Vivian Lee, the president of Health Platforms at Verily, opened the second session of the workshop by remarking that estimates of annual medical care spending waste in the United States range from $601 billion to $1.9 trillion (Speer et al., 2020). Although U.S. per capita health costs can be up to twice those of other high-income nations, health outcomes in the United States are worse than in those countries (IOM, 2012, 2013a). Overuse and missed prevention opportunities account for the largest proportion of waste, followed by administrative waste, clinical inefficiencies, excessive prices, and fraud and abuse (for the sizes of the categories of waste, see IOM, 2013b). During this session, panelists explored approaches to driving down the cost of care—drawing upon lessons learned at the federal, state, and local levels—and considered why the problems of wasteful health care expenditures seem so intractable.
A STATE ROADMAP TO DEVELOPING HEALTH CARE COST GROWTH BENCHMARKS
Rachel Block, a program officer at Milbank Memorial Fund (MMF), noted that U.S. health care spending has been rising rapidly for more than four decades, accounting for an ever-larger portion of the nation’s gross domestic product (Peterson-KFF Health System Tracker, 2020). While an increasing portion of the economy is devoted to health expenditures, other categories of spending have decreased, making it difficult for state leaders to balance efforts to address the full spectrum of the population’s needs, including education, housing, and other social determinants of health (U.S. GAO, 2019b). Block said that the first step toward controlling health care costs will be to measure state-level per-person health care spending across all payers, including Medicare, Medicaid, and employer-based coverage. Then, factoring in the growth of the economy and of household incomes, the state can set a benchmark for the statewide growth of health care costs. States traditionally focus on state-funded health spending, but this approach extends to all health spending within the state. Data on all-payer spending can then be used to extract information on broad market segments, on individual health care systems, and ultimately on individual payers and providers.
In 2012, Massachusetts was the first state to establish a health care cost growth benchmark, Block said. This strategy resulted from the state’s efforts to expand health care coverage to its entire population and to make the system affordable, both for the state and for participants in the system. Legislation was passed that set the growth target, then the Health Policy Commission was created to evaluate factors driving health care cost growth in Massachusetts by using a global health spending perspective. Since the implementation of this strategy, annual all-payer health care
spending growth per enrollee has been below the target level, and the state growth rate averages have been lower than the national rate of growth (Massachusetts Health Policy Commission, 2019). Block highlighted the importance of flexibility in allowing the system to adjust to unexpected events. For example, the introduction of the hepatitis C drug Sovaldi and the COVID-19 pandemic have both caused unanticipated increases in expenditures.1 Block noted that a target does not represent a hard cap on health care spending; rather, it allows growth in spending to be monitored.
Massachusetts has been successful in limiting growth in commercial health care spending, with a growth rate below that of the entire United States in recent years, Block said. MMF is exploring the extent to which having a cost growth target in place contributes to curbing spending growth. Rhode Island, Delaware, Connecticut, and Oregon have issued executive orders or enacted legislation to establish efforts focused on elucidating total health care spending and addressing factors driving health care cost growth. Partnering with the Peterson Center on Healthcare, MMF supports several states undertaking this work—including Connecticut, Nevada, New Jersey, Oregon, and Washington—by providing technical assistance to build state policy and technical capacity to implement cost growth targets.
Once a state authority has been established, Block explained, the process for developing growth targets proceeds to convening a stakeholder group to provide input on the different variables involved. The next step is determining the types of spending to be measured, including claims and non-claims payments. She said that measuring non-claim-related health care spending is an area of ongoing learning. Each state develops a cost growth target methodology by selecting the indicator(s) to be used for the benchmark. The next steps involve identifying the components of the health care system to be assessed against that benchmark and determining the data collection methods. Data analytic capacity must be built in order to monitor and publicly report cost growth performance against the benchmark, Block added. Then a data use strategy is created that can be used to identify the biggest cost drivers. The final step is to develop state policy responses that are informed by the cost driver analysis and other activities. Block emphasized that transparency is critical throughout this process. MMF assists states in evaluating health care spending across the system, identifying factors driving health care costs, and choosing appropriate policy solutions that address those drivers. As cost growth targets are set, states must also determine how to hold the system accountable.
