Assessment of the Medicare Advantage Risk Adjustment Model for Measuring Veterans Affairs Hospital Performance

Almenoff, Peter L.; Francis, Joseph; Jacobs, Josephine; Chee, Christine Pal

doi:10.1001/jamanetworkopen.2018.5993

Cited by 10 publications

(7 citation statements)

References 42 publications

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“…First, the VA population is different from the general population, and patients are more likely to be male, older, and have more comorbidities. 24 Although these differences would likely make this population more likely to suffer adverse outcomes associated with delays, it is unclear if other populations would have similar experiences with canceled surgical cases due to COVID-19. Furthermore, our analysis was limited to VA data and did not include referrals to or care from non-VA or community providers due to lag in claims data.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…This study has several limitations. First, the VA population is different from the general population, and patients are more likely to be male, older, and have more comorbidities 24 . Although these differences would likely make this population more likely to suffer adverse outcomes associated with delays, it is unclear if other populations would have similar experiences with canceled surgical cases due to COVID-19.…”

Section: Discussionmentioning

confidence: 99%

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Short-term Effects of Canceled Elective Procedures Due to COVID-19

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Short-term Effects of Canceled Elective Procedures Due to COVID-19

et al. 2021

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“…Third, since risk adjustment models are developed on historical data, they tend to perpetuate inequality of past spending trends if no explicit adjustments are made to account for the endogeneity of spending. Prior work has investigated methods to develop fairer healthcare payment models through data manipulation and modeling changes [39,41,50], which can be pursued in future studies. Fourth, the SDH indicators used in this study are at the area-level which may lead to bias or ecological fallacy in the risk adjustment models.…”

Section: Discussionmentioning

confidence: 99%

Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments

Irvin

Kondrich

et al. 2020

BMC Public Health

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Background Risk adjustment models are employed to prevent adverse selection, anticipate budgetary reserve needs, and offer care management services to high-risk individuals. We aimed to address two unknowns about risk adjustment: whether machine learning (ML) and inclusion of social determinants of health (SDH) indicators improve prospective risk adjustment for health plan payments. Methods We employed a 2-by-2 factorial design comparing: (i) linear regression versus ML (gradient boosting) and (ii) demographics and diagnostic codes alone, versus additional ZIP code-level SDH indicators. Healthcare claims from privately-insured US adults (2016–2017), and Census data were used for analysis. Data from 1.02 million adults were used for derivation, and data from 0.26 million to assess performance. Model performance was measured using coefficient of determination (R2), discrimination (C-statistic), and mean absolute error (MAE) for the overall population, and predictive ratio and net compensation for vulnerable subgroups. We provide 95% confidence intervals (CI) around each performance measure. Results Linear regression without SDH indicators achieved moderate determination (R2 0.327, 95% CI: 0.300, 0.353), error ($6992; 95% CI: $6889, $7094), and discrimination (C-statistic 0.703; 95% CI: 0.701, 0.705). ML without SDH indicators improved all metrics (R2 0.388; 95% CI: 0.357, 0.420; error $6637; 95% CI: $6539, $6735; C-statistic 0.717; 95% CI: 0.715, 0.718), reducing misestimation of cost by $3.5 M per 10,000 members. Among people living in areas with high poverty, high wealth inequality, or high prevalence of uninsured, SDH indicators reduced underestimation of cost, improving the predictive ratio by 3% (~$200/person/year). Conclusions ML improved risk adjustment models and the incorporation of SDH indicators reduced underpayment in several vulnerable populations.

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“…Prior research has found that the inclusion of mental health variables in risk adjustment models for Veterans significantly improves model fit when predicting health care costs. 59 Fifth, we conducted analyses among the sub-set of individuals who self-identify as Veterans in the HRS. Finally, we used panel generalized estimating equations with a binomial distribution and log link for binary outcomes and a Gaussian distribution and identity link for continuous variables.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

Long‐term care service mix in the Veterans Health Administration after home care expansion

Jacobs

Trivedi

Lorenz

et al. 2021

Health Services Research

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Objective: To determine whether the Veterans Health Administration's (VHA) efforts to expand access to home-and community-based services (HCBS) after the 2001 Millennium Act significantly changed Veterans' utilization of institutional, paid home, and unpaid home care relative to a non-VHA user Medicare population that was not exposed to HCBS expansion efforts.Data Sources: We used linkages between the Health and Retirement Study and VHA administrative data from 1998 until 2012. Study Design:We conducted a retrospective-matched cohort study using coarsened exact matching to ensure balance on observable characteristics for VHA users (n = 943) and nonusers (n = 6106). We used a difference-in-differences approach with a person fixed-effects estimator.Data Collection/Extraction Methods: Individuals were eligible for inclusion in the analysis if they were age 65 or older and indicated that they were covered by Medicare insurance in 1998. Individuals were excluded if they were covered by Medicaid insurance at baseline. Individuals were considered exposed to VHA HCBS expansion efforts if they were enrolled in the VHA and used VHA services.Principal Findings: Theory predicts that an increase in the public allocation of HCBS will decrease the utilization of its substitutes (e.g., institutional care and unpaid caregiving). We found that after the Millennium Act was passed, there were no observed differences between VHA users and nonusers in the probability of using institutional long-term care (0.7% points, 95% CI: À0.009, 0.022) or in receiving paid help with activities of daily living (0.06% points, 95% CI: À0.011, 0.0125). VHA users received more hours of unpaid care post-Millennium Act (1.48, 95% CI: À0.232, 3.187), though this effect was not significant once we introduced controls for mental health.Conclusions: Our findings indicate that mandating access to HCBS services does not necessarily imply that access to these services will follow suit.

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Assessment of the Medicare Advantage Risk Adjustment Model for Measuring Veterans Affairs Hospital Performance

Abstract: This cohort study of administrative data from the Veterans Affairs Health Care System assesses the applicability of using the Medicare Advantage risk adjustment system version 21 for comparing Veterans Affairs hospitals with nonfederal hospitals.

Cited by 10 publications

References 42 publications

Short-term Effects of Canceled Elective Procedures Due to COVID-19

Short-term Effects of Canceled Elective Procedures Due to COVID-19

Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments

Long‐term care service mix in the Veterans Health Administration after home care expansion

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