The Impact of Simultaneous Hospital Participation in Accountable Care Organizations and Bundled Payments on Episode Outcomes.

Among hospitals accepting bundled payments, simultaneous "co-participation" in accountable care organizations (ACOs) could impact episode outcomes compared to bundled payment participation alone. Difference-in-differences (DID) analysis of 1 857 653 ACO-attributed Medicare beneficiaries. The study exposure was hospitalization for 24 procedure-based and 24 condition-based episodes at hospitals participating in bundled payments and ACOs (co-participant) versus only bundled payments. Study outcomes included episode quality, postacute utilization, and spending. For procedure-based episodes, patients hospitalized at co-participant and bundled payment hospitals did not exhibit differential changes in risk-adjusted mortality (DID 0.04 percentage points [p.p.], 95% confidence interval [CI] -0.28 p.p. to 0.37 p.p., P = 0.79), readmissions (DID -0.32 p.p., 95% CI -1.5 p.p. to 0.82 p.p., P = 0.59), postdischarge institutional spending (DID $119, 95% CI -$216 to $455, P = 0.49), or postacute utilization. Similarly, outcomes for condition-based episodes did not vary between co-participant and bundled payment hospitals. Payment model co-participation may produce neither synergistic benefits nor negative effects for patients.

The Association between Bundled Payment Participation and Changes in Medical Episode Outcomes among High-Risk Patients.

Background: Bundled payments for medical conditions are associated with stable quality and savings through shorter skilled nursing facility (SNF) length of stay. However, effects among clinically higher-risk patients remain unknown. Objective: To evaluate whether the association between participation in bundled payments for medical conditions and episode outcomes differed for clinically high-risk versus other patients. Design: Retrospective difference-in-differences analysis; Participants: 471,421 Medicare patients hospitalized at bundled payment and propensity-matched non-participating hospitals. Exposures were 5 measures of clinically high-risk groups: advanced age (>85 years old), high case-mix, disabled, frail, and prior institutional post-acute care provider utilization. Main Measures: Primary outcomes were SNF length of stay and 90-day unplanned readmissions. Secondary outcomes included quality, utilization, and spending measures. Key Results: SNF length of stay was differentially lower among frail patients (aDID −0.4 days versus non-frail patients, 95% CI −0.8 to −0.1 days), patients with advanced age (aDID −0.8 days versus younger patients, 95% CI −1.2 to −0.3 days), and those with prior institutional post-acute care provider utilization (aDID −1.1 days versus patients without prior utilization, 95% CI −1.6 to −0.6 days), compared to non-frail, younger, and patients without prior utilization, respectively. BPCI participation was also associated with differentially greater SNF LOS among disabled patients (aDID 0.8 days versus non-disabled patients, 95% CI 0.4 to 1.2 days, p < 0.001). Bundled payment participation was not associated with differential changes in readmissions in any high-risk group but was associated with changes in secondary outcomes for some groups. Conclusions: Changes under medical bundles affected, but did not indiscriminately apply to, high-risk patient groups.

Association of Patient Outcomes With Bundled Payments Among Hospitalized Patients Attributed to Accountable Care Organizations.

Importance: It is unknown how outcomes are affected when patients receive care under bundled payment and accountable care organization (ACO) programs simultaneously. Objective: To evaluate whether outcomes in the Medicare Bundled Payments for Care Improvement (BPCI) program differed depending on whether patients were attributed to ACOs in the Medicare Shared Savings Program. Design Setting and Participants: This cohort study was conducted using Medicare claims data from January 1, 2011, to September 30, 2016, and difference-in-differences analysis to compare episode outcomes for patients admitted to BPCI vs non-BPCI hospitals. Outcomes were stratified for patients who were and were not attributed to an ACO. Participants included Medicare fee-for-service beneficiaries receiving care for medical and surgical episodes at US hospitals. Data were analyzed between October 1, 2018, and June 10, 2021. Exposures: Hospitalization for any of the 48 episodes (24 medical, 24 surgical) included in the BPCI at US hospitals participating in the BPCI for those episodes. Main Outcomes and Measures: The primary outcome was change in 90-day postdischarge institutional spending, and secondary outcomes included changes in quality and utilization. Results: A total of 7 108 146 beneficiaries (mean [SD] age, 76.9 [12.2] years; 4 101 081 women [58%]) received care for medical episodes, and 3 675 962 beneficiaries (mean [SD] age, 74.8 [10.1] years; 2 074 921 women [56%]) received care for surgical episodes. Compared with patients who were not attributed to ACOs, the association between bundled payments and changes in postdischarge institutional spending was larger among patients attributed to ACOs (-$323 difference; 95% CI, -$607 to -$39; P = .03) for medical episodes, but not surgical episodes. Attribution to an ACO also increased the strength of the association between bundled payments and changes in 90-day readmissions for both medical episodes (-0.98 percentage point difference; 95% CI, -1.55 to -0.41; P = .001) and surgical episodes (-0.84 percentage point difference; 95% CI, -1.32 to -0.35; P = .001). Conclusions and Relevance: In this cohort study, compared with inclusion in bundled payments alone, simultaneous inclusion in both ACOs and bundled payment programs was associated with lower institutional postacute care spending and readmissions for medical episodes and lower readmissions but not spending for surgical episodes. Receiving care under models such as ACOs may improve episode outcomes under bundled payments.