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1 Sovaldi, an effective treatment for hepatitis C, became available to patients in 2013. At a cost of $84,000 per treatment course, paying for the drug strained state Medicaid budgets (Pollack, 2016).
CASE EXAMPLE: DELAWARE’S JOURNEY TOWARD HEALTH AND QUALITY BENCHMARKS
Kara Odom Walker, a senior vice president and the chief population health officer at Nemours Children’s Health System (Nemours) and a former secretary of the Delaware Department of Health and Social Services (Delaware DHSS), described Delaware’s efforts to use data-driven performance and community engagement to move the health care system toward increased value and patient-centered care. In 2014, Delaware’s per capita personal health care expenditures were the third highest in the nation, yet the state ranked 35th for overall health (CMS, 2017; Lassman et al., 2017; United Health Foundation, 2014). Furthermore, Delaware’s total health spending was anticipated to double from 2014 to 2025, resulting in a projected cost of $21.5 billion (Delaware DHSS, 2018). Walker noted that because the growth in health care spending exceeded the growth of state revenues, health care was in competition with priorities such as infrastructure, education, public safety, and state salaries, all of which had more stable growth. In the midst of a substantial budget deficit, health care spending growth garnered the attention of the governor, legislators, and stakeholders.
Walker said that in the fall of 2017, Delaware began exploring the benchmark concept outlined by Block. The process of holding public dialogues, summits, and advisory council meetings increased support from legislators, and in November 2018, Governor John Carney issued an executive order establishing a framework and process for setting quality benchmarks and for reviewing annual health care cost targets.2 The executive order set the 2019 spending growth benchmark at 3.8 percent and then decreased it annually until 2022, at which point the benchmark will be a 3 percent growth rate. Established with public input, Delaware’s benchmark is equal to the potential gross state product (GSP), The GSP accounts for changes due to inflation and the labor force, and it takes a long-term outlook on health care spending, Walker said. The measures were taken from publicly available data sources to enable calculation on an ongoing basis by any interested party.
Walker outlined three areas of Delaware’s health care quality benchmarks: (1) access to primary care, as measured by emergency department (ED) visits for ambulatory care–sensitive conditions; (2) opioid-related deaths and complications; and (3) cardiovascular disease prevention and treatment.3 Health care quality benchmarks include measures for both
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2 Executive Order 25 can be accessed at https://governor.delaware.gov/executive-orders/eo25 (accessed May 25, 2021).
3 Ambulatory care–sensitive conditions are those that can be prevented, controlled, or managed with effective outpatient care, thus reducing the risk for hospitalization.
health status and health care. The health status measures quantify population-level characteristics of Delaware residents, including adult obesity, the activity levels of teenagers, opioid-related deaths, and tobacco use. Health care measures quantify the performance of health care processes or outcomes and include ED usage, the persistence of beta-blocker treatment after a heart attack, statin therapy, and an opioid-related measure currently being developed.
At the time Walker left the Delaware DHSS in July 2020, the department was in the process of establishing baselines from collected data. State health care expenditures—totaling approximately $7.8 billion in 2018—were disaggregated by payer and service type. Walker said that while much was learned from preliminary data, the data suggested the potential for methodological inconsistencies. To strengthen future data collection and analysis, technical assistance is being provided to payers in order to improve data submission consistency; 2018 data will also be recollected to ensure better year-over-year comparisons moving forward.
As Delaware continues to heighten its focus on value, the state is exploring mechanisms to improve health at lower costs, Walker said. Strategies that have been adopted or that are under consideration include: using the benchmark as a guideline for state employee benefit design; supporting global budgeting for rural hospitals, including value-based and quality thresholds in managed care contracts; allowing a 1332 Medicaid waiver4 process to implement reinsurance programs and ensure affordability; developing pediatric accountable care organization (ACO) models; creating a Medicaid ACO that includes adult dental coverage; and updating substance abuse payment to include value-based payment. Walker added that policy discussions have also focused on investing in primary care and measuring the effect of that investment.
