Effects of the Million Hearts Model on Myocardial Infarctions, Strokes, and Medicare Spending: A Randomized Clinical Trial.

Importance: The Million Hearts Model paid health care organizations to assess and reduce cardiovascular disease (CVD) risk. Model effects on long-term outcomes are unknown. Objective: To estimate model effects on first-time myocardial infarctions (MIs) and strokes and Medicare spending over a period up to 5 years. Design, Setting, and Participants: This pragmatic cluster-randomized trial ran from 2017 to 2021, with organizations assigned to a model intervention group or standard care control group. Randomized organizations included 516 US-based primary care and specialty practices, health centers, and hospital-based outpatient clinics participating voluntarily. Of these organizations, 342 entered patients into the study population, which included Medicare fee-for-service beneficiaries aged 40 to 79 years with no previous MI or stroke and with high or medium CVD risk (a 10-year predicted probability of MI or stroke [ie, CVD risk score] ≥15%) in 2017-2018. Intervention: Organizations agreed to perform guideline-concordant care, including routine CVD risk assessment and cardiovascular care management for high-risk patients. The Centers for Medicare & Medicaid Services paid organizations to calculate CVD risk scores for Medicare fee-for-service beneficiaries. CMS further rewarded organizations for reducing risk among high-risk beneficiaries (CVD risk score ≥30%). Main Outcomes and Measures: Outcomes included first-time CVD events (MIs, strokes, and transient ischemic attacks) identified in Medicare claims, combined first-time CVD events from claims and CVD deaths (coronary heart disease or cerebrovascular disease deaths) identified using the National Death Index, and Medicare Parts A and B spending for CVD events and overall. Outcomes were measured through 2021. Results: High- and medium-risk model intervention beneficiaries (n = 130 578) and standard care control beneficiaries (n = 88 286) were similar in age (median age, 72-73 y), sex (58%-59% men), race (7%-8% Black), and baseline CVD risk score (median, 24%). The probability of a first-time CVD event within 5 years was 0.3 percentage points lower for intervention beneficiaries than control beneficiaries (3.3% relative effect; adjusted hazard ratio [HR], 0.97 [90% CI, 0.93-1.00]; P = .09). The 5-year probability of combined first-time CVD events and CVD deaths was 0.4 percentage points lower in the intervention group (4.2% relative effect; HR, 0.96 [90% CI, 0.93-0.99]; P = .02). Medicare spending for CVD events was similar between the groups (effect estimate, -$1.83 per beneficiary per month [90% CI, -$3.97 to -$0.30]; P = .16), as was overall Medicare spending including model payments (effect estimate, $2.11 per beneficiary per month [90% CI, -$16.66 to $20.89]; P = .85). Conclusions and Relevance: The Million Hearts Model, which encouraged and paid for CVD risk assessment and reduction, reduced first-time MIs and strokes. Results support guidelines to use risk scores for CVD primary prevention. Trial Registration: ClinicalTrials.gov Identifier: NCT04047147.

Effect of the Million Hearts Cardiovascular Disease Risk Reduction Model on Initiating and Intensifying Medications: A Prespecified Secondary Analysis of a Randomized Clinical Trial.

