to risk.

Discontinuation, also known as de-prescribing, has been defined as a “systematic process of identifying and discontinuing drugs in instances in which existing or potential harms outweigh existing or potential benefits within the context of an individual patient’s care goals, current level of functioning, life expectancy, values, and preferences. De-prescribing is part of the good prescribing continuum.”

 Although discontinuing a medication can be considered “doing less,” it often requires more provider effort, as patients may require more frequent visits and closer monitoring after de-prescribing. Moreover, discussions about medication discontinuation may take time during already busy clinical encounters, especially to ensure accurate communication between patients and clinicians.

Prescribers’ voice concerns about the adverse consequences of inappropriate medication use and general support for the notion of discontinuing unnecessary drugs.

 Nonetheless, many factors impede clinicians’ ability to de-prescribe, including patient complexity, clinical uncertainty, and shared management with other healthcare providers, all of which can contribute to “clinical inertia” around medication discontinuation.

 Taking these findings in the context of the broader literature on de-implementation, we undertook the present study to develop and administer a survey to a national sample of VA primary care providers (PCPs) to characterize their experiences, attitudes, and beliefs toward medication discontinuation.

Based on our qualitative work with PCPs assessing their attitudes toward discontinuation and on the literature on medication prescribing during the past 30 years, we developed a conceptual model of factors that influence medication discontinuation decisions.

 The hierarchical model identified 4 larger, overarching domains: Medications, Patients, Providers, and System Factors. Within these broad domains, we distinguish 10 more specific dimensions (

). Each dimension represents a construct, or abstract idea, that we sought to measure in the survey instrument.

 Medication includes 2 dimensions: “medication characteristics,” such as dosing frequency, and “medication uncertainty,” which includes medication reconciliation difficulties. Patients comprises 4 dimensions: “clinical status” includes the patient’s current health conditions, “desired role” reflects patient activation and shared decision making, “adherence” is taking medication as prescribed, and “patient knowledge and beliefs about medications.” Providers has 2 dimensions: “providers’ personal beliefs” reflects respondents’ views about medications; and “professional identity” encompasses responsibility, jurisdiction, and authority regarding medications. System Factors has 2 dimensions: “multiple providers” addresses complexities of care, and “workplace structure and process” refers to external factors.

Based on these 10 dimensions, we generated a pool of 75 items, ensuring that each dimension was represented by at least 3 items. An additional item asked providers to indicate their current overall comfort level with deciding to discontinue a medication on a 0 to 10 scale ranging from “not at all comfortable” to “completely comfortable.” To assess providers’ general attitudes regarding the use and efficacy of medications, we included the 4-item Beliefs about Medications Questionnaire (BMQ) overuse scale.

We circulated the draft Provider Perceptions of Medication Discontinuation survey to a 7-member expert panel of researchers and PCPs, including experts in survey development and medication safety; all provided feedback on the draft items, which were revised accordingly. The updated draft was then presented to a non-VA academic research forum composed of 20 to 30 internists and researchers similar to the target population of survey respondents, where the items were reviewed and suggested improvements were again incorporated.

Next, we used a semi-structured cognitive interview protocol with specific probes in 1-on-1 sessions with VA PCPs.

 Using a modified form of retrospective debriefing, subjects completed the survey 1 section at a time and answered questions about how they interpreted the items and decided on a response.

 Participants included professionals representing the most likely future respondents: physicians, nurse practitioners, and clinical pharmacy specialists. The cognitive interviews were conducted in 2 rounds (8 and 4 clinicians, respectively). Between rounds, we modified items and instructions based on feedback with additional edits after the second round. The resulting pilot-ready version of the survey included 56 items related to medication discontinuation, organized around the 10 dimensions noted above, plus 8 demographic items. The substantive content of the pilot-ready survey is summarized in Table 1.

 We surveyed VA PCPs with prescribing privileges. Our sampling frame was the Primary Care Management Module, a centralized VA database containing information for all PCPs. From this listing, we identified all providers nationwide with the title of physician—primary care, physician–attending, PCP, nurse practitioner (NP), or physician assistant (PA). Using another centralized database, we identified clinical pharmacy specialists by selecting “pharmacy service providers” who had primary care clinical encounters. We determined  the sample size based on our primary objective to achieve adequate power (0.80) for the multi-trait analysis (MTA), assuming, based on our general experience with survey-based attitude measures, that item-scale correlations would be moderate (0.3-0.5). This yielded an estimated effect size for the difference between correlations in the moderate range (Jacob Cohen’s effect size index [q] = 0.30) and a target sample of 180 for both the derivation and validation samples. Assuming a response rate of 20%, we randomly selected an initial mail-out sample of 2500 providers from those eligible, we stratified evenly across 4 geographic regions, and oversampled NP/PAs and pharmacists to ensure adequate representation and to enable comparisons across the 3 provider types.

 We sent each provider an e-mail introducing the survey objectives and containing a link to the survey website. If an e-mail was undeliverable, we selected a replacement subject of the same provider type and geographic stratum. Nonrespondents received up to 2 reminder e-mails at 1-week intervals. The survey remained open for 3 months. All responses were anonymous.

 We applied MTA to evaluate the psychometric properties of the proposed scales. In MTA, scale reliability is assessed by Cronbach’s alpha coefficient. Item convergence was evaluated by examining the correlation of each item with its assigned scale (item-scale correlations), and item discrimination compared each item’s item-scale correlations with its correlations with all other scales.

 We randomly split respondents into derivation and validation groups and ran the initial MTA in the derivation sample. We made iterative modifications guided by both empirical findings and conceptual considerations, reassigning items to scales to improve the psychometric properties of the scales while also clarifying and focusing scale content. After arriving at a final model in the derivation sample, we tested it by repeating the MTA in the validation sample. To evaluate the proposed final questionnaire produced by the MTA, 4 expert-panel members reviewed the results for face validity via independent appraisal and group discussion. We assessed nonresponse bias by comparing respondents and nonrespondents on 4 factors available for all subjects from VA centralized databases: geographic region, job type, age, and gender. All analyses were conducted in SAS version 9.3 (SAS Institute Inc, Cary, North Carolina).

A total of 411 prescribers completed online questionnaires. After accounting for unreachable prescribers (n = 25), the response rate was 16.6%. Nonresponders were more likely to be physicians than NP/PAs or pharmacists, but were otherwise similar with respect to age, gender, and geographic region. Details regarding respondent demographics are in 

. Regarding data quality, the median percent of missing responses per item on the substantive questions was 11.7% (range = 0.01%-16%; n = 4-65); the median percent missing on the demographic questions was 17.2% (range = 16%-19%; n = 64-79).

