When the Affordable Care Act passed, accountable care organizations (ACOs) seemed like the future hope for health care in the United States, but the reality 10 years later is that implementing ACOs and changing health care has not been as simple as initially expected, said Francis J. Crosson, MD, immediate past chair of the Medicare Payment Advisory Commission (MedPAC), during a session at the National Association of ACOs fall meeting.

He was joined by Michael E. Chernew, PhD, the current MedPAC chair and a co-editor-in-chief of 

, to discuss the future of value-based care.

Crosson noted that it has become apparent to those involved in the day-to-day work of managing ACOs that implementing them successfully has been challenging due to a number of factors. During his presentation, Crosson focused on 4 major barriers outlined in a June 2020 report from MedPAC.

“We believe that if these can be addressed or resolved, it can make a major difference in the success of the ACO movement,” Crosson said. “ACOs have a big fan in MedPAC.”

1. Medicare beneficiaries may not understand what ACOs are and feel no particular inclination to join them or cooperate with them.

Crosson highlighted the success of Medicare Advantage and pointed to how much these plans advertise and explain what the program is around enrollment time. In contrast, ACOs need to do a better job of educating beneficiaries and implementing some practice incentives to get beneficiaries to accept and maintain a relationship with ACOs.

2. ACOs do not have incentives to manage Medicare Part D drug costs at a time when these costs are increasing.

Rising prescription drug costs are a well-known issue and have the attention of the broader public, and “it would be good if ACOs were seen as and were actually the solution here,” Crosson said. “But that’s not the case at the moment.”

One option would be to add Part D spending to the ACO benchmark, but most of the MedPAC members didn’t think that was a good idea, he said. Another more feasible idea would be to have CMS facilitate a voluntary association of ACOs with Part D plans with incentive sharing.

3. Most hospitals having conflicting financial incentives with ACOs.

Few hospitals operate off global payments; instead, their revenue is based on “heads in beds.” However, savings come from managing the cost of hospital care and drug costs.

“It’s always been my concern that to achieve what ACOs can achieve without an ability to manage hospital costs and manage costs of prescription drugs leaves ACOs with a relatively narrow set of options on the cost savings side,” Crosson said.

It remains a challenge to align hospital financial incentives with those of ACOs, which may be why hospital-owned ACOs don’t do as well as physician-led ACOs, he conjectured.

4. Most ACOs are still wedded to fee-for-service (FFS) payment.

If it is known that FFS causes overutilization, Crosson asked, why aren’t we pushing for a different form of payment? In Europe, FFS works because it is paid in the context of a global budget. In the United States, ACOs should look at how they pay their physicians and use less FFS payment, he said.

Chernew then noted that the federal government is facing serious financial pressures, so despite talk about value with alternative payment models (APMs), what’s really driving the move to APMs is fiscal performance. However, the commercial sector is facing many of the same financial problems as the Medicare program.

In Medicare, the demographics are a challenge. “It doesn’t take a genius to understand that if there are more beneficiaries per worker, the workers have to pay more to fund the care for the beneficiaries,” Chernew said. As a result, the country has to pay close attention to financing the care of baby boomers, but “many of the potential sources of savings have already been harvested, in a budget sense,” he said.

Because no more money is coming from the federal government, the United States needs to be more efficient in producing care, Chernew said. The theory behind APMs is that they provide flexibility in how to provide health care services to produce health outcomes. The question is how to get them right.

ACOs do save money, but they don’t save that much money and they either maintain quality of care or improve it by just a little. “Right now, I think unambiguously these programs are a success, just not that much of a success,” Chernew said. “Not yet.”

However, it is clear that ACOs can perform better. In general, the move to value is a long-term endeavor, which will provide time to learn more about doing a better job, and research has shown that ACOs do better over time.

Progress has been slow with ACOs because they face operational difficulties, such as risk adjustment and the benchmark design, as well as cultural issues. Chernew’s biggest concern is that CMS is providing mixed and overwhelming messages with too many programs. He recommends that the number of models be reduced in the future and that the existing models be aligned and harmonized.

As for risk and benchmarks, Chernew noted that although he is a fan of downside risk, he believes it’s overrated. Not only is the impact more modest than once thought, but there are participation concerns. As for benchmarks, they currently put organizations in the situation whereby their benchmark goes down if they save a lot of money, and if they save more money, the benchmark goes down again.

Finally, Chernew pointed out that what works is targeting low-value care broadly, as opposed to setting up broad disease-management programs. Evidence has shown that organizations do not necessarily save money by spending money or bringing more patients in and testing them all. Instead, getting rid of unnecessary care or shifting to more efficient sites of care works to save money and improve quality.