COLLABORATIVE APPROACH TO PUBLIC GOODS INVESTMENT
Ben Miladin, the director of health at United Way of Greater Cleveland (UWGC), described how UWGC is working to address poverty through a project starting during its implementation of the accountable health communities (AHC) model with funding from the Centers for Medicare & Medicaid Services (CMS). CMS launched the AHC model to determine whether linking Medicare and Medicaid recipients with community social services can improve health outcomes and reduce health care costs.5 CMS
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4https://www.cms.gov/CCIIO/Programs-and-Initiatives/State-Innovation-Waivers/Section_1332_State_Innovation_Waivers- (accessed September 22, 2021).
5https://innovation.cms.gov/data-and-reports/2020/ahc-first-eval-rpt (accessed September 21, 2021).
has funded 29 organizations across the United States to test the model. Participants are required to convene an advisory board to identify gaps in social services and then attempting to fill those gaps through collaboration with hospitals, managed care, policy makers, academia, service recipients, and social service providers. Miladin remarked that as health providers and insurance companies develop value- and risk-based frameworks to bring down cost and improve the quality of care, addressing upstream issues surrounding chronic disease management—including housing, access to food, utility assistance, personal safety issues, transportation, and workforce needs—should theoretically be incentivized by this process. Nonetheless, he continued, the health sector continues to focus funding on downstream, reactive treatment once someone is already sick.
Overview of the Collaborative Approach to Public Goods Investment Model
The UWGC advisory board consulted with Len Nichols of George Mason University and Lauren Taylor of Harvard Business School, currently at New York University, about the Collaborative Approach to Public Goods Investment (CAPGI) model they developed for funding social services in a way that simultaneously meets social needs and positively affects health outcomes (Nichols and Taylor, 2018). Miladin explained that CAPGI is a funding mechanism for facilitating upstream investments on social service projects that align the financial incentives of insurers and providers. The collaborative investment model incentivizes investment while removing some concerns about “free riders,” or the unequal distribution of the burden of the responsibility. In a competitive market in which individuals switch insurers as their jobs or life circumstances change, an insurer may perceive covering a preventative service for an individual who then moves to another company a year later as helping the competition realize the benefits of the preventive service. However, if all insurers and health providers are collaboratively investing in the same service, it can alleviate concerns about individuals who move from provider to provider or insurer to insurer, thus increasing the security of investing upstream. Miladin noted that emerging evidence indicates that social services can lead to health savings. A “trusted broker” works with insurance, health providers, and other stakeholders to facilitate upstream investment.
Miladin illustrated UWGC’s role as “trusted broker” with a simplified example. UWGC asks investors—in this case insurers, hospitals, and non-vendor community-based organizations—to provide confidential bids on the worth of a social service intervention to their organizations. For instance, these parties might bid on a complex case management program that costs $180,000. Each insurance company provides a bid, and the
collective bid from all insurers totals $110,000; the bids from hospitals total $50,000, and the community-based organizations collectively bid $40,000. Because the $200,000 sum of all bids surpasses the cost, the intervention moves forward, and each investor is charged 90 percent of their bid in order to collect the $180,000 needed. In this example, investors are paying less than the valuation they assigned to the service. In Ohio, managed care bids exceed hospital bids, as was expected, which may be related to the different incentives for plans in comparison to providers, Miladin said. In service-based fee structures, hospitals may lose revenue if upstream efforts result in decreased services that are traditional high-revenue generators such as intensive care unit and ED visits. In capitated payment models such as managed care, the hospitals have a stronger financial incentive to lower the total cost of care.
The “trusted broker” fulfills multiple roles, the first of which is recruiting investors to the general concept of collaborative upstream investment, Miladin explained. As investors gradually become more familiar with the framework, the broker facilitates the establishment of investors’ parameters for investment. Examples of investor parameters include: investment in the scale-up of an existing intervention versus the creation of a service; specific investment areas of focus, such as food access or services for seniors; and getting as close as possible to a guaranteed return on investment (ROI) on an investment with strong research backing showing an ROI and low risk for losing dollars. The broker then seeks projects and assists investors in selecting initiatives for investment. Miladin noted that UWGC hosted an event styled after the Shark Tank television show, at which nonprofit organizations presented project proposals and the coalition of investors voted on which project to fund. Once a project was selected, then the trusted broker accepted confidential bids, while guiding the investors on what types of information would likely be helpful in a bid. After accepting confidential bids, the broker determines whether the bids are sufficient to fund the project and then sets prices for each investor. Next, the broker ensures the launch of the service through data management and the formation of legal agreements. Finally, the broker conducts a large-scale evaluation of the project.