The respondents were randomly divided into a derivation sample (n = 205) and validation sample (n = 206). We conducted a series of MTAs in the derivation sample, beginning with the hypothesized model of 10 scales. Upon review of the data, we felt 2 of the hypothesized scales (Indication Uncertainty and Multiple Providers) represented the frequency with which various events occurred rather than representing respondents’ beliefs. The items combine to describe and define a scale (ie, a formative measure) rather than reflect an underlying latent construct driving item responses (ie, a reflective measure).

 Formative measures will, by their nature, not necessarily exhibit high internal consistency, reliability, or item convergence and discrimination. Therefore, we omitted these questions from the MTA, but retained them as indices for use in future analyses.

Thus, we began the series of MTAs with 8 hypothesized dimensions. Consistent with the recommendations of Ware and colleagues, we included only the subset of 167 respondents (81%) who answered at least half of the items in all of the hypothesized scales (the half-scale criterion), estimating values for the unanswered items based on the within-subject average of the answered items in the scale.

 Item-scale correlations were corrected for overlap, and differences between 2 item-scale correlations were deemed significant if their 95% confidence intervals did not intersect. After each iteration, an item was moved if: 1) it was more highly correlated with a competing scale than with its current scale (ie, scaling failure), 2) the re-assignment was consistent with the content of the destination scale, and 3) its removal did not reduce the internal consistency reliability of the current scale below the minimum of 0.70 recommended for group comparisons.

 After 8 iterations, the original model evolved to a more parsimonious model with 5 scales: Medication Characteristics, Current Patient Clinical Factors, Predictions of Future Health States, Patients’ Resources to Manage Their Own Health, and Education and Experience. One of those scales—Medication Characteristics&mdash;consisted of only 2 items and demonstrated relatively low internal consistency reliability. However, there were no scaling failures, and thus, we tested the 5-scale model in the validation sample.

The pattern of convergent and discriminant correlations generated by the MTA in the validation sample demonstrated strong support for the 5-scale model (

). Correlations of 0.40 or higher between an item and its overall scale score (adjusted for overlap) is indicative of adequate item convergence.

 In the confirmation sample, this criterion was met by all but 3 of the items. Additionally, items were significantly more highly correlated with their own scale than with other scales in 65 of 76 comparisons (86%), and higher—albeit not significantly&mdash;in another 9 comparisons. Thus, appropriate item discrimination was seen for 99% of item-to-scale correlations. Scale internal consistency reliabilities were adequate, ranging from 0.75 to 0.82, with the exception of the 2-item Medication Characteristics scale (Cronbach’s alpha of 0.59 in the derivation sample and 0.33 in the validation sample).

Basic descriptive statistics for the 5 scales are reported in 

Scale scores computed as the mean across relevant items—all on 1-to-5 response scales&mdash;ranged from 2.7 (Medication Characteristics) to 4.2 (Education and Experience). The percentage of respondents with scores at the floor ranged from 0% (Current Patient Clinical and Education and Experience) to 2% (Medication Characteristics), whereas the range of responses at the ceiling ranged from 1% (Current Patient Clinical) to 20% (Education and Experience).

 reports the correlations between scales in the off-diagonal entries and the scale internal consistency reliability estimates in the diagonal entries. These results are consistent with those expected of a set of measures of related, yet distinguishable, factors that influence medication discontinuation decisions. Specifically, the inter-scale correlations range from 0.03 to 0.44 (median = 0.14). With the exception of Medication Characteristics, scale alpha coefficients were substantially higher than the scale-to-scale correlations, further supporting discrimination between the scales; if the scale reliabilities did not exceed the inter-scale correlations, the scales would be interchangeable (ie, not measures of unique and distinguishable factors).

Finally, to evaluate construct validity, we conducted a multiple linear regression analysis using the 5 scales as predictors of self-rated comfort with medication discontinuation decisions. The mean response for comfort was 7.5 (standard deviation = 1.8), with generally normal distribution (skew, —0.47; kurtosis, –0.52), supporting its use as a linear outcome variable.

 Other included predictors were 8 provider demographics, 9 survey items regarding patient medication-taking behaviors, medication reconciliation, side effects, workplace support for monitoring patients, prior discontinuation experience, and the BMQ overuse scale. The model was built using forward selection; given the exploratory nature of the exercise, a 

value of .10 was used for entry into the model. The model overall was significant (

 <.0001) and explained 27.6% of the variance in prescriber comfort with de-prescribing. Three of the new attitude scales were significant predictors (standardized beta estimates): Current Patient Clinical Factors (0.17; 

 = .005), Predictions of Future Health States (—0.11; 

 = .005), and Education and Experience (0.41; 

 <.0001). The zero-order correlations between these scales and self-rated comfort were 0.16, 0.01, and 0.37, respectively. Additional significant predictors included age, race, physician provider, region, and prior discontinuation experience.

We developed and administered a survey instrument designed to characterize PCPs’ and clinical pharmacists’ experiences, attitudes, and beliefs regarding medication discontinuation. To our knowledge, this is the first instrument of its kind, and it should enable better understanding of the facilitators of and barriers to de-prescribing in a general ambulatory population—an area in need of further study.

To examine the instrument’s psychometric properties, we applied MTA to responses from more than 400 VA primary care prescribers. We identified 5 scales related to constructs of clinical de-prescribing decisions: Medication Characteristics, Current Patient Clinical Factors, Predictions of Future Health States, Patients’ Resources to Manage Their Own Health, and Education and Experience. These scales were similar, but not identical, to the dimensions of our a priori conceptual model. Where the 2 differed, the empirically derived scales exhibited logical face-value relationships. For example, parsing the hypothesized dimension “Clinical Status” into Current Patient Clinical Factors and Predictions of Future Health is reasonable inasmuch as the latter may involve decreased certainty, and thus, influence decision making differently than the former.

Our analyses demonstrated that 3 of the 5 final scales predicted prescribers’ self-rated comfort with medication discontinuation decisions. We observed an apparent example of classical suppression with regard to Predictions of Future Health States, given that the simple correlation was essentially 0; however, this variable had a negative coefficient in the regression model.

 The scale items are phrased such that a higher score indicates a higher likelihood of recommending discontinuation even though such action might be associated with patient concerns about symptom return or other negative consequences. The negative coefficient in the regression model suggests that, after controlling for other factors, endorsing discontinuation even if it may lead to worse health outcomes is associated with lower levels of comfort with discontinuation. Finally, Current Patient Clinical Factors and Education and Experience were both significant positive predictors of providers’ comfort with discontinuation.