“It’s really about reducing things that weren’t needed,” Chernew said.

1. Medicare Payment Advisory Commission. Report to the Congress: Medicare and the health care delivery system. Medicare Payment Advisory Commission. June 15, 2020. Accessed November 10, 2020. http://www.medpac.gov/docs/default-source/reports/jun20_reporttocongress_sec.pdf

Articles

The American Journal of Accountable Care

Coverage of the National Association of Accountable Care Organizations (ACOs) fall meeting.

The Future Success of ACOs Depends on Fixing Current Challenges

There is no doubt that 2020 will long be remembered as the year when the SARS-CoV-2 virus led to a pandemic of coronavirus disease 2019 (COVID-19), causing significant morbidity and mortality and changing almost every dimension of life and activity for individuals, governments, and commerce around the globe. Looking toward 2021, it seems likely that the new year will be busier than ever for health policy and health services research, and 

) stands at the ready to publish and disseminate critical research and expert thinking about the challenges that face the health of populations and the operations of health and public health systems. As the year ends and results from several successful phase 3 COVID-19 vaccine trials are used in submissions to the FDA for emergency use authorizations, it will be critically important for the research and policy community to follow a number of important issues pertaining to the vaccine in the coming year. Important topics include policies for allocating initial scarce supply to the most vulnerable populations, willingness of the individuals to trust the scientific and regulatory approval process and get vaccinated, and the ability of the supply chain and providers to keep up with vaccine demand. Ethical issues are critically important, such as access to vaccines in underdeveloped and developing countries, as well as for disadvantaged populations at home in the United States. Of course, it will also be important to monitor the real-world effectiveness of COVID-19 vaccines to determine if the promising results in the phase 3 trials hold up in the larger population and to better understand the length of protection from COVID-19 that the vaccines afford.

As if these questions are not enough, there will be additional critical questions to investigate as the vaccines eventually provide both protection and relief from COVID-19 for significant portions of the population. Specifically, it will be important to see if and how health care systems in the United States and around the world are able to rebound, and whether the heavy toll of the pandemic results in significant investments in public health infrastructures in an attempt to prevent a similar situation in the future. Evidence regarding the health impact of delayed care—such as missed well and preventive care visits during the pandemic—as well as whether rates of telehealth visits and virtual consults continue in the postacute period of the pandemic will be important to track. It is virtually certain that there will be unexpected new findings that will be attributed to the pandemic that in time may seem obvious, but at the moment may be merely hypotheses. For example, at a recent visit to an eye clinic, an optometrist predicted that the pandemic will have a significant impact on vision, particularly for young students or working professionals who have endured increased use of computers and other online devices and technologies to study and work. In another recent conversation, an orthopedic surgeon mused about the possibility that work-from-home policies, likely in spaces that were not ergonomically designed, could increase the likelihood of musculoskeletal and other related conditions unless the home workspace allowed for some protective impact, such as more convenient ability to get up and move frequently. There is no doubt that we will see a rash of research studies and evidence that report on the impact that the pandemic has had on the health of populations, and ultimately the care that will be needed to be delivered—and paid for—going forward. 

 welcomes these contributions as an outlet that publishes good empirical science in a manner that is accessible for health care professionals and policy makers.

Speaking of public policy, the new year will bring a new presidential administration in the United States, as well as leadership changes in critical cabinet positions and at some federal agencies responsible for health care financing, organization, and delivery, as well as public and population health. During any shift in administration, there are key changes in health policy to track, and one expects this to be even more pronounced coming out of COVID-19. As several articles published in the December issue of 

 illustrate, new evidence to evaluate current policy approaches, or to suggest new approaches, is what occupies the pages of our journal. In the current issue, for example, there are articles that address critical components of the health care financing and delivery landscape in the United States. An article by Clark et al compares the performance of 2 types of Medicare accountable care organization (ACO) models: the shared savings model and the Next Generation ACO model. Another article by Gascon et al focuses on the process of developing bundled payments for specific clinical conditions, an approach that has garnered significant attention as remedy for the perverse incentives of the fee-for-service payment system. An article by Highfield et al puts forth a conceptual framework for addressing the social needs that are believed to contribute to the health of patients, such as safe housing, food security, and protection from domestic danger. Given growing levels of income inequality in the United States, as well as the economic impact of the pandemic, this issue is now more important to understand than ever. Finally, in a summary of a recent panel session at the National Association of ACOs meeting, Laura Joszt highlights views about the effectiveness and future of ACOs by the current and immediate past chairs of the Medicare Payment Advisory Commission: Michael E. Chernew, PhD, and Francis J. Crosson, MD, respectively. Importantly, 4 changes are suggested to make Medicare’s ACO program more impactful.

