Medicare Accountable Care Organizations: Clinical Performance for Patients With Heart Failure

ABSTRACT

Objective: To understand factors that distinguish higher-performing accountable care organizations (ACOs) in the Medicare Shared Savings Program from those with average levels of clinical performance for patients with heart failure (HF), a common and complex condition.

Study Design: We used a 2-step design. First, we measured ACOs’ clinical performance using Medicare data to calculate risk-standardized admission rates over time (2014-2017) for patients with HF. Next, we conducted 16 videoconference interviews with key informants in 2 ACOs that were average performers on this measure and 2 that were higher performers. At each ACO, we interviewed at least 1 of 4 key informant types: top leader, manager or director, clinical-level staff, and HF clinician or cardiologist.

Methods: We developed semistructured interview guides that included questions about ACO history and geographic context, organization and governance, management and leadership, and care management practices.

Results: A combination of factors distinguishes higher-performing ACOs from average performers: fewer ACO staff and offices, majority-White beneficiary populations, and primary care physician leaders. In addition, higher-performing ACOs were characterized by close communication, real-time information sharing, regular feedback, physician buy-in, and strong care management programs.

Conclusions: The results suggest a challenge in scaling up the best practices of small, primary care–centered ACOs to improve the performance of larger, complex hospital-dominated health systems. The results also suggest prioritizing the development of data and information systems that track patients in real time and provide clinicians with regular feedback on the quality of their work.

Takeaway Points

Results from qualitative case studies of Medicare Shared Savings Program accountable care organizations (ACOs) that performed higher vs average on rates of unplanned hospital admissions for patients with heart failure show the following:

• Higher-performing ACOs are likely to be relatively small, primary care centered, and physician led.
• ACOs perform better when they have strong care management programs that provide frequent data about their patients, especially during care transitions.
• Real-time feedback on performance to clinicians is associated with better ACO performance.
• Regular, transparent data sharing and communication help create perceptions of ownership and inclusion among ACO clinicians and staff.

What factors distinguish higher-performing accountable care organizations (ACOs) in the Medicare Shared Savings Program (MSSP) from average- or lower-performing ACOs? Addressing this question is timely and critical: The MSSP ACO program has been Medicare’s flagship for payment reform for more than a decade and as of October 2025 included 476 ACOs providing care to an estimated 10.3 million beneficiaries.^1-3

Results from prior studies show that ACOs that perform better on cost and clinical measures tend to have several distinguishing structural characteristics.^4-13 Specifically, higher-performing ACOs are smaller, physician led, and primary care focused; are not associated with hospitals^4,7,8,12; and are early adopters of the ACO model.^4,9,14,15 Prior research also shows that Medicare ACOs serving higher proportions of Black and Indigenous persons and persons of color perform worse across several quality measures compared with ACOs with a lower proportion of patients from these populations.^7,16,17

Yet these studies reporting associations between structure and outcomes provide little understanding about how ACOs achieve these results—that is, we know relatively little about the processes and practices that ACOs employ to achieve higher levels of performance.^11,18,19 We aimed to address this gap in understanding of ACO performance by focusing on the managerial and care management (CM) practices employed by high- and average-performing MSSP ACOs.

Further, because we wanted to understand the role of ACOs in improving clinical processes, we examined a specific clinical population: patients with heart failure (HF). We chose HF because the care coordination needs of patients with HF are high and because patients with HF are among the highest utilizers of care in the Medicare fee-for-service population, making HF a critical target of ACO CM and performance.^17,20-23

METHODS

Study Design

This study included a quantitative phase and a qualitative phase. The quantitative phase consisted of several sequential steps, summarized later (also see eAppendix A [eAppendices available at ajmc.com] and in a prior publication⁷). We then conducted a qualitative phase of the study, collecting data through video conference interviews with 4 ACOs with varying performance levels. The Baystate Health Institutional Review Board approved this study.

Identifying a Cohort of Patients With HF

We identified fee-for-service Medicare beneficiaries who were older than 65 years and had at least 1 hospital claim (Part A) with a principal diagnosis code for HF or 2 claims (Part A or Part B, which tend to occur in outpatient settings such as physician offices) with codes for HF in the years 2014-2017.¹⁷ We included beneficiaries in the cohort through 2018 or until death, whichever was earlier.

Next, we limited the sample to beneficiaries who were enrolled in ACOs between 2014 and 2017 and attributed patients to ACOs. Using the MSSP ACO Provider-Level Research Identifiable File (which defines each ACO as a collection of provider taxpayer identification numbers), we attributed beneficiaries to physicians within MSSP ACOs where they received the plurality of care in each study year.