Altogether, Current Patient Clinical Factors, Predictions of Future Health States, and Education and Experience, along with other factors, accounted for 27.6% of the variance in provider comfort. Provider comfort, in turn, likely influenced the ability and willingness of clinicians to address discontinuation. As VA implements the Patient Aligned Care Team (PACT) model of the patient-centered medical home, more types of clinicians will encounter potential discontinuation opportunities. Building on more than a decade of increasing responsibilities within VA, clinical pharmacists play a significant role in PACT.

 Although pharmacists do not have the jurisdiction to make diagnoses, they can initiate, titrate, and discontinue medications in the management of chronic conditions. Additionally, NPs and PAs are gaining increased autonomy, often operating on par with physicians, supporting the need for better understanding of their comfort and willingness to de-prescribe. Therefore, using this survey instrument to assess the attitudes, beliefs, and comfort of various care clinicians may identify those prescribers who need additional training in proactive medication discontinuation.

Several limitations should be noted. First, the response rate was low. Multiple factors may have contributed to this low response, including clinicians’ busy schedules and the ability to only access the survey from behind the organizational firewall. Clinicians have lower response rates than the general population,

 and there was no incentive provided. However, for the primary purpose of scale development, rather than seeking to characterize clinicians’ attitudes through a representative sample, the sample size provided adequate power. Future studies that provide an incentive, use a shortened survey, or have enhanced follow-up may yield higher response rates.

 Second, the survey was developed in VA, and there may be differences in the provider characteristics themselves (ie, those who choose to practice within VA may differ from those who do not) or in the practice culture influencing them. Future research should evaluate the instrument and confirm its psychometric properties outside VA.

For individual items, the missing response rate was as high as 16% for some of the substantive questions and 19% for demographic questions. Many of these items were near the end of the survey, suggesting a role for response fatigue. Eliminating unnecessary items and shortening the survey could improve response rates and reduce missing data. The MTA analysis supported only a 2-item Medications Characteristics scale, which consequently demonstrated more limited reliability. Creating and testing additional items on this topic should strengthen the scale’s psychometric properties. When considered along with the face validity of the items, the pattern of item/scale relationships reported here preliminarily supports the proposed interpretation of the new scales.

 However, as this is based only on the analysis of data from the proposed measures themselves, such interpretation is tentative. We do not know from these data whether the scales measure what they purport to measure; only that the items assigned to a given scale are probably related to the same construct, and that proposed scales appear to measure different constructs.

Additional testing is needed to more definitively establish the meaning of these measures, including criterion and construct validation. Finally, 60% of respondents scored at the ceiling of the Education and Experience scale, potentially limiting that scale’s ability to discriminate between subgroups or detect change over time. This scale could benefit from additional items regarding the details of training or experience that contribute in a less uniform manner to clinicians’ comfort with de-prescribing.

Using separate derivation and validation samples, we created replicable and psychometrically sound scales representing key dimensions that contribute to primary care prescribers’ medication discontinuation decisions. Prior research on overuse in healthcare has tended to focus on practices related to screening and diagnostic testing.

 It is also thought that de-implementation, of which medication discontinuation is a particular example, is qualitatively different than preventing inappropriate initiation, and is associated with its own difficulties.

 For example, loss of clinical information between healthcare providers and systems may make it more difficult to obtain an accurate medication list; without these data, clinicians will have less certainty about de-prescribing. Therefore, using this survey instrument in future evaluations to understand how clinicians who work in different care settings perceive and incorporate de-prescribing into their practice can identify provider characteristics or specific decision-making factors associated with reluctance to discontinue. Those findings can then be leveraged to design future interventions that improve the quality and safety of prescribing via reduction of inappropriate medication use.

Center for Healthcare Organization and Implementation Research (AL, SRS, KS) and Section of General Internal Medicine (AL, SRS), VA Boston Healthcare System, Boston MA; Edith Nourse Rogers Memorial Veterans Affairs Medical Center (BGB), Bedford, MA; Section of General Internal Medicine, Boston Medical Center (AL, SRS), Boston, MA; Health Law, Policy and Management, Boston University School of Public Health (BGB, MM), Boston MA; Performance Measurement, VHA Office of Analytics and Business Intelligence (MM), Bedford, MA.

The principal investigator (AL) was supported by a Department of Veterans Affairs (VA), Veterans Health Administration, Health Services Research and Development Career Development Award (CDA12-166), and the study was conducted using resources of the VA Boston Healthcare System. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; nor the decision to submit the manuscript for publication.

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 (BGB, AL, MM, SRS); acquisition of data (AL); analysis and interpretation of data (BGB, AL, MM, KS, SRS); drafting of the manuscript (AL); critical revision of the manuscript for important intellectual content (BGB, AL, MM, KS, SRS); statistical analysis (AL, MM, KS); provision of patients or study materials (AL); obtaining funding (AL); administrative, technical, or logistic support (AL, MM); and supervision (SRS). 

Amy Linsky, MD, MSc, General Internal Medicine (152G), VA Boston Healthcare System, 150 S. Huntington Ave, Boston, MA 02130. E-mail: amy.linsky@va.gov.

1. Bushardt RL, Massey EB, Simpson TW, Ariail JC, Simpson KN. Polypharmacy: misleading, but manageable. 

2. Nebeker JR, Barach P, Samore MH. Clarifying adverse drug events: a clinician’s guide to terminology, documentation, and reporting. 

3. Gandhi TK, Weingart SN, Borus J, et al. Adverse drug events in ambulatory care. 

Articles

METHODS

The authors developed and validated a survey instrument to assess primary care providers’ and pharmacists’ experiences, attitudes, and beliefs regarding medication discontinuation.

Prescribers' Perceptions of Medication Discontinuation: Survey Instrument Development and Validation

As the boomer population ages, there is a growing need for integrated care organizations (ICOs) that can integrate both medical care and long-term services and supports in the home. This paper presents a policy proposal to support the creation of ICOs, redesign care, and provide financing for home- and community-based services (HCBS), with the goal of enhancing financial protection for beneficiaries, coordinating care, and preventing costly hospital and nursing home use.

 This study used the 2012 Medicare Current Beneficiary Survey (MCBS) Cost and Use File, inflated to 2016 figures, to describe the characteristics of Medicare beneficiaries and their healthcare utilization and spending. The costs of covering up to 20 hours of personal care services a week were estimated using MCBS population counts, participation assumptions based on the literature, and financing design parameters.

A targeted HCBS benefit could be added to Medicare and financed with income-related cost sharing ranging from 5% to 50%, a premium paid by Medicare beneficiaries of approximately $42 a month, and payroll taxes estimated at around 0.4% of earnings on employers and employees.