In closing, the quality of content in our current issue illustrates the importance of the work we publish at 

. As mentioned earlier, we look forward to publishing important future research, and as always we invite our readers to formally share their thoughts on these published articles in the form of commentaries or informed opinion pieces pertaining to public health policy or clinical heath practice.

A letter from the editor-in-chief of The American Journal of Accountable Care®.

Health Services Research and The American Journal of Accountable Care®: More Important Than Ever as We Enter 2021

 To present a conceptual framework to address social needs through accountable health programs in the United States. We present our implementation of the Accountable Health Communities (AHC) Model as an example.

Our conceptual framework of the AHC Model is adapted from the UK Rainbow Model of Integrated Care for the US health care context based on current literature. Our approach is further underpinned by Medical Research Council guidance on intervention development stages, recent framework development in intervention coproduction, and quality improvement methods.

Our team used the adapted framework coupled with Medical Resource Council stages for intervention development to create a program scope and sequence. Standard operating procedures and an implementation plan were created and approved by the Center for Medicare and Medicaid Innovation. Implementation was successfully launched at 13 clinical delivery sites and completed screenings of more than 10,000 patients have been done to date.

Our conceptual framework, which we are applying as a bridge organization in the AHC Model, can serve as a model that organizations can use to successfully design interventions to address social needs in clinical care settings in the United States. Further application and testing of the framework are warranted to advance understanding of social needs interventions in the United States.

Recognition of the driving role of social determinants in health outcomes, coupled with the untenable cost curve of US health care, has led to calls for action in the health care system. Major professional groups

 have challenged health systems to assess and address social determinants of health (SDOH) in a variety of ways. This has led to a plethora of activity across the US health system, including recent efforts to delineate between upstream SDOH interventions and midstream social needs interventions.

 Recent efforts driven through health system reform have focused primarily on social needs. These are individual-level factors, such as housing instability, that can be assessed and addressed within a specific patient interaction (visit) or as part of a patient panel, as opposed to a broader whole-population or community effort to address upstream SDOH.

 Although interventions addressing social needs are new in the United States, the United Kingdom has been engaged in similar social prescribing innovations for several years and offers the opportunity to inform practice development in the US context.

Although there is no standard definition, the concept focuses on expanding the role of health care (typically primary care) to include factors outside medicine such as social needs.

 Programs implemented to date generally have included 3 components: (1) referral from a health care professional related to an identified social need, (2) consultation with a link worker, such as a community health worker or patient navigator, to set goals and action plans, and (3) use of local community organizations and programs to address identified needs.

 These align with programs to address social needs that are gaining traction in the United States. The CMS Accountable Health Communities (AHC) Model is one social needs intervention that is currently undergoing testing and has similarities to social prescribing programs in the United Kingdom. The AHC Model focuses on screening beneficiaries in 5 domains: housing instability, difficulty paying utility bills, food insecurity, transportation, and interpersonal violence. Screening is coupled with referral to community resources and patient navigation (typically through a link worker).

 A bridge organization serves as the “hub” to coordinate activities.

The AHC Model is designed to be flexible and allows bridge organizations to use theories, conceptual frameworks, and strategies best suited to their clinical delivery system and community needs.

 The literature published to date on accountable health–type programs and social needs screening in the United States has discussed some factors that influence implementation, such as readiness for change, organizational factors, culture or climate, capacity, and use of implementation strategies (eg, facilitation). However, a number of gaps remain in the evidence base, including a lack of specification on how authors have designed specific social needs interventions and chosen implementation strategies and whether theory or frameworks were used in these processes.

 In other words, the AHC Model and social needs intervention movement nationally are very much an opportunity for learning. The current state of the field is further complicated by the lack of integrated health services research and dissemination and by the lack of implementation sciences frameworks to guide the development of interventions that require both intra- and interorganizational change.

 Our objective with this paper is to propose a conceptual framework for social prescribing tailored to the US context and to share our application of the framework as a bridge organization participating in the CMS AHC Model.

The overarching goals of social needs interventions align with the Triple Aim and include improved connections of patients to community services, reduced health care utilization, reduced health care costs, and improved health outcomes. We hypothesize that adoption and implementation of social needs interventions in health care systems is similar to adoption of any innovation and is influenced by characteristics ranging from the individual to environmental. Using the 2016 version of the Rainbow Model of Integrated Care (RMIC) conceptual framework,

 we present a conceptual framework. The RMIC was originally developed by Valentin et al in 2013 to combine integrated care and primary care, with particular attention to the multicomponent, complex nature of integrating social and health care interventions, including social prescribing.

