Collecting Data to Ensure Equity in Payment Policy

 

 

 

 

Collecting Data to Ensure Equity in Payment Policy

NAM Discussion Proceedings – published June 8, 2022

 

Background Information

The Center for Medicare & Medicaid Innovation (CMMI) was established as a key provision of the Affordable Care Act (ACA) to develop, test, and disseminate care and payment models to enhance health care quality and reduce spending. A decade later, with the combined learnings from more than 50 alternative payment models and the federal government’s commitment to expand access to care and lower costs, CMMI is building on and expanding that foundation to catalyze a “stronger and more sustainable path forward” (Brooks-LaSure et al., 2021). To this end, the National Academy of Medicine (NAM) and CMMI have cooperated on a two-phase initiative to engage leading authorities in comprehensive consideration of key learnings and opportunities as CMMI lays the groundwork for a broader transformation of the nation’s health and health care system. In Phase 1, a NAM-convened Expert Panel undertook a broad review of priority opportunities for CMMI to catalyze progress toward high-value, high-quality health and health care with enhanced effectiveness and efficiency in improving individual and population health. The Expert Panel issued a NAM-published Review outlining anchor commitments and action steps in support of CMMI’s role as a catalyst for change (NAM, 2021).

In Phase 2, the Expert Panel transitioned to serve as a Steering Committee to guide the NAM in developing and convening a discussion series designed to provide operational and action-oriented steps to help address critical issues and challenges in two areas: Multi-Payer Alignment on Value-Based Care and Collecting Data to Ensure Equity in Payment Policy. This Discussion Proceedings will highlight priority areas and key themes that arose throughout the meeting, focused on collecting data to ensure equity. Through a combination of individual presentations and open discussions, the discussion series engaged a range of field leaders and experts to describe the landscape of challenges and opportunities, highlight multi-stakeholder perspectives and examples of progress, and identify concrete steps to improve health system preparedness, effectiveness, efficiency, equity, and beneficiary experience. Through this work, six elements were identified as key component processes in CMMI’s approach to advancing work on multi-payer alignment and health equity in every activity:

  • signaling,
  • mapping,
  • measuring,
  • modeling,
  • partnering, and
  • demonstrating.

 

These elements are both aligned with and necessary to the achievement of the five Innovation Center Strategic Objectives outlined in the October 2021 Innovation Center Strategy Refresh white paper, namely:

  1. drive accountable care,
  2. advance health equity,
  3. support care innovations,
  4. improve access by improving affordability, and
  5. partner to achieve system transformation (CMMI, 2021).

 

Meeting Summary

The Landscape of Data Collection to Support Equity Keynote

Cara James, Grantmakers In Health, defined health equity as the fair and just opportunity to be as healthy as possible as measured by the reduction and elimination of health disparities and their determinants that affect marginalized groups. Important types of data that the ACA requires to help address health equity include race, ethnicity, primary language, sex, and disability. However, the Secretary of the U.S. Department of Health & Human Services has the authority to add other measures, including socioeconomic status, sexual orientation, and gender identity. A report from the National Committee for Quality Assurance and Grantmakers in Health funded by the Commonwealth Fund found that data to understand and advance equity are severely lacking across U.S. government departments and agencies, except for data collected by Federally Qualified Health Centers (GIH and NCQA, 2021). Additionally, a 2016 National Academies report titled Accounting for Social Risk Factors in Medicare Payments found a lack of data on the social drivers of health, social relationships and context, and environmental context (NASEM, 2017). More concerning, the Centers for Medicare & Medicaid Services (CMS) reported that race and ethnicity data in Medicaid in 22 states are unusable (CMS, 2022). In James’s view, data is also often missing at the local level for people of color, people who have disabilities, older people, and those living with mental and behavioral health issues.

