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The American Journal of Managed Care February 2017
Synchronized Prescription Refills and Medication Adherence: A Retrospective Claims Analysis
Jalpa A. Doshi, PhD; Raymond Lim, MA; Pengxiang Li, PhD; Peinie P. Young, PharmD, BCACP; Victor F. Lawnicki, PhD; Andrea B. Troxel, ScD; and Kevin G. Volpp, MD, PhD
Addressing the Chronification of Disease
Michael E. Chernew, PhD, Co-Editor-in-Chief, The American Journal of Managed Care
Economic Burden of Hypoglycemia With Basal Insulin in Type 2 Diabetes
Vivian Fonseca, MD; Engels Chou, MS; Hsing-Wen Chung, PhD; and Charles Gerrits, PhD, PharmD
Treating Medicaid Patients With Hepatitis C: Clinical and Economic Impact
Zobair Younossi, MD; Stuart C. Gordon, MD; Aijaz Ahmed, MD; Douglas Dieterich, MD; Sammy Saab, MD; and Rachel Beckerman, PhD
An Examination of the Relationship Between Care Management With Coaching for Activation and Patient Outcomes
Cindy Reistroffer, DSc; Larry R. Hearld, PhD; and Jeff M. Szychowski, PhD
Sustained Participation in a Pay-for-Value Program: Impact on High-Need Patients
Dori A. Cross, BSPH; Genna R. Cohen, PhD; Christy Harris Lemak, PhD; and Julia Adler-Milstein, PhD
Currently Reading
Value-Based Contracting Innovated Medicare Advantage Healthcare Delivery and Improved Survival
Aloke K. Mandal, MD, PhD; Gene K. Tagomori, BSc; Randell V. Felix, BSc; and Scott C. Howell, DO, MPH&TM
Perceptions of the Medical Home by Parents of Children With Chronic Illnesses
Emily B. Vander Schaaf, MD, MPH; Elisabeth P. Dellon, MD, MPH; Rachael A. Carr, BA; Neal A. deJong, MD; Asheley C. Skinner, PhD; and Michael J. Steiner, MD, MPH
Patient Characteristics and Healthcare Utilization of a Chronic Pain Population Within an Integrated Healthcare System
Robert J. Romanelli, PhD; Sonali N. Shah, RPh, MBA, MPH; Laurence Ikeda, MD; Braden Lynch, PharmD, MS, CPEHR; Terri L. Craig, PharmD, CPEHR; Joseph C. Cappelleri, PhD, MPH, MS; Trevor Jukes, MS; and D
Patients With Diabetes in Pay-for-Performance Programs Have Better Physician Continuity of Care and Survival
Chien-Chou Pan, MD, PhD; Pei-Tseng Kung, ScD; Li-Ting Chiu, MHA; Yu Pei Liao, MHA; and Wen-Chen Tsai, DrPH

Value-Based Contracting Innovated Medicare Advantage Healthcare Delivery and Improved Survival

Aloke K. Mandal, MD, PhD; Gene K. Tagomori, BSc; Randell V. Felix, BSc; and Scott C. Howell, DO, MPH&TM
Elderly Medicare Advantage members with multiple chronic conditions attained a survival benefit from more cost-effective care when a private plan developed gainshare and monetary risk-bearing arrangements with its contracted providers.
ABSTRACT

Objectives: In Medicare Advantage (MA) with its CMS Hierarchical Condition Categories (CMS-HCC) payment model, CMS reimburses private plans (Medicare Advantage Organizations [MAOs]) with prospective, monthly, health-based or risk-adjusted, capitated payments. The effect of this payment methodology on healthcare delivery remains debatable. How value-based contracting generates cost efficiencies and improves clinical outcomes in MA is studied.
 
Study Design: A difference in contracting arrangements between an MAO and 2 provider groups facilitated an intervention-control, preintervention–postintervention, difference-in-differences approach among statistically similar, elderly, community-dwelling MA enrollees within one metropolitan statistical area.
 
Methods: Starting in 2009, for intervention-group MA enrollees, the MAO and a provider group agreed to full-risk capitation combined with a revenue gainshare. The gainshare was based on increases in the Risk Adjustment Factor (RAF), which modified the CMS-HCC payments. For the control group, the MAO continued to reimburse another provider group through fee-for-service. RAF, utilization, and survival were followed until December 31, 2012.
 
Results: The intervention group’s mean RAF increased significantly (P <.001), estimating $2,519,544 per 1000 members of additional revenue. The intervention increased office-based visits (P <.001). Emergency department visits (P <.001) and inpatient hospital admissions (P = .002) decreased. This change in utilization saved $2,071,293 per 1000 enrollees. By intensifying office-based care for these MA enrollees with multiple comorbidities, a 6% survival benefit with a 32.8% lower hazard of death (P <.001) was achieved. 
 
Conclusions: Value-based contracting can drive utilization patterns and improve clinical outcomes among chronically ill, elderly MA members.

