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How Medicare Advantage Plans Use Data for Supplemental Benefits Decision-Making

The American Journal of Managed CareApril 2022
Volume 28
Issue 4

This article presents findings from interviews conducted with executives from 29 Medicare Advantage plans regarding plan decision-making processes related to new social risk factor–related benefits.


Objectives: Health care payers are increasingly experimenting with interventions to address social risk factors. With enactment of the 2018 Bipartisan Budget Act, Medicare Advantage (MA) plans have new opportunities to offer supplemental benefits that are not “primarily health-related.” This article presents findings from interviews conducted with executives from MA plans regarding plan decision-making processes related to new social risk factor benefits.

Study Design: Semistructured qualitative interviews with MA plan leadership.

Methods: A total of 63 plan representatives from 29 unique MA plans were interviewed about the rationale for social risk–related interventions and how data are used to inform benefits expansion decisions. This paper combines qualitative interview data from 2 separate studies with similar target groups and interview guides. Interview transcripts were qualitatively analyzed to examine underlying themes.

Results: Three main themes emerged: (1) Plans use multiple data sources to determine how to target benefits; (2) evidence gaps hinder decision-making to expand or offer new supplemental benefits; and (3) in the absence of sufficient evidence, some plans have their own research and quality improvement processes to maximize effectiveness.

Conclusions: Findings provide insights about opportunities and challenges that MA plans face in making decisions related to supplemental benefits designed to address members’ social risk factors. Barriers include collecting, generating, and analyzing data critical to informing investments. Results highlight the need to ensure interoperability of new and existing data sources, foster shared learning opportunities, and narrow evidence gaps about specific social care interventions to inform the design and implementation of effective supplemental benefits.

Am J Manag Care. 2022;28(4):e132-e139. https://doi.org/10.37765/ajmc.2022.88866


Takeaway Points

Despite enthusiasm around expanding Medicare Advantage plans’ supplemental benefits to social care programs, innovation or activity has been slow.

  • Plans use multiple data sources to determine how to target benefits, and evidence gaps persist, which hinder decision-making to offer new supplemental benefits.
  • In the absence of sufficient evidence, plans have their own research and quality improvement processes to maximize effectiveness.
  • These findings highlight the need to ensure interoperability of new and existing data sources, foster shared learning opportunities, and narrow evidence gaps about specific social care interventions to inform the design and implementation of supplemental benefits.


As evidence about the impact of social drivers of health grows, opportunities to address patients’ social and economic adversity—such as food and housing insecurity and the lack of social connections1,2—have increased across the US health care sector. Medicaid plans have been at the leading edge of developing payment models that incentivize or otherwise support social care, including expanding opportunities for plans and affiliated providers to provide nonmedical services and supports related to food and housing.3,4 New opportunities are emerging in Medicare5 to address the barriers to achieving good health that many older adults experience, such as poverty,6 social isolation,7,8 food insecurity,9 and transportation challenges.10,11

The 2018 Bipartisan Budget Act, which included the Creating High-Quality Results and Outcomes Necessary to Improve Chronic Care Act (referred to as the CHRONIC Care Act),12 and the 2018 CMS call letter (an annual set of guidelines and clarifications for Medicare Advantage [MA] plans) announced greater flexibility for MA plan supplemental benefits to address social drivers of health. MA plans, which currently enroll one-third of all Medicare beneficiaries,13 are required to cover traditional Medicare services, but they may also cover services not traditionally available under Medicare. MA plans now have further flexibility in offering supplemental benefits. For the first time, the federal government has authorized MA plans to offer supplemental benefits that are not “primarily health-related”14,15 and has given MA plans more room and discretion to design supplemental benefits that “have a reasonable expectation of improving or maintaining the health or overall function”16 of members.

