Outcomes for Hospitals Participating in More- and Less-Mature ACOs

ABSTRACT

Objective: This study examines the impact of accountable care organization (ACO) maturity on inpatient costs, quality of care, and patient safety for hospitals participating in ACOs initiated by CMS relative to nonparticipants.

Study Design: Quasi-experimental evaluation of hospitals before and after joining a CMS ACO using a difference-in-differences design during the period from 2010 to 2013.

Methods: Propensity score–matched groups of hospitals were used to estimate the combined effects of ACO maturity and CMS ACO participation on inpatient costs, quality, and patient safety outcomes. Total treatment costs, mortality rates for 4 common conditions, and 4 perioperative adverse events were investigated. Analyses were based on state-level data from the Healthcare Cost and Utilization Project.

Results: We matched 121 CMS ACO–participating hospitals and 853 nonparticipating hospitals. Hospitals with an ACO maturity score of 0 had significantly worse acute myocardial infarction mortality and perioperative pulmonary embolism or deep vein thrombosis rates than nonparticipants. These differences were no longer significant with increasing ACO maturity. Higher ACO maturity was associated with significant improvements in accidental punctures and lacerations among hospital CMS ACO participants. No other significant trends were observed.

Conclusions: Findings suggest a potential positive effect of hospital CMS ACO participation with increasing maturity. However, use of early ACO data, a short follow-up period, and other limitations hindered the ability to identify significant trends. Nonetheless, ACO maturity scores and new outcome measures may offer a promising approach for tracking the long-term impact of hospital ACO participation in future research and policy evaluations.

Am J Manag Care. 2025;31(Spec. No. 6):SP322-SP336

Accountable care organizations (ACOs) are a significant innovation in health care delivery and payment.¹ ACOs, formed by various health care providers, including acute care hospitals, expanded through commercial health plans and Medicaid agencies even before Medicare ACOs grew under the Affordable Care Act.^2,3 CMS launched the Medicare Shared Savings Program (MSSP) and Pioneer ACOs in 2012. The MSSP primarily employed a low-risk payment model, rewarding providers with shared savings for meeting spending and quality benchmarks without risking losses.³ In contrast, the Pioneer ACO program utilized a moderate-risk model, sharing both savings and losses, which saw modest growth in provider participation.^4,5 Early evaluations indicate some savings and quality improvements in CMS ACOs,⁵ but research on Medicaid ACOs’ effects is limited.⁶ Commercial ACOs show mixed results in spending and quality of care.^7,8 The number of ACOs with multiple payer contracts has increased over time,⁹ but there is no research distinguishing whether treatment costs and quality of care are affected by ACO maturity (ie, as the number and risk level of contracts and time in accountable care arrangements increase).¹⁰

Our study fills a knowledge gap by evaluating the impact of ACO maturity on inpatient costs, mortality, and perioperative adverse events in hospitals participating in CMS ACOs. Acute care hospitals are key drivers of US health care spending, yet inpatient quality and patient safety require improvement.^11,12 Hospitals provide the most intensive services, which are susceptible to care process breakdowns, leading to high treatment costs and poor outcomes.^11,12 However, hospitals are also central to ACOs’ cost containment efforts⁵ as they aim to establish comprehensive delivery networks and manage all health care needs of ACO-attributed populations. Hospitals in CMS ACOs invest in new personnel, such as care coordinators,¹³ and upgrade their health information technology (HIT)¹⁴ to develop advanced care coordination programs, enhance data sharing, and improve overall performance. Those with prior experience in commercial and Medicaid ACOs are typically better equipped to manage financial risks and care for large ACO-attributed populations upon joining CMS ACOs. We expect that hospitals in more mature ACOs will reduce inpatient treatment costs while enhancing quality of care and patient safety.

We selected total treatment costs, 4 leading causes of US inpatient mortality, and 4 preventable perioperative adverse events as outcomes for this study. ACO maturity scores were used for propensity score matching and estimating the combined effects of CMS ACO participation and ACO maturity on the studied outcomes. To our knowledge, this is the first study to utilize ACO maturity scores alongside a comprehensive set of outcome measures, representing a significant methodological contribution. We also used a longitudinal pre-/post-ACO design with a propensity score–matched control group in difference-in-differences analyses, which is a valid methodological approach commonly used in similar policy evaluations. We hypothesized that as ACO maturity scores rose for hospitals participating in CMS ACOs, there would be a corresponding decrease in their treatment costs, inpatient mortality rates, and perioperative adverse events in comparison with hospitals that did not participate in CMS ACOs.