Am J Manag Care. 2017;23(2):e41-e49
Take-Away Points

This study tested the hypothesis that payer-provider risk contracting promotes high-value care.
  • In the future, clinicians increasingly will have to bear the monetary risks associated with healthcare utilization.
  • The Medicare Advantage program provides a unique milieu for investigating provider groups that have either risk-bearing or fee-for-service contracts with private health plans.
  • Full-risk capitation combined with a revenue gainshare agreement promulgated a clinical practice transformation at the provider group level, associated with increased office-based care and decreased hospital-based services.
  • The clinical practice transformation resulted in a 6% survival benefit and lowered the hazard of death by 32.8%.
  • Value-based contracting benefits all stakeholders of the Medicare Advantage program.
In caring for traditional Medicare beneficiaries, primary care physicians will need to transform their clinical practice and assume more fiduciary risk. CMS recently published “new policies to address and incentivize participation in alternative payment models” (APMs).1 An APM broadly can be defined as any reimbursement model other than strict fee-for-service (FFS). Currently, 30% of traditional FFS Medicare is reimbursed through APMs, with the goal of 50% involvement by 2018.2,3 Clinicians who want to become qualifying APM participants can expect to “bear more than a nominal amount of risk for monetary losses.”1 Through APMs and increasing risk assumption, these new policies aspire to promulgate high-value healthcare, as defined by “better care, smarter spending, and healthier people.”1,2 Whether FFS divestiture in favor of APMs and increasing capitation can generate cost efficiencies and also improve clinical outcomes remains debatable.4,5

Medicare Advantage (MA) provides an alternative to traditional FFS Medicare. It has been a commercial success, accounting for 17.5 million (30.6%) of all Medicare enrollees and $204.7 billion (28.9%) of Medicare’s 2017 gross spending budget.6,7 Because it is regulated by its own federal statutes,8 MA is classified as an “Other Payer APM” and excluded from CMS’ Proposed Rule for Medicare FFS.1 For over a decade, MA has used the CMS Hierarchical Condition Categories (CMS-HCC) payment model to reimburse private plans (Medicare Advantage Organizations [MAOs]) with prospective, monthly, risk-adjusted or health-based capitated payments for the care of MA enrollees. The value of subsidizing MA often has been challenged.9-14 Consequently, on October 3, 2016, CMS’ Innovation Center (CMMI) announced its Medicare Advantage Value-Based Insurance Design (VBID) model to test whether new initiatives can “improve health outcomes and lower expenditures for Medicare Advantage enrollees.”15

CMS adopted the CMS-HCC payment model with the concept that MAO recompense should reflect the disease and related cost burdens of the pertinent population and, thus, fundamentally changed how MAOs are reimbursed.16-19 In return for providing healthcare benefits to MA enrollees during the calendar year (CY), MAOs receive risk-adjusted payments during the following payment year (PY), modified by a Risk Adjustment Factor (RAF). Along with demographic data, specific diagnosis codes grouped into HCCs and associated with increased future expenditures impact the RAF. Certain HCC-HCC and demographic-HCC interactions have further additive effects on the RAF. Recalibration of the model occurs every 2 years so that the “typical” FFS Medicare beneficiary’s total RAF is 1.00.20 Therefore, when compared with an FFS Medicare beneficiary, a healthier MA enrollee’s total RAF would be less than 1.00 and a sicker one’s would be greater than 1.00.

The capitated revenue for MAOs is dependent on how its contracted providers document and code. For example, based on calculations derived from CMS’ most recent policy update and data on both MA enrollment and risk-adjusted benchmark rates,6,21,22 each 0.1 of the RAF has a nationally averaged valuation of $74.85 per member per month (PMPM), or $898.20 per member per year (PMPY). If providers document and code for a specific diabetic complication in CY2016, an MAO could anticipate a risk-adjusted revenue in 2017 (PY2016) that is 3-fold greater ($238.02 PMPM or $2856.28 PMPY) than if diabetes were coded without any specified complication ($77.84 PMPM or $934.13 PMPY). Assuming diagnostic coding accuracy, an MAO would be reimbursed at a higher rate during the PY for enrolling sicker MA members during the CY.

The CMS-HCC payment model thus should provide incentives to MAOs that reward continuous, high-value care from its contracted providers.19,20 Of all the different types of MA plans available, coordinated care plans are the majority, of which health maintenance organizations (HMOs) account for 52% of all MA plans.6 In applying for an HMO-type MA plan, the MAO must include “a network of providers that are under contract or arrangement with the organization to deliver the benefit package approved by CMS.”8 How MAOs reimburse providers has gone without much input from CMS, and most providers prefer FFS reimbursement.8,23,24 For example, in Oregon, the HMO-type MA plan denoted by CMS contract #H3805 has capitated agreements with only a quarter of its contracted providers, and the rest are reimbursed on an FFS schedule (eAppendix [eAppendices available at www.ajmc.com]). If the MAO’s capitated reimbursement from CMS is greater than its FFS disbursement to providers, there might be no incentive to encourage high-value care.9,10,13,14

The primary aim of this study tested the following hypothesis: value-based contracts augment the CMS-HCC payment model’s ability to generate cost efficiencies and improve clinical outcomes. Because local provider groups remain the core business unit for both MAO revenue generation and overall cost-of-care management, a secondary aim was to delineate the specific clinical practice transformations implemented by a provider group pursuant to such contracting changes.