 The model was expanded in 2016 to include the Triple Aim outcomes of cost and utilization, population health, and care experience.

 Related studies have conducted international expert consensus panels to determine a taxonomy of constructs for the framework.

 The framework is premised on population and patient-focused care, which includes personal preferences, values, and medical, psychological, and social needs of individuals, and on service delivery concerns that are inclusive of health, economic, social, and environmental needs and promote health equity and well-being.

Macro-level integration includes alignment of inter­organizational frameworks and policy decisions that aid the delivery of integrated care to populations.

 Determinants of system integration that apply to social prescribing interventions include resources, engagement of stakeholders, and the political, economic, and social climate in which integration is taking place.

Meso-level integration includes organizational and professional integration. Determinants of organizational integration can include culture, language, backgrounds, bureaucratic structures, professional roles and responsibilities, organizational visions, and regulations among social and health organizations.

 Professional integration focuses on inter- and intraorganizational relationships.

 Determinants for professional integration can include trust; shared understanding of roles, responsibilities, ethics, and common language; and respect and communication.

Micro-level integration includes clinical integration. This refers to the extent to which services are effectively coordinated.

 Determinants of clinical integration can include continuity of care, quality of interaction between the professional and patient, use of care plans, and resources such as personnel and competing demands.

The framework further delineates functional and normative integration as key to success. Functional integration is the coordination of key support functions and activities—for example, financial and information management, strategic planning, human resources, communication, support systems, regular feedback, and quality improvement.

 Normative integration refers to the development and maintenance of a common frame of reference among organizations, professional groups, and individuals.

 Key determinants of normative integration are trust, visionary leadership, conflict management, and perceived need and purpose to collaborate.

 Normative integration is important to facilitate integration at all levels and to achieve a sustainable intervention aimed toward achieving the Triple Aim outcomes.

 Previous research has found that normative integration is a critical enabler to the implementation of integrated care.

Proposed Adapted Conceptual Framework for Social Needs (Social Prescribing) Interventions in the United States

We propose the addition of 3 categories to the RMIC model as shown in 

: intervention characteristics, hub organization characteristics, and community integration; these would increase alignment with the US health care and social services delivery systems and current implementations of social needs programs. We also suggest several determinants to be considered and specified by authors under each integration level of the RMIC as they relate to social needs intervention implementation as shown in 

 References for selection of factors in each category are noted in that figure.

Case Study: Applying the Social Needs Conceptual Framework and Medical Research Council Guidance to Designing Our AHC Model

) identifies the selected factors from the framework that we are targeting in our implementation (shown in white). Our research team chose factors that we believed to be modifiable, and we identified those which were not readily modifiable but could moderate the impact of our implementation, as shown in red in the eAppendix Figure. We selected the constructs for planning our implementation based on literature review and previous research.

Our implementation plan for the AHC Model was further developed using the Medical Research Council (MRC) guidance on intervention development and quality improvement methods, as shown in 

 We operationalized our framework using the MRC process stages on intervention development: (1) evidence review and stakeholder consultation, (2) coproduction, and (3) prototyping. We used quality improvement cycles to help us improve readiness to implement and also to address trialability and adaptability of the intervention in each clinical delivery setting at the preimplementation phase. Following prototyping, we created a final intervention scope and sequence (

Stage 1: Evidence Review and Stakeholder Consultation

As shown in the Table, we began with a series of meetings with all levels of the clinical delivery site (CDS) and implementers to promote normative integration by garnering buy-in, support, and a sense of shared purpose and vision for the AHC Model in our community. We focused on functional integration by understanding the CDS’ current workflows and any potential constraints we needed to solve as a leadership team for successful AHC Model implementation. We conducted site visits and developed process maps of patient flows and workflows for intervention development as a team. At the intervention level, we discussed the trialability and relative advantage of implementing the AHC Model with executive leadership through frontline staff in a series of meetings to discuss how the program would be operationalized. We identified opinion leaders and a program champion at each CDS. A series of meetings were held with information technology leadership and staff to inform them about the AHC Model and to create and prioritize data transfer protocols among organizations. Meetings were held with link workers to discuss patient interactions with a focus on social needs, present the AHC Model, discuss intervention strategies for patient navigation, and garner their input and buy-in for using a theory-based navigation script. At the management level, we focused on both normative and functional integration through meetings held with CDS leadership to ensure executive-level engagement and support and to assign roles and responsibilities for program staff. The input of CDS leadership was sought on the approach to navigation, alignment with broader organizational goals, and brainstorming potential barriers and facilitators to implementation.