METHODS

Data Sources

All nonfederal, short-term, general medical and surgical hospitals identifiable in all-payer inpatient discharge data from the Healthcare Cost and Utilization Project State Inpatient Databases (HCUP SID) for 14 states (Arkansas, Arizona, California, Colorado, Florida, Kentucky, Maryland, Massachusetts, New Jersey, New York, North Carolina, Vermont, Utah, and Washington) were included.^15,16 These states provided consistent annual data over the study period (2010-2013) and offered a rich diversity of local health care system, socioeconomic, market, and geographic environments. The states had many organizations participating in the ACOs and provided sufficient intrastate variation in measures. We identified hospitals participating in CMS ACO programs using CMS announcements and databases containing participants’ legal business names and taxpayer identifiers associated with MSSP ACOs.¹⁷ For Pioneer ACOs, we used CMS announcements that identified 32 Pioneer ACOs,¹⁸ then used the American Hospital Directory online resource¹⁹ to verify that these providers were hospitals and obtain hospitals’ addresses and Medicare identifiers. Leavitt Partners LLC provided their ACO maturity scores and participating hospitals’ Medicare identifiers.¹⁰ We used ACO maturity scores and information on hospital organizational and market characteristics derived from American Hospital Association surveys, CMS demonstration projects and evaluation reports,²⁰ Healthcare Information and Management Systems Society surveys, and the Area Health Resources Files for propensity score matching of hospital CMS ACO participants and nonparticipants.

Analysis

This study used propensity score–matched groups of hospitals in a quasi-experimental pre- and post-ACO study design to estimate the combined effects of hospital CMS ACO participation and increases in ACO maturity scores on inpatient total costs, quality, and patient safety outcomes relative to nonparticipating hospitals.

Study Variables

Three types of dependent variables were developed. First, total treatment costs per discharge were calculated using HCUP SID data, which provide uniform information on Medicare total hospital charges per discharge. Hospital charges were converted to costs using the HCUP cost-to-charge ratio files,²¹ adjusted for each hospital and year, and then divided by the patient’s diagnosis-related group weight to obtain case mix–adjusted costs. The case mix–adjusted cost per hospital Medicare discharge was obtained and transformed using the natural log, a common approach in hospital cost analyses.

Second, risk-adjusted inpatient quality indicators (IQIs) were developed using Agency for Healthcare Research and Quality (AHRQ) methodology,¹⁵ focusing on mortality rates for acute myocardial infarction (AMI; IQI 15), congestive heart failure (IQI 16), stroke (IQI 17), and pneumonia (IQI 20). These IQIs are validated measures with high precision and discriminant validity, reflecting deficiencies in inpatient care processes.¹⁵

Finally, patient safety indicators (PSIs) were used to measure the following adverse events: central venous catheter–related bloodstream infection (PSI 7), perioperative pulmonary embolism or deep vein thrombosis (PPE/DVT) (PSI 12), postoperative sepsis (PSI 13), and accidental puncture or laceration (PSI 15).¹⁶ These PSIs are validated indicators of hospital-acquired conditions, which are often preventable and associated with poorly coordinated care.^22-24 Addressing these conditions offers CMS ACO participants an opportunity to improve surgical care quality and reduce costs.²⁴

An indicator variable for hospital CMS ACO participants was created by combining information on hospitals that joined MSSP and Pioneer ACOs relative to nonparticipating hospitals. Hospitals with varying degrees of CMS ACO contract risk were combined, as they were matched on ACO maturity scores, which incorporate contract risk information (Table 1).

Propensity Score Matching and Difference-in-Difference Regressions

Table 1 outlines the variables and data sources used for hospital matching and analyses. ACO maturity scores, developed by Leavitt Partners, were calculated as a weighted mean based on the total number of active ACO contracts, contract risk levels (low: shared savings, moderate: shared risk, high: capitation), and years in ACOs since the earliest active payment arrangement.¹⁰

Previous research indicates that hospitals affiliated with different health system types (centralized, moderately centralized, decentralized, independent) show varying quality performance, which was accounted for in matching.^25,26 Additional factors that may enhance ACO performance, such as physician-hospital integration,²⁷ HIT capabilities,²⁸ and hospital integration with ambulatory providers,²⁹ were also considered. Measures included the number of affiliated physicians, basic and advanced HIT capabilities, and hospital linkages with ambulatory facilities.

Table 1 describes variables and their data sources that were used for hospital matching and analyses. Hospitals were also matched on their organizational and market characteristics^30-35and on pre-ACO performance (Table 1).