 Adoption of an HCBS benefit in Medicare would improve financial protection for beneficiaries with physical and/or cognitive impairment and provide the financing for health organizations to better integrate medical and social services. ICOs and delivery models of care emphasizing care at home would improve accessibility of care and avoid costly institutionalization; additionally, it would also reduce beneficiary reliance on Medicaid.

	This paper presents a policy proposal to finance Medicare home- and community-based care, create integrated care organizations (ICOs), and redesign care to deliver both medical and long-term services and supports (LTSS) in the home. 

		Financing home- and community-based care under Medicare and creating ICOs to be accountable for LTSS would provide the incentive and means to coordinate care, support family care partners, and better meet beneficiary preferences for independent living and care at home. 

		Providing Medicare coverage of home- and community-based care would reduce beneficiary reliance on Medicaid’s safety net coverage.

As the boomer generation ages, a growing number of Medicare beneficiaries will experience difficulty functioning without assistance. The Medicare benefit package was originally aimed at covering acute care, including the cost of hospitalization and short-term postacute care following hospitalization

; however, beneficiaries are becoming increasingly in need of long-term services and supports to enable them to maintain independent living. Medicare does not pay for this, nor does it coordinate care across acute and long-term services and supports, and sites of care. Further, it does not provide training and support to care partners, whose assistance is often critical to good health outcomes, adherence to medications and recommended care, and beneficiary functioning.

To address these issues, this commentary presents a policy proposal to finance Medicare home and community-based care, create integrated care organizations (ICOs), and redesign care to deliver both medical and long-term services and supports (LTSS) in the home. Financing home- and community-based care under Medicare and creating ICOs to be accountable for LTSS would provide the incentive and means to coordinate care, support family care partners, and better meet beneficiary preferences for independent living and care at home. Providing Medicare coverage of home and community-based care would reduce beneficiary reliance on Medicaid’s safety net coverage.

Beneficiaries With Physical and/or Cognitive Impairment 

Based on the Medicare Current Beneficiary Survey for 2012 with population and cost figures inflated to 2016 levels,

 almost one-fourth of the community-dwelling (noninstitutionalized) Medicare beneficiary population has physical and/or cognitive impairment, representing 12 million noninstitutionalized individuals living in the community.

 describes the demographic, socioeconomic, and health status characteristics of the community-dwelling Medicare population with and without physical and/or cognitive impairment. The population with physical and/or cognitive impairment has disproportionately lower income; 64% have incomes below 200% of the federal poverty level, with 29% on Medicaid and 35% not on Medicaid. The latter is the group most at risk of not being able to afford care not covered by Medicare.

The overwhelming majority of individuals with physical and/or cognitive impairment have multiple health problems in addition to functional impairment. More than three-fourths of the population with physical and/or cognitive impairment has 3 or more chronic conditions compared with half of the population without physical or cognitive impairment. More than half (53%) of the group with physical and/or cognitive impairment rates their health as fair or poor compared with 17% of beneficiaries without such functional limitations.

“Medicare Help at Home” Policy Proposal

The policy option we call “Medicare Help at Home” could be offered as a supplement to Medicare, and with financing, it would share the costs across all ages.

 The policy has 3 elements: 1) a Medicare home and community-based benefit for those with 2 or more functional limitations or dementia, covering up to 20 hours a week of personal care services or an equivalent dollar amount for a range of home and community-based care; 2) creation of new ICOs accountable for the delivery and coordination of both acute and long-term services and supports that meet quality standards, honor beneficiary preferences, and support care partners; and 3) innovative models of healthcare delivery, including a team approach to care in the home, building on models of service delivery that improve patient outcomes, reduce emergency department (ED) use, prevent avoidable hospitalizations, and delay or reduce long-term institutional care.

Medicare Home and Community-Based Benefits

Those eligible include Medicare beneficiaries with serious physical and/or cognitive impairment, such as limitations in 2 or more activities of daily living or a diagnosis of Alzheimer’s disease or other forms of dementia. Beneficiary functional limitation would be assessed by organizations designated by Medicare and individualized care plans developed based on beneficiary preferences (or those of a legal guardian) and the degree and nature of functional limitations. All enrollees would complete an advance directive indicating preferences regarding end-of-life care.

Under this proposal, Medicare’s current home health and hospice benefits would continue. The new home- and community-based benefit, however, would not have the restrictions that apply to home health services, such as being limited to homebound beneficiaries who require skilled nursing care, physical therapy, occupational therapy, speech-language pathology services, or hospice services that require giving up treatment for the beneficiary’s terminal condition (other than pain and symptom management). Instead, the home- and community-based services benefit would permit palliative care in the home and personal care services that help beneficiaries maintain independent living.

For modeling purposes, we assume that 60% of eligible beneficiaries would receive the maximum benefit (full year, 20 hours per week) of personal services and 40% would receive partial benefit for care (either partial year or partial support, averaging 10 hours a week).

 The cost is estimated at $15 per hour, plus an additional 33% allowance for fringe benefits and overhead. For the maximum benefit of 20 hours per week, the cost amounts to $400 per week, or $20,800 a year. Beneficiaries (or their legal guardians) could, however, elect to use a portion or all of their allowance on other approved home and community services. Beneficiaries could select their own paid personal attendant (“consumer-directed care”), including family members other than their legal guardian.

Coinsurance based on income would range from 5% for beneficiaries with incomes below 150% of the federal poverty level (FPL) to 15% for incomes between 150% to 199% of the FPL, 25% between 200% to 399% of the FPL, and 50% for incomes 400% of the FPL or greater.

Based on take-up rates in other programs, such as Medicaid and Medicare Part B, it is estimated that 75% of those eligible for the home care benefit who are not already on Medicaid would participate each year—6.3 million of a total eligible population of 8.5 million.

 Beneficiary coinsurance, which varies with income (as outlined above), would cover an average of 20% of the total cost of the benefit. As illustrated in 

, we estimated that the total program costs with these participation assumptions—net of beneficiary cost-sharing payments&mdash;would be around $82 billion a year.

Financing through a combination of Medicare beneficiary premiums (25%) and payroll taxes (75%) is suggested to share the costs over the life span and across Medicare beneficiaries with and without physical and/or cognitive impairment. The required premium to cover one-fourth of the cost would amount to about $42 a month. The remaining 75% of the cost of the benefit—$61 billion&mdash;could be financed through an increase in the payroll tax. This could be split evenly on employers and employees, with each contributing 0.4% of earnings.