Measuring ACO Clinical Performance for HF

We used previously described methods to calculate ACOs’ risk-standardized rates of unplanned admissions and mortality (eAppendix A).¹⁷ We first identified a set of 320 ACOs that were active in the MSSP for 3 years between 2014 and 2017; this approach enabled us to examine ACO performance over time. We then computed each ACO’s risk-standardized, all-cause, unplanned admission rates (RSAARs) to acute care hospitals per 100 beneficiaries with HF in the outcome years (2015-2018) for each ACO, winsorizing at the 99th percentile to exclude extreme outliers.

After risk adjustment, the mean (SD) and median RSAAR were 87 (7.5) admissions per 100 people. We observed variation in RSAARs across MSSP ACOs, ranging from 61 (minimum) to 109 (maximum) admissions per 100 beneficiaries, with the 25th and 75th percentile RSAARs at 82 and 92 admissions per 100 beneficiaries.

Sample of ACOs for Interviews

We used group-based trajectory modeling of RSAARs^24,25 to cluster the 320 ACOs in our sample into groups with similar patterns of change over time, identifying low-performing (higher RSAARs) (n = 48), average-performing (n = 239), and high-performing (low RSAARs) (n = 33) ACOs. We then stratified the high- and average-performing groups by factors defined in the literature^4-13 as key to ACO performance: geographic region, size, beneficiary race, and percentage of primary care physicians (PCPs). Using these strata and procedures described in prior mixed-methods work, ^26-28 we identified a subsample of 65 higher- and average-performing ACOs that we contacted for site interviews. Similar to prior work,^7,16,17 we found that high-performing ACOs were more likely to be smaller (have fewer beneficiary members), have a higher percentage of PCPs, have fewer Black beneficiaries, and be located in the West.

Recruiting ACOs

We contacted leaders from the selected subset of high- and lower-performing ACOs using the US Postal Service, email, and phone. Given that the recruitment began in 2020, we experienced some difficulty due to the COVID-19 pandemic. After contacting approximately 65 ACOs and receiving responses from 6 ACOs, we recruited 4 ACOs for interviews across 2 performance strata: 2 average performers and 2 high performers. Our final sample of 4 ACOs varied across key demographic and organizational factors: number of hospitals, safety-net status, timing of the first MSSP contract, and the presence of a cardiovascular center. Data from the Public Use Files also showed that the 2 high performers were PCP-led, early-adopter ACOs not located in the Northeast, had no affiliated hospitals, and served medium-size beneficiary populations. Table 1 shows descriptive statistics for the 2 high-performing ACOs (ACO A and ACO B) and the 2 average-performing ACOs (ACO C and ACO D).

Qualitative Data Collection and Analysis

Because of the onset of the COVID-19 pandemic, we opted to conduct interviews using videoconferencing, which can produce results consistent with in-person interviews.^29,30

Between February 2020 and July 2021, we conducted interviews with 16 key informants, 4 from each of the participating MSSP ACOs. We aimed to interview 4 types of informants: top leader (CEO, chief operating officer [COO]); manager or director; clinical and CM staff; and HF clinician or cardiologist. We achieved this aim at ACO C and interviewed 3 of the 4 types at the other 3 ACOs. ACO A did not have a HF clinician or cardiologist, and we were unable to interview clinicians at ACO B and D. eAppendix Table 1 shows the titles and numbers of respondents for each ACO. eAppendix Table 2 identifies the topic areas of each interview guide (see eAppendix B for full interview guides).

Interviews were recorded, transcribed, and imported into NVivo (QSR International Pty Ltd) qualitative analysis software. Three investigators coded the transcripts from the first 4 interviews and resolved discrepancies to establish interrater reliability.^31-34 Each coder then took a portion of the codebook and coded all subsequent transcripts to those codes. After completing each transcript, the coders each drafted a memo outlining key themes, points for discussion, and questions; the team then reviewed and resolved coding discrepancies. Two team members (CD, TD) reviewed the completed case memos and created a list of themes distinguishing high-performing ACOs from average performers. We obtained the results presented here by reporting practices that more than 1 respondent per ACO mentioned; if not a single respondent mentioned the practice for an ACO, we concluded that the practice was not in use.

RESULTS

Our interview data revealed 7 key practices that distinguished high-performing ACOs. Table 2 shows quotations from interviewees that illustrate the use of these practices in their ACOs.

Information Shared in Useful Ways for Physicians and Care Managers

Although all 4 ACOs had ways to track patients and share information, only the high performers delivered data to physicians and care managers in near real time and in formats directly useful for their daily workflows. At ACO A, the care managers connected to the state health information exchange, which allowed them to track and “get a clue for why [patients] were even [in the hospital] before calling hospitalized patients for follow-up.” The chief medical officer (CMO) at ACO B explained that because of electronic health record (EHR) access, they did not rely on health information exchanges; physicians and care managers at ACO B could make use of the data in daily operations via an EHR dashboard.