For our difference-in-differences analysis, we required a balanced panel, including CMS ACO hospital participants and matched nonparticipants, excluding hospitals with missing HCUP SID data from 2010-2013. The final sample included 156 CMS ACO participants and 853 nonparticipants with the necessary data. The difference-in-differences model estimated the proportional change in cost, quality, and patient safety outcomes for CMS ACO participants, with maturity scores considered, compared with nonparticipants. We also assessed the combined effects of CMS ACO participation and increasing ACO maturity scores (from 2 to 10) on outcomes.

For total treatment costs, we estimated a fixed-effects panel linear regression with the natural log of cost per Medicare discharge as the dependent variable. For IQIs and PSIs, we estimated a nonlinear Poisson fixed-effects panel regression model with risk adjustment via including the natural log of the number of expected incidents (based on the AHRQ risk-adjustment software³⁴), with the coefficient for this variable constrained to 1 (ie, an elasticity of 1 between expected and actual incidents).

To determine nonparticipating hospitals that were most similar to those participating, we followed previous studies and used propensity score matching³⁶ based on 2011 data on hospital organizational and market characteristics. Propensity score–matching variables and their sources are defined in Table 2. Because we had several different outcome measures, the propensity score–matching model included both the 2010 and 2011 values for the natural log of costs and the number of adverse outcomes for each of the PSI and IQI measures. We applied a 1:1 propensity score match with replacement, allowed for a single nonparticipating hospital to serve as the match for more than 1 participating hospital, and used weighted estimation to account for nonindependence of hospitals serving as matches for more than 1 participant. We required a common support, excluding participating hospitals if their propensity was outside the range of propensity scores for nonparticipants. We also required a caliper of 0.01 (ie, a maximum difference of 0.01 in propensity scores between a participant and the closest match).

The difference-in-differences regression models included fixed effects for hospitals and time. To test the parallel trends assumption, we included interaction variables between the CMS ACO participation indicator and indicator variables for 1 and 2 years before participation to estimate the proportional change in outcomes 2 years and 1 year prior to the year of participation. In addition, we controlled for the percentages of hospital patients who were female, in different racial/ethnic categories, in 5 age categories, and categorized as urgent admissions; who had any of 29 comorbidities³⁷; and interactions of percentage female with all the racial/ethnic, age, admission type, and comorbidity categories. This study was reviewed and deemed exempt by the Virginia Commonwealth University Institutional Review Board.

RESULTS

Table 2 shows means and standardized bias statistics for all variables in the propensity score–matching model. Of 156 hospitals participating in a CMS ACO in 2011 or 2012, 35 were excluded because they had no suitable control match within the 0.01 probability caliper. Standardized bias reflects the difference in means between participants and nonparticipants, divided by the square root of the mean sample variances. A value above 0.25 indicates imbalance.³⁶ Participants were more likely to be system hospitals, teaching hospitals, or nonprofit institutions; in addition, they had more beds, higher ACO maturity scores, and advanced HIT. They were also more likely to be in urban areas with higher income, have fewer Medicare enrollees, and have higher numbers of physicians. Differences in characteristics, such as primary care physicians per 1000 population, decreased significantly in matched comparisons, with standardized bias measures mostly below 0.10 and always less than 0.25.

Figure 1 presents the estimates and CIs based on the ACO maturity score at the end of 2011 and compares hospital CMS ACO participants with an ACO maturity score that equals 0 against hospitals not participating in CMS ACOs. There is evidence to suggest that hospitals participating in CMS ACOs with an ACO maturity score of 0 had statistically significant positive (worse) proportional changes for IQI 15 and PSI 12 vs nonparticipants. Hospital CMS ACO participants with an ACO maturity score of 0 were not able to achieve significantly better (lower) treatment costs, inpatient quality outcomes, and patient safety outcomes for all other IQIs and PSIs compared with hospitals not participating in CMS ACOs. The difference between these 2 slopes is not statistically significant at the conventional 5% level for any of these outcomes.

Figure 2 presents the differential proportional change in studied outcomes per 1-unit increase in ACO maturity score for hospital CMS ACO participants relative to nonparticipants. There was a statistically significant better (lower) differential proportional change per 1-unit increase in maturity score for accidental puncture or laceration (PSI 15). There were no other statistically significant differences for CMS ACO participants vs nonparticipants. The estimates for CMS ACO participants were often lower than those for nonparticipants as ACO maturity scores increased, including the estimates for IQI 15 and PSI 12 that were statistically significantly higher for hospitals participating in CMS ACOs with an ACO maturity score of 0 relative to nonparticipants (Figures 1 and 2).

