Making Health Equity a Sustainable Business Priority for Health Plans

INTRODUCTION

Driven by the moral imperative to address health disparities and objectives to improve health outcomes, many healthcare organizations have committed to achieving health equity. However, to create sustainable change, organizations must prove the return on investment (ROI) to build on this foundation by ultimately proving the ROI of their health equity efforts.

Doing so shows decision-makers why prioritizing the integration of health equity as a core business component will benefit the organization’s financial outcomes, value to patients, and long-term viability. In this paper, we define and evaluate the current state of the health equity continuum and explore how health plans can address operational hurdles to establish a business case for health equity and begin to integrate it across business priorities.

Defining the Health Equity Continuum

Health equity has long been a topic of conversation in the healthcare sector, emerging as a convenient buzzword. However, it is often discussed without substantive action or consistent prioritization beyond communitybased organizations. In recent years, there has been a shift in how US healthcare approaches health equity. The Centers for Medicare and Medicaid Services (CMS) recently incorporated health equity into the CMS Innovation Center’s 2030 vision. Given this and other signals, we expect to see this ethos continue to emerge in government requirements, such as Star Ratings and Medicaid incentives. With the broader acceptance and government focus on health equity, many plans are evolving from simply talking about health equity to taking tangible action to address disparities in care. Health plans fall into various stages of maturity across the health equity continuum. The four main stages we have identified are outlined and discussed below.

Expanding Health Equity Beyond Charity into ROI

The shift from pilot and charity-based efforts to ongoing operational and capability changes will require plans to prove health equity’s ROI to ensure continued investment in development. When health plans begin to promote health equity as a priority within their organizations, many approach it from a moral perspective and focus initial efforts on charitable and ideological support. Unfortunately, a charity-based approach is not enough to ensure that health equity endures as a priority within health plans. Health plans must not only show that addressing health disparities is the right thing to do, but also demonstrate that it will ultimately sustain the company’s bottom line.

To move along the continuum and make progress in reducing disparities, plans must develop new and tailored capabilities. These capabilities will require financial investment and proof of impact within the organization. Plans should consider ROI from the inception of their health equity framework development to design a thoughtful strategy that enables collection and production of the right data and reporting. Data-enabled approaches allow plans to serve members more effectively, monitor and measure financial and clinical impacts, and show where outcomes and costs are improving because of health equity efforts.

Anchoring organizational programs in reliable data and measurable goals will be critical to prove programs’ effectiveness and justify ongoing investment. For example, if a plan is interested in addressing the mental health crisis for their LGBTQ+ population, they may consider investing in different tools or partnerships that improve a member’s access to primary care and behavioral health providers (e.g., FOLX, Included Health, Plume). To substantiate the value of this investment, plans need to: (1) obtain reliable Sexual Orientation or Gender Identity (SOGI) data so they can identify their LGBTQ+ members and (2) identify measured outcomes that can validate the investment. Such measures include improved utilization of primary care and behavioral health providers, decreased psych inpatient admissions, and decreased acute behavioral health diagnoses. Proving these outcomes via data will emphasize the value of health equity investments for both financial stakeholders and members experiencing improved wellbeing via these targeted resources.

Tackling Health Equity Data Complexities

Whether it faces challenges with defining and measuring success, identifying data sources, or using data in analyses, every health plan will find itself in a unique set of circumstances when trying to develop health equity programming. To understand the problem and potential solutions, we have outlined some initial questions that plans can ask to begin framing problem areas and identifying associated solutions.

Defining and Measuring Success

Plans must find a way to determine the effectiveness of their programming and justify their investment by showing a positive return. In some instances, the definition of success may be straightforward. As an example, programming intended to decrease the maternal mortality rates among women of color should track disaggregated maternal mortality rates over time for a plan’s membership. It should leverage the plan’s existing race and clinical data to show the impact of the given health equity initiative on patient outcomes and associated pre- and post-natal health costs. In other instances, it may be much more difficult to quantify a problem and associated success indicators, such as addressing the declining mental health of the LGBTQ+ community. Even with adequate collection of member SOGI data, plans would need to identify the specific measures that prove the efficacy of their efforts and subsequently attribute savings or value attached to the improved mental health outcomes.

Questions to Ask:

• Are we seeking to change a specific health outcome or disparity?
• What other sources of information can show progress (e.g., member surveys)?
• What ROI will the programming need to yield to justify the investment in time and resources?

Data Sources and Aggregation

Health plans have a wealth of population and member-level data they can leverage to identify the existence of inequities. However, they often need additional data to segment their populations, understand root causes of inequities, and ultimately develop meaningful programming to close gaps in outcomes. These additional data points need to be from a reliable source, mapped to existing member data, and actively managed for accuracy.

Macro Population Data: Plans can use existing data, publicly available information, and proprietary data sources to develop an understanding of a population. However, this information will only get organizations so far in identifying and addressing needs at the individual level.

Micro Member Data: Plans can leverage macro population-level findings using member data (e.g., zip codes and social deprivation index), but they must keep in mind that this quantitative information does not indicate an individual member’s lived experience. Plans must find ways to systematically collect more qualitative, sensitive information from members (e.g., living conditions and existence of stressors) in order to better support them and ultimately improve their health outcomes.

Questions to Ask:

• What data does the plan have currently?
• What data is available publicly?
• What data is available for purchase?
• What data is relevant, and which data elements are highest priority?
• How can we systematically collect relevant data?
• Which other stakeholders or partners can collect and share data (e.g., providers, community-based organizations)?
• How can we collect, manage, and confirm the accuracy of this data?

Analysis and Interpretation

Once plans have qualitative and quantitative information ingested and stored, they must develop capabilities to analyze and interpret the data. With data coming from many different sources, the plan must ensure adequate ID management so that collected information about members can be aggregated into a singular set for analysis.

Another issue plans may face is the appropriate application and interpretation of the data they have collected. Plans need to discern how they can leverage different data types to understand either population or member-specific characteristics.

Population-level data may help a plan understand the existence of a problem for a larger segment of its membership but not necessarily provide insight into the needs of individuals. As an example, large member enrollment in a specific zip code may reveal a significant portion of membership lives in a food desert. However, it does not indicate which members are experiencing food insecurity, which members have issues with obtaining healthy/high-quality nutrition, or which members do not have any issues with acquiring healthy food.

Questions to Ask:

• What tools or platforms are required to aggregate and summarize information into an interpretable format?
• What information can be used to identify population issues versus member-level issues?
• What assumptions are being made within the interpretation of this data?
• With whom is data shared? What groups need to review data to build in accountability for change?

HealthScape Can Help

Our team has extensive experience supporting health plans in developing and integrating databased solutions across the healthcare ecosystem. Now a part of the Chartis portfolio of companies, HealthScape brings new expertise and capabilities from the Chartis Center for Health Equity & Belonging (CCHEB). The CCHEB is focused on creating a liberated healthcare system free of bias, discrimination, and disparities—resulting in equitable health for all. Together, we can help your organization define and sustainably address your health equity goals. The authors would like to thank Lee Swartz, Abby Schur and Bridget Halleran for their support and contributions to this article.