Current CAPGI projects in Cleveland include the home delivery of medically tailored meals and weekly socialization visits to approximately 400 seniors experiencing chronic health conditions, food insecurity, and social isolation, Miladin said. The deliveries and visits are carried out via the Benjamin Rose Institute of Aging. Research indicates that such services can have a positive effect on health and decrease health care spending (Berkowitz et al., 2018). A desired long-term outcome is that investors will have a positive experience and become more willing to invest in larger, riskier projects using the bidding infrastructure established by the trusted
broker. Additionally, UWGC hopes that success in creating health savings will eventually broaden the scope of CAPGI projects to extend to the legal system, an area where social interventions could lead to savings by municipal governments. Miladin noted that in the process of redesigning its Medicaid program, the Ohio Department of Medicaid included a requirement that plans collectively invest part of the profits in community services, which indicates, he said, that momentum is gathering toward the collaborative approach.
Miladin acknowledged that a substantial challenge to CAPGI implementation is a fear of data breaches associated with sharing data with social service providers. Generally, hospitals do not want to administer social services programs directly, preferring to partner with experts in the field to deliver these programs. However, these partnerships introduce data-sharing concerns. Hospital legal departments have required UWGC to accept strict indemnification provisions that would be difficult for many nonprofits to accept. Additionally, the stigma felt by many toward social services and investor concerns of creating dependency on these services can hinder participation. However, evidence indicates that providing people with food, housing, and transportation assistance can improve health outcomes and save money.
A FEDERAL-LEVEL PERSPECTIVE
Spencer Carrucciu, a vice president at Oxeon Venture Studio and recent former senior advisor at the Center for Medicare & Medicaid Innovation (CMMI), discussed CMMI’s goal of changing payment design and innovating models to improve patient care, reduce costs, and better align the system to promote patient-centered practices. He said that CMMI’s mandate is to design incentives that encourage changes in the CMS system as a whole. Carrucciu described three models developed by CMMI to drive value-based care within communities. The AHC model involves providing CMMI grants to agencies to implement health screenings for Medicare and Medicaid beneficiaries within a given community. The Community Health Access and Rural Transformation (CHART) model features CMS grant funding for organizations working to implement care redesign; it also supports capitated payments and global budgets for hospitals as well as providing a pathway to create rural-based ACOs. The Geographic Direct Contracting (Geo) model assigns the full risk for an entire community to ACO-type entities within that community, which enables communities to approach investments in new ways.
The multiple incentive tools that CMS uses in these models include funding, capitation, risk, and flexibility, Carrucciu said. The funding tool involves providing money to organizations to try something new. For example, funding is a tool that enables the health screenings featured in the AHC
model, which evaluations indicate has an impact on lowering ED use for the beneficiaries screened, he said. The CHART Community Transformation Track provides lead organizations in rural areas with the infrastructure and funding needed to redesign local care in partnership with community stakeholders. Capitation is a tool that involves providing global payments to providers in lieu of traditional fee-for-service payments. For instance, in the CHART Community Transformation Track CMMI provides hospitals with a global budget for inpatient and outpatient services. This can enable hospitals to allocate resources toward keeping people healthier. In the Geo model, CMMI provides additional flexibility to providers that accept capitation to enable investment in population health activities, care management tools, and technologies. Risk is a tool that can help to align provider incentives with the ultimate spending cost and quality of a given beneficiary. In the CHART Accountable Care Organization Transformation Track, CMMI enables ACOs to enter into the Medicare Shared Savings Program and provides advance payments to these ACOs to invest in the technology, processes, and people needed to drive local health care change. In the Geo model, CMMI selects direct contracting6 organizations that take the full risk for Medicare beneficiaries in an effort to align risk within the entire region of a community. Carrucciu expressed optimism that this will spur advanced investment and CAPGI implementation. Flexibility is a tool that, for example, allows payments to be made for care that typically is not covered by Medicare. Within all three models, CMMI is able to provide waivers and beneficiary engagement incentives that enable organizations to deliver care in new ways. The CHART and Geo models each feature a suite of benefit enhancements and incentives that allow organizations to provide transportation and meal vouchers to beneficiaries.