James highlighted other considerations and challenges associated with collecting and using health equity data among CMS programs. First, data collection efforts could be more mindful of beneficiary and respondent burden in answering questions. Second, inconsistent data collection makes it hard to compare data, indicate and describe challenges, or even harmonize different aspects of data collected, such as different questions to beneficiaries about shelter and homelessness. Third, there is a lack of data collection standards, especially around race and ethnicity; James suggested a better data standard that is more congruent with the U.S.’s current population and a consistent execution strategy for collecting these data. Fourth, existing federal government data collection efforts have trouble screening for an issue versus measuring a problem. Fifth, James suggested educating the health professionals on best practices to collect data, helping people distinguish between real and perceived barriers, and ensuring people feel safe answering questions. Sixth, she noted the challenge of limited implementation resources, including the funding, staffing, and technical assistance required to collect, capture, analyze, and report data insights. Seventh, James noted the need for increased interoperability to ensure the data is transferable and shareable across different settings. Finally, she noted a window of opportunity due to a substantial amount of current attention placed upon advancing the health of marginalized populations. The present is the time needed to implement changes, keeping in mind that change will not happen overnight.

James suggested several paths forward to improve the collection and use of the critical data she discussed. She suggested the Health Equity Summary Score as a foundational measure upon which CMMI could build a comprehensive health data infrastructure nationwide. The multidimensional measure, built from Healthcare Effectiveness Data and Information Set, Consumer Assessment of Healthcare Providers and Systems, and Medicare Advantage measures, includes measures about dual-eligible beneficiaries and the ability to incorporate other factors, such as rural living areas or people receiving low-income subsidies (Agniel et al., 2021). The blended score accounts for current data as well as changes in these data over time and compares performance between groups to a national benchmark and within a health plan (Agniel et al., 2021).

This session also underscored the need to add race and ethnicity data to the Medicare Part C and D application, incorporating available data toward payment options to inform value-based payment efforts and drive health care value and better outcomes; enhancing CMMI’s ability to collect, analyze, and report the demographic data necessary to monitor and evaluate the impact of programs and policies; providing technical assistance and resources to ensure seamless implementation of CMMI models at the state level; engaging meaningfully the communities whose voices could be incorporated into these efforts; and conceiving and implementing a plan to build a usable data infrastructure.

 

Multi-Stakeholder Perspectives on Collecting Data to Support Equity

Following the keynote address by Cara James, five invited speakers with perspectives on collecting race, ethnicity, language, disability, sexual orientation, gender identity, and the social drivers of health data shared their practical implementation experiences in collecting and applying health data. They described new understandings of health data and its relationship to reforming payment policy and incentives, highlighted populations excluded from current efforts to inform future community engagement efforts and suggested best practices for CMMI to account for underserved communities with intersecting and additional needs.

 

Race and Ethnicity

David R. Nerenz, Henry Ford Health System, described Henry Ford Health System’s process of developing staff training to capture patient information on race, ethnicity, and language in electronic health records. The Epic-based system is flexible, allowing patients to type directly into medical records or fill out data. With information on over one million people, the data are used for research, quality improvement, and individual medical care. To build trust and proactively collect data to inform care delivery, Henry Ford’s efforts focused on medical records and patient care issues. Data were recorded as part of a health risk or general care management risk appraisal instead of insurance billing or payment processes. Nerenz indicated that CMMI could include race and ethnicity data for billing for Medicare patients hosted on an interoperable electronic health database, leverage data from private payers participating in collaborative quality initiatives or registries, and identify hospitals with the capability to transmit data.

Reacting to David Nerenz’s presentation, Gary Puckrein, National Minority Quality Forum, added that CMMI could find ways to align measurement efforts to calculate patient risk, standardize data collection, and improve data access with risk reduction, outcomes improvement, and preventing emergency hospitalizations. Meanwhile, Lenny Lopez, University of California, San Francisco, and Sean Cahill, The Fenway Institute, noted that one-off systems approaches are always challenging, including the lack of interoperability and divergent IT systems. It remains uncertain how health systems with differing financial and human resources can transform without expending resources on establishing electronic health record systems and training staff.