Criteria for benefit structure were also expanded, such that benefits should meet at least 1 of the following criteria: diagnose, prevent, or treat an illness or injury; compensate for physical impairments; act to ameliorate the functional or psychological impact of injuries or health conditions; or reduce avoidable health care utilization.17 Benefit offerings under the expanded structure can be diverse, including meal delivery, home modifications, personal support services, and cooking classes,5,15,18 and they can be targeted toward groups at highest risk of poor health, as the CHRONIC Care Act waived the requirement of uniform access to benefits.5 Plans therefore have the option to target these benefits to enrollees with specific chronic conditions.19 Importantly, insurers do not receive additional funds to offer supplemental benefits. Supplemental benefits can be paid through charging all MA enrollees a premium, through cost sharing, or through a plan’s rebate dollars.20

Our previous research has found that adoption of new or expanded offerings among MA plans has been gradual and limited.5,18,20 However, research has yet to examine the underlying reasons for this slow uptake. Although MA plans have a role in addressing the social risks of their members, little is known about how plans address these risks and how they collect data to inform their approaches to addressing social risk. This paper addresses this gap in the literature by explicitly exploring MA plan decision-making, including how plans anticipate defining eligibility parameters around at-risk populations and how plans collect and apply social risk data to improve health outcomes.


This paper combines data from 2 separate qualitative studies (study 1 and study 2), both of which involved interviews with MA plan leaders. Study 1 examined how MA plans address the health of MA plan members; how they design, implement, and assess the efficacy of new programs and benefits; and plans’ receptivity to alternative payment models. Study 2 explored how MA plans were using the additional flexibility offered by CMS to direct supplemental benefits toward supports that address health-related social risk factors in their 2020 bid cycles and what shaped the plans’ decisions to offer new or expanded benefits. Although these 2 studies were funded, designed, and conducted independently, interview topics described in this report overlapped. In total, the 2 studies involved interviews with 63 leaders from 29 different MA plans. Recruitment and data collection from each of the 2 studies and the combined analysis methods are described below.


The research teams conducted semistructured interviews with health plan leaders in 2 qualitative studies. Study 1 interviewed 38 representatives from 17 plans; study 2 interviewed 25 representatives from 14 plans, yielding 31 total plans. Two plans overlapped across both studies, accounting for 16 of the 63 total participants. The 29 unique plans together cover 75% of the national MA population. The study 1 research team employed a purposive and snowball sampling strategy. Initially, participants from plans of varying size, geographic location, and quality rating were purposively recruited, and interview participants were asked for recommendations of potential participants from other plans at the completion of each interview. The study 2 project team used a random sampling approach to identify plans from a complete list of MA plans nationally, ensuring representation of plans from the following categories: geographical coverage and location, special needs plan (SNP) vs non-SNP, tax status (nonprofit vs for profit), and plan type (eg, health maintenance organizations, private fee-for-service plans). In instances in which the study team received no response to a request for an interview after multiple outreach attempts, the next plan was randomly selected from the list of MA plans. Twice when initial outreach attempts failed, plan representatives were contacted through professional network connections using a convenience sampling approach.

Interview guides in both studies included questions focused on health plans’ responses to the CHRONIC Care Act and on factors shaping plan decision-making around offering new or expanded supplemental benefits. Common interview guide topics included organizational strategy and work regarding social risk factors; eligibility for benefits that address social risk factors; current and future plans around expanding or offering new supplemental benefits that address social risk factors; new benefit decision-making processes; evaluation plans and research capacity; and barriers and facilitators to new or expanded benefit offerings. Both interview guides are included in the eAppendix (available at ajmc.com). Both study teams piloted their respective interview guides with leaders from a health plan, and both guides were subsequently refined for clarity.

Recruitment and interviews were conducted via telephone between July and November 2018 (study 1) and between April and November 2019 (study 2). The study 1 team conducted interviews immediately after the CHRONIC Care Act was passed; interviews for study 2 were conducted while organizations were planning 2020 bids in response to the CHRONIC Care Act. This approach allows for a longer range of time from which to understand MA plans’ decisions regarding supplemental benefits and provides an opportunity to understand more anticipated responses from plans (study 1) and more real-time responses from MA plans in the midst of supplemental benefits planning and implementation (study 2).


In both studies, interviews were conducted with executive leadership team members (eg, chief medical officer, chief executive officer, chief organizational officer, department heads, or institutional focal points for enrollees’ social risk factors) who could speak to overall strategy and mission related to addressing social risk factors, as well as to the plans’ bid processes. When more than 1 representative was interviewed in any given plan, group interviews were conducted. Of all plans approached in study 1, 12 plans did not respond to the request for interview and 2 refused to participate. In study 2, 5 plans did not respond to the request for interview and 1 refused to participate.