We then moved to development of standard operating procedures (SOPs). Coproduction was used to create the project SOPs. The bridge organization first created draft SOPs based on our in-person meetings and process maps using the prespecified Center for Medicare and Medicaid Innovation (CMMI) format. These were presented to our clinical delivery partners in an in-person meeting in which the collective team reviewed the content and edited the documents in real time. The resulting SOP draft was then emailed to CDS leadership and staff for final edits and approval.

The SOPs were prototyped and tested in each CDS prior to development of our AHC Model training protocols. Bridge organization leadership and the AHC Model program manager met CDS managers and staff on each unit to pilot-test our procedures. We visited a minimum of 4 patients in each unit to complete screening, referral, and navigation processes using the SOPs. After each patient, the team debriefed about the process and identified any steps that needed additional review or changes. Once final processes were agreed upon, staff were provided formal training for AHC Model implementation. Training was done on multiple levels: first in a structured didactic environment and then with hands-on training conducted in each CDS. Hands-on training consisted of embedding the academic team in the CDS and having the members work alongside the staff (screening, referring, navigating) until they expressed readiness to start program components. Structured debriefings took place during the hands-on training. Once staff began implementation, we moved to quality improvement and assurance. A linkage system with mirrored operational teams housed at the academic site was set up to provide ongoing technical support and assistance and to continue to foster the conditions for successful implementation through expert facilitation. Monthly meetings were scheduled with CDS leadership and staff to support implementation. Meeting agendas were cocreated by the CDS and bridge organization.

Our approach to AHC Model evidence review and stakeholder consultation resulted in the creation of process maps detailing current patient encounters in each CDS. Based on our meetings, all 13 CDSs expressed readiness to implement the AHC Model. All sites continue to successfully implement the model. To support implementation and based on our meetings, the bridge organization embedded behavior change and theory into our navigation protocols. We scripted these for staff using the transtheoretical model of change.

 Clinical integration focused on creating secure data transmission protocols using HL7 messages or flat files from each CDS to our secure CMS Certified Data Center. Our software was created to allow staff across all locations to log in and complete all 3 processes (screening, referral, and navigation) in a seamless fashion. We used funds from the cooperative agreement to support packaging the intervention through the creation of a software platform and development of training protocols for implementation. At the professional level, we discussed each CDS’ philosophy of delivering care so we could tailor scripts for their patients and staff based on this. For example, one CDS focuses on wellness as a core tenet. We embedded the use of the term throughout our introductory scripting and in the text provided to patients in the community referral summary. We used standard language for staff across the CDSs when discussing the AHC Model and provided definitions for key model terms.

Coproduced SOPs were successfully created for all 13 CDSs. Our process of creating SOPs crossed functional and normative integration levels by incorporating operational factors and focusing on collaborative creation of an implementation approach. As we moved toward implementation, we evaluated, adapted, or created any necessary data systems and data transmission protocols using human-centered design approaches to ensure fit with the individual implementers and to support overall program uptake, coordination, and maintenance.

The prototyping step allowed our team to identify missing components needed for successful implementation. For example, we discovered that even clinical staff needed a scripted introduction to use when introducing the AHC Model screening tool to patients. During the prototyping step, we were able to try different scripts and develop tailored language for each CDS. We also identified that use of laptops was impractical in the clinical delivery environment and switched to tablets in rolling kiosks to improve ease of use. By switching to rolling kiosks, we were able to streamline the AHC Model workflow for staff members during the screening. The kiosks housed the screening tablet as well as a printer to instantly distribute program material. The kiosk also housed hospital-grade cleaning wipes. Staff members no longer needed to return to patients’ rooms after retrieving documents from a stationary workstation printer as would have occurred with the use of laptops. The kiosks also allowed for an additional level of privacy for participants completing the screening with the addition of a 4-way privacy screen. With the privacy screen in place, only the participant completing the screening tool could see the questions and responses, so clinical staff could confidently let patients self-complete the screening questions. Functional integration, including identification of where to store kiosks and supplies, was done in the prototyping step, as this practical need was not raised by any stakeholders in previous interactions. Thirty-two staff members successfully completed training and expressed readiness to implement. Following training, staff achieved an average 57.1% survey completion rate.

Final Scope and Sequence for AHC Model Implementation

As shown in the Table, our team created the final scope and sequence for developing our AHC Model implementation.

The adapted RMIC conceptual framework was successfully applied across 13 CDSs including emergency departments (EDs), labor and delivery departments, and ambulatory clinics. Previous studies have shown the importance of stakeholder involvement in program design.