Table 3 reports the combined estimates of the CMS ACO participants’ effects on studied outcomes relative to nonparticipants as ACO maturity scores increased from 2 to 10. Although not statistically significantly different, the cost per discharge and inpatient mortality for all studied IQIs decreased for CMS ACO participants relative to nonparticipants as ACO maturity scores increased from 2 to 10 (Table 3).

The adverse events for accidental puncture or laceration (PSI 15) remained statistically significantly lower for hospital CMS ACO participants in comparison with nonparticipants (Table 3). However, the differences for PSI 7, PSI 12, and PSI 13 were not statistically significant and did not uniformly show a decreasing trend in proportional changes for these adverse events with increasing ACO maturity (Table 3).

DISCUSSION

We hypothesized that as ACO maturity scores increased, hospital treatment costs, inpatient mortality, and perioperative adverse events would decrease for CMS ACO participants compared with nonparticipants. We expected that higher ACO maturity scores would improve inpatient care processes and care coordination, enhancing performance. Hospitals with an ACO maturity score of 0 had significantly worse AMI mortality and PPE/DVT rates than nonparticipants. As ACO maturity increased, these negative differences were no longer statistically significant. Additionally, CMS ACO participants with high ACO maturity scores showed significant reductions in accidental punctures or lacerations (PSI 15) compared with nonparticipants.

Although we observed a tendency for hospital CMS ACO participants to improve quality outcomes and reduce treatment costs, the lack of statistically significant findings may be explained by several factors. Early ACOs primarily focused on enhancing care coordination and strengthening primary care,^38,39 with limited transformation of inpatient care processes.⁴⁰ ACOs improved care coordination in ambulatory settings, supported patients outside hospitals, and developed a team-based approach to care delivery.³⁸ These efforts led to modest reductions in spending, decreased utilization of inpatient and emergency department services, and improvements in preventive care and chronic disease management, largely attributable to the restructuring of primary care delivery.³⁹ However, ACOs undertook only limited boundary-spanning activities during that period and did little to transform inpatient care delivery in the first 18 months of performance contracts.³⁹ The insufficient financial incentives of early ACO programs likely delayed broader inpatient transformations for hospital ACO participants.⁴⁰ Achieving major organizational transformation may have required additional time for participating hospitals to align and optimize care processes across multiple ACOs and improve performance.

Additionally, certain cost containment strategies implemented by ACOs are payer specific. For instance, CMS ACOs may reduce volume or direct patients toward lower-cost settings, a practice not commonly observed in commercial ACOs.⁴¹ Conversely, commercial ACOs can achieve savings through price negotiations, a strategy not relevant to CMS ACOs, where prices are fixed.⁴² As our cost and quality measures were developed for Medicare patients, the strategies employed by commercial ACOs to contain costs were less likely to impact Medicare-specific outcomes. However, ACOs with both public and private contracts are increasingly streamlining quality reporting, using similar patient engagement, quality, satisfaction, and cost-reduction measures across payers.^42-45 Moreover, findings from a qualitative study indicate that once care coordination programs are established, ACOs are likely to employ these methods to enhance care not only within their networks but also for non-ACO providers and in underserved communities.⁴⁶ Mature and latest ACO models are also enhancing care coordination by integrating services across primary, specialty, acute, and postacute care; using data analytics and risk scoring to address unmet needs in at-risk populations.^47,48 As ACO maturity increases, we expect improved performance among CMS ACO hospitals due to spillover effects and greater integration across care settings. Future research should further explore how ACO maturity impacts inpatient costs and quality, using more recent data and comprehensive quality and cost measures.

Discernable declines in perioperative adverse events were less evident among hospital CMS ACO participants, even as ACO maturity scores increased. This aligns with prior research showing no significant changes in surgical spending and outcomes.⁴⁹ The studied perioperative events were not originally used to benchmark CMS ACO quality,⁵⁰ making them less of a priority for improvement. The only significant finding, for PSI 15, likely reflects better reporting and coding of accidental punctures or lacerations by CMS ACO hospitals, aided by investments in advanced data analytics and HIT.^13,14 Although PSI 15 is a validated measure, 25% of these events involve minor injuries, which can be misinterpreted due to variations in coding and documentation of surgical notes.⁵¹ Because perioperative adverse events indicate poor surgical processes, further research is needed to better understand how to incentivize hospital ACO participants to enhance their surgical performance. It is also important for both public and private payers to regularly update quality and safety measures to ensure continued improvements in ACO performance, including improved reporting of perioperative adverse events by ACO participants.

