Regulator Leniency and Mispricing in Beneficent Nonprofits

Heese, Jonas; Krishnan, Ranjani; Moers, Frank

doi:10.2139/ssrn.2545972

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…Silverman and Skinner (2004) found that for-profit hospitals in particular bias their claims reports towards higher-paying diagnoses (DRGs) in order to maximize reimbursement; however, this form of upcoding has greatly declined after increased auditing pressure from Medicare. In fact, concurrent work by Heese et al (2015) finds that upcoding occurs more frequently among non-profit providers since Medicare preferentially avoids auditing them; based on these results, the authors hypothesize that Medicare allows "beneficient" non-profit hospitals to make some profit from upcoding in order to recover losses from other factors (e.g., treating poorer patients). From a methodological standpoint, both these papers use a provider's fraction of claims that correspond to the highestpaying DRG as a proxy for that provider's level of upcoding.…”

Section: Related Literaturementioning

confidence: 99%

Evidence of Upcoding in Pay-for-Performance Programs

Bastani

Goh

Bayati³

2019

Management Science

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Recent Medicare legislation has been directed at improving patient care quality by financially penalizing providers for hospital-acquired infections (HAIs). However, Medicare cannot directly monitor HAI rates, and instead relies on providers accurately self-reporting HAIs in claims data to correctly assess penalties. Consequently, the incentives for providers to improve service quality may disappear if providers upcode, i.e. mis-report HAIs (possibly unintentionally) in a manner that increases reimbursement or avoids financial penalties. Identifying upcoding in claims data is challenging due to unobservable confounders such as patient risk. Our approach leverages state-level variations in adverse event reporting regulations and instrumental variable techniques to discover contradictions in HAI and present-on-admission (POA) infection reporting rates that are strongly suggestive of upcoding. We conservatively estimate that over 10,000 out of nearly 60,000 annual reimbursed claims for POA infections (18.5%) were upcoded HAIs, resulting in an added cost burden of $200 million to Medicare. Our findings suggest that self-reported quality metrics are unreliable and thus, recent legislation may result in unintended consequences. In particular, contrary to widely-held beliefs, increasing financial penalties may not reduce HAI incidence and may even exacerbate the problem. We make several policy recommendations based on our results, including a new measure for targeted HAI auditing and suggestions for effective adverse event reporting systems.

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Section: Related Literaturementioning

confidence: 99%

Evidence of Upcoding in Pay-for-Performance Programs

Bastani

Goh

Bayati³

2019

Management Science

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“…Their work suggests that whistleblower-initiated civil suits reduce Medicaid spending on prescription drugs and off-label prescription drug use. Similarly, Heese et al (2015) examine cases of civil fraud filed by whistleblowers against California hospitals. They find that teaching hospitals, which provide high levels of charity care, tend to receive weaker punishments than other hospitals.…”

Section: Related Researchmentioning

confidence: 99%

Should We Do More to Police Medicaid Fraud? Evidence on the Intended and Unintended Consequences of Expanded Enforcement

Perez

Wing

2019

American Journal of Health Economics

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We develop a model of hospital responses to the possibility of a MFCU investigation, which is similar to models of tax evasion and crime (Allingham and Sandmo, 1972;Yitzhaki, 1987;Becker, 1968). In our model, the conditional probability of a MFCU investigation is higher when the MFCU has a larger budget, and higher at hospitals that produce a large number of fraud prone claims. We assume that complying with the investigation imposes administrative costs on the hospitals, and we examine how profit-maximizing hospitals respond to these incentives.3 This share is capped at a maximum of 0.25% of the total cost of the state Medicaid program, but states tend to request less than this limit. 4 Figures are based on authors' own analysis of the data and are presented in 2016 dollars. 5 00130 2019If IG, IFG, IF , and IF F are all positive given the prevailing MFCU investigation rates, then we would expect that dF/dG < 0. Increases in the MFCU budget will lead hospitals to reduce their production of fraud prone health services. C. LEGITIMATE AND ILLEGITIMATE FRAUD PRONE SERVICESEven when focusing on fraud-prone health conditions, the MFCU cannot distinguish between legitimate and fraudulent services without investigating. It is unclear whether hospital management systems can independently control the hospitals production of fraudulent and legitimate fraud-prone health services. If the hospital does have independent control over illegitimate and legitimate activity, then an interesting question is how changes in MFCU budgets will affect the hospitals behavior with respect to these two separate types of activity. That is, in response to a MFCU expansion, will hospitals reduce only their fraudulent activities? Or will they also reduce their production of legitimate health services for the fraud-prone health conditions that attract MFCU attention? To study the possibility of such defensive practice patterns in more detail, we augment the basic model to allow hospitals to produce a mix of legitimate and illegitimate health services for fraud prone health conditions. Suppose that the MFCU's fraud prone activity index is Fi = li + zi, where li represents legitimate service production and zi represents illegitimate service production. Assume that the hospital receives the same revenue and incurs the same costs

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“…For any procedure and/or provider, one could compare suspicious billings with historical claims data. For instance, Silverman and Skinner [7] show that profit driven hospitals biased their claims toward higher-paying diagnoses to maximize reimbursement while [8] discuss cases of upcoding at non-profit hospitals. However, as [9] noted, identifying upcoding in claims data is difficult due to confounding variables such as patient risk.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Health Care Billing via Quantile Variable Selection Models

Ekin

Damien

2021

Healthcare

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Fraudulent billing of health care insurance programs such as Medicare is in the billions of dollars. The extent of such overpayments remains an issue despite the emerging use of analytical methods for fraud detection. This motivates policy makers to also be interested in the provider billing characteristics and understand the common factors that drive conservative and/or aggressive behavior. Statistical approaches to tackling this problem are confronted by the asymmetric and/or leptokurtic distributions of billing data. This paper is a first attempt at using a quantile regression framework and a variable selection approach for medical billing analysis. The proposed method addresses the varying impacts of (potentially different) variables at the different quantiles of the billing aggressiveness distribution. We use the mammography procedure to showcase our analysis and offer recommendations on fraud detection.

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Regulator Leniency and Mispricing in Beneficent Nonprofits

Cited by 3 publications

References 32 publications

Evidence of Upcoding in Pay-for-Performance Programs

Evidence of Upcoding in Pay-for-Performance Programs

Should We Do More to Police Medicaid Fraud? Evidence on the Intended and Unintended Consequences of Expanded Enforcement

Analysis of Health Care Billing via Quantile Variable Selection Models

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