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4 COLLECTING DATA TO ENSURE EQUITY IN PAYMENT POLICY DISCUSSION HIGHLIGHTS During a meeting with experts on collecting data to support eq- uity in payment policy, various stakeholders, including the Center for Medicare & Medicaid Innovation (CMMI), shared their perspec- tives and engaged in a discussion on the critical steps necessary for transformation. Below is a summary of the multi-stakeholder per- spectives and the discussion. For more information on the meet- ingâs content, please see the full Discussion Proceedings of this meeting in Appendix C. The meeting agenda and invited attendees are featured in Appendixes G and H. ⢠The landscape of data collection to support equity. Cara James, Grantmakers in Health, deï¬ned health equity as the fair and just opportunity to be as healthy as possible as mea- sured by the reduction and elimination of health disparities and their determinants that affect marginalized groups. Ac- cording to the Affordable Care Act (ACA), race, ethnicity, pri- mary language, sex, and disability data are important to ad- dress health equity. However, data available to understand these aspects are severely lacking across U.S. government departments and agencies, except for data collected by Fed- erally Qualiï¬ed Health Centers. For example, the Centers for Medicare & Medicaid Services (CMS) reported that race and ethnicity data in Medicaid in 22 states are unusable. Data if 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 raised several consider- ations that could serve as areas for CMMI to target, incorpo- rate, or consider as it embarks on its efforts to accomplish 17
18 | Catalyzing Innovative Health System Transformation goals. These considerations include adding race and ethnic- ity data to the Medicare Part C and D application, incorporat- ing available data toward payment options to inform value- based payment efforts and drive health care value and better outcomes, enhancing CMMIâs ability to collect, analyze, and report the demographic data necessary to monitor and evalu- ate the impact of programs and policies, providing technical assistance and resources to ensure seamless implementation of CMMI models at the state level, and engaging meaningful- ly the communities whose voices could be incorporated into these efforts, and conceiving and implementing a plan to build a usable data infrastructure. Finally, James suggested several policy actions to facilitate CMMIâs measurement related ef- forts. James suggested that CMMI use the multidimensional and comprehensive Health Equity Summary Score (HESS) da- taset as a template to incorporate population health factors across timeframes, populations, and benchmarks. ⢠Race and ethnicity. It remains uncertain how health systems can transform without substantial ï¬nancial support to build electronic health record systems and train staff to collect race and ethnicity data. If data to support equity remain siloed or restricted to well-resourced health systems, many provid- ers will struggle to utilize race and ethnicity data to inform treatment and interventions at the point of care. CMMI could include race and ethnicity data for billing for Medicare pa- tients hosted on an interoperable electronic health database to build trust and proactively collect data to inform care deliv- ery. It could also collect presently available data through pri- vate payers, existing collaboratives and registries, and health systems with advanced data-sharing capabilities. Ultimately, these efforts to standardize and collect race and ethnicity data can be leveraged to calculate patient risk, improve outcomes, and prevent emergency hospitalizations. ⢠Preferred language. Another dimension intersecting with race and ethnicity is preferred language. Collecting language data that is actionable at the point of care is essential to improv- ing the cultural competence, experience, and quality of care. By collecting and applying available data in this area, provid- PREPUBLICATION COPY - Uncorrected Proofs
Collecting Data to Ensure Equity in Payment Policy Discussion Highlights | 19 ers could work to enhance access to interpreter services and systems navigation. Furthermore, beneï¬ciaries from diverse backgrounds would beneï¬t through obtaining, understand- ing, and utilizing health information to make well-informed decisions for themselves or their next of kin. To support these advancements at a systems level, CMMI could provide guid- ance on collecting patient demographic and background ques- tions. CMMI could also create uniï¬ed expectations around the purpose and use of these data, especially related to its appli- cations in population health and quality improvement. ⢠Disabilities. The conversation then focused on another over- looked area, the lack of disability status data and accommo- dations for disabled people. Additionally, optional standards for accessible medical equipment under the Affordable Care Act make it challenging to provide high-quality care across the care continuum for people with disabilities. For example, people with disabilities might require different levels of sup- port and treatment, such as physical accommodations for health screenings for people with different kinds of disabili- ties. CMMI could beneï¬t from having qualitative and quan- titative data illustrating the experiences of people with dis- abilities within health and health care systems. Additionally, CMMI could take an intersectional approach to understand health outcomes in various subpopulations who are more likely to experience worse health and screen positive for a so- cial driver of healthâfor example, people of color from the LGBTQ+ community who also have disabilities. ⢠Sexual orientation and gender identity. Discreetly collecting sexual orientation and gender identity (SOGI) data is critical to protect the privacy, discretion, and trust of beneï¬ciaries and improve care quality. To ensure both aims are achieved, CMMI could partner with other organizations and use existing government agency structures to collect sexual orientation and gender identity (SOGI). To incorporate SOGI measures in existing data and diagnostic tools, CMMI could partner with the Bureau of Primary Healthcare to amend the uni- form data set to include sexual orientation and gender iden- tity data ï¬elds and eventually expand this to programs such PREPUBLICATION COPY - Uncorrected Proofs
