Association Between a Bundled Payment Program for Lower Extremity Joint Replacement and Patient Outcomes Among Medicare Advantage Beneficiaries.

Importance: Much of the evidence for bundled payments has been drawn from models in the traditional Medicare program. Although private insurers are increasingly offering bundled payment programs, it is not known whether they are associated with changes in episode spending and quality. Objective: To evaluate whether a voluntary bundled payment program offered by a national Medicare Advantage insurer was associated with changes in episode spending or quality of care for beneficiaries receiving lower extremity joint replacement (LEJR) surgery. Design, Setting, and Participants: Cross-sectional study of 23 034 LEJR surgical episodes that emulated a stepped-wedge design by using the time-varying, geographically staggered rollout of the bundled payment program from January 1, 2012, to September 30, 2019. Episode-level multivariable regression models were estimated within practice to compare changes before and after program participation, using episodes at physician practices that had not yet begun participating in the program during a given time period (but would go on to do so) as the control. Data analyses were performed from July 1, 2021, to June 30, 2022. Exposures: Physician practice participation in the bundled payment program. Main Outcomes and Measures: The primary outcome was episode spending (plan and beneficiary). Secondary outcomes included postacute care use (skilled nursing facility and home health care), surgical setting (inpatient vs outpatient), and quality (90-day complications [including deep vein thrombosis, wound infection, fracture, or dislocation] and readmissions). Results: The final analytic sample included 23 034 LEJR episodes (6355 bundled episodes and 16 679 control episodes) from 109 physician practices participating in the program. Of the beneficiaries, 7730 were male and 15 304 were female, 3057 were Black, 19 351 were White, 447 were of other race or ethnicity (assessed according to the Centers for Medicare & Medicaid Services beneficiary race and ethnicity code, which reflects data reported to the Social Security Administration), and 179 were of unknown race and ethnicity. The mean (SD) age was 70.9 (7.2) years. Participation in the bundled payment program was associated with a 2.7% (95% CI, 1.3%-4.1%) decrease in spending per episode (mean episodic spending, $21 964 [95% CI, $21 636-$22 296] vs $22 562 [95% CI, $22 346-$22 779]), as well as reductions in skilled nursing facility use after discharge (21.3% for bundled episodes vs 25.0% for control episodes; odds ratio [OR], 0.81 [95% CI, 0.67-0.98]) and increased use of the outpatient surgical setting (14.1% for bundled episodes vs 8.4% for control episodes; OR, 1.79 [95% CI, 1.53-2.09]). The program was not associated with changes in quality outcomes, including 90-day complications (8.8% for bundled episodes vs 8.6% for control episodes; OR, 1.02 [95% CI, 0.86-1.20]) and readmissions (4.3% for bundled episodes vs 4.6% for control episodes; OR, 0.92 [95% CI, 0.75-1.13]). Conclusions and Relevance: In this study of an LEJR bundled payment program offered by a national Medicare Advantage insurer, findings suggest that physician practice participation in the program was associated with a decrease in episode spending without changes in quality. Bundled payments offered by private insurers, including Medicare Advantage plans, are an alternate payment option to fee for service that may reduce spending for LEJR episodes while maintaining quality of care.

Association Between Community-Level Social Risk and Spending Among Medicare Beneficiaries: Implications for Social Risk Adjustment and Health Equity.