Interview participants were approached by email or phone with a request to participate in an interview focusing on MA and social risk factors. Interviews were conducted by 1 to 2 members of each research team by phone. Interviews ranged between 30 and 60 minutes; the mean interview length was 45 minutes. Participants provided verbal consent to record the interview, and the audio recordings were transcribed before analysis. The institutional review boards at the study 1 and study 2 institutions exempted the qualitative study from ethical review, as it was not considered human subjects research.


Both teams followed a similar content analysis approach for initial transcript review.21 Initial coding trees based on the study-specific interview guide topics were discussed using a consensus-based team approach to identify major concepts. Two coders individually coded each transcript, met to reconcile codes, and drafted a master codebook for use in the next stage of coding. Audit trails of ongoing decisions including revision of existing codes and emergent codes were kept for both sets of interviews to ensure analytic rigor.

For this cross-study analysis, 2 members from each research team reviewed a random sample of the partner study’s transcripts and engaged in open coding. After developing a preliminary codebook for themes emerging from sets of interviews, all 4 coders reviewed the separate codebooks for relevant overlapping codes and discussed reasoning for any discrepancies before reaching a consensus on a shared codebook. Coded excerpts from interviews were shared with study partners for review and analysis.

To ensure anonymity of the participating MA plans, organizations were identified by unique numbers assigned in the study. The label “ID” for identifier represents a unique MA plan, and ID numbers were assigned to study 1 and study 2 participants. This study is reported in accordance with the Consolidated Criteria for Reporting Qualitative Research reporting guideline.22


Three main themes emerged across both studies: (1) Plans use multiple data sources to determine how to target benefits; (2) evidence gaps hinder decision-making to develop new or expand existing supplemental benefits, and (3) in the absence of sufficient evidence, participants recognize they will need to implement their own research and quality improvement processes to maximize effectiveness. These themes are described below, and supportive quotes are included in Table 1, Table 2 [part A and part B], and Table 3 for themes 1, 2, and 3, respectively.

Plans Use Multiple Data Sources and Considerations to Determine How to Target Benefits

Plan representatives described using multiple sources of data, including patient-reported data, patient-level purchased consumer data, and population-level data, to inform decisions about eligibility and services. Some plans use both individual and population-level data sources to stratify risk and determine eligibility for social care benefits. These methods included building predictive models using different social risk factors (eg, food insecurity), behavioral risk factors (eg, depressive symptoms), and medical risk factors (eg, chronic renal failure) in efforts to proactively identify “at-risk” enrollees (ID6, study 1), as well as hot-spotting techniques combining socioeconomic, medical, and demographic data (ID17, study 1). Actively screening for social risk factors in different health care settings was another strategy used to gauge members’ needs (ID6, study 1). Informants also described using community needs assessments as important data sources to better understand plan membership in a particular coverage area (ID13, study 1). Some plan representatives mentioned exploring relationships with community-based organizations to acquire and leverage data points to more accurately identify community needs and social risk factors (as noted by ID7, study 2). Several plan representatives mentioned taking the opportunity to build data ecosystems or data banks that pull in data from multiple sources to target member needs more accurately, leveraging data sources such as claims data, provider data, or third-party vendor data (ID1; ID7, both study 2). See Table 1 for supportive quotes.

Evidence Gaps Hinder Decision-Making to Expand or Offer New Supplemental Benefits

Although plan representatives acknowledged the importance of evidence-based approaches to decision-making, they highlighted the need for better data to build the evidence base around which social needs to target and what interventions are effective in addressing social risk factors. Plan representatives recognized that some of the data generation linking social needs, interventions, and outcomes needs to happen externally and is beyond the scope of what plans can realistically do themselves. Participants articulated that the current national evidence base is not robust enough to inform plan decisions. In particular, plan representatives expressed the challenge of obtaining “cause and effect” data to inform investment decisions (ID1, study 2). Plan representatives noted compelling evidence linking food insecurity and housing instability to health outcomes (ID8, study 2), which facilitated internal decisions related to including benefits targeting these 2 social risk factors.