METHODS

Study Population, Study Design, and Provider Groups

The study population consisted of community-dwelling MA members 65 years or older, enrolled in 1 HMO-type MA plan (CMS contract #H3805),6 and ascribed to 1 of 2 provider groups for their primary care in the Portland, Oregon, metropolitan statistical area (MSA). These members had to be enrolled entirely through CY2008, which was the preintervention period (eAppendix and eAppendix Figure 1). The intervention occurred in CY2009 when the MAO’s contract with Provider Group A differed from that with Provider Group B (Table 1). Effective CY2009, Provider Group A became financially responsible for most healthcare services (full-risk capitation) for intervention-group members. In turn, starting in 2010 (PY2009), the provider group would receive most of the CMS-HCC risk-adjusted monthly capitated payment (RAF gainshare). In response, Provider Group A appointed an “HCC Physician Champion” (HPC) in CY2009. The HPC’s mission was to improve HCC documentation and coding, as well as to develop cost-effective approaches to primary care delivery. The contracting change and HPC assignation were considered a single intervention—all initiated in CY2009. In the control group, the MAO continued FFS reimbursement for Provider Group B. The postintervention period began on January 1, 2009, and ended on December 31, 2012, after which a revised CMS-HCC model (V22) was introduced and would have produced nonuniform changes in the RAF.25,26

Based on the timing of this intervention, an intervention–control, preintervention–postintervention, difference-in-differences (DID) approach among matching cohorts sought to determine the intervention’s effect on the RAF, utilization, and survival. This approach within 1 MSA inherently controlled for variations in care because of geography, time of enrollment, MAO plan administration, provider groups, and enrollee characteristics.27-29 A smaller, well-controlled study at the provider-group level with greater internal validity was chosen over one that compiled and summarized large datasets, which, although may provide greater external validity, would obfuscate exploring the specific clinical practice transformations implemented at the provider-group level. Provider Group A had 7 clinic locations, and Provider Group B had 5 locations, all within 2 to 18 miles from one another. Provider Group A had 25 primary care specialists (16 in internal medicine, 9 in family medicine) and Provider Group B had 19 (5 in internal medicine and 14 in family medicine). By the end of the study period, both provider groups were Oregon Health Authority–certified Patient-Centered Primary Care Homes (PCPCHs). Six of Provider Group A’s 7 clinic locations were PCPCH-accredited in December 2012, with the last one certified in 2014. All of Provider Group B’s clinic locations were accredited slightly earlier, in June 2012.

Propensity Score Model and Nearest-Neighbor Matching

Plan enrollment and provider-group election are by enrollee choice and subject to selection bias. Covariates known to affect healthcare utilization and expenditure include age, sex, ethnicity, original reason for Medicare entitlement, and disease burden.30 By not applying the appropriate preprocessing methodologies to ensure comparison of statistically similar groups,31 previous conclusions on MA healthcare delivery vis-à-vis traditional FFS Medicare may have been compromised.27,28,32-35 For example, past studies relied on self-reported survey data for health status and were subject to recall bias.27,28,32,34,35 This study’s proprietary access to full encounter claims data for MA enrollees facilitated 2 objective measures of disease burden: the Charlson Comorbidity Index (CCI) and the number of CMS Chronic Conditions Warehouse (CCW) categories (eAppendix). Using the aforementioned covariates, the logistic regression model created propensity scores for each member.31 Nearest-neighbor matching based on propensity scores created 2 well-balanced, statistically similar groups for subsequent analysis (eAppendix Table 1 and eAppendix Figure 2), as delineated in Table 1.

Statistical and Economic Analyses

Primary outcomes included: 1) RAF data at the member level obtained from CMS’ Monthly Membership Report, 2) utilization based on full-encounter claims data, and 3) survival data obtained from CMS’ Daily Transactional Reply Report (eAppendix). A linear regression model evaluated the RAF as a continuous variable. Poisson regression models examined utilization as count data. DID analyses compared postintervention (CY2009-CY2012) with preintervention (CY2008) data. For survival, the log-rank test—using either time-on-study or age of the enrollees on the time scale—evaluated Kaplan-Meier survival curves for the 2 groups.36,37 Permutation testing (randomization inference) validated the DID and survival analyses (eAppendix).38 Statistical analyses were performed within the R statistical computing environment (R Foundation for Statistical Computing, Vienna, Austria).

RAF-based revenue was estimated using CMS’ published rates for Multnomah County for each year (eAppendix).22 FFS Medicare expenditures extracted from the Medical Expenditure Panel Survey data files provided cost-of-care data according to place of service (eAppendix). Since the last CMS-HCC-derived capitated payment was disbursed in 2013 (PY2012), all amounts were indexed to 2013 dollar values using the appropriate component of the Personal Healthcare Expenditure Index (eAppendix).

RESULTS

Effect on RAF

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
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