Importance: The Million Hearts Cardiovascular Disease (CVD) Risk Reduction Model pays provider organizations for measuring and reducing Medicare patients' cardiovascular risk. Objective: To assess whether the model increases the initiation or intensification of antihypertensive medications or statins among patients with blood pressure or low-density lipoprotein (LDL) cholesterol levels above guideline thresholds for treatment intensification. Design, Setting, and Participants: This prespecified secondary analysis of a cluster-randomized, pragmatic trial included primary care and cardiology practices, health care centers, and hospital-based outpatient departments across the US. Participants included Medicare patients who were enrolled into the model in 2017 by participating organizations and who were at high risk and at medium risk of a myocardial infarction or stroke in 10 years. Patient outcomes were analyzed for 1 year postenrollment (through December 2018) using an intent-to-treat design. Analysis began November 2019. Interventions: US Centers for Medicare & Medicaid Services paid organizations for risk stratifying Medicare patients and reducing CVD risk among high-risk patients through discussing risk scores, developing individualized risk reduction plans, and following up with patients twice yearly. Main Outcomes and Measures: Initiating or intensifying statin or antihypertensive therapy within 1 year of enrollment, measured in Medicare Part D claims, and LDL cholesterol and systolic blood pressure levels approximately 1 year after enrollment, measured in usual care and reported to Centers for Medicare & Medicaid Services via a data registry (data complete for 51% of high-risk enrollees). The study's primary outcome (incidence of first-time myocardial infarction and stroke) is not reported because the trial is ongoing. Results: A total of 330 primary care and cardiology practices, health care centers, and hospital-based outpatient departments and 125 436 Medicare patients were included in this analysis. High-risk patients in the intervention group had a mean (SD) age of 74 (4.1), 15 213 (63%) were male, 21 657 (90%) were receiving antihypertensive medication at baseline, and 16 558 (69%) were receiving statins. Almost all (21 791 [91%]) high-risk intervention group patients had above-threshold systolic blood pressure level (>130 mm Hg), LDL cholesterol level (>70 mg/dL), or both. Patients in the intervention group with these risk factors were more likely than control patients (8127 [37.3%] vs 4753 [32.4%]; adjusted difference in percentage points, 4.8; 95% CI, 2.9-6.7; P < .001) to initiate or intensify statins or antihypertensive medication. Centers for Medicare & Medicaid Services did not pay for CVD risk reduction for medium-risk enrollees, but initiation or intensification rates for these enrollees were also higher in the intervention vs control groups (12 668 [27.9%] vs 7544 [24.8%]; adjusted difference in percentage points, 3.1; 95% CI, 1.9-4.3; P < .001). Among high-risk enrollees with clinical data approximately 1 year after enrollment, LDL cholesterol level was slightly lower in the intervention vs control groups (mean [SD], 89 [31.8] vs 91 [32.1] mg/dL; adjusted difference in percentage points, -1.8; 95% CI, -2.9 to -0.6; P = .002), as was systolic blood pressure (mean [SD], 133 [15.7] vs 135 [16.4] mm Hg; adjusted difference in percentage points, -1.7; 95% CI, -2.8 to -0.6; P = .003). Conclusions and Relevance: In this study, a pay-for-performance model led to modest increases in the use of CVD medications in a range of organizations, despite high medication use at baseline.

Parent Partners: Evaluation of a Novel Peer-Support Intervention for the Caregivers of Children Hospitalized for Behavioral Health Conditions.

OBJECTIVE: Nearly 10% of all hospitalized children have a primary behavioral health diagnosis, but the effectiveness of treatment can be limited by caregivers' challenges navigating the behavioral health system. In this study, we assessed a novel peer-support intervention ("parent partners") designed for the caregivers of children admitted to an inpatient psychiatric unit. METHODS: We used a mixed-methods approach including 1) document review and interviews to assess implementation and 2) a difference-in-differences analysis using claims for Medicaid-enrolled children admitted to the intervention inpatient psychiatric unit and matched comparison children admitted to other inpatient psychiatric units to assess the impacts on health care use after discharge. RESULTS: Ninety-six percent of caregivers who were offered the intervention engaged with a parent partner. The primary challenges to implementation were accommodating the needs of parent partners to address behavioral health crises among their own children and initial limited engagement from behavioral health clinicians. The intervention leaders reported success in addressing these through adjustments to staffing policies, training parent partners in engagement with clinicians, and incorporating parent partners into team rounds. We did not find a statistically significant difference in follow-up outpatient behavioral health visits (adjusted treatment to comparison difference +3% [90% CI = -2%, +9%]), readmissions (+5% [-33%, +43%]), or behavioral health ED visits (-15% [-44%, +14%]). CONCLUSIONS: This novel intervention was implemented successfully, and although our study did not find statistically significant impacts on health care use after discharge, the findings for ED visits are suggestive of benefits. Parent peer support in inpatient settings warrants additional investigation.

Effects of a Behavioral Health and Chronic Illness Care Intervention on Patient Outcomes in Primary Care Practices in the Dakotas.