 

Preferred Language

Alice Hm Chen, Covered California, raised the responsibility of the health care delivery system to collect and standardize data to understand patients better, allocate care responsibly, adjust for risk, transform systems, and reduce administrative burden. In 2003, the California Legislature required the California Department of Managed Health Care to “adopt regulations ensuring access to language assistance and culturally competent health systems.” The legislature also required health plan services to assess beneficiary needs and “provide translation, interpretation, and culturally competent services” (Roat, 2005). Yet, according to Chen, the provision of interpreter services under this act has been variable, with only safety net systems achieving substantial progress. In 2023, according to Chen, California’s ACA health insurance marketplace Covered California will require health plans to collect preferred language data. This data will guide investments in interpreter services and systems navigation, benefiting members who speak Spanish and other languages, who are 20% of Covered California members. These services could also help beneficiaries with limited health literacy to obtain, understand, and use health-related information and services to make well-informed decisions for themselves or their next of kin (CDC, 2022). Chen indicated that CMMI could create improved standards for asking about patient demographic data and background questions. Additionally, Chen suggested that CMMI create unified expectations around the purpose and use of this data, especially related to its applications in population health and quality improvement.

Frederick Isasi, Families USA, noted that while fee-for-service health systems understand the need for data, they are concerned about the reputational impact of publicly reporting these data. Specifically, health systems may be considered if the data show that they are not sufficiently addressing disparities. Ignatius Bau, Independent Consultant, and Alice Hm Chen, Covered California, added that data collection should use Office of the National Coordinator for Health Information Technology interoperability standards to reduce respondent fatigue and administrative burden, especially in communities with reduced digital literacy and access to digital infrastructure, tools, and devices. Attendees also highlighted the potential of learning from efforts that train and certify interpreters in a more diverse set of languages without relying on a patient’s family and community. Attendees also discussed the advantages of training providers to frame and focus questions about language preference to bridge cultural divides that may arise due to stigma and unconscious bias and effectively meet patient needs.

 

Disabilities

Barbara Kornblau, National Disability Mentoring Coalition, discussed how current data on disability status or the need for accommodations is not actively collected or considered by the broader health system, negatively impacting patient care experiences. For example, people with disabilities might require different levels of support and treatment, such as physical accommodations for health screenings for people with different kinds of disabilities. The lack of data and visibility, as well as optional ACA standards for accessible medical equipment, makes it challenging to provide high-quality care across the care continuum for people with disabilities. Kornblau noted that CMMI could benefit from having qualitative and quantitative data illustrating the experiences of people with disabilities within health and health care systems.

Affirming Kornblau’s remarks, Michelle Doty Cabrera, County Behavioral Health Directors Association, stressed the importance of expanding data collection for individuals with intellectual and developmental disabilities and taking an intersectional approach to understanding health outcomes in various subpopulations who are more likely to experience worse health and screen positive for social drivers of health—for example, people of color from the LGBTQ+ community who also have disabilities. Cabrera added that death-related outcomes data could identify early mortality for individuals with disabilities or even Black, Indigenous, and People of Color (BIPOC) individuals by identifying discrimination and bias from providers within care settings.

 

Sexual Orientation and Gender Identity

Sean Cahill, The Fenway Institute, noted that Fenway Health and 1400 other federally qualified health centers across the country routinely collect data, including sexual orientation and gender identity (SOGI), assigned sex at birth, anatomical inventory, surgical history, pronouns, and preferred name—for their 21 million adult patients. A Johns Hopkins/Brigham and Women’s Hospital study found that 78% of emergency room doctors worried their patients would refuse to answer questions about SOGI. In fact, only 10% of patients said they would refuse to answer SOGI questions, demonstrating that most patients are willing to share this data especially if questions are asked through an electronic portal or a tablet (Haider et al., 2017). Cahill suggested opportunities for CMMI to advance SOGI data collection efforts, including partnering with the Bureau of Primary Health Care to examine the Uniform Data Set that has included SOGI data fields and expand this as part of programs like the State Innovation Models; using ICD-10 Clinical Modification measures for gender dysphoria, endocrine disorders, and hormone therapies to identify transgender beneficiaries; providing technical assistance and training via the National LGBTQIA+ Health Education Center and demonstrations around collecting SOGI data; leveraging collaboratives and networks such as the CMS Quality Innovation Network-Quality Improvement Organizations Learning Action Networks to support and use SOGI data; and supporting clinical demonstrations and pilots in the collection of SOGI data with beneficiaries.