Plan representatives mentioned the difficulty in the health care domain of attributing which inputs generate improvements in health outcomes and the lack of evidence to support the granular data that are needed to effectively guide plan decision-making. The challenge of investing resources using rebate dollars was highlighted among several interviews. Rebate dollars are one way to finance supplemental benefits, and typically rebate dollars are invested specifically into benefits that will have the greatest impact on health outcomes and generate a return on investment (ROI) (ID14, study 1). Some representatives described having to make more pragmatic decisions in the absence of data, basing decisions to offer new or expanded benefits more on whether it would be a market opportunity for the plan, such as whether a certain benefit will be competitive in a particular market segment or have high uptake among enrollees (ID16, study 1; ID14, study 2). One plan representative expressed the need for more guidance or improved tools to calculate the potential ROI of interventions targeting social risk factors to synthesize emergent evidence (ID10, study 2). See Table 2 for supportive quotes.

In the Absence of Sufficient Evidence, Participants Recognize They Will Need to Implement Their Own Research and Quality Improvement Processes to Maximize Effectiveness

To address the challenge of limited data, MA plans are developing pilot programs to generate and test the feasibility of implementation and new benefit effectiveness as a means to shape future benefit design. Plan representatives noted that internal research and quality improvement processes are important to have in place to maximize learning from experimentation in offering benefits that address social risk factors. Plan representatives described this as the process of learning from what they are doing, tweaking processes and benefit offerings accordingly, and measuring outcomes (ID1, study 1).

Several plan representatives shared that their plan had a systematic internal procedure to review evidence and impact to steer modifications to benefit offerings (as noted by ID12, study 1); however, many plans lacked the ability or resources to build internal research capacity. Plan representatives with internal research capacity and infrastructure emphasized that data on social needs have only very recently been collected internally and that it takes time to build a credible data set on which actuaries base their decisions (ID11, study 2). Correspondingly, plan representatives noted that a longer time frame is needed to show an ROI when providing a benefit that addresses a social need compared with other outcomes such as preventing a hospital admission (ID1, study 2).

Operational and implementation data were noted as important information sources for MA plans, including uptake rates, acceptability of the benefit to members, and member engagement with the benefit over time (ID1, study 1; ID8, study 2). These data inform investment decisions or benefit modifications that are needed, as these are more granular data on whether a certain benefit works in reducing social risk factors among one target population more than another. Some plan representatives stated that once the benefit is offered, data collection processes are put in place to monitor uptake of the benefit among members and the impact of the benefit on health outcomes such as emergency department visits. In particular, one plan representative (ID7) from study 2 shared an example that internal monitoring of the implementation of a new meal benefit demonstrated low member uptake, leading the plan to only gradually expand the benefit and monitor utilization rates in 2019. See Table 3 for supportive quotes.


We leveraged 2 unique studies that involved interviews with MA plan executives to better understand plan decision-making around supplemental benefits to address social risk factors. Despite enthusiasm among plan representatives to expand benefits to social care programs, innovation or activity around the new supplemental benefits is slow, consistent with hypotheses and findings of several recent reports.23,24 There may be several reasons for the slow uptake, including lack of evidence, which in part stems from lack of data; not enough clarity and guidance from CMS about what is allowed; and insufficient time to develop thoughtful proposals.

Paucity of evidence around how to use existing data in determining which risk factors to address and for which populations within the member constituency is a feasible reason for plan hesitation to structure new benefits. This hesitation is reflected in the low 4.6% adoption rate of new special supplemental benefits for the chronically ill among MA plans in 2020.19 A recent analysis found that the largest areas of growth within benefit expansion were pest control, meal programs, and produce.19 Given the evidence gaps and lack of additional funding for providing new or expanded supplemental benefits, there may be hesitation among plans to invest additional resources into new benefits.5 MA plan representatives stated that limited evidence was available to guide decision-making about programs or benefits that address health-related social risk factors, including ROI in terms of both financial and health impacts. This has also been reflected in other recent studies among MA plan leadership.5 MA plans identified that both financial ROI and health impact ROI were important factors in decision-making about which services to offer or provide related to social determinants of health. Recent (2021) guidance from CMS has noted that spending on nonmedical services is generally expected to produce health returns and therefore lower health spending. Although the evidence base was considered strong for some health-related social risk factors (eg, food insecurity), there was less evidence on the operational implementation of these benefits and what the returns were financially or healthwise and over what time horizon. MA plan representatives stated that they need more granular, tailored evidence to make financial investments, with some representatives indicating that they were collecting some of this information internally.