ICOs that both meet the current requirements for accountable care organizations (ACOs) and agree to be accountable for the delivery and coordination of medical care and LTSS, would be authorized under the Help at Home proposal. Beneficiaries would receive reduced cost sharing on their Medicare Help at Home benefit if they enrolled in an ICO, and ICOs would receive financial incentives for reduced long-term institutional placement subject to meeting quality targets and reporting on quality and other performance metrics. ICOs would be responsible for supporting family care partners, including training, respite, and other support services (eg, mental health services).

ICOs would be encouraged to incorporate promising models of care using a team approach to care found to improve patient outcomes, as well as reduce the use of EDs, hospitalizations, and long-term institutionalization.

 This could include, for example, Independence at Home, a model of home-based primary care using home-based primary care teams directed by physicians and advanced practice nurses.

 The Hospital at Home program provides hospital services within the patient’s home to avoid the necessity of admission for patients presenting at the ED or ambulatory clinic with pneumonia, exacerbations of chronic heart failure, chronic obstructive pulmonary disease, or cellulitis.

ICOs would also be encouraged to adopt innovative models of care delivery that help maintain independent living. For example, CAPABLE (The Community Aging in Place, Advancing Better Living for Elders), which uses an interdisciplinary team consisting of a nurse, occupational therapist, and a handyman, has been shown to improve functioning as defined by activities of daily living.

 MIND (Maximizing Independence for Dementia patients) at Home, which uses memory care coordinators supervised by a geriatric psychiatrist and nurse, has been found to enable participants to safely stay in their homes.

The proposal would benefit Medicare beneficiaries who face the challenge of serious physical or cognitive functioning. It could improve access to home and community long-term services and supports, reduce the financial burden of out-of-pocket costs, and assist family care partners. Beneficiaries with physical and/or cognitive impairment now face serious financial burdens. As shown in 

, twice as many Medicare beneficiaries with physical and/or cognitive impairment spent over 10% of their income on out-of-pocket spending for medical services compared with those without physical and/or cognitive impairment (33% vs 18%). In particular, the proposal could be expected to reduce the high out-of-pocket costs Medicare beneficiaries now spend on long-term services and supports.

Medicaid does not cover two-thirds of those with physical and/or cognitive impairment. As a result, the cost of such services poses barriers to care access and represents a major financial burden on beneficiaries with modest incomes. By intervening before beneficiaries become impoverished by the purchase of home and community services, and moving to long-term nursing facilities, this proposal could achieve savings by reducing the number of Medicaid enrollees. The innovative delivery options that would be adopted by ICOs can be expected to yield significant savings in reduced hospitalizations and ED use.

Several important caveats should be noted. First, the numbers of Medicare beneficiaries will grow over time, including those with physical and/or cognitive limitations, subsequently increasing the cost of care over time. Some Medicaid beneficiaries may opt for coverage under Medicare rather than Medicaid given serious restrictions on eligibility and home- and community-based benefits in many states. Further, there may be adverse risk selection with beneficiaries at greater risk of requiring LTSS being more likely to participate than those with lower risk. Low-income individuals with lower cost sharing are also more likely to participate than high-income beneficiaries who are required to pay 50% of the cost of services. However, the fact that there is a severe penalty for failing to enroll at the time of Medicare eligibility, and the substantial financing provided by payroll taxes are likely to be significant deterrents to forgoing the benefit, as has been the experience with Part B of Medicare.

ACOs will increasingly grapple with the need for LTSS among their aging patients. Financing home- and community-based services under Medicare and expanding ACOs to be accountable for LTSS would provide the incentive and means to coordinate care, support family care partners, and better meet beneficiary preferences for independent living and care at home. It would also reduce beneficiary reliance on Medicaid’s safety net coverage of institutional care. This policy proposal is worthy of serious consideration as the nation grapples with the need for Medicare redesign to meet the needs of an aging population. It could serve as an important first step toward more far-reaching Medicare benefit redesign proposals to improve the affordability of services. Unlike Medicare’s original focus on inpatient hospital care, it recognizes that many services can be provided in noninstitutional settings, including the home, and realigns benefits to provide care in a way that meets beneficiary preferences. 

Johns Hopkins Bloomberg School of Public Health (KD, AW), Baltimore, MD; New York Academy of Medicine (CS), New York, NY. 

Dr Davis is on the Board of nonprofit Geisinger Health (without compensation). 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 (KD, CS); acquisition of data (AW); analysis and interpretation of data (KD, CS, AW); drafting of the manuscript (KD); critical revision of the manuscript for important intellectual content (KD, CS, AW); statistical analysis (AW); obtaining funding (KD); administrative, technical, or logistic support (KD, AW); and supervision (KD). 

Karen Davis, PhD, 624 N. Broadway, Room 693, Baltimore, MD 21205. E-mail kadavis@jhsph.edu.

	1. Blumenthal D, Davis K, Guterman S. Medicare at 50—origins and evolution. 

 2015;372(5):479-486. doi: 10.1056/NEJMhpr1411701.

	2. Wolff JL, Spillman B. Older adults receiving assistance with physician visits and prescribed medications and their family caregivers: prevalence, characteristics, and hours of care. 

 2014;69(suppl 1):S65-S72. doi: 10.1093/geronb/gbu119.

	3. National health expenditure projections 2015-2025. CMS website. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsProjected.html. Accessed May 2016.

	4. CMS Medicare Current Beneficiary Survey cost and use file 2012 [data file]. CMS website. https://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/LimitedDataSets/MCBS.html. Accessed January 2016.

	5. Davis K, Willink A, Schoen C. Medicare help at home. 

 website. http://healthaffairs.org/blog/2016/04/13/medicare-help-at-home/. Published April 13, 2016. Accessed April 2016.

	6. Wolff J, Davis K, Leeds M, Narawa L, Stockwell I, Woodcock C. Family caregivers as paid personal care attendants in Medicaid. Johns Hopkins Bloomberg School of Public Health website. http://www.jhsph.edu/research/centers-and-institutes/roger-c-lipitz-center-for-integrated-health-care/issue-brief-family-caregivers.html. Accessed October 2016

	7. Sommers B, Kronick R, Finegold K, Po R, Schwarts K, Glied S. Understanding participation rates in Medicaid: implications for the Affordable Care Act. Office of the Assistant Secretary for Planning and Evaluation website. https://aspe.hhs.gov/basic-report/understanding-participation-rates-medicaid-implications-affordable-care-act. Published March 16, 2012. Accessed March 2016.

	8. Options for reducing the deficit: 2015 to 2024. Congressional Budget Office website. https://www.cbo.gov/budget-options/2014. Published November 20, 2014. Accessed April 2016. 