Importance: Payers are increasingly using approaches to risk adjustment that incorporate community-level measures of social risk with the goal of better aligning value-based payment models with improvements in health equity. Objective: To examine the association between community-level social risk and health care spending and explore how incorporating community-level social risk influences risk adjustment for Medicare beneficiaries. Design, Setting, and Participants: Using data from a Medicare Advantage plan linked with survey data on self-reported social needs, this cross-sectional study estimated health care spending health care spending was estimated as a function of demographics and clinical characteristics, with and without the inclusion of Area Deprivation Index (ADI), a measure of community-level social risk. The study period was January to December 2019. All analyses were conducted from December 2021 to August 2022. Exposures: Census block group-level ADI. Main Outcomes and Measures: Regression models estimated total health care spending in 2019 and approximated different approaches to social risk adjustment. Model performance was assessed with overall model calibration (adjusted R2) and predictive accuracy (ratio of predicted to actual spending) for subgroups of potentially vulnerable beneficiaries. Results: Among a final study population of 61 469 beneficiaries (mean [SD] age, 70.7 [8.9] years; 35 801 [58.2%] female; 48 514 [78.9%] White; 6680 [10.9%] with Medicare-Medicaid dual eligibility; median [IQR] ADI, 61 [42-79]), ADI was weakly correlated with self-reported social needs (r = 0.16) and explained only 0.02% of the observed variation in spending. Conditional on demographic and clinical characteristics, every percentile increase in the ADI (ie, more disadvantage) was associated with a $11.08 decrease in annual spending. Directly incorporating ADI into a risk-adjustment model that used demographics and clinical characteristics did not meaningfully improve model calibration (adjusted R2 = 7.90% vs 7.93%) and did not significantly reduce payment inequities for rural beneficiaries and those with a high burden of self-reported social needs. A postestimation adjustment of predicted spending for dual-eligible beneficiaries residing in high ADI areas also did not significantly reduce payment inequities for rural beneficiaries or beneficiaries with self-reported social needs. Conclusions and Relevance: In this cross-sectional study of Medicare beneficiaries, the ADI explained little variation in health care spending, was negatively correlated with spending conditional on demographic and clinical characteristics, and was poorly correlated with self-reported social risk factors. This prompts caution and nuance when using community-level measures of social risk such as the ADI for social risk adjustment within Medicare value-based payment programs.

Comparison of Health Care Utilization by Medicare Advantage and Traditional Medicare Beneficiaries With Complex Care Needs.

Importance: Medicare beneficiaries with co-occurring chronic conditions and complex care needs experience high rates of acute care utilization and poor outcomes. These patterns are well described among traditional Medicare (TM) beneficiaries, but less is known about outcomes among Medicare Advantage (MA) beneficiaries. Compared with TM, MA plans have additional levers to potentially address beneficiary needs, such as network design, care management, supplemental benefits, and value-based contracting. Objective: To compare health care utilization for MA and TM beneficiaries with complex care needs. Design, Setting, and Participants: This cross-sectional study analyzed beneficiaries enrolled in MA and TM using claims data from a large, national MA insurer and a random 5% sample of TM beneficiaries. Beneficiaries were segmented into the following cohorts: frail elderly, major complex chronic, and minor complex chronic. Regression models estimated the association between MA enrollment and health care utilization in 2018, using inverse probability of treatment weighting to balance the MA and TM cohorts on observable characteristics. The study period was January 1, 2017, through December 31, 2018. All analyses were conducted from December 2020 to August 2022. Exposures: Enrollment in MA vs TM. Main Outcomes and Measures: Hospital stays (inpatient admissions and observation stays), emergency department (ED) visits, and 30-day readmissions. Results: Among a study population of 1 844 326 Medicare beneficiaries (mean [SD] age, 75.6 [7.1] years; 1 021 479 [55.4%] women; 1 524 458 [82.7%] White; 223 377 [12.1%] with Medicare-Medicaid dual eligibility), 1 177 896 (63.9%) were enrolled in MA and 666 430 (36.1%) in TM. Beneficiary distribution across cohorts was as follows: frail elderly, 116 047 with MA (10.0% of the MA sample) and 104 036 with TM (15.6% of the TM sample); major complex chronic, 320 954 (27.2%) and 158 811 (23.8%), respectively; and minor complex chronic, 740 895 (62.9%) and 403 583 (60.6%), respectively. Beneficiaries enrolled in MA had lower rates of hospital stays, ED visits, and 30-day readmissions. The largest relative differences were observed for hospital stays, which ranged from -9.3% (95% CI, -10.9% to -7.7%) for the frail elderly cohort to -11.9% (95% CI, -13.2% to -10.7%) for the major complex chronic cohort. Conclusions and Relevance: In this cross-sectional study of Medicare beneficiaries with complex care needs, those enrolled in MA had lower rates of hospital stays, ED visits, and 30-day readmissions than similar beneficiaries enrolled in TM, suggesting that managed care activities in MA may influence the nature and quality of care provided to these beneficiaries.

Comparison of Low-Value Services Among Medicare Advantage and Traditional Medicare Beneficiaries.