Jerry Peterson, Ruth Ellis Center, noted that current data ownership laws, which place responsibility for data for youth under 18 years with parents or adult guardians, may harm the well-being of youth who have not revealed their sexual identity to their family or whose families do not accept their child’s SOGI. Aparna Sridhar, Epic, suggested that training via scripts and role-playing alongside patients using computers or tables to input responses to data fields related to care could promote the increased collection of SOGI data.

 

Social Drivers of Health

Rocco Perla, The Health Initiative, noted that many CMMI models have functionally included social drivers of health screening or navigation for years—in some cases with formal requirements to do so and, in others, with model participants choosing to do so as a means to improve patient health outcomes and succeed in APMs. He cited the CMMI Accountable Health Communities (AHC) model’s Year 1 Evaluation, which reported that 33% of beneficiaries screen positive for one or more drivers of health, with racial and ethnic minorities overrepresented in the population eligible for navigation toward resources such as food and housing (RTI International, 2020). However, only 14% of beneficiaries had a social need resolved after one year of support, with the main barrier being inadequate community resources (RTI International, 2020). He noted the opportunity for CMS to draw on this extensive field testing to leverage their regulatory authorities to adopt the AHC drivers of health screening measures in CMS quality and payment programs, like Medicare and Medicaid, and for CMMI to make these same measures standard across its models, as well as to ensure stratification of these measures by race and ethnicity. Perla noted CMS had made progress by including the first-ever drivers of health measure derived from AHC. These measures were the only patient-level equity measure on CMS’s 2021 Measures Under Consideration list that also received conditional support through the 2021–2022 National Quality Forum Measure Applications Partnership (The White House, 2021). According to Perla, this heightened attention to the drivers of health present an unprecedented opportunity for CMMI and CMS to elevate this first instance of measures from a CMMI model toward inclusion in CMS quality and payment programs and models. Perla also noted that since the AHC model has drivers of health screening data and hierarchical condition category (HCC) risk scores on beneficiaries, CMMI could also continue its efforts to facilitate cross-referencing these data sets better understand the impact of social risk on cost (AAFP, 2022; Billioux et al., 2017). Incorporating drivers of health measures in CMMI programs and models could help CMMI demonstrate the possibility of efficiently and effectively scaling up drivers of health services and programs and signal their wide-reaching impacts on the health and well-being of beneficiaries.

In support of Perla’s remarks, Frederick Isasi, Families USA, noted linking AHC social driver data with HCC data could create the case for investments and actions linking infrastructure, social drivers of health, federal spending, and health outcomes to restructure the health care system.

 

Open Discussion

Observations by Attendees

Opening remarks (CMMI’s Liz Fowler, Dora Hughes, and Kathryn Davidson) affirmed President Biden’s Executive Order On Advancing Racial Equity and Support for Underserved Communities Through the Federal Government (The White House, 2021). Fowler, Hughes, and Davidson identified the challenges of limited staffing, resources, and staff bandwidth as the overarching issue in collecting population-level data to address equity. CMMI is focused on assessing models for reach and their impact on underserved communities to inform their future approach. CMMI is launching successor models to Primary Care First and Comprehensive Primary Care Plus with considerations for data collection, geographic penetration, impact on underserved communities, specialty care, and implicit bias in eligibility criteria and payment algorithms. Additionally, they are studying data attribution methodology and assessing the impact of application criteria and eligibility as a barrier to reaching their goal of ensuring 100% of Medicare beneficiaries and the vast majority of Medicaid beneficiaries are in an accountable provider relationship by 2030. Finally, they are clarifying the legal basis upon which they could mandate race, ethnicity, and language data collection, as well as their ability to share these data with providers. The conversation moved toward suggestions for CMMI to overcome resource constraints and ensure meaningful stakeholder engagement, then toward the need to account for beneficiary and provider concerns, and finally, toward an urgent call to action for CMMI to begin moving rapidly on building the systems necessary to collect data that will ensure more equitable payment policies.