20 | Catalyzing Innovative Health System Transformation as the State Innovation Models; use ICD-10 Clinical Modiï¬ca- tion measures for gender dysphoria, endocrine disorders, and hormone therapies to identify transgender beneï¬ciaries; pro- vide technical assistance and demonstrations via the National LGBTQIA+ Health Education Center on best SOGI data collec- tion practices; leverage collaboratives and networks such as the CMS Quality Innovation Network-Quality Improvement Organizations Learning Action Networks to support and use SOGI data; and support clinical demonstrations and pilots in SOGI data collection. ⢠Social drivers of health. Another cross-cutting issue raised was the incorporation of upstream social drivers of health into CMMI models. Many CMMI models have included social driv- ers of health screening or navigation for factors such as hous- ing, food, and income. However, while the CMMI Accountable Health Communities (AHC) modelâs Year 1 Evaluation report- ed that 33% of beneï¬ciaries screen positive for one or more drivers of health, only 14% of beneï¬ciaries had a social need resolved after one year of support, with the main barrier being inadequate community resources. Within this sample, racial and ethnic minorities were overrepresented. The heightened attention to the drivers of health presents an opportunity for CMMI to include these measures in CMS quality and payment programs and models. CMMI could also continue its efforts to facilitate cross-referencing hierarchical condition category risk scores and health screening data to better understand the impact of social risk on cost. Ultimately, incorporating DOH measures in CMMI programs and models could help CMMI demonstrate the possibility of efficiently and effectively scal- ing up DOH services and programs and signal their wide- reaching impacts on the health and well-being of beneï¬cia- ries. OPEN DISCUSSION ⢠CMMIâs Liz Fowler, Dora Hughes, and Kathryn Davidson be- gan by affirming President Bidenâs Executive Order on ad- vancing equity in payment policy and identiï¬ed the chal- PREPUBLICATION COPY - Uncorrected Proofs
Collecting Data to Ensure Equity in Payment Policy Discussion Highlights | 21 lenges of limited resources and staffing as data-collection challenges. CMMI is currently focused on launching succes- sor models to Primary Care First and Comprehensive Primary Care Plus with considerations for data collection, geographic penetration, impact on underserved communities, specialty care, and bias in eligibility criteria and payment algorithms. Additionally, they are studying the impact of data attribution methodology, application criteria, and eligibility as barriers to CMMIâs 2030 goal of ensuring 100% of Medicare beneï¬- ciaries and that the vast majority of Medicaid beneï¬ciaries 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 and their ability to share these data with providers. ⢠Despite these efforts from CMMI, the agencyâs resource con- straints to coordinate multi-stakeholder efforts to collect population-data to support equity is a core concern. CMMI could issue a call to action with designated roles and respon- sibilities for ï¬eld stakeholders to overcome this barrier. While designing new models and payment reforms, CMMI could publicly discuss available data and employ creative mecha- nisms such as secret shopper programs to evaluate provid- er quality of care. Additionally, CMMI could employ multi- stakeholder networks to aggregate complaints data, identify systemic problems, and use qualitative evaluations and anal- yses. ⢠Additionally, attendees highlighted that CMMI is best served by more actively involving stakeholders throughout its data collection and implementation efforts. For example, while designing new models and value-based payment innovations, CMMI could publicly discuss available data and employ cre- ative mechanisms such as secret shopper programs to evalu- ate providers on the quality of care given to patients. Multi- stakeholder networks could be engaged and coordinated by CMMI to aggregate complaints data, identify systemic prob- lems, and use qualitative evaluations and analyses. ⢠Attendees also encouraged CMMI to provide guidance, stan- dards, and training for providers to collect data and discuss PREPUBLICATION COPY - Uncorrected Proofs
22 | Catalyzing Innovative Health System Transformation identifying questions for critical factors such as disabilities, race, ethnicity, language, SOGI, and immigration status. These standards and guidelines could be based on the Insti- tute of Medicine 2009 report on standardizing race/ethnicity and language data collection and disseminated across health systems, states, and providers. This effort would help increase provider conï¬dence and build the patient trust needed to ob- tain critical data points. ⢠Beyond CMMI, attendees also raised that CMMI could help guide providers with more operational clarity and incentives to share, use, and apply data to support equity. By reducing administrative burden for providers, more capacity could be directed toward collecting and using data at the point of care. CMMI could also explore incentives, technical assistance, and other strategies to help providers overcome the high costs of creating electronically-enabled data collection systems and the staff and resources necessary to maintain them. ⢠In the process of data collection efforts to support equity, it is critical to engage behavioral health and developmental dis- ability providers. Involving these providers could galvanize the ï¬eld more comprehensively toward population-level health data collection and documentation efforts. However, these providers may require substantially more resources, support, and attention to improve their data infrastructure and technology to achieve progress in data collection. Some attendees noted that they have been omitted from previous data interoperability initiatives such as the Health Informa- tion Technology for Economic and Clinical Health Act of 2009. 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 burn- out, stress, and trauma in the U.S. population. ⢠With a supportive federal administration, new CMS leader- ship, and the fresh urgency of the nationâs reckoning with structural racism and inequities amidst the COVID-19 pan- demic, CMMI has an unprecedented opportunity to unite PREPUBLICATION COPY - Uncorrected Proofs
Collecting Data to Ensure Equity in Payment Policy Discussion Highlights | 23 stakeholders by collecting population-level data to embed equity in payment policy. CMMI also has the opportunity to advocate for, measure, and pay for systems transformation and capacity-building efforts, strengthen ï¬nancial incen- tives, and relax eligibility requirements. PREPUBLICATION COPY - Uncorrected Proofs