The Effects of Medicare Accountable Organizations on Inpatient Mortality Rates

Association of Clinician Health System Affiliation With Outpatient Performance Ratings in the Medicare Merit-based Incentive Payment System

IMPORTANCE

Integration of physician practices into health systems composed of hospitals and multispecialty practices is increasing in the era of value-based payment. It is unknown how clinicians who affiliate with such health systems perform under the new mandatory Centers for Medicare & Medicaid Services Merit-based Incentive Payment System (MIPS) relative to their peers.

OBJECTIVE

To assess the relationship between the health system affiliations of clinicians and their performance scores and value-based reimbursement under the 2019 MIPS.

DESIGN, SETTING, AND PARTICIPANTS

Publicly reported data on 636 552 clinicians working at outpatient clinics across the US were used to assess the association of the affiliation status of clinicians within the 609 health systems with their 2019 final MIPS performance score and value-based reimbursement (both based on clinician performance in 2017), adjusting for clinician, patient, and practice area characteristics.

EXPOSURES

Health system affiliation vs no affiliation.

MAIN OUTCOMES AND MEASURES

The primary outcome was final MIPS performance score (range, 0-100; higher scores intended to represent better performance). The secondary outcome was MIPS payment adjustment, including negative (penalty) payment adjustment, positive payment adjustment, and bonus payment adjustment.

RESULTS

The final sample included 636 552 clinicians (41% female, 83% physicians, 50% in primary care, 17% in rural areas), including 48.6% who were affiliated with a health system. Compared with unaffiliated clinicians, system-affiliated clinicians were significantly more likely to be female (46% vs 37%), primary care physicians (36% vs 30%), and classified as safety net clinicians (12% vs 10%) and significantly less likely to be specialists (44% vs 55%) (P < .001 for each). The mean final MIPS performance score for system-affiliated clinicians was 79.0 vs 60.3 for unaffiliated clinicians (absolute mean difference, 18.7 [95% CI, 18.5 to 18.8]). The percentage receiving a negative (penalty) payment adjustment was 2.8% for system-affiliated clinicians vs 13.7% for unaffiliated clinicians (absolute difference, -10.9% [95% CI, -11.0% to -10.7%]), 97.1% vs 82.6%, respectively, for those receiving a positive payment adjustment (absolute difference, 14.5% [95% CI, 14.3% to 14.6%]), and 73.9% vs 55.1% for those receiving a bonus payment adjustment (absolute difference, 18.9% [95% CI, 18.6% to 19.1%]).

CONCLUSIONS AND RELEVANCE

Clinician affiliation with a health system was associated with significantly better 2019 MIPS performance scores. Whether this represents differences in quality of care or other factors requires additional research.

Ten Modifiable Health Risk Factors and Employees' Medical Costs-An Update

PURPOSE

To estimate the relationship between employees' health risks and health-care costs to inform health promotion program design.

DESIGN

An observational study of person-level health-care claims and health risk assessment (HRA) data that used regression models to estimate the relationship between 10 modifiable risk factors and subsequent year 1 health-care costs.

SETTING

United States.

PARTICIPANTS

The sample included active, full-time, adult employees continuously enrolled in employer-sponsored health insurance plans contributing to IBM MarketScan Research Databases who completed an HRA. Study criteria were met by 135 219 employees from 11 employers.

MEASURES

Ten modifiable risk factors and individual sociodemographic and health characteristics were included in the models as independent variables. Five settings of health-care costs were outcomes in addition to total expenditures.

ANALYSIS

After building the analytic file, we estimated generalized linear models and conducted postestimation bootstrapping.

RESULTS

Health-care costs were significantly higher for employees at higher risk for blood glucose, obesity, stress, depression, and physical inactivity (all at P < .0001) than for those at lower risk. Similar cost differentials were found when specific health-care services were examined.

CONCLUSION

Employers may achieve cost savings in the short run by implementing comprehensive health promotion programs that focus on decreasing multiple health risks.