Plan leaders also noted that gaps in the availability of data limited their ability to make informed decisions. A number of plan representatives in this study explained that they contract out services for data collection, monitoring, and evaluation efforts, particularly for evaluating pilot programs. It could also be valuable to set requirements for consistent and reliable MA plan encounter data for plans to learn to use data to drive decisions around modifications to benefit offerings to ensure better population reach or uptake and better outcomes. To advance this effort, consensus is needed on standardization of measures and metrics to drive plan decisions.

Although lack of data and financial resources may partially explain slow uptake of the new supplemental benefits, other reasons may be the relatively short period of time between the call announcement and the proposal deadline,23 a challenge reflected in several interviews in this study. CMS has an important role to play in setting requirements and guidelines to steer MA plans in this new process. The ability for MA plans to learn from each other is also crucial, and currently there are limited shared data mechanisms under the MA program. CMS could be an important player in setting requirements for shared data and in monitoring and evaluating emerging data on these new supplemental benefits. Slow uptake of new supplemental benefits may result, in part, from hesitancy on the part of plans to adjust their roles. In our prior work that used interview data from study 1,5 we found that among MA plan representatives interviewed, although there was significant interest in identifying ways in which MA plans can have greater social and health impacts, there were differences of opinion regarding the best approach. Whereas some plan representatives expressed a desire to offer supplemental benefits to meet members’ holistic needs, others preferred to engage with community-based organizations to provide support for members’ social needs. This lack of consensus across MA plans may contribute to limited benefit uptake.


There are several limitations to this work. First, although we were able to sample leaders representing 75% of the MA market, our findings may not represent the entire MA market in the United States. Second, plans may not have shared the full picture of their decision-making processes with respect to offering benefits or strategies to address members’ social risk factors, and qualitative interviews provide only a part of the picture with respect to this fast-evolving payer policy landscape. A strength of employing qualitative methods is that it is the only way to understand the contextual factors of MA decision-making. Although the interview guides were not identical, the advantages of combining data outweighed that limitation because they enabled us to develop a data set reflecting the opinions and perspectives of a larger set of MA plan leaders. Our study is among the few to provide insights into how a large sample of MA plans make decisions to address members’ social risk factors in response to the CHRONIC Care Act and covering a longer time range.


The CMS rule and CHRONIC Care Act together have provided the MA market with more flexibility to address nonmedical needs of chronically ill members. Uptake of this flexibility has been slow, overall, among plans. Emphasizing limited effectiveness evidence, our participants expressed caution about making long-term investments in this area. Efforts to strengthen data availability and interoperability will improve the quality of research and thereby support MA investments in social care programs. A coordinated research agenda would help target future research efforts in ways that can support public and private payers looking to advance social care initiatives.

Author Affiliations: Department of Family and Community Medicine, University of California San Francisco (LSS-Z, MT, LMG), San Francisco, CA; Brown University School of Public Health (EAG, JFB, KT), Providence, RI.

Source of Funding: This research project (study 1) has been made possible by a grant from the Non-Profit Finance Fund.

Author Disclosures: Dr Shields-Zeeman received personal grant funding from The Commonwealth Fund via the 2018-2019 Harkness Fellowship in Healthcare Policy and Practice.Dr Gottlieb is involved in a collaborative research project with Humana. 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.

Authorship Information: Concept and design (LSS-Z, EAG, LMG, KST); acquisition of data (LSS-Z, EAG, MT, JFB, LMG, KST); analysis and interpretation of data (LSS-Z, EAG, MT, JFB, LMG, KST); drafting of the manuscript (LSS-Z); critical revision of the manuscript for important intellectual content (LSS-Z, EAG, MT, JFB, LMG, KST); obtaining funding (LSS-Z, LMG, KST); administrative, technical, or logistic support (LSS-Z, JFB, LMG); and supervision (LSS-Z, LMG, KST).

Address Correspondence to: Laura S. Shields-Zeeman, PhD, MS, Netherlands Institute of Mental Health and Addiction, Da Costakade 45, 3521 VS, Utrecht, the Netherlands. Email: lshields-zeeman@trimbos.nl.


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