Upcoding Medicare: Is Healthcare Fraud and Abuse Increasing?

Medicare fraud has been the cause of up to $60 billion in overpaid claims in 2015 alone. Upcoding occurs when a healthcare provider has submitted codes for more severe conditions than diagnosed for the patient to receive higher reimbursement. The purpose of this study was to assess the impact of Medicare and Medicaid fraud to determine the magnitude of upcoding inpatient and outpatient claims throughout reimbursements. The methodology for this study utilized a literature review. The literature review analyzed physician upcoding throughout present on admission infections, diagnostic related group upcoding, emergency department, and clinic upcoding. It was found that upcoding has had an impact on Medicare payments and fraud. Medicare fraud has been reported to be the magnitude of upcoding inpatient and outpatient claims throughout Medicare reimbursements. In addition, fraudulent activity has increased with upcoding for ambulatory inpatient and outpatient charges for patients with Medicare and Medicaid

Regulator Leniency and Mispricing in Beneficent Nonprofits

We posit that nonprofits that provide a greater supply of unprofitable services (beneficent nonprofits) face lenient regulatory enforcement for mispricing in price-regulated markets. Consequently, beneficent nonprofits exploit such regulatory leniency and exhibit higher mispricing. Drawing on organizational legitimacy theory, we argue that both regulators and beneficent nonprofits seek to protect their legitimacy with stakeholders, including those who demand access to unprofitable services. Using data from hospitals, we examine mispricing via “upcoding”, which involves misclassifying ailment severity. Archival analysis indicates less stringent regulatory enforcement of upcoding for beneficent nonprofit hospitals, defined as hospitals that provide higher charity care and medical education. After observing regulator leniency, beneficent hospitals demonstrate higher upcoding. Our results suggest that lenient enforcement assists beneficent nonprofits to obtain higher revenues in price-regulated markets

Private Hospital Behavior Under Government Insurance: Evidence from Reimbursement Changes in India

In a major shift away from direct public provision, governments around the world are expanding public insurance programs that contract the private sector to deliver health services at pre-specified reimbursement rates. These rates are a key policy lever to shape provider incentives, but there is little evidence on their effects in lower-income contexts with limited regulatory capacity. Using over 1.6 million insurance claims and 20,000 patient surveys, and exploiting a policy-induced natural experiment, this paper provides evidence on private hospital responses to reimbursement rate changes under government health insurance in India. It shows that: 1) Private hospitals engage in coding manipulation to increase revenues at government expense. Manipulation is highly responsive to changes in the relative reimbursement rates of similar services. 2) Rate increases also induce an increase in service volumes. 3) Hospitals charge patients for care that should be free under program rules. Raising rates reduces these charges significantly, but hospitals capture about half of the increase. Pass-through is driven entirely by less concentrated markets, suggesting that competition limits hospital capture of public subsidies. There is no evidence of changes in care quality or patient composition. These findings highlight the critical role of prices and market structure when contracting the private sector for delivery of social services

Four Contributions to Experimental Health Economics

Chapter 1 “The effects of audits and fines on upcoding in neonatology”: The chapter provides causal evidence on the effect of random audits with different probabilities and financial consequences on dishonest reporting of birth weights in neonatology. The results show that audits with low detection probabilities only reduce fraudulent birth-weight reporting, when they are coupled with fines for fraudulent reporting. For audit policies with fines, increasing the probability of an audit only effectively enhances honest reporting, when switching from detectable to less gainful undetectable upcoding is not feasible. Chapter 2 “Physicians’ incentives, patients’ characteristics, and quality of care: A systematic experimental comparison of fee-for-service, capitation, and pay for performance”: This chapter presents causal evidence from a series of controlled experiments with physicians, medical students, and non-medical students about the effects of performance pay on the quantity and quality of care with heterogeneous patient types. Behavioral data show that performance pay significantly reduces non-optimal service provision under fee-for-service and capitation and enhances the quality of care. The effect sizes depend on the patients’ severity of illness. The effect of performance pay and fee-for-service on the quality of care decreases in the patients’ severity of illness, while it increases in severity for performance pay and capitation. Chapter 3 “Physician performance pay and personality traits”: The chapter explores how responses to performance incentives for physicians aimed at improving the quality of care relate to an individual’s personality traits. This analysis uses the experimental data introduced in Chapter 2. Beyond the experimental evidence that performance pay significantly improves the quality of care, I find differences in the provided quality of care for some personality traits under capitation payments but not under fee-for-service. More conscientious and more agreeable individuals respond significantly less strong to incentives under performance pay blended with capitation. Other personality traits are not significantly related to individuals’ behavior. These findings can be informative for incentivizing physicians better and sorting them into incentive schemes. Chapter 4 “Physician altruism: The role of medical education”: The chapter presents structural estimates on experimental data from a large sample of German medical students varying in their study progresses. The estimates reveal substantial heterogeneity in altruistic preferences across study cohorts. Patient-regarding altruism is highest for freshmen, declines for the students in the pre-clinical and clinical study phase, and tends to increase for practical-year students who are assisting in clinical practice. Across individuals, patient-regarding altruism is higher for females and increases in general altruism. Altruistic subjects have lower income expectations and are more likely to choose surgery and pediatrics as their preferred specialty