CMMI has a framework for evaluating the quality of care and spending changes within each model, Carrucciu said. Models are initially implemented in a few geographic areas or with subsets of providers. CMMI then evaluates whether models can be scaled to additional providers and patients and, if so, identifies the infrastructure, tools, and data that are needed to facilitate this scaling. Further, CMMI evaluates the extent to which a model is generalizable in that it is able to deliver similar results in different communities. It also identifies features of the model that may make it unique to specific communities. Finally, it evaluates the model’s ability to improve the quality of services while also driving savings. This process involves identifying interventions within the model that led to improved quality and reduced
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6https://www.jonesday.com/-/media/files/publications/2018/05/direct-contracting-101-collaborations-between-empl/files/direct-contracting-101/fileattachment/direct-contracting-101.pdf (accessed September 17, 2021); https://www.cms.gov/newsroom/fact-sheets/direct-contracting (accessed September 17, 2021).
cost, then examining the variance of interventions across providers or specific sub-populations. This evaluation informs decisions about potential model expansion or the construction of successor models, with the ultimate goal of bringing system change to the national scale, Carrucciu said.
DISCUSSION
State Factors in Adopting Spending-Related System Changes
Lee asked about the factors preventing some states from participating in system changes to improve quality of care and to lower health care spending. Block responded that health care cost growth targets have been established in three states and that an additional five states are moving in this direction, but this remains unfamiliar territory for the majority of the United States. Generally, some states are more willing to try new approaches than others. State health policy innovations tend to emerge in those states; when evidence on the impact of those policies becomes available, it is used by other states as they determine whether they have the political and technical wherewithal for implementation. For example, Block said, Massachusetts independently created an elegant design for health care cost growth targets that MMF borrowed from in developing designs for other states. Additionally, the combination of political will and data capacity is needed to address these systems-level issues. Block posited that building political will begins with raising awareness of the cost curve of health spending trends and its impact on household and state budgets. Change will require leadership from state politicians and across the entire health care community, she added. Additionally, the requisite data capacity benefits greatly from the use of all-payer claims databases, which not all states have. Furthermore, the all-payer claims database may not include data from self-insured employers. In addition, most all-payer claims databases lack the capability to collect non-fee-for-service and non-claims spending, limiting the database’s capacity to measure total health care spending.
Walker remarked that at the time she became secretary of the Delaware DHSS in 2017, Delaware had already committed to innovation through state initiatives and receipt of a CMMI state innovation award—enabling access to flexible resources and funding—and a newly elected governor had just entered office with a budget crisis to address. She added that crises can create opportunities for change, with the COVID-19 pandemic providing an opportune time to raise the issue of health care costs. As Medicaid expenses account for 25 percent of Delaware’s budget, the budget deficit increased the urgency and pace of discussion around health care spending. Walker emphasized the role of leadership and political will in this process. The state held public and private dialogues and used communication experts
to elevate the issue through the use of op-eds, written brochures, and the media. Because the same individuals involved in health care system change are often responsible for pandemic response, the COVID-19 pandemic has challenges in resources and workforce that create bandwidth limitations for states working to address health care spending, Walker said. She added that MMF is meeting this bandwidth issue with technical expertise in states where there is some political will for change. States with supportive governors, stakeholders championing change, and the willingness to build bandwidth and capacity can capitalize on the expertise created by states like Massachusetts to move toward innovative system change.
Miladin said that CMS’s emphasis on social determinants of health and an intensive statewide focus on reducing costs and improving quality of health care have enabled UWGC’s work. He added that Ohio’s governor and the Medicaid director both came to office during the process of advocating for approval of the CAPGI project and their support for more collaboration and attention to social need bolstered the local effort.