Similarly, all ACOs focused on social determinants of health (SDOH), but the high performers had greater formalization of SDOH pathways and readily available access to SDOH data.

High Levels of Communication Among ACO Staff and Between ACO Staff and Physicians

In contrast to the average performers, both high-performing ACOs reported small numbers of participating physician offices with strong communication and a culture of support across the staff and physicians. Staff at all levels described similar cultures, from upper management to on-the-ground clinical staff. At ACO A, the COO, director of care management, and care managers all described a culture of shared responsibility and open communication across all staff levels. Similarly, at ACO B, the CMO and director of care management each noted that their small ACO team fosters a culture of communication and improvement.

Fostering Relationships Among Physicians

The higher-performing ACOs, but not the average performers, had strong bonds between physicians, either fostered by the ACO (ACO A) or through a history of shared ownership (ACO B). The physician groups comprising ACO A had no prior history together and formed through a management company. Their ACO staff, particularly the care managers, focused on building a relationship of trust and respect with the physicians, primarily by helping patients feel more connected to their ACO physicians. The CMO of ACO B stated that it had been a “confederacy of different little practices” for more than 2 decades and drew upon that history of shared leadership.

The 2 average-performing ACOs were strongly mission driven, with a historical and cultural commitment to deep-seated values of serving the underserved. One of these ACOs was located in a safety-net system with a population that had high levels of health-related social needs and need for care coordination. The fact that this ACO was an average performer rather than a low performer suggests that these efforts were somewhat successful.

Timely, Data-Driven Feedback

Both high performers publicly shared clinician-level data directly via dashboards and/or monthly in-person meetings. By contrast, the average-performing ACOs either did not provide performance feedback at all (ACO D) or only did so in a particular yearly initiative and not for the purpose of timely performance improvement (ACO C).

Physicians Involved in Running of ACO

In contrast to the average-performing ACOs, at both high-performing ACOs, the physician-owners were involved in decision-making. ACO A is owned, operated, and governed by PCPs. The physician board members worked closely with the COO to manage the ACO and guide its development. The ACO staff met monthly both with themselves and physicians to coordinate ACO operations. ACO B’s physician board was comprised of representative members from across the various ACO practices. In addition to an active, representative board, ACO B had long-standing physician leaders who were early adopters of value-based payment models and in whom the physicians—both board members and others—had great trust.

CM Programs With High Internal Coordination and Many Touchpoints With Patients

Neither average-performing ACO reported having CM programs. In contrast, both high-performing ACOs credited strong CM programs as fundamental to their success. Both ACOs invested early in developing CM programs and integrated evidence-based CM practices, such as triaging patients to focus on those at highest risk. Within each ACO, CM teams consistently worked closely together, sharing knowledge and resources and problem-solving in real time via phone or messaging. Both offered frequent touchpoints with patients, including giving direct phone numbers and calling or seeing them at regular intervals.

Some differences existed between these 2 CM programs. At ACO A, case managers worked remotely and separate teams conducted chronic case management and transitional care management. ACO A also documented all chronic care management work and sent that information back to practices. This allowed the primary care practices to bill Medicare using chronic care management codes, which increased revenue to the practices. At ACO B, the same CM teams handled chronic and transitional care management and they were embedded in physician practices. In contrast to the average-performing ACOs, both ACOs A and B focused on highest-risk patients, integrated patient data into their approach, and prioritized close contact between patients and providers.

Overlay and Practice Change

The high performers used 2 approaches to improve clinical outcomes: practice change and overlay.^11,16 In the former, ACOs focus on reorganizing and transforming care in existing practice settings. In contrast, the overlay approach occurs when an ACO adds new services or programs to existing practices and providers. Informants reported that the practice change approach improved CM in clinicians’ offices, whereas the overlay approach added resources for CM during important transitions between care providers and home (eg, discharges from hospitals and emergency departments).¹⁶ The average performers engaged only an overlay approach.

DISCUSSION

In one of the few qualitative studies to examine the processes of care that help MSSP ACOs succeed, we identified key processes and practices that high-performing ACOs use and built on previous analyses of 320 ACOs.7 Many of these practices were also used, to some extent, in lower-performing ACOs, but in general, the lower performers reported less frequency or intensity of implementation, and in some cases, they reported a less comprehensive focus compared with the higher performers (eg, providing general performance data yearly rather than clinician-level data monthly).

This study adds to what we know about factors that promote higher performance in MSSP ACOs by focusing on their managerial and CM practices and has important implications. First, managerial practices of high-performing ACOs center on frequent, real-time communication among members, including both clinical and nonclinical staff. Perhaps the most important feature of these communication practices is the use of timely, data-driven feedback to physicians on their clinical performance.²³ Timely and transparent data sharing likely promotes physician buy-in and contributes to building strong bonds within ACOs. Small ACO size and physician leadership appear to facilitate this level of communication and data sharing.