	9. Davis K, Buttorff C, Leff B, et al. Innovative care models for high-cost Medicare beneficiaries: delivery system and payment reform to accelerate adoption. 

	10. Affordable Care Act payment model continues to improve care, lower costs [press release]. Baltimore, MD: Center for Medicare and Medicaid Services; August 9, 2016. https://www.cms.gov/Newsroom/MediaReleaseDatabase/Press-releases/2016-Press-releases-items/2016-08-09.html. Accessed October 2016.

TRENDS FROM THE FIELD

This paper presents a policy proposal to integrate care for Medicare beneficiaries through creation of integrated care organizations and a Medicare home care benefit.

Integrated Care Organizations: Medicare Financing for Care at Home

 To compare home health utilization and clinical outcomes between Medicare beneficiaries in the fee-for-service (FFS) and Medicare Advantage (MA) programs, and to compare regional variation.

 We used the 2010 and 2011 Outcome and Assessment Information Set to identify all home health episodes begun in 2010 and to measure 7 clinical home health outcomes that are defined by CMS for public reporting.

 We modeled the probability of home health use, the duration of home health episodes, and each clinical outcome measure as a function of MA versus FFS enrollment and model-specific risk adjustors. Empirical Bayes predictions from generalized linear mixed models were aggregated by hospital referral region (HRR) to create standardized regional measures of home health utilization and mean episode duration

We identified 30,837,130 FFS and 10,594,658 MA beneficiaries (excluding those dually eligible for Medicaid). After adjusting for demographic and clinical patient characteristics, the odds of receiving home health among FFS enrollees were 1.83 times those of MA (95% CI, 1.82-1.84). Adjusted home health duration was 34% longer for FFS (95% CI, 32%-34%). Outcomes differences were small in magnitude and inconsistent across measures. Regional variations in use and duration were substantial for both FFS and MA enrollees. Within HRRs, correlations between FFS and MA utilization rates and between FFS and MA episode durations were 0.51 and 0.94, respectively.

 MA beneficiaries use less home health than their FFS counterparts, but regional factors affect utilization, independent of insurance status.

Home health utilization is a substantial source of Medicare costs; however, whether enrollment in a Medicare Advantage (MA) plan affects home heathcare use is unknown. We compared nationwide and intra-regional use of home health between beneficiaries enrolled in fee-for-service (FFS) Medicare and MA plans. Our findings were as follows:

		FFS beneficiaries used substantially more care, even after adjusting for patient and demographic characteristics.

		Functional outcomes were similar, although FFS episodes were 33% longer in duration.

		Adjusted utilization varied markedly across regions for both FFS and MA, suggesting that MA plans cannot entirely overcome non—health-related regional forces driving use.

Although more than 30% of all Medicare beneficiaries are enrolled in the Medicare Advantage (MA) program,

 studies focusing on the MA patient population are relatively rare, in large part because there are no claims to use as a data source. The capitated payment model introduces a financial incentive to limit wasteful care, whereas none exists in the fee-for-service (FFS) system. However, few published studies have directly measured and compared how much a particular service is used in the 2 systems.

One venue where care is documented for all Medicare beneficiaries is home health. All patients receiving care through CMS-certified home health agencies are required to have a comprehensive clinical assessment at the start and end of care, using the Outcome and Assessment Information Set (OASIS) instrument. For both FFS and MA beneficiaries, there is therefore a record not only that care occurred, but also of the clinical status at the start of care and of the functional outcome. Thus, home health provides a unique window through which the 2 programs can be compared.

The use of home health within Medicare is a topic ripe for review. Home health accounted for more than 5% of all FFS Medicare spending ($18.4 billion) in 2011,

 and an Institute of Medicine (IOM) report identified postacute care as the leading source of unexplained regional variation in healthcare spending via FFS.

 In a report to Congress, the Medicare Payment Advisory Commission (MedPAC) suggested that much of the variation in home health use reflected “fraud, waste, and abuse,” and noted that “the broad program standards and fragmented nature of the FFS program do not encourage effective targeting of the benefit.”

 Whether MA plans, with their incentives to avoid unnecessary care, are more effective at managing home health use is a natural next question. As a first step toward answering it, we compared home health utilization, regional variation, and clinical outcomes among beneficiaries enrolled in the MA and FFS programs.

We identified all home health episodes among Medicare beneficiaries that started in 2010 and ended by December 31, 2011. We first performed a beneficiary-level analysis, comparing the probability (after adjusting for patient characteristics) that an FFS versus an MA beneficiary would have at least 1 home health episode. For those who received home health, we also compared the total number of days enrolled and 7 clinical outcomes that are publicly reported by CMS on the “Home Health Compare” website: improvement in the 3 measures of activities of daily living; acute care hospitalizations; and improvements in pain, dyspnea, and management of oral medications.

 Each of these measures is risk-adjusted (per CMS specifications) using the results of the OASIS assessment at the start of care.

 With the exception of the hospital utilization measure, outcomes are defined by comparing end-of-care and start-of-care OASIS assessments, and are defined only for patients who remain living in the community at the end of the episode.

We first compared each study endpoint as adjusted averages across the FFS and MA populations. Then, to better understand patterns of utilization differences between FFS and MA, we compared FFS and MA beneficiaries within each of the 306 hospital referral regions (HRRs). HRRs were chosen as the geographic unit of interest because the system partitions the country into mutually exclusive regional healthcare markets, which were felt to be the best available approximation for regions serviced by home health agencies.

Using the 100% OASIS file, we grouped start-of-care and end-of-care assessments into episodes according to CMS specifications.

 OASIS episodes represent a complete cycle of care that ends only when a patient is hospitalized, dies, or is discharged to the community. An OASIS episode may encompass 1 or more 60-day billing episodes, as defined by the home health prospective payment system.

 To each OASIS episode (observation), we merged in data from the  100% denominator file and from a file containing Hierarchical Condition Category (HCC) scores (which represent the ratio of an individual’s expected cost to that of an average beneficiary).

The Medicare health insurance claim number or the combination of Social Security number and date of birth were used as unique identifiers.  We also added demographic data from the American Community Survey, using 5-year estimates for 2007 to 2011, by linking via the zip code of each beneficiary.

Because patients who were dually eligible for Medicare and Medicaid may have received home health benefits through Medicaid—which would not have been visible to us&mdash;we excluded dual-eligible beneficiaries from the analysis. All others were included.