Importance: Low-value care in the Medicare program is prevalent, costly, potentially harmful, and persistent. Although Medicare Advantage (MA) plans can use managed care strategies not available in traditional Medicare (TM), it is not clear whether this flexibility is associated with lower rates of low-value care. Objectives: To compare rates of low-value services between MA and TM beneficiaries and explore how elements of insurance design present in MA are associated with the delivery of low-value care. Design, Setting, and Participants: This cross-sectional study analyzed beneficiaries enrolled in MA and TM using claims data from a large, national MA insurer and a random 5% sample of TM beneficiaries. The study period was January 1, 2017, through December 31, 2019. All analyses were conducted from July 2021 to March 2022. Exposures: Enrollment in MA vs TM. Main Outcomes and Measures: Low-value care was assessed using 26 claims-based measures. Regression models were used to estimate the association between MA enrollment and rates of low-value services while controlling for beneficiary characteristics. Stratified analyses explored whether network design, product design, value-based payment, or utilization management moderated differences in low-value care between MA and TM beneficiaries and among MA beneficiaries. Results: Among a study population of 2 470 199 Medicare beneficiaries (mean [SD] age, 75.6 [7.0] years; 1 346 777 [54.5%] female; 229 107 [9.3%] Black and 2 126 353 [86.1%] White individuals), 1 527 763 (61.8%) were enrolled in MA and 942 436 (38.2%) were enrolled in TM. Beneficiaries enrolled in MA received 9.2% (95% CI, 8.5%-9.8%) fewer low-value services in 2019 than TM beneficiaries (23.1 vs 25.4 total low-value services per 100 beneficiaries). Although MA beneficiaries enrolled in health management organization and preferred provider organization products received fewer low-value services than TM beneficiaries, the difference was largest for those enrolled in health management organization products (2.6 fewer [95% CI, 2.4-2.8] vs 2.1 fewer [95% CI, 1.9-2.3] services per 100 beneficiaries, respectively). Across primary care payment arrangements, MA beneficiaries received fewer low-value services than TM beneficiaries, with the largest difference observed for MA beneficiaries whose primary care physicians were reimbursed within 2-sided risk arrangements. Conclusions and Relevance: In this cross-sectional study of Medicare beneficiaries, those enrolled in MA had lower rates of low-value care than those enrolled in TM; elements of insurance design present in the MA program and absent in TM were associated with reduction in low-value care.

Association Between Self-reported Health-Related Social Needs and Acute Care Utilization Among Older Adults Enrolled in Medicare Advantage.

Importance: There is increased focus on identifying and addressing health-related social needs (HRSNs). Understanding how different HRSNs relate to different health outcomes can inform targeted, evidence-based policies, investments, and innovations to address HRSNs. Objective: To examine the association between self-reported HRSNs and acute care utilization among older adults enrolled in Medicare Advantage. Design Setting and Participants: This cross-sectional study used data from a large, national survey of Medicare Advantage beneficiaries to identify the presence of HRSNs. Survey data were linked to medical claims, and regression models were used to estimate the association between HRSNs and rates of acute care utilization from January 1, 2019, through December 31, 2019. Exposures: Self-reported HRSNs, including food insecurity, financial strain, loneliness, unreliable transportation, utility insecurity, housing insecurity, and poor housing quality. Main Outcomes and Measures: All-cause hospital stays (inpatient admissions and observation stays), avoidable hospital stays, all-cause emergency department (ED) visits, avoidable ED visits, and 30-day readmissions. Results: Among a final study population of 56 155 Medicare Advantage beneficiaries (mean [SD] age, 74.0 [5.8] years; 32 779 [58.4%] women; 44 278 [78.8%] White; and 7634 [13.6%] dual eligible for Medicaid), 27 676 (49.3%) reported 1 or more HRSNs. Health-related social needs were associated with statistically significantly higher rates of all utilization measures, with the largest association observed for avoidable hospital stays (incident rate ratio for any HRSN, 1.53; 95% CI, 1.35-1.74; P < .001). Compared with beneficiaries without HRSNs, beneficiaries with an HRSN had a 53.3% higher rate of avoidable hospitalization (incident rate ratio, 1.53; 95% CI, 1.35-1.74; P < .001). Financial strain and unreliable transportation were each independently associated with increased rates of hospital stays (marginal effects of 26.5 [95% CI, 14.2-38.9] and 51.2 [95% CI, 30.7-71.8] hospital stays per 1000 beneficiaries, respectively). All HRSNs, except for utility insecurity, were independently associated with increased rates of ED visits. Unreliable transportation had the largest association with increased hospital stays and ED visits, with marginal effects of 51.2 (95% CI, 30.7-71.8) and 95.5 (95% CI, 65.3-125.8) ED visits per 1000 beneficiaries, respectively. Only unreliable transportation and financial strain were associated with increased rates of 30-day readmissions, with marginal effects of 3.3% (95% CI, 2.0%-4.0%) and 0.4% (95% CI, 0.2%-0.6%), respectively. Conclusions and Relevance: In this cross-sectional study of older adults enrolled in Medicare Advantage, self-reported HRSNs were common and associated with statistically significantly increased rates of acute care utilization, with variation in which HRSNs were associated with different utilization measures. These findings provide evidence of the unique association between certain HRSNs and different types of acute care utilization, which could help refine the development and targeting of efforts to address HRSNs.