Limitations

This study has several notable limitations. First, the propensity score–matching approach, although helpful in reducing bias, may not fully account for unmeasured confounding variables. Second, the reliance on older data and the short follow-up period both limit the ability to capture potential long-term trends and broader ACO impacts. Third, the study outcomes may not fully capture the broader impact of ACO participation, because these outcomes were not originally used to benchmark early CMS ACO performance. Future research needs more recent and additional years of data to enhance statistical power and provide a more contemporary understanding of ACO performance, as major organizational changes often take more than 2 years to show effects.⁵² ACOs and participating hospitals likely need more time to fully transform inpatient care processes and implement advanced care coordination. Because our study uses data from the early stages of the CMS ACO models, it establishes a baseline for future research. Additional studies with more recent data are needed to assess whether mature ACOs can significantly impact inpatient outcomes. Despite the cost of new data, future investigators should consider HCUP and American Hospital Association data sources to assess the long-term impact of hospital ACO participation.

Given its limitations, the findings of this study should not be used to guide current policy decisions. However, the study’s methodological contributions and introduction of a comprehensive set of inpatient mortality outcomes and treatment costs provide a valuable baseline for future research and evaluation of health policy. Specifically, the use of ACO maturity scores and inpatient measures offers an opportunity to evaluate ACO development over time while extending assessments beyond traditional metrics of benchmarking ACO performance. Future research should incorporate additional years of data to enhance statistical power and better understand the evolving impacts of ACO maturity on inpatient performance. Moreover, including comprehensive quality and cost measures that align with public and private payer priorities will be essential for fully evaluating ACO effectiveness.

Nevertheless, as only 1450 of more than 5000 Medicare-enrolled hospitals currently participate in CMS ACOs,^53,54 many hospitals have yet to join. Our results indicate that ACO maturity does not immediately improve inpatient care, as hospitals need time to observe the positive effects of organizational transformation on quality and costs. However, hospitals in mature ACOs are likely to develop key capabilities, such as better access to capital, advanced data analytics, and care management protocols. Hence, nonparticipating hospitals can learn from early adopters and strategically invest in these ACO capabilities to accelerate progress once they join. Thus, a lack of prior ACO experience does not imply that hospitals that have yet to join CMS ACOs will be unable to improve performance in the future.

CONCLUSIONS

This study is the first to evaluate whether hospital CMS ACO participants improve their performance as their ACO maturity increases compared with nonparticipants. Our findings indicate that ACO maturity does not immediately lead to improved inpatient performance. Hospitals in more mature ACOs may require more than 2 years to observe significant positive effects. Nonetheless, this study is innovative in its use of ACO maturity scores and a comprehensive set of inpatient outcome measures, which are relevant for future research and policy evaluations. Finally, this work serves as a baseline for future studies on the effectiveness of hospital participation in ACOs.

To meet its goal of transitioning all traditional Medicare beneficiaries to accountable care by 2030, CMS needs to involve more acute care hospitals currently not participating in ACOs. We expect that newly joining hospitals will learn from experienced counterparts and invest in effective ACO capabilities such as advanced care coordination, data analytics, and care management. Such investments may help these hospitals adapt quickly to high-risk payment models. Recent findings support this, as long-standing CMS ACOs moving to high-risk models have generated more shared savings.⁵⁵ As performances of public and private ACOs that increasingly implement high-risk payment models are showing promise,^55-57 hospitals not yet engaged in accountable care should start now. As the proverb says, “The best time to plant a tree was 20 years ago; the second best time is now.”

Author Affiliations: Department of Health Policy, School of Public Health, Virginia Commonwealth University (AC), Richmond, VA; Department of Economics, School of Business, Virginia Commonwealth University (DWH), Richmond, VA; Simple Healthcare (DBM), Sanford, FL; Margolis Institute for Health Policy, Duke University (DBM), Durham, NC.

Source of Funding: This research was supported through a grant from the Agency for Healthcare Research and Quality, R01 HS023332.

Author Disclosures: Dr Muhlestein was employed by Health Management Associates, which consults around accountable care organizations (ACOs), during the writing of this article; has spoken on ACOs at several conferences; and is a member of the Population Health, Equity & Outcomes editorial board. 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 (AC, DWH); acquisition of data (AC, DBM); analysis and interpretation of data (AC, DWH); drafting of the manuscript (AC, DWH); critical revision of the manuscript for important intellectual content (AC, DWH, DBM); statistical analysis (AC, DWH); obtaining funding (AC); administrative, technical, or logistic support (AC, DBM); interpretation and policy relevance (DBM); and supervision (AC).

Send Correspondence to: Askar Chukmaitov, MD, PhD, Department of Health Policy, School of Public Health, Virginia Commonwealth University, PO Box 980430, Richmond, VA 23298-0430. Email: achukmaitov@vcu.edu.

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