Linear mixed-effects models for estimation of pulmonary metastasis growth rate: implications for CT surveillance in patients with sarcoma.

OBJECTIVES: Sarcoma patients often undergo surveillance chest CT for detection of pulmonary metastases. No data exist on the optimal surveillance interval for chest CT. The aim of this study was to estimate pulmonary metastasis growth rate in sarcoma patients. METHODS: This was a retrospective review of 95 patients with pulmonary metastases (43 patients with histologically confirmed metastases and 52 with clinically diagnosed metastases) from sarcoma treated at an academic tertiary-care center between 01 January 2000 and 01 June 2019. Age, sex, primary tumor size, grade, subtype, size and volume of the pulmonary metastasis over successive chest CT scans were recorded. Two metastases per patient were chosen if possible. Multivariate linear mixed-effects models with random effects for each pulmonary metastasis and each patient were used to estimate pulmonary metastasis growth rate, evaluating the impact of patient age, tumor size, tumor grade, chemotherapy and tumor subtype. We estimated the pulmonary metastasis volume doubling time using these analyses. RESULTS: Maximal primary tumor size at diagnosis (LRT statistic = 2.58, df = 2, p = 0.275), tumor grade (LRT statistic = 1.13, df = 2, p = 0.567), tumor type (LRT statistic = 7.59, df = 6, p = 0.269), and patient age at diagnosis (LRT statistic = 0.735, df = 2, p = 0.736) were not statistically significant predictors of pulmonary nodule growth from baseline values. Chemotherapy decreased the rate of pulmonary nodule growth from baseline (LRT statistic = 7.96, df = 2, p = 0.0187). 95% of untreated pulmonary metastases are expected to grow less than 6 mm in 6.4 months. There was significant intrapatient and interpatient variation in pulmonary metastasis growth rate. Pulmonary metastasis volume growth rate was best fit with an exponential model in time. The volume doubling time for pulmonary metastases assuming an exponential model in time was 143 days (95% CI (104, 231) days). CONCLUSIONS: Assuming a 2 mm nodule is the smallest reliably detectable nodule by CT, the data suggest that an untreated pulmonary metastasis is expected to grow to 8 mm in 8.4 months (95% CI (4.9, 10.2) months). Tumor size, grade and sarcoma subtype did not significantly alter pulmonary metastasis growth rate. However, chemotherapy slowed the pulmonary metastasis growth rate. ADVANCES IN KNOWLEDGE: CT surveillance intervals for pulmonary metastases can be estimated based on metastasis growth rate. There was significant variation in the pulmonary metastasis growth rate between metastases within patient and between patients. Pulmonary nodule volume growth followed an exponential model, linear in time.

Changes in Outpatient Imaging Utilization and Spending Under a New Population-Based Primary Care Payment Model.

OBJECTIVE: To evaluate whether the implementation of a new population-based primary care payment system, Population-Based Payments for Primary Care (3PC), initiated by Hawaii Medical Service Association (HMSA; the Blue Cross Blue Shield of Hawaii), was associated with changes in spending and utilization for outpatient imaging in its first year. METHODS: In this observational study, we used claims data from January 1, 2012, to December 31, 2016. We used a propensity-weighted difference-in-differences design to compare 70,284 HMSA patients in Hawaii attributed to 107 primary care physicians (PCPs) and 4 physician organizations participating in 3PC in its first year of implementation (2016) and 195,902 patients attributed to 312 PCPs and 14 physician organizations that used a fee-for-service model during the study period. The primary outcome was total spending on outpatient imaging tests, and secondary outcomes included spending and utilization by modality. RESULTS: The study included 266,186 HMSA patients (mean age of 43.3 years; 51.7% women) and 419 PCPs (mean age of 54.9 years; 34.8% women). The 3PC system was not significantly associated with changes in total spending for outpatient imaging. Of 12 secondary outcomes, only 3 were statistically significant, including changes in nuclear medicine spending (adjusted differential change = -20.1% [95% confidence interval = -27.5% to -12.1%]; P < .001) and utilization (adjusted differential change = -18.1% [95% confidence interval = -23.8 to -11.9%]; P < .001). DISCUSSION: The HMSA 3PC system was not associated with significant changes in total spending for outpatient imaging, though spending and utilization on nuclear medicine tests decreased.