We used logistic regression with robust standard errors to model the odds of receiving home health and the odds of each of the 7 clinical home health outcomes. We used a generalized linear model with a log link and Gaussian distribution to model the geometric mean number of days enrolled in home health (for those who received care). For models of utilization, risk adjustors included age, sex, HCC score, and 2 socioeconomic indicators from the 2007 to 2011 American Community Survey: deciles of median household income in the Zip Code Tabulation Area (ZCTA) and the proportion of households in the ZCTA with only 1 resident (among those with 1 or more residents aged ≥65), as surrogates for the beneficiary’s income and the probability that they live alone. For home health duration, we used 2 additional risk adjustors: whether the initial home health episode started after a hospitalization (postacute) and whether it followed a surgical procedure. For each clinical outcome, we used an extensive set of outcome-specific risk adjustors (derived from the baseline OASIS assessment) that is specified by CMS for the Home Health Compare program.

To generate region-specific standardized utilization rates, we used generalized linear mixed models (with logit link for the probability of receiving home health and log link for home health duration) to model home health use as described for the national analysis, but added a random intercept for HRR. We then aggregated recycled empirical Bayes predictions to create a standardized utilization rate (or mean duration) for MA and FFS, for each of the 306 HRRs.

 We presented our results graphically by plotting each region’s FFS utilization rate against the MA utilization rate, and reported the correlation coefficients for MA versus FFS across the 306 HRRs.

In keeping with recent reports by MedPAC and the IOM, we quantified regional variation by reporting the ratio of the 90th percentile versus the 10th percentile of regional utilization.

 We reported these ratios separately for the FFS and MA populations.

After excluding 8,254,838 individuals dually eligible for Medicare and Medicaid, we identified 30,837,130 FFS beneficiaries and 10,594,658 MA beneficiaries. Baseline characteristics and unadjusted home health utilization rates are summarized in 

. Among the general population, MA patients were slightly older, whereas in the population using home health, the opposite was true. HCC scores were higher for MA in both populations.

Of those enrolled in FFS, 6.3% had at least 1 home health episode; of those in MA, 3.9% did. Among FFS beneficiaries who used home health, the average number of enrolled days was 75.8; among MA beneficiaries, it was 56.3 days. Although MA patients using home health had higher HCC scores than their FFS counterparts (suggesting a higher disease burden), they had less baseline disability according to each of 6 different measures at the start-of-care OASIS assessment. Home health more commonly started after a hospitalization and/or a surgical procedure for MA patients.

FFS Versus MA: Adjusted National Estimates

. We estimated that among the general Medicare population, the odds of starting home health during 2010 were 1.82-fold higher for those enrolled in FFS versus MA, after adjusting for patient characteristics and socioeconomic indicators. Among those who did start home health in 2010, we estimated that the FFS patients were enrolled an average of 34% more days than their MA counterparts, after risk adjustment.

The relationship between FFS versus MA, in regard to clinical home health outcomes, was inconsistent, and observed differences were small in magnitude: FFS outcomes were better with regard to pain (odds ratio [OR], 1.08; 95% CI, 1.07-1.09), ambulation (OR, 1.01; 95% CI, 1.00-1.02; 

 = .03), and management of medications (OR, 1.06;  95% CI, 1.04-1.07); and MA outcomes were better with regard to bed transfer (OR, 0.98;  95% CI, 0.97-0.99), dyspnea (OR, 0.99;  95% CI, 0.98-1.00; 

 = .02), and hospital utilization (an adverse outcome) (OR, 1.16; 95% CI, 1.15-1.17). Improved bathing was not significantly related to FFS versus MA enrollment (OR, 1.00; 95% CI, 0.98-1.02). Hospital utilization, which measures whether home health ended in hospitalization rather than discharge to the community, was the outcome with the greatest difference (favoring MA). Of note, this outcome is not adjusted for duration of home health. Because FFS patients—who have longer home health episodes&mdash;spend more time at risk for hospitalization while enrolled, this relationship may be confounded.

Among the 10% of HRRs (ie, 31 of 306 HRRs) with the highest proportions of FFS beneficiaries with 1 or more home health episodes (90th-percentile FFS), we estimated that 9.4% of all FFS beneficiaries used home health, whereas among the 31 HRRs with the lowest proportion (10th-percentile FFS), only 4.2% did. The ratio of the 90th percentile/10th percentile for FFS was, therefore, 2.2. For MA patients, the 90th percentile for use of home health was 6.3%, the 10th percentile was 2.2%, and the ratio was 2.8. The higher 90th/10th percentile ratio for MA means that by this measure, regional variation in home health use was higher for MA patients than for FFS patients.

An analogous evaluation of the duration of home health episodes yielded the following results: in the 90th-percentile HRR (with regard to average episode duration), mean duration was estimated to be 92 days for FFS and 67 days for MA. The 10th percentile was 39 days for FFS and 31 days for MA. The ratios of 90th/10th percentiles were 2.4 for FFS and 2.2 for MA.

 shows the standardized proportions of FFS and MA beneficiaries receiving home health in each of the 306 HRRs. 

 shows the analogous mean (standardized) duration of home health episodes. These figures illustrate the correlation between FFS and MA rates of home health use (Figure 1), and average home health duration (Figure 2) across regions. Correlation coefficients for MA versus FFS across regions were 0.51 for the proportion using home health and 0.94 for mean duration, meaning regions with longer home health episodes among MA patients also tend to have longer episodes among FFS patients and that this relationship exists to a lesser extent with regard to the probability that an MA or FFS beneficiary in a given region will use home health.

We have shown that Medicare beneficiaries enrolled in FFS use substantially more home health than those in MA, both in terms of the proportion of beneficiaries who receive care and the duration of home health among those who do. This is true after adjusting for age, disease burden, and regional differences in household size and income. We found that despite the average home health episode duration being 34% longer for FFS, differences in clinical outcomes were small in magnitude and inconsistent. Of the 7 outcomes that we measured, FFS fared slightly better on 3, MA fared slightly better on 3 others, and there was no difference on 1.

Assertions of “fraud, waste, and abuse” in the home health industry are based on data from the FFS program and have been made in the context of a history of rapid fluctuations in home health use that have closely paralleled changes in home health agency reimbursement policy.

 In 1997, after Medicare spending for home health quadrupled over a 5-year period, cost-based reimbursement was abruptly replaced with a forerunner of today’s home health prospective payment system, whereupon spending and utilization rates were cut in half within a year. Subsequently, utilization and expenditures have again crept upward, and since 2002, total FFS home health expenditures have nearly doubled.