From 2012 to 2015, Sanford Health, a large health care system, integrated behavioral health services and chronic condition care management in some of its primary care practices in the Dakotas and rural Minnesota. Using difference-in-differences analyses for fee-for-service Medicare beneficiaries attributed to 22 participating practices and 91 matched comparison practices, we found that the program increased the receipt of four recommended diabetes care processes by 8.6% (p=.048) and, by slowing the increase in emergency department (ED) visits, reduced them by 4.9% (p=.07) relative to the comparison group. However, the findings are mixed: the program did not affect hospital admissions, readmissions, or Medicare spending. In addition, the program increased admissions for ambulatory care-sensitive conditions by 13.6% (p=.07) relative to the comparison group. Sanford's program provides a concrete example of how to incorporate behavioral health services in primary care in underserved areas with some positive results on quality-of-care processes and ED utilization.

Rural hospital transitional care program reduces Medicare spending.

OBJECTIVES: To evaluate impacts of a telephonic transitional care program on service use and spending for Medicare fee-for-service beneficiaries at a rural hospital. STUDY DESIGN: Observational cohort study. METHODS: Patients discharged from Atlantic General Hospital (AGH) with an AGH primary care provider were assigned a nurse care coordinator for 30 days. The nurse reviewed the patient's conditions, assessed needs for transition support, conducted weekly telephone calls (beginning 24-72 hours after discharge) to monitor adherence to treatment plans, and scheduled follow-up appointments. Using claims data, we evaluated impacts on service use and spending using a difference-in-differences design with a matched comparison group. RESULTS: The intervention reduced Medicare spending in the 6-month period after discharge by 30.8%, or $1333 per beneficiary per month (90% CI, -$2078 to -$589), which was partly driven by a 39.4% reduction in spending for inpatient claims (difference, -$729; 90% CI, -$1234 to -$225). There were no statistically significant changes in the 14-day ambulatory care follow-up rate, 30-day unplanned readmission rate, number of inpatient admissions, or number of emergency department visits, although this may be due to modest statistical power to detect effects. CONCLUSIONS: The estimated $5.4 million in savings from this intervention more than offset the costs of the $1.1 million funding for the award. Although other studies have found that care transitions programs can improve outcomes, this study was unique in the size of the impacts relative to the low-touch intervention and the location in a small rural healthcare system.

The Impact of a Health Information Technology-Focused Patient-centered Medical Neighborhood Program Among Medicare Beneficiaries in Primary Care Practices: The Effect on Patient Outcomes and Spending.

BACKGROUND: The Center for Medicare & Medicaid Innovation (CMMI) tests new models of paying for or delivering health care services and expands models that improve health outcomes while lowering medical spending. CMMI gave TransforMED, a national learning and dissemination contractor, a 3-year Health Care Innovation Award (HCIA) to integrate health information technology systems into physician practices. This paper estimates impacts of TransforMED's HCIA-funded program on patient outcomes and Medicare parts A and B spending. RESEARCH DESIGN: We compared outcomes for Medicare fee-for-service (FFS) beneficiaries served by 87 treatment practices to outcomes for Medicare FFS beneficiaries served by 286 matched comparison practices, adjusting for differences in outcomes between the 2 groups during a 1-year baseline period. We estimated impacts in 3 evaluation outcome domains: quality-of-care processes, service use, and spending. RESULTS: We estimated the program led to a 7.1% reduction in inpatient admissions and a 5.7% decrease in the outpatient emergency department visits. However, there was no evidence of statistically significant effects in outcomes in either the quality-of-care processes or spending domains. CONCLUSIONS: These results indicate that TransforMED's program reduced service use for Medicare FFS beneficiaries, but also show that the program did not have statistically significant favorable impacts in the quality-of-care processes or spending domains. These results suggest that providing practices with population health management and cost-reporting software-along with technical assistance for how to use them-can complement practices' own patient-centered medical home transformation efforts and add meaningfully to their impacts on service use.

Association Between Extending CareFirst's Medical Home Program to Medicare Patients and Quality of Care, Utilization, and Spending.