 We found that the MRC stages were easy to apply and fit well with our pragmatic orientation to intervention development. Extending the MRC stages to include implementation through the use of quality improvement cycles and ongoing technical assistance and support for CDSs has been helpful to support implementation of the model. To date, our collaborative team has successfully completed screening on more than 10,000 patients across these locations, and 43% of patients screened were found to be high risk (positive for a need and had been in the ED 2 or more times in the past 12 months) (n = 4300). The needs reported with the highest frequency by all patients were food insecurity (40%), housing instability (30%), and transportation (28%). The primary outcome measures of focus for the AHC Model currently are health care utilization (ED) and health care costs, both of which will be evaluated post model by CMMI and the contracted evaluation organizations using patient claims data (beyond 2022). We are unable to provide details on the evaluation plan for the AHC Model given that our role as a bridge organization is focused on our implementation. That said, our perspective as a bridge organization is that navigation and the creation of new navigation cases represent the hypothesized mechanism through which these outcomes will be changed, and related data may serve as useful indicators for the model’s implementation. Each bridge organization is expected to create new navigation cases for 2048 patients annually. To date, our team has achieved approximately 95% of the goal across all sites. Navigation cases created do vary by site, with ambulatory sites having fewer high-risk patients and generating fewer navigation cases than ED locations. The data system created for this project allows for the generation of reports showing the number of navigation cases created by each site at custom time periods, which allows us to monitor and adjust staffing at regular intervals. Of patients deemed eligible to receive navigation, 96% agreed to receive the service (opt in) and 92% were successfully contacted by our navigators to create a patient action plan and for ongoing follow-up. The team held weekly staff and monthly leadership meetings to discuss model metrics (specifically navigation) and barriers and facilitators of implementation related to model constructs that could affect success, including self-efficacy, communication, outcome expectations, and competing demands. During staff training, we created curricula to focus on building self-efficacy and communication skills along with targeting perceptions of intervention constructs (eg, adaptability, complexity, trialability). Training also focuses on constructs at the integration levels including buy-in, communication, readiness for change, and organizational support. To date, 92% of staff reported feeling confident to implement the model following training.

Implementation using this model was not a quick process. As with any endeavor aiming to build integration among organizations, finding time to consistently come together to collaborate was a challenge. To ensure participation, meetings had to be scheduled far enough in advance to accommodate busy schedules. Navigating the mandatory policy and procedures of multiple bureaucratic organizations delayed the speed at which decisions were ratified. The differences in the types of organizations also led to different and sometimes competing outcome expectations, goals, and priorities. We addressed the issues of navigating organizational polices, outcome expectations, goals, and priorities through transparency and the sharing of information using community-based participatory research principles. We adapted project tools to meet organizations’ concerns and changing needs wherever possible, treating our approach as quality improvement in all facets. These accommodations directly affected the perception of our partners’ packaging, adaptability, and trialability of the intervention. We scheduled regular check-ins with staff members to continually build their self-efficacy. We devised advanced organizers and reports to provide managers with additional support while tracking their staff’s activities. These organizers reminded managers of critical implementation functions that needed to be routinely monitored, and the reports offered an avenue for progress to be checked. We also reframed retraining sessions as opportunities to address the fit of the current SOPs. Lastly, personnel turnover is a challenge, as buy-in, trust, and the transfer of knowledge were necessary for successful implementation. Packaging of trainings (eg, videos, online) could serve as a tool to reduce training burden for future organizations. Future studies implementing and testing the model constructs proposed herein would further the field of social needs interventions in the United States. Application of the MRC framework in the United States also warrants further application and testing.

Department of Management, Policy and Community Health Practice, UTHealth School of Public Health (LH, GF, RM), Houston, TX; Department of Internal Medicine, McGovern Medical School at UTHealth (LH, KOH), Houston, TX; Ambulatory and Population Health (RW) and Community Benefit Corporation (CP, DG), Memorial Hermann Health System, Houston, TX.

 This publication was supported by the Centers for Medicare and Medicaid Services (CMS) of the US Department of Health and Human Services (HHS) as part of a financial assistance award totaling $529,632 with 100% funded by CMS/HHS and $0 amount and 0% funded by nongovernment source(s). The contents are those of the author(s) and do not necessarily represent the official views of nor an endorsement by CMS/HHS or the US government.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (LH, RW, KOH, RM); acquisition of data (LH, GF, RW); analysis and interpretation of data (LH, RM); drafting of the manuscript (LH, GF, KOH, RM); critical revision of the manuscript for important intellectual content (LH, GF, KOH, RM); statistical analysis (LH); provision of study materials or patients (LH, CP, DG); obtaining funding (LH, RW, RM); administrative, technical, or logistic support (LH, RW, CP, DG, RM); and supervision (LH, CP, DG).

