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Detecting bad actors in value-based payment models.

ABSTRACT: The U.S. federal government is spending billions of dollars to test a multitude of new approaches to pay for healthcare. Unintended consequences are a major consideration in the testing of these value-based payment (VBP) models. Since participation is generally voluntary, any unintended consequences may be magnified as VBP models move beyond the early testing phase. In this paper, we propose a straightforward unsupervised outlier detection approach based on ranked percentage changes to identify participants (e.g., healthcare providers) whose behavior may represent an unintended consequence of a VBP model. The only data requirements are repeated measurements of at least one relevant variable over time. The approach is generalizable to all types of VBP models and participants and can be used to address undesired behavior early in the model and ultimately help avoid undesired behavior in scaled-up programs. We describe our approach, demonstrate how it can be applied with hypothetical data, and simulate how efficiently it detects participants who are truly bad actors. In our hypothetical case study, the approach correctly identifies a bad actor in the first period in 86% of simulations and by the second period in 96% of simulations. The trade-off is that 9% of honest participants are mistakenly identified as bad actors by the second period. We suggest several ways for researchers to mitigate the rate or consequences of these false positives. Researchers and policymakers can customize and use our approach to appropriately guard VBP models against undesired behavior, even if only by one participant.

Supplementary information

The online version contains supplementary material available at 10.1007/s10742-021-00253-9.

SUBMITTER: Lissenden B

PROVIDER: S-EPMC8237252 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionValue-based healthcare has potential for cost control and quality improvement. To assess this, we review the evidence on the impact of value-based payment (VBP) models in the context of networks of care (NOC) and transmural care.MethodsWe used the PRISMA guidelines for this systematic literature review. We searched eight databases in July 2021. Subsequently, we conducted title and abstract and full-text screenings, and extracted information in an extraction matrix. Based on this, we assessed the evidence on the effects of VBP models on clinical outcomes, patient-reported outcomes/experiences, organization-related outcomes/experiences, and costs. Additionally, we reviewed the facilitating and inhibiting factors per VBP model.FindingsAmong articles studying shared savings and pay-for-performance models, most outline positive effects on both clinical and cost outcomes, such as preventable hospitalizations and total expenditures, respectively. Most studies show no change in patient satisfaction and access to care when adopting VBP models. Providers' opinions towards the models are frequently negative. Transparency and communication among involved stakeholders are found to be key facilitating factors, transversal to all models. Additionally, a lack of trust is an inhibitor found in all VBP models, together with inadequate targets and insufficient incentives. In bundled payment and pay-for-performance models, complexity in the structure of the program and lack of experience in implementing required mechanisms are key inhibitors.ConclusionsThe overall positive effect on clinical and cost outcomes validates the success of VBP models. The mostly negative effects on organization-reported outcomes/experiences are corroborated by findings regarding providers' lack of awareness, trust, and engagement with the model. This may be justified by their exclusion from the design of the models, decreasing their sense of ownership and, therefore, motivation. Incentives, targets, benchmarks, and quality measures, if adequately designed, seem to be important facilitators, and if lacking or inadequate, they are key inhibitors. These are prominent facilitators and inhibitors for P4P and shared savings models but not as prominent for bundled payments. The complexity of the scheme and lack of experience are prominent inhibitors in all VBP models, since all require changes in several areas, such as behavioral, process, and infrastructure.

Project description:Background:When risk adjustment is inadequate and incentives are weak, pay-for-performance programs, such as the Value-Based Payment Modifier (Value Modifier [VM]) implemented by the Centers for Medicare & Medicaid Services, may contribute to health care disparities without improving performance on average. Objective:To estimate the association between VM exposure and performance on quality and spending measures and to assess the effects of adjusting for additional patient characteristics on performance differences between practices serving higher-risk and those serving lower-risk patients. Design:Exploiting the phase-in of the VM on the basis of practice size, regression discontinuity analysis and 2014 Medicare claims were used to estimate differences in practice performance associated with exposure of practices with 100 or more clinicians to full VM incentives (bonuses and penalties) and exposure of practices with 10 or more clinicians to partial incentives (bonuses only). Analyses were repeated with 2015 claims to estimate performance differences associated with a second year of exposure above the threshold of 100 or more clinicians. Performance differences were assessed between practices serving higher- and those serving lower-risk patients after standard Medicare adjustments versus adjustment for additional patient characteristics. Setting:Fee-for-service Medicare. Patients:Random 20% sample of beneficiaries. Measurements:Hospitalization for ambulatory care-sensitive conditions, all-cause 30-day readmissions, Medicare spending, and mortality. Results:No statistically significant discontinuities were found at the threshold of 10 or more or 100 or more clinicians in the relationship between practice size and performance on quality or spending measures in either year. Adjustment for additional patient characteristics narrowed performance differences by 9.2% to 67.9% between practices in the highest and those in the lowest quartile of Medicaid patients and Hierarchical Condition Category scores. Limitation:Observational design and administrative data. Conclusion:The VM was not associated with differences in performance on program measures. Performance differences between practices serving higher- and those serving lower-risk patients were affected considerably by additional adjustments, suggesting a potential for Medicare's pay-for-performance programs to exacerbate health care disparities. Primary Funding Source:The Laura and John Arnold Foundation and National Institute on Aging.

Project description:ObjectiveTo examine the impact of a scaled implementation of workforce training intervention on value-based payment measures in a large home-based Medicaid managed long-term care plan population in New York.Data sourcesManaged long-term care clients' health assessments from the Uniform Assessment System of New York merged with paid claims, home health aide operational visit data, and workforce training rosters between 2018 and early-2020.Study designA quasi-experimental design was used. Exposure and control groups were constructed using the proportion of service hours delivered by trained aides between clients' baseline and follow-up/outcome assessments. Multivariate logistic generalized linear and additive models were estimated to examine associations between exposure to trained aides and value-based payment measures.Data collection/extraction methodsThe analytic sample consisted of 19,212 pairs of assessments from 13,320 long-term care clients continuously enrolled in the plan between baseline and follow-up/outcome assessments. Matched assessment pairs were 6-10 months apart.Principal findingsOver 27% of the study population (n = 3656 clients) received services from one or more of 8683 trained aides. Statistically significant associations were observed for four of seven value-based payment measures; however, the presence and magnitudes of positive training effects differed by client service needs. With covariate adjustment, workforce training had the largest estimated positive impacts on rates of flu vaccination among average-need clients (1.60%, standard error [SE] = 0.01), not experiencing uncontrolled pain among above-average-need clients (0.69%, SE = 0.001), stable/improved pain intensity among heavy-need clients (1.25%, SE = 0.01), and stable/improved shortness of breath among light-need clients (0.88%, SE = 0.003).ConclusionAlthough we found mixed associations between scaled workforce training implementation and value-based payment metrics, we noted workforce training could benefit high-need long-term care recipients. Health indicators more sensitive to the daily support provided by direct care workers should be integrated into value-based health care models.

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Detecting bad actors in value-based payment models.

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