Reacting to CMMI’s presentation of their ongoing activities, attendees noted a key concern was how CMMI could overcome resource constraints and encourage a broad, coordinated, multi-stakeholder effort on population-level data collection. These constraints prompted reflections on the need to act now and a call to action for the field with designated roles and responsibilities for all potential stakeholders. While waiting for more extensive efforts, such as new models and value-based payment innovations, CMMI could use already collected data on specific diseases such as diabetes and employ creative mechanisms such as secret shopper programs to evaluate providers.

To further increase stakeholder capacity and confidence, CMMI could also engage multi-stakeholder networks to aggregate complaints data, identify systemic problems, and use qualitative evaluations and analyses to increase systems capacity and inform a substantial increase in nationwide population-level data collection. Throughout its ongoing efforts, attendees highlighted that CMMI more actively involving stakeholders throughout its data collection and implementation efforts would move the organization’s present consultation-based approach toward a more comprehensive power-sharing and meaningfully engaged approach.

Attendees then refocused the conversation on the need for communication and understanding of beneficiary needs. A common point raised was that a substantial interpersonal barrier for providers was anxiety around asking sensitive questions about patient identity, with the main fear being around breaking patient trust and offending patients. To reduce these anxieties and better prepare providers to discuss these topics with patients, CMMI could develop standards, guidelines, training, and educational resources on appropriate methods to ask beneficiaries identifying questions for critical factors such as disabilities, race, ethnicity, language, SOGI, and immigration status. These standards and guidelines could be based on the Institute of Medicine 2009 report on standardizing race/ethnicity and language data collection (IOM, 2009). CMMI could then disseminate and deploy these practices across health systems, states, and providers to increase provider competency and confidence and build the patient trust needed to obtain critical data points.

Additionally, meeting attendees cautioned against overlooking the unique needs of people with mental health and behavioral health issues and disabilities and the sometimes-intersecting impact of these issues, including, but not limited to, diverse race, language, sexual orientation, gender identities. Attendees also noted the importance of addressing the needs of behavioral health providers and professionals in CMMI’s value-based models, efforts, and policies. These providers are essential to the health system due to their critical role in beneficiary health and well-being and their ability to collect unique mental health-related data. To this end, CMMI could consider including mental health and behavioral health provider needs as part of their efforts to increase care access, achieve optimal care utilization rates across providers, and adequately incentivize and reimburse behavioral health providers for their services. Finally, CMMI’s efforts could also help invest in cultural competence in behavioral health through interpreter training and certifications or access to culturally competent care, providers, and services.

In addition to beneficiaries, attendees noted that providers need more clarity, engagement, and advice on the operational standards and demands of collecting data. First, providers are unsure how to share, use, and apply the health data. Second, they are uncertain of the burden of data collection on themselves and respondents; this fear of acting is a strong deterrent to data collection efforts in a health ecosystem lacking strong incentives to collect data. Third, CMMI could explore incentives, technical assistance, and other strategies to successfully overcome the high costs required in creating electronically enabled systems collecting health data, training staff, and maintaining these systems across health care and behavioral health services and settings. While incentives might help, they must successfully overcome the substantial expenses of building, testing, and maintaining such systems. Fourth, it is critical to engage behavioral health and developmental disability providers to galvanize the field more comprehensively toward population-level health data collection and documentation efforts. Behavioral health and developmental disability providers have been omitted from previous efforts, such as the 2009 Health Information Technology for Economic and Clinical Health Act. They may also require substantially more resources, support, and attention to improve their data infrastructure and technology. These considerations require attention to the pressing need for a strong and sustainable workforce, especially in the face of substantial health care worker burnout and shortages due to the COVID-19 pandemic, an increase in chronic disease and mental health needs, and the associated high rates of burnout, stress, and trauma in the U.S. population (Dzau et al., 2022).

In concluding the discussion, attendees emphasized that CMMI has an unprecedented opportunity to unite stakeholders by collecting population-level data to embed equity in payment policy. With a supportive federal administration, new CMS leadership, and the fresh urgency of the nation’s reckoning with structural racism and inequities amid the COVID-19 pandemic, CMMI and CMS has the opportunity to advocate for, measure, and pay for systems transformation and capacity building efforts, strengthen financial incentives, and relax eligibility requirements. Finally, a strong refrain underscored the need to act expeditiously to maintain momentum.