Linda Highfield, PhD, UTHealth School of Public Health, 1200 Pressler St, RAS Bldg E909, Houston, TX 77030. Email: Linda.d.highfield@uth.tmc.edu.

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No published studies exist on use of intervention design, implementation strategies, and theory in the CMS Accountable Health Communities Model, the largest-scale test of social prescribing in the United States.

A Conceptual Framework for Addressing Social Needs Through the Accountable Health Communities Model

In this manuscript, we provide a case report of a novel methodology for the analytic development of bundled payments designed to be implemented in the commercial market. In winter 2016, an integrated health system’s senior leadership tasked the system with the development of episode-based bundled payment programs for its employer-sponsored self-insured health plan. A steering committee was formed to manage the effort, and 6 inter­dependent teams were organized to address each work stream. Analytic objectives included the development of a methodology for clinical opportunity identification, data analysis and communication, and bundle development. Two data sources were used to understand clinical opportunity: the health plan’s administrative claims to understand practice patterns before, during, and after a bundled payment episode, and facility charge detail to understand utilization variation within and across providers in a bundle. Through statistical analysis, data visualization, clinical consultation, and feedback, 6 bundled payments were developed within 24 months.

Episode-based bundled payment programs provide patients, employers, and third-party payers with a prescribed set of health care goods and services related to a medical procedure or condition at a fixed charge. Payments for the care are distributed on a contractual basis to each provider who contributes to patient care during the episode to the extent that quality goals are met; facilities utilized during the episode of care are reimbursed at prearranged rates. The bundled payment organizes care in such a way as to produce optimal patient outcomes efficiently, taking all aspects of care before and after the procedure or treatment into account. In a commercial market, contracts for episode-based bundled payment programs are negotiated between a provider organization and a third-party payer. The patient typically pays less than they would for the episode of care had the program not been available.

Research regarding the development of bundled payment programs in the private sector has focused on the establishment of a bundled payment program,

 a description of their clinical parameters,

 a description of their operational development,

 the administration of a bundled payment program,

 This paper contributes to the literature by providing a case study of their analytic development.

In winter 2016, OhioHealth senior leadership advised the leader of population health analytics (G.I.S.) to prepare several episode-based bundled payment programs for the consideration of its employer-sponsored self-insured health plan (hereafter referred to as “health plan”). The intent behind the request was to work with the clinical service teams to improve the value of care provided across several high-volume procedures and conditions in order to support its go-to-market strategy. A steering committee was formed across service lines to identify goals, objectives, and milestones.

Six interdependent work streams were organized to address the foundational aspects of bundled payment program development. This manuscript focuses on the efforts of the first work team, clinical opportunity development. Pursuant to its objectives, analysts from a variety of departments collaborated to develop the data architecture required to analyze spending and utilization across the episode. The analyses were conducted in 2 phases: the first using the claims of the integrated health system’s health plan to develop baseline pricing given clinical opportunities to increase the efficiency of care delivery, and the second using charge detail from a financial decision support system to understand opportunities to reduce spending, resulting in an estimated target price.

Phase 1: Using Administrative Claims to Understand Procedural and Medical Episode Costs

In the first phase, the analytic team submitted an administrative claims file to its analytic business associate, Aver Inc, in December 2016. Aver applied bundle methodologies to the claims using the episode of care definitions developed by PROMETHEUS Analytics maintained by Altarum. The PROMETHEUS algorithm provided logic to group claims into episode phases such as preprocedure, procedure, and post procedure. PROMETHEUS’ Episodes of Care Definition Summary Descriptions are only available to licensees and partners of PROMETHEUS Analytics, but their format is described generally in the literature.

 Using the methodology, Aver produced initial high-level results for 90 episodes in March 2017. In 1 case, the team chose to use the CMS bundle definition of an episode as opposed to the PROMETHEUS definition since the operations team was already participating in the same bundle for the Medicare fee-for-service population and did not want multiple definitions for the same episode. The analytic team validated the initial results in SAS 9.4 (SAS Institute), shared them with the integrated health system’s clinical leaders to obtain feedback on the components of each bundle, and by April 2017 prioritized 29 episodes of care for bundled payment development consideration based upon existing volume and potential clinical variation.

Beyond clinical variation, several other prioritization criteria were employed in order to reduce the number of bundled payment candidates, including the ability to manage outcomes (where procedural bundles would be prioritized over chronic conditions, acute conditions, and system failures), physician and service line engagement, providing a level playing field for all providers in the integrated health system’s clinically integrated network, interest from an employer’s commercial populations, case volume, and synergies with existing system quality or process improvement activities. These criteria further reduced the list of candidate bundles from 29 to 10.

