The aim of our healthcare research is to inform policymakers, industry leaders, students, and the public.
The Healthcare Analytics Laboratory at Chicago Booth brings the real-world business problems of the modern healthcare industry into the classroom.
Explore Dan Adelman's Healthcare Analytics Laboratory ResearchOur faculty are recognized for their significant contributions to scholarship on the business of healthcare. See recent publications below.
Operating room (OR) teamwork quality is associated with familiarity among team members and their individual specialization. We describe novel measures of OR team familiarity and specialty experience. Surgeon-scrub (SS) and surgeon-circulator (SC) teaming scores, defined as the pair’s proportion of interactions relative to the surgeon’s total cases in the preceding 6 months were calculated between 2017 and 2021 at an academic medical center. Nurse service-line (SL) experience scores were defined as the proportion of a nurse’s cases performed within the given specialty. SS, SC, and nurse-SL scores