Health Care and Social Services Quality Measures
Lee asked Walker about quality measure selection and data collection in Delaware’s initiative. She replied that health care providers and specialists were solicited for input about investing in population health. However, the resulting quality measures are not the Healthcare Effectiveness Data and Information Set measures that quality experts tend to be familiar with. Instead, the Delaware DHSS selected from among available measures that have validity in assessing year-over-year change and lead to improved long-term health or reduced cost. A list of approximately 20 measures was presented to Governor John Carney, who suggested increasing the priority of issues that had stakeholder buy-in, Walker said. She remarked that as a primary physician, she is aware of the challenges of trying to address a quality measure—such as the rate of obesity—in isolation within the primary care setting because additional resources, partnerships, policies, and communication platforms are needed to adequately address this widespread health issue. Lee added that this involves strategies to encourage different ways of thinking about diet among communities and the population as a whole. Walker noted that problem-solving to address complex issues such as obesity benefits from participation by a range of stakeholder groups.
A participant asked how programs providing food access can be evaluated to determine whether health was improved and whether housing should also be provided. Miladin responded that initially the evaluation process concentrated on financial ROI, as investors identified that as a priority. Because assessing quality is a developing process, UWGC is partnering with a data quality improvement collaboration—Better Health Partnership in
Cleveland—to examine quality and outcomes metrics. Furthermore, agreements are being developed to enable the tracking of program participants’ ED usage rates. Participant screeners are also used to gather self-reported data on food insecurity. Carrucciu said that CMMI is continuing to innovate about beneficiary engagement incentives to include in models, which can serve as a mechanism for exploring additional flexibility needs.
Data Capability and Determining Return on Investment
Given the complicated nature of projecting a return on investment in social services for health systems, Lee asked how the entities approach bid calculation in the CAPGI process. Miladin replied that having Nichols serve as a consultant on this project was invaluable in training UWGC staff and in helping the organization develop the bidding process. Nichols used research to arrive at returns on investment for aspects of services, which were provided to investors. Additionally, investors were asked to calculate their risk tolerance because replicating the exact results of previous studies within a new setting cannot be guaranteed and therefore involves some level of risk. Miladin said that the majority of investors do not share the factors they consider in their bid calculations but added that the rates that investors are willing to pay cluster according to managed care versus hospital settings.
Lee asked about the system capabilities needed to shift investment risk more effectively for population health overall. Carrucciu replied that hospitals would need access to data to calculate ROI; this approach to data needs to expand from a focus on the individuals receiving specific services in the hospital’s buildings to the patient population that is attributed to the entity and the broader issues they are facing, i.e., as reflected in data from social services systems. For example, CMMI is working to provide claims data at a more rapid pace in order to provide insight into how populations evolve over time and to enable a better understanding of risk and the associated interventions for those populations. To create a longitudinal record, organizations will need to take advantage of the clinical data captured in providing care by merging that evidence with claims data, Carrucciu said. Moreover, understanding trends in entire populations will require standardizing data collection to take into account social needs. Scalable data collection and scalable analyses would enable more nuanced perspectives of the ROI for entities as well as for patients.
Health entities often have insufficient data regarding individual episodes of care, Lee said. For example, while hospital budget totals for labor and pharmacy are known, the labor or pharmacy expenses for an average patient with pneumonia or pregnancy are typically unknown, making it challenging to manage cost. Lee asked Walker how cost growth targets
and quality metrics operate within Nemours. Walker replied that Medicaid is a primary payer for Nemours and that Medicaid programs vary in the two states where Nemours has hospitals, Delaware and Florida. The health system is launching a Medicaid ACO in an effort to accelerate movement toward value. Nemours is collecting data on the social determinants of health via health screeners with the hope of creatively connecting patients to community resources. Walker highlighted the need for improvements in data collection practices and systems. State data systems are not linked, which limits the ability to identify families using services from multiple state agencies. Moreover, while Nemours has data expertise and capability, it requires data from state agencies to make patient connections. Once additional data streams can be established, Nemours plans to create dashboards to track equity measures. The work ahead in increasing care value and in improving social determinants of health by connecting patients to resources presents challenges and opportunities for Nemours. Walker added that the organization has momentum toward this work.