 

 

Areas of Future Focus and Priorities for Action

Mandy Cohen, MD, MPH, Aledade Care Solutions; Julian Harris, MD, MBA, MSc, Healthcare Services, Deerfield; Rebecca Onie, JD, The Health Initiative; Edwin Park, JD, Georgetown University; and Rocco Perla, EdD, The Health Initiative

Throughout this meeting, the presentations and discussions highlighted several key themes and priorities for action that CMMI could use in developing concrete implementation steps to advance data collection and policy to support equity. Here the authors discuss and elevate the primary considerations for forward momentum and identify six areas for action.

Several considerations are prominent as CMMI discusses the next steps to advance equity. It is critical that CMMI capitalize on the current national discourse on equity in moving forward. President Biden’s executive order on advancing racial equity and support for underserved communities and efforts by state and local entities to address justice and well-being for communities of color and the underserved present a launching pad for CMMI to add specificity to the call to action. Given the data, resources, partners, and political momentum available to leverage this current moment, there is substantial urgency to act as soon as possible to realize these goals.

This juncture also presents the opportunity for adopting a more intersectional approach to equity by collecting more granular and specific population health data that accounts for subpopulations and intersectionality across identities, circumstances, and lived experiences. At the point of care, it is critical that data allow for instant access and can inform the evaluation and treatment of care for beneficiaries. Data to ensure equity in payment policy also requires a more targeted and specific approach that examines how some populations suffer even more significant disparities in the nation’s health care system, especially if they experience one or more factors that could lead to greater disparities. These factors and characteristics include, but are not limited to, race and ethnicity, language, SOGI, geographic location and environmental context, disability status, behavioral and mental health issues, immigration and refugee status, and social drivers of health such as income, education, food access, health care access, and housing. Accounting for these intersecting and often overlapping identities can accurately capture the experiences and challenges faced by people in their daily lives and the impact on their health and well-being. These considerations can also inform efforts to reduce the cost barriers to accessing care and increase care utilization by populations disproportionately affected by a lack of health care affordability.

In conjunction with stakeholders, CMMI could also reexamine how value is defined. While cost containment is critical and an important part of CMMI’s objectives, it is impossible to arrive at value without health and health care equity. CMMI and other stakeholders would benefit from examining the need for community investment as a strategy to arrive at a value-based care health system. CMMI could work synergistically with existing collaboratives, partnerships, and stakeholders engaged in equity data collection and use. Much work on equity data collection has been done, and many best and promising practices are available. To ensure these efforts can be implemented quickly, CMMI has an opportunity to learn from others and build off known successes, efforts, resources, and knowledge. The areas where CMMI could immediately learn from stakeholders include training providers and payers to collect race, ethnicity, language, and SOGI data to minimize patient concerns. These learnings could then be scaled to increase equity data collection capacity.

Additionally, CMMI has the potential and opportunity to work alongside its federal partners to implement equity data collection. During this discussion, many suggestions and comments about advancing equity in payment policy are also relevant for CMS, such as adding race/ethnicity data to the Medicare Part C and D application. For example, CMMI could elevate the first instance of measures from a CMMI model, such as the drivers of health measures from AHC, potentially becoming part of CMS quality and payment programs as an example of how model elements can scale across the government, drive alignment across federal programs, galvanize stakeholders, and send a powerful market signal to commercial/private payers.

Progress initiated by CMS on equity will greatly influence and impact CMMI and vice versa. CMMI and CMS could continue to work across the federal government, including the Office of the National Coordinator for Health Information Technology and the Office of Civil Rights, to ensure alignment in approaches to collecting, sharing, and using data to support equity. These organizations could help address a data-sharing landscape where sharing health data occurs unevenly across communities and in the volume, speed, and frequency with which health data is shared (Greene et al., 2021). These efforts would work on targeting populations disproportionately affected by institutional bias and discrimination across the aforementioned qualities and characteristics. Several priorities for action warrant emphasis.