Across all 10 episodes of care, the total cost was determined by summing the reimbursement levels associated with codes for the pretrigger (episode) period, trigger professional payments, trigger facility payments, and posttrigger periods. In the aggregate, facility costs comprised approximately 79% of the total spend. Given this, the next goal was to identify actionable information about variation in physician practice patterns within the facility trigger period in order to reduce the cost of each episode, as facility costs would comprise the majority of baseline price for each bundled payment.

Phase 2: Using Financial Data to Understand Treatment Variability and Set Baseline Prices

In the second phase, the analytics team drilled down into each episode to understand the clinical variation associated with the facility charge component of each episode using the health plan’s claims data and internal OhioHealth facility revenue and charge data from EPSi (Chesterfield, MO), a financial decision analytics platform. Thus, 2 related but independent analyses were occurring simultaneously to develop a comprehensive perspective for clinical leader input. Initial analyses of the 29 bundled candidates revealed the high variability of clinical costs within and across sites of service.

Charge data were indispensable in this effort because some health plans pay the facility portion of a claim as a percentage of total charges. Thus, all line items that were included as charges to the patient had to be reviewed. Utilization of financial decision support system data enabled review of line item detail for drugs, supplies, and services (claims detail is summarized at the revenue code level). Charge data provided a way to analyze physician practice patterns at a level that claims could not provide, but it came at the cost of low sample sizes, because the cases under analysis were limited to health plan patients. Low per-physician per-facility sample sizes created the need for analysts to review patient acuity and outliers carefully. As a proxy for patient acuity, physician variation was analyzed at a diagnosis-related group level, which helped to ensure that patient heterogeneity was dampened; cost outliers were defined using a heuristic from statistical process control theory.

Even with these precautions, subsequent analyses demonstrated the need for larger sample sizes in order to have reliable and valid results at a physician level. One potential solution was to identify the extent to which patients from multiple payer populations could be combined with the health plan to analyze clinical variation within and across facilities. A hypothesis was developed and tested to identify if the charge distributions were similar in the health plan and other commercial payer populations. Satisfied that the assumptions of the test were met, a Kruskal-Wallis H test was performed. In each case, no statistically significant differences were detected. Thus, the combined populations were used to analyze average charge per case at the individual line item level across all provider groups, providing analysts and clinical leaders a perspective on provider utilization variation. Because the goal was to determine a target price for each bundle, the average charge per case for all commercial payers was adjusted to the employer-sponsored health plan contract rate for all cases. This analysis provided the core definitions for each bundle: the pretrigger, trigger, and posttrigger time frames, and the baseline total cost of care. It also highlighted the need to drill into the trigger facility portion of the baseline spend. Enterprise charge data were used to augment the analysis and provided the detail necessary to understand the opportunities for cost reduction. 

 illustrates the relationship between the information gained in the 2 phases.


Presenting Phases 1 and 2 Data to Clinical Leaders

To identify clinical opportunities to increase the value of procedural and medical episodes of care that could be developed into bundled payments, analysts developed a presentation platform in Microsoft Excel for clinical leaders, consisting of detail and variance tabs. The variance tab described spending patterns at the revenue code level (spending categories describing items and services charged to a patient). The 

 illustrates the information reviewed at a revenue code level to help focus analysis in areas that would provide the greatest impact. Because the goal was to determine a target price for each bundle, the average charge per case for all commercial payers was adjudicated to the health plan contract rate for all cases. This converted the average charge per case to an average baseline price per case for the facility component of the trigger event. The intent was to inform leaders as to which spending category had not only high variation in dollars but also high utilization. The detail tab was subtotaled at 3 levels—individual line item, revenue code, and grand total—and displayed the unit charge amount for each item, the number of cases using that item, and the average charge per all cases. This information was displayed by physician or physician group, as appropriate for the episode, and provided a complete view of utilization, highlighted variation among providers, and was reviewed with clinical leaders who helped to identify clinical opportunities and provide the basis for making change recommendations. 

This case report of a novel methodology for the analytic development of bundled payments addresses statistical analysis, data visualization, and clinical consultation.

December 2020

The Future Success of ACOs Depends on Fixing Current Challenges

Health Services Research and The American Journal of Accountable Care®: More Important Than Ever as We Enter 2021

A Conceptual Framework for Addressing Social Needs Through the Accountable Health Communities Model

A Case Report of the Analytic Development of Bundled Payments for the Commercial Market

Differences in Savings and Quality by Type of ACO Model