Importance: CareFirst, the largest commercial insurer in the mid-Atlantic Region of the United States, runs a medical home program focusing on financial incentives for primary care practices and care coordination for high-risk patients. From 2013 to 2015, CareFirst extended the program to Medicare fee-for-service (FFS) beneficiaries in participating practices. If the model extension improved quality while reducing spending, the Centers for Medicare and Medicaid Services could expand the program to Medicare beneficiaries broadly. Objective: To test whether extending CareFirst's program to Medicare FFS patients improves care processes and reduces hospitalizations, emergency department visits, and spending. Design, Setting, and Participants: This difference-in-differences analysis compared outcomes for roughly 35 000 Medicare FFS patients attributed to 52 intervention practices (grouped by CareFirst into 14 "medical panels") to outcomes for 69 000 Medicare patients attributed to 42 matched comparison panels during a 1-year baseline period and 2.5-year intervention at Maryland primary care practices. Main Outcomes and Measures: Hospitalizations (all-cause and ambulatory-care sensitive), emergency department visits, Medicare Part A and B spending, and 3 quality-of-care process measures: ambulatory care within 14 days of a hospital stay, cholesterol testing for those with ischemic vascular disease, and a composite measure for those with diabetes. Interventions: CareFirst hired nurses who worked with patients' usual primary care practitioners to coordinate care for 3656 high-risk Medicare patients. CareFirst paid panels rewards for meeting cost and quality targets for their Medicare patients and advised panels on how to meet these targets based on analyses of claims data. Results: On average, each of the 14 intervention panels had 9.3 primary care practitioners and was attributed 2202 Medicare FFS patients in the baseline period. The panels' attributed Medicare patients were, on average, 73.8 years old, 59.2% female, and 85.1% white. The extension of CareFirst's program to Medicare patients was not statistically associated with improvements in any outcomes, either for the full Medicare population or for a high-risk subgroup in which impacts were expected to be largest. For the full population, the difference-in-differences estimates were 1.4 hospitalizations per 1000 patients per quarter (P = .54; 90% CI, -2.1 to 5.0), -2.5 outpatient ED visits per 1000 patients per quarter (P = .26; 90% CI, -6.2 to 1.1), and -$1 per patient per month in Medicare Part A and B spending (P = .98; 90% CI, -$40 to $39). For hospitalizations and Medicare spending, the 90% CIs did not span CareFirst's expected impacts. Hospitalizations for the intervention group declined by 10% from baseline year to the final 18 months of the intervention, but this was matched by similar declines in the comparison group. Conclusion and Relevance: The extension of CareFirst's program to Medicare did not measurably improve quality-of-care processes or reduce service use or spending for Medicare patients. Further program refinement and testing would be needed to support scaling the program more broadly to Medicare patients.

Testing the Replicability of a Successful Care Management Program: Results from a Randomized Trial and Likely Explanations for Why Impacts Did Not Replicate.

OBJECTIVES: To test whether a care management program could replicate its success in an earlier trial and determine likely explanations for why it did not. DATA SOURCES/SETTING: Medicare claims and nurse contact data for Medicare fee-for-service beneficiaries with chronic illnesses enrolled in the trial in eastern Pennsylvania (N = 483). STUDY DESIGN: A randomized trial with half of enrollees receiving intensive care management services and half receiving usual care. We developed and tested hypotheses for why impacts declined. DATA EXTRACTION: All outcomes and covariates were derived from claims and the nurse contact data. PRINCIPAL FINDINGS: From 2010 to 2014, the program did not reduce hospitalizations or generate Medicare savings to offset program fees that averaged $260 per beneficiary per month. These estimates are statistically different (p < .05) from the large reductions in hospitalizations and spending in the first trial (2002-2010). The treatment-control differences in the second trial disappeared because the control group's risk-adjusted hospitalization rate improved, not because the treatment group's outcomes worsened. CONCLUSION: Even if demonstrated in a randomized trial, successful results from one test may not replicate in other settings or time periods. Assessing whether gaps in care that the original program filled exist in other settings can help identify where earlier success is likely to replicate.