Implications of upcoding on Medicare

The complexity of and amount of funds involved in Medicare has led to a significant increase in the incidence of Medicare fraud. A type of Medicare fraud, upcoding, has contributed to excessive and unnecessary health care spending. Upcoding has been an illegal strategy that some providers have used to increase their Medicare reimbursement for certain conditions. This is accomplished by coding a provided service as a more expensive service than what was actually performed. With the proliferation of upcoding, there has been an astonishing $12.5 billion in fraudulent Medicare charges since 2007. The fraudulent strategy of upcoding to increase Medicare reimbursement for organizational financial gain has been a common occurrence and has resulted in substantial Medicare overpayments. While solving the problem of upcoding will not eliminate the myriad contributing factors to this enormous healthcare expenditure, it certainly would be a start

Empirical Characteristics of Affordable Care Act Risk Transfer Payments

Under the Affordable Care Act (ACA), insurers cannot engage in medical underwriting and thus face perverse incentives to engage in risk selection and discourage low-value patients from enrolling in their plans. One ACA program intended to reduce the effects of risk selection is risk adjustment. Under a risk adjustment program, insurers with less healthy enrollees receive risk transfer payments from insurers with healthier enrollees. Our goal is to understand the elements driving risk transfers. First, the distribution of risk transfers should be based on random health shocks, which are unpredictable events that negatively affect health status. Second, risk transfers could be influenced by factors unique to each insurer, such as certain plans attracting certain patients, the extent to which carriers engage in risk selection, and the degree of upcoding. We create a publicly available dataset using Centers for Medicare and Medicaid Services data that includes insurer risk transfer payments, costs, and premiums for the 2014-2017 benefit years. Using this dataset, we find that the empirical distribution of risk transfer payments is not consistent with the lack of risk selection as measured by the ACA risk transfer formula. Over all states included in our dataset, at least 60% of the volume of transfers cannot be accounted for by a purely normal model. Because we find that it is very unlikely that risk transfer payments are caused solely by random shocks that reflect health events of the population, our work raises important questions about the causes of heterogeneity in risk transfers.Comment: 33 pages, 2 figure

Law, Technology and Patient Safety

Medical error is the third leading cause of death in the United States, In an effort to increase patient safety, various regulatory agencies require reporting of adverse events, but reported counts tend to be inaccurate. In 2005, in an effort to reduce adverse event rates, Congress proposed a list of “never events,” adverse events, such as wrong-site surgery, that should never occur in hospitals, and authorized CMS to refuse payment for care required following such events. CMS has since pushed for further regulation, “such as putting more payment at risk, increasing transparency, increasing frequency of quality data reviews, and stepping up media scrutiny.” Evidence suggests these public reporting and pay-for-performance initiatives compel hospitals to manipulate reports, and in some cases patient treatment, to conceal adverse events. The purpose of this Essay is to consider how we might use law coupled with technological advances to increase adverse event count accuracy. On the technology front, three advances are particularly relevant. First, digitization of medical records, billing data, and other sources of germane information has made collecting large amounts of data easier than ever. Second, current adverse event counters employ powerful computer algorithms, and we’re likely moving towards detecting adverse events through analysis of large datasets using artificial intelligence. Third, governmental entities have started to team up with computer scientists who use cryptographic techniques to collect sensitive data in ways that protect the anonymity of data producers. We explore how law might harness the power of these technological developments to increase adverse event count accuracy without creating incentives for providers to hide data or alter treatment practices in harmful or wasteful ways. This Essay is organized as follows. Part II describes current methods used by hospitals, CMS and researchers to count adverse events. It also attempts to explain the wide disparities in counts produced by various counting methods. A close look at count disparities illuminates two problems with today’s methods. First, the most reliable count estimates are not generalizable. Second, evidence suggests that providers act to shroud true counts, sometimes in ways that put patients at risk. Part III suggests that recent technological advances might make it possible to use law to improve the accuracy of adverse event counts. In particular, we explore the law’s possible annexing of three technological advances—digitized patient data, artificial intelligence, and cryptography—to assemble a state-of-the-art adverse events dataset that could make it possible for policy makers, in conjunction with providers, to take well-informed steps towards increasing patient safety. Part IV discusses a number of possible hurdles and concludes

Essays in health economics: volume-outcome effect, upcoding behaviour and the evaluation of a short-term intervention program

Evidence in the literature suggests a negative relationship between volume of medical procedures and mortality rates in the health care sector. In general, high-volume hospitals appear to achieve lower mortality rates, although considerable variation exists. However, most studies focus on US hospitals, which face different incentives than hospitals in a National Health Service (NHS). In order to add to the literature, this study aims to understand what happens in a NHS. Results reveal a statistically significant correlation between volume of procedures and better outcomes for the following medical procedures: cerebral infarction, respiratory infections, circulatory disorders with AMI, bowel procedures, cirrhosis, and hip and femur procedures. The effect is explained with the practice-makes-perfect hypothesis through static effects of scale with little evidence of learning-by-doing. The centralization of those medical procedures is recommended given that this policy would save a considerable number of lives (reduction of 12% in deaths for cerebral infarction).Fundação para a Ciência e a Tecnologia - FCT PTDC/ECO/71867/2006 and SFRH/BD/71799/201