The MA versus FFS comparison of home health utilization across regions is of interest because MA plans, which are paid a capitated rate to cover all needed services, have a direct incentive to minimize financial costs, whereas there is no real equivalent incentive for FFS. It is unclear how financial incentives might affect an MA plan’s “propensity to provide home health.” On the one hand, MA plans might be expected to minimize the direct costs of paying home health agencies and restrict the use of home health. On the other, they might substitute home health for more costly inpatient services and be more inclined to promote its use. Whichever the predominant effect, whether MA enrollees actually use less home health than their FFS counterparts also depends on the proportion of FFS home health that is truly wasteful and the practical ability of MA plans to curb such waste. Small studies from the pre-prospective payment system era suggested that Medicare health maintenance organizations (the forerunner of today’s MA plans) used substantially less home health than their FFS counterparts at the time, and our study confirms that this is still true (albeit to a lesser degree).

Regional variation is of interest because it provides insight into the practical limitations to optimizing the use of a service: if MA plans were perfectly able to optimize health outcomes or their own financial interests, then unexplained regional variation (ie, beyond what can be explained by differences between patients) would not be expected. The strong correlation between the use of home health by MA and FFS patients within regions therefore implies that MA plans’ ability to manage utilization is limited; MA plans limit use, but cannot entirely overcome the same regional forces (eg, healthcare market, local practice styles) that drive variation across regions in the FFS population.

We found a greater correlation between the duration of home health episodes for MA versus FFS patients within a region compared with the regional correlation in the proportion of beneficiaries enrolled in MA versus FFS who receive any home health at all. One explanation might be that home health agencies have more leverage in determining duration of care than they do in determining whether beneficiaries are referred to them. If most home health agencies treat a mix of MA and FFS beneficiaries, and if they provide care in a “payer-blind” way, then this finding might be expected.

There has been little previous study of regional variation in the use of a service by MA beneficiaries. One study did compare the use of cardiovascular procedures across 32 of 306 HRRs and found regional FFS and MA rates to be correlated, albeit less so than we find here.

 An IOM report has also noted regional correlation (0.66) in total per-capita spending.

 Regional variation among the FFS population has been extensively studied, but we found more variation in FFS home health use than has been recently described for most other services or for postacute care in general.

 We noted that postacute care encompasses more than home health and that not all home health is post acute. However, a 1996 study of FFS home health use during an era prior to the prospective payment reform found a degree of regional variation that appears to be on par with what we find here (methodological differences preclude an exact comparison).

We cannot directly determine whether greater home health use by FFS beneficiaries means that they receive unnecessary care or whether MA beneficiaries are not receiving necessary care; it is likely that both are sometimes true (as would sometimes be the converse). Measurement limitations notwithstanding, our finding that MA and FFS patients have similar clinical outcomes suggests that the shorter MA episodes may not be detrimental to health. We do not address patient preferences, however; nor do we explore the clinical effect of differing rates of home health use (compared with differing episode durations).

Our study has several other limitations. First, because MA plans have a financial incentive to select for healthier patients in ways that are not captured by CMS payment models,

 it is possible that unobservable differences between the populations remain after risk adjustment. Although this could account for some of the difference that we see in a beneficiary’s probability of receiving home health, we would expect less influence on our estimates of home health duration or outcomes, which are conditional on receiving home health and which use a larger set of observables (from OASIS) for risk adjustment. Second, functional outcome measures rely on clinical assessments at the start and end of each home health episode. Because each agency’s performance on these measures is publicly reported, there may be an incentive for agencies to underestimate functional status at the start of care or overestimate it at the end of care, biasing the results toward better outcomes, which would have the effect of minimizing any true outcomes differences between the FFS and MA populations. Moreover, since the end-of-care assessment does not take place when patients die or are transferred, outcomes are only measured for patients who are discharged back to the community at the end of home health. Finally, we do not have a direct window onto the actual number of home health visits that a patient receives, and rely instead on the duration of a home health episode as a proxy. We cannot be certain that FFS and MA beneficiaries receive the same number of visits per unit of time enrolled in home health.

MedPAC has reported to Congress that the FFS system is not structured to limit wasteful use of the home health benefit.

 Our study uses MA as a counterexample of care under an alternative reimbursement structure. The Government Accountability Office cites the lack of standards for what constitutes appropriate and necessary care as one of the challenges to curbing excess. Although we do not offer such a standard, we suggest that the type of within-region, between-payer comparison performed here can help to better define a reasonable range of use and to identify high-use and low-use regional outliers (for both FFS and MA). This methodology may be more widely applicable using MA encounter data, which are still being collected.

The authors acknowledge Patty Smith for editorial assistance, Cheryl Damberg and Rodger Madison for assistance with data access, and Emmett Keeler and Jeffrey Wasserman for comments on a previous version of this paper. 

Department of Emergency Medicine (DAW) and David Geffen School of Medicine (NSW, DAG), University of California, Los Angeles, Los Angeles, CA; RAND Corporation (DAW, LM, CMS, MH, NSW, DAG), Santa Monica, CA; University of Michigan Medical School (LM), Ann Arbor, MI; VA Ann Arbor Healthcare System (LM), Ann Arbor, MI; VA Greater Los Angeles Healthcare System (DAG), Los Angeles, CA.

The analyses upon which this article is based were performed under contract HHSM-500-2005-00028I, task order number HHSM-500-T0004, entitled “Evaluation and Development of Outcome Measures for Quality Assessment in MAOs and SNPs,” funded by CMS and HHS. The content of this article does not necessarily reflect the views or policies of HHS, nor does the mention of trade names, commercial products, or organizations imply endorsement by the US government. The author assumes full responsibility for the accuracy and completeness of the ideas presented.

Dr Waxman was supported by the Department of Veterans Affairs (VA) Office of Academic Affiliations through the VA Health Services Research and Development Advanced Fellowship Program; he had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Ganz was part of the project team on contracts awarded to RAND from CMS and the Health Services Advisory Group, and was part of the project team on a contract awarded to UCLA from SCAN Health Plan. 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 (DAG, MH, LM, DAW); acquisition of data (DAW); analysis and interpretation of data (DAG, MH, LM, CMS, DAW); drafting of the manuscript (LM, CMS, DAW); critical revision of the manuscript for important intellectual content (DAG, LM, DAW); statistical analysis (CMS, DAW); obtaining funding (DAG, DAW); administrative, technical, or logistic support (MH); and supervision (DAW). 

Daniel A. Waxman, MD, PhD, RAND Corporation, 1776 Main St, Santa Monica, CA 90407. E-mail: dwaxman@rand.org.

1. Medicare Advantage. Kaiser Family Foundation website. http://kff.org/medicare/fact-sheet/medicare-advantage/. Published May 11, 2016. Accessed February 13, 2016.

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