Second, although prior studies have examined the role of CM in ACO performance,^4-13 they have not linked specific CM strategies to ACO outcomes. We found that the high-performing ACOs made early investments in CM programs that were both high touch and connected to data drawn from the EHR.

Third, our results are consistent with those of several prior studies that identified structural features of ACOs that contribute to their performance.^4-13 In addition to being consistent with prior work, our results also identified another organizational characteristic that distinguished high-performing ACOs from average performers: their early adoption of the ACO model. Our results for early adopters are consistent with study findings showing that early adopters were more likely to show success in cost control.^9,14,15 These results suggest that there is a benefit to time spent learning to work together to improve performance.

Our results also suggest that policy makers can promote ACO performance by finding ways to encourage ACO investment in robust CM strategies and practices. In particular, CMS should ensure that ACOs more consistently use the billing codes it created to support CM practices.^11,35-37 In addition, given the important role that primary care providers play in ACO performance, it is critical for CMS and other policy makers to strengthen their efforts to build the nation’s primary care workforce.

Limitations

We note the following limitations. First, the number of ACOs included for interviews is low; this is a direct result of difficulty collecting data during the COVID-19 pandemic (2020-2021). However, the pattern of data across cases is strong, and the results are consistent with prior work; further, the depth of the qualitative data we collected from each site is of high value. Second, we do not have data on ACOs’ cost outcomes, but hospital care utilization is strongly associated with cost in many cases. Third, because we limited analyses to patients with HF, our findings might not be generalizable to other conditions. Last, we collected data before and during the onset of the pandemic, and given changes in the MSSP ACO program and developments in health care delivery more generally, our work may not reflect recent changes in ACO practices and performance.

CONCLUSIONS

This study identified important opportunities for ACO leaders and managers to improve the performance of lower-performing ACOs. The results emphasize the need for managers to establish strong communication channels and mechanisms to ensure that timely data get to clinicians and other decision makers. ACO leaders and clinicians benefit from frequent, regular data about their patients, especially during care transitions. Further, regular and transparent data sharing and related communication also help create perceptions of ownership and inclusion among ACO leaders, clinicians, and staff, which can build motivation and commitment to performance improvement, creating a virtuous cycle of organizational effectiveness.

Acknowledgments

We would like to acknowledge Dr Lauren Gilstrap for her help with interviews and data collection and dedicate this manuscript to her memory.

Author Affiliations: Department of Healthcare Delivery and Population Science, UMass Chan Medical School–Baystate (CD, BJ, PKL), Springfield, MA; Robert F. Wagner Graduate School of Public Service, New York University (TD), New York, NY; Department of Sociology and Interdisciplinary Social Sciences, San Jose State University (YW), San Jose, CA; Division of Health Systems Science, Department of Medicine, UMass Chan Medical School, (YW), Worcester, MA; Section of Cardiovascular Medicine, Yale School of Medicine; Department of Epidemiology of Microbial Diseases, Yale School of Public Health; Center for Outcomes Research and Evaluation, Yale School of Medicine (ESS), New Haven, CT; Leonard Davis Institute of Health Economics and Perelman School of Medicine, University of Pennsylvania (RMW), Philadelphia, PA; Corporal Michael J. Crescenz Department of Veterans Affairs Medical Center (RMW), Philadelphia, PA; Center for Health Services and Outcomes Research, Institute of Public Health and Medicine, and Division of Hospital Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine (TL), Chicago, IL.

Source of Funding: National Institutes of Health; National Heart, Lung, and Blood Institute.

Author Disclosures: Dr Werner is a consultant for City Block Health, advising on evaluation strategy; provided expert testimony in St Francis v Hartford (Trinity Health); has received grants from the Agency for Healthcare Research and Quality, Arnold Ventures, National Institutes of Health, and SCAN Foundation; and has received honoraria from Geisinger Health, JAMA Network Open, and Wiley & Sons. Dr Lagu received grant R01HL146884 and is now employed by a for-profit company focused on value-based care (the work for this paper was completed prior to that employment). The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (CD, TD, BJ, PKL, RMW, TL); acquisition of data (CD, TD, BJ, TL); analysis and interpretation of data (CD, TD, BJ, YW, PKL, ESS, RMW, TL); drafting of the manuscript (CD, TD, YW, TL); critical revision of the manuscript for important intellectual content (CD, TD, PKL, ESS, RMW); statistical analysis (ESS); provision of patients or study materials (CD, BJ, TL); obtaining funding (TL); administrative, technical, or logistic support (CD, BJ, YW, TL); and supervision (TL).

Address Correspondence to: Thomas D’Aunno, PhD, Wagner Graduate School of Public Service, New York University, 105 E 17th St, New York, NY 10003. Email: tdaunno@nyu.edu.

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