  1. Signaling: Reemphasize the key actions and steps that will be taken by CMMI and asked of payers, providers, and purchasers to prioritize equity, as well as indicate the short-, intermediate-, and long-term targets for collecting, sharing, and using equity data, including collecting reliable data that can be disaggregated, categorized, and targeted by subpopulations and communities.
  2. Mapping: Develop, identify, and communicate the approaches, activities, and timelines used to meet targets through a comprehensive and actionable roadmap (e.g., providing culturally appropriate guidance through training to empower health and health care stakeholders to clarify perceived and real barriers as well as motivate widespread data collection). The roadmap would ensure field alignment by communicating guidance, best practices, incentives, requirements, and core measures.
  3. Measuring: Co-develop core data sets that measure performance on equity dimensions and display significance to field stakeholders, as well as patients, families, and communities. These measures, which should be both universal and targeted, could comprehensively account for, assess, and evaluate underserved populations’ health, care, and outcomes. The measures should include considerations across race and ethnicity, language, SOGI, geographic and environmental context, disability status,
    behavioral and mental health issues, immigration and refugee status, and social drivers of health such as income, education, food access, health care access, and housing.
  4. Modeling: Provide more substantial incentives to drive multi-stakeholder collaboration to collect equity data while providing learning tools, technical assistance, mechanisms, and funding for states, providers, and communities. States could use these resources to develop, coordinate, and lead innovations while proactively and continuously coordinating community stakeholders to inform these efforts. The resources could also help providers build and maintain the necessary capacity to collect data. Over time, the assistance would provide the collected data, documented community-based best practices and experiences, and align state, provider, and community stakeholders to test, implement, and craft community-level approaches and policies. These actions could address the needs of people disproportionately affected by institutional racism, multidimensional disparities through factors such as education and income, and the fee-for-service chassis.
  5. Partnering: Develop a continuous and comprehensive multi-stakeholder community engagement approach that includes diverse and intersectional beneficiary perspectives as well as providers, payers, purchasers, and community-based organizations. These stakeholders would work to partner in collecting and sharing the needed data in an open, transparent, and timely manner. Through these collected data, stakeholders can better understand various perspectives, lived experiences, efforts, and unique needs at a more specific and granular scale. In addition to developing and refining approaches to advancing equity in payment policy, a multi-stakeholder partnership could help CMMI build broad-scale support and adopt their guidance and deliverables.
  6. Demonstrating: Test innovative and creative strategies that incentivize and facilitate the adoption of health data collection in multiple contexts and diverse, intersectional populations. These innovations could, through evidence-based mechanisms, provide promising practices on measurably reducing bias and care malpractice, improving care outcomes, and driving investments into the community and social drivers of health. These learnings could then be incorporated into future CMMI efforts and disseminated across stakeholders collaborating with CMMI.

 

References

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  17. The White House. 2021. Executive Order on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government. Available at: https://www.whitehouse.gov/briefi ng-room/presidential-actions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/ (accessed December 16, 2021).

 

Disclaimer

This Discussion Proceedings were prepared by Peak Sen Chua, Jennifer Lee, and Ayodola Anise as a factual summary of what occurred at the meeting and areas of future focus and priorities for action. The statements made are those of the rapporteurs or individual meeting participants and do not necessarily represent the view of all meeting participants; the planning committee; members of the associated program; the National Academy of Medicine; or the National Academies of Sciences, Engineering, and Medicine.

 

Reviewers

To ensure that it meets institutional standards for quality and objectivity, this Discussion Proceedings was reviewed by Ignatius Bau, Independent Consultant, and Jorge Petit, Coordinated Behavioral Care, Inc.

 

Sponsors

This workshop was held with support from The Commonwealth Fund. Any opinions, findings, or conclusions expressed in this publication do not necessarily reflect the views of any organization or agency that assisted in the development of this project.

 

Suggested Citation

Chua, P. S., J. Lee, and A. Anise, rapporteurs. 2022. Collecting Data to Ensure Equity in Payment Policy. Discussion Proceedings, National Academy of Medicine, Washington, DC.


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