Miladin added that data infrastructure has long been neglected in the social service sector. Building this infrastructure in partnership with health sector entities could make possible the calculation of more precise unit costs for various services. Lee asked about optimal methods of state entities and social service organizations partnering to build data systems. Miladin replied that a convening organization like United Way or a health collaborative can be central to these efforts, sparing hospitals from carrying out simultaneous data collection efforts with multiple social service organizations. In Cleveland’s CAPGI model, UWGC serves as a conduit for data flow between individual investors and social service providers. This creates a streamlined process and allows UWGC to identify areas in which the social service sector needs to increase its capabilities. Miladin noted there may a role for an organization such as UWGC to collaborate with organizations and health care providers to identify growth needs and assist in building the relevant capacity.
Lee asked about the pros and cons of building large state- or federal-level databases to address these data needs. Block replied that the state level is best suited for the current decentralized nature of the U.S. health system. Whereas a national database may seem ideal on paper, the realities of health care delivery and payment in the United States call for systems tailored to ground-level operations, she explained. Currently, state databases lack the ability to require the submission of self-insured data. Lee added that the No Surprises Act included two things that will be helpful to states: (1) for the first time, federal funding will be available for states to establish or improve all-payer claims databases, and (2) the act encourages self-insured employers to submit their data voluntarily to these databases. (The No Surprises Act was passed as part of the Consolidated Appropriations Act
of 2021, HR 133, 116th Cong., 2nd sess., [December 27, 2020]). Block remarked that while a rationale exists for a national-level approach to aggregating data, practical considerations for the foreseeable future point to the need for building databases at the state level.
Carrucciu added that each state has a unique structure for its Medicaid program, so the individual states may be best equipped to combine data sources. The federal government can play a role in setting standards and requirements—for example, by enabling standardization across state databases. The Office of the National Coordinator for Health Information Technology has set powerful standards concerning the ability to scale data collection, he said. Standards enable states to more rapidly provide data to the federal level and to communicate data more effectively with one another. Regarding data-sharing considerations, Carrucciu remarked that the Health Insurance Portability and Accountability Act (HIPAA) framework for traditional care, which pertains to individuals, does not meet the data needs for population-focused, value-based care. A shift toward value-based care will involve the evolution of many systems, including the frameworks currently structuring data sharing, he added. Lee commented on the need to consider ways to ensure that benefits accruing from dataset integration benefit the people most in need of improved health.
Patient Role in Health Spending
Lee asked about the role of patient care expectations and the perception that more care equates to better care in addressing rising health costs. Block suggested that relying on individual decisions is less likely to be effective than a coordinated strategy that enables all participants in the health care system to work together toward collective goals. She added that high health care costs translate into high insurance premiums for individuals and families. Carrucciu said that CMMI is considering how to simplify and enhance beneficiary education materials to better explain the benefits and implications of value-based care. He added that beneficiary education will play a role in successfully scaling up value-based models.
Accelerating Momentum Toward Value-Based Care
Given the years of research and experiments on reducing health spending waste, controlling costs, and improving value, Lee asked what is needed to make meaningful progress on a national level. Carrucciu answered that while some local efforts have been effective, scaling to the national level will require investments in value-based infrastructure and capacity building. He added that the ability to share claims and referrals data with community-based organizations could accelerate value-based care. Miladin noted that
building prototypes, learning from them, and scaling them takes time. He suggested that the next steps in bending the cost curve could include supporting initiatives such as CAPGI and investing funding and resources into historically underfunded social services. Walker said that leadership is needed to direct greater investment in the health and social services sectors and to possibly combine these sectors. Investments made in infants and toddlers can require a 10-year timeframe to show results, yet managed care contracts operate on 1- to 3-year cycles that are too short to allow for the full ROI of a public good to emerge. Block added that while the body of evidence regarding value-based care has grown, work remains in connecting leaders with that evidence. The MMF model begins with governance and trust in common data and collective problem-solving; governance and trust then drive efforts toward achieving a shared goal.
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