Health-Related Social Needs Among Older Adults Enrolled In Medicare Advantage.

Among older adults enrolled in Medicare Advantage, health-related social needs are highly prevalent, with financial strain, food insecurity, and poor housing quality the most commonly reported. The distribution of health-related social needs is uneven, with significant disparities according to race, socioeconomic status, and sex.

Remote Care Management for Older Adult Populations With Elevated Prevalence of Depression or Anxiety and Comorbid Chronic Medical Illness: A Systematic Review.

BACKGROUND: Comorbidity of psychiatric and medical illnesses among older adult populations is highly prevalent and associated with adverse outcomes. Care management is a common form of outpatient support for both psychiatric and medical conditions in which assessment, care planning, and care coordination are provided. Although care management is often remote and delivered by telephone, the evidence supporting this model of care is uncertain. OBJECTIVE: To perform a systematic review of the literature on remote care management programs for older adult populations with elevated prevalence of depression or anxiety and comorbid chronic medical illness. METHODS: A systematic review was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A multidatabase search was performed. Articles were included for review if they studied fully remote care management for older adult populations with elevated prevalence of depression or anxiety and chronic medical illness or poor physical health. A narrative synthesis was performed. RESULTS: A total of 6 articles representing 6 unique studies met inclusion criteria. The 6 studies included 4 randomized controlled trials, 1 case-matched retrospective cohort study, and 1 pre-post analysis. Two studies focused on specific medical conditions. All interventions were entirely telephonic. Five of 6 studies involved an intervention that was 3 to 6 months in duration. Across the 6 studies, care management demonstrated mixed results in terms of impact on psychiatric outcomes and limited impact on medical outcomes. No studies demonstrated a statistically significant impact on health care utilization or cost. CONCLUSIONS: Among older adult populations with elevated prevalence of depression or anxiety and comorbid chronic medical illness, remote care management may have favorable impact on psychiatric symptoms, but impact on physical health and health care utilization is uncertain. Future research should focus on identifying effective models and elements of remote care management for this population, with a particular focus on optimizing medical outcomes.

Health Costs And Financing: Challenges And Strategies For A New Administration.

It is likely that 2021 will be a dynamic year for US health care policy. There is pressing need and opportunity for health reform that helps achieve better access, affordability, and equity. In this commentary, which is part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2021 initiative, we draw on our collective backgrounds in health financing, delivery, and innovation to offer consensus-based policy recommendations focused on health costs and financing. We organize our recommendations around five policy priorities: expanding insurance coverage, accelerating the transition to value-based care, advancing home-based care, improving the affordability of drugs and other therapeutics, and developing a high-value workforce. Within each priority we provide recommendations for key elected officials and political appointees that could be used as starting points for evidence-based policy making that supports a more effective, efficient, and equitable health system in the US.

Association Between Primary Care Payment Model and Telemedicine Use for Medicare Advantage Enrollees During the COVID-19 Pandemic.

This cohort study examines the association between the primary care payment model and telemedicine use for Medicare Advantage enrollees during the COVID-19 pandemic.

The Beneficial Effects Of Medicare Advantage Special Needs Plans For Patients With End-Stage Renal Disease.

Patients with end-stage renal disease (ESRD) are a vulnerable population with high rates of morbidity, mortality, and acute care use. Medicare Advantage Special Needs Plans (SNPs) are an alternative financing and delivery model designed to improve care and reduce costs for patients with ESRD, but little is known about their impact. We used detailed clinical, demographic, and claims data to identify fee-for-service Medicare beneficiaries who switched to ESRD SNPs offered by a single health plan (SNP enrollees) and similar beneficiaries who remained enrolled in fee-for-service Medicare plans (fee-for-service controls). We then compared three-year mortality and twelve-month utilization rates. Compared with fee-for-service controls, SNP enrollees had lower mortality and lower rates of utilization across the care continuum. These findings suggest that SNPs may be an effective alternative care financing and delivery model for patients with ESRD.

Impact of complex care management on spending and utilization for high-need, high-cost Medicaid patients.

OBJECTIVES: Complex care management programs have emerged as a promising model to better care for high-need, high-cost patients. Despite their widespread use, relatively little is known about the impact of these programs in Medicaid populations. This study evaluated the impact of a complex care management program on spending and utilization for high-need, high-cost Medicaid patients. STUDY DESIGN: Randomized quality improvement trial conducted at CareMore Health in Memphis, Tennessee. A total of 253 high-need, high-cost Medicaid patients were randomized in a 1:2 ratio to complex care management or usual care. METHODS: Intention-to-treat analysis compared regression-adjusted rates of spending and utilization between patients randomized to the complex care program (n = 71) and those randomized to usual care (n = 127) over the 12 months following randomization. Primary outcomes included total medical expenditures (TME) and inpatient (IP) days. Secondary outcomes included IP admission, emergency department (ED) visits, care center visits, and specialist visits. RESULTS: Compared with patients randomized to usual care, patients randomized to complex care management had lower TME (adjusted difference, -$7732 per member per year [PMPY]; 95% CI, -$14,914 to -$550; P = .036), fewer IP bed days (adjusted difference, -3.46 PMPY; 95% CI, -4.03 to -2.89; P <.001), fewer IP admissions (adjusted difference, -0.32 PMPY; 95% CI, -0.54 to -0.11; P = .014), and fewer specialist visits (adjusted difference, -1.35 PMPY; 95% CI, -1.98 to -0.73; P <.001). There was no significant impact on care center or ED visits. CONCLUSIONS: Carefully designed and targeted complex care management programs may be an effective approach to caring for high-need, high-cost Medicaid patients.

Prioritizing Primary Care Patients for a Communication Intervention Using the "Surprise Question": a Prospective Cohort Study.

BACKGROUND: Communication about priorities and goals improves the value of care for patients with serious illnesses. Resource constraints necessitate targeting interventions to patients who need them most. OBJECTIVE: To evaluate the effectiveness of a clinician screening tool to identify patients for a communication intervention. DESIGN: Prospective cohort study. SETTING: Primary care clinics in Boston, MA. PARTICIPANTS: Primary care physicians (PCPs) and nurse care coordinators (RNCCs) identified patients at high risk of dying by answering the Surprise Question (SQ): "Would you be surprised if this patient died in the next 2 years?" MEASUREMENTS: Performance of the SQ for predicting mortality, measured by the area under receiver operating curve (AUC), sensitivity, specificity, and likelihood ratios. RESULTS: Sensitivity of PCP response to the SQ at 2 years was 79.4% and specificity 68.6%; for RNCCs, sensitivity was 52.6% and specificity 80.6%. In univariate regression, the odds of 2-year mortality for patients identified as high risk by PCPs were 8.4 times higher than those predicted to be at low risk (95% CI 5.7-12.4, AUC 0.74) and 4.6 for RNCCs (3.4-6.2, AUC 0.67). In multivariate analysis, both PCP and RNCC prediction of high risk of death remained associated with the odds of 2-year mortality. LIMITATIONS: This study was conducted in the context of a high-risk care management program, including an initial screening process and training, both of which affect the generalizability of the results. CONCLUSION: When used in combination with a high-risk algorithm, the 2-year version of the SQ captured the majority of patients who died, demonstrating better than expected performance as a screening tool for a serious illness communication intervention in a heterogeneous primary care population.

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

BACKGROUND: Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients. OBJECTIVE: To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients. DESIGN: We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics. PARTICIPANTS: Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154). MAIN MEASURES: Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures. KEY RESULTS: Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering. CONCLUSIONS: Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.