Upcoding: Evidence from Medicare on Squishy Risk Adjustment

Geruso, Michael; Layton, Timothy J.

doi:10.1086/704756

Cited by 129 publications

(91 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, the MEPS provides comprehensive information on socioeconomic and health status, which are associated with rates of health services use but are imperfectly captured in claims data. Third, clinician coding patterns may be associated with differences in health risks as constructed from claims data; studies from 2014 23 and 2020 24 suggest differences in coding between MA and TM. However, the MEPS offers self-reported health status, which allowed us to control for a measure of health that is comparable across TM and MA populations.…”

Section: Methodsmentioning

confidence: 99%

Trends in Use of Low-Value Care in Traditional Fee-for-Service Medicare and Medicare Advantage

et al. 2021

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Trends in Use of Low-Value Care in Traditional Fee-for-Service Medicare and Medicare Advantage

et al. 2021

View full text Add to dashboard Cite

show abstract

“…As a result, diagnosis codes can create ambiguities because of overlap and hierarchy in codes. Moreover, facilities have incentives to underreport (81) and overreport (85,86) outcomes, yielding differences in model representations.…”

Section: Clinical Diagnosismentioning

confidence: 99%

Ethical Machine Learning in Healthcare

Chen

Pierson

Rose

et al. 2021

Annu. Rev. Biomed. Data Sci.

253

171

View full text Add to dashboard Cite

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

show abstract

“…While perfect risk adjustment is a useful thought experiment, most markets include an imperfect form of risk adjustment where transfers are based on individual risk scores computed from diagnoses appearing in health insurance claims. (See Geruso and Layton (2015) for an overview.) For instance, in the ACA Marketplaces, the per-enrollee transfer to plan j is determined by the following formula: 19 T j (P) = R j (P) R(P) − 1 • P(P) (9)…”

Section: Risk Adjustment Transfersmentioning

confidence: 99%

The Two-Margin Problem in Insurance Markets

Geruso

Layton

McCormack

et al. 2023

The Review of Economics and Statistics

Self Cite

View full text Add to dashboard Cite

Insurance markets often feature consumer sorting along both an extensive margin (whether to buy) and an intensive margin (which plan to buy). We present a new graphical theoretical framework that extends a workhorse model to incorporate both selection margins simultaneously. A key insight from our framework is that policies aimed at addressing one margin of selection often involve an economically meaningful trade-off on the other margin in terms of prices, enrollment, and welfare. Using data fromMassachusetts, we illustrate these trade-offs in an empirical sufficient statistics approach that is tightly linked to the graphical framework we develop.

show abstract

Upcoding: Evidence from Medicare on Squishy Risk Adjustment

Cited by 129 publications

References 36 publications

Trends in Use of Low-Value Care in Traditional Fee-for-Service Medicare and Medicare Advantage

Trends in Use of Low-Value Care in Traditional Fee-for-Service Medicare and Medicare Advantage

Ethical Machine Learning in Healthcare

The Two-Margin Problem in Insurance Markets

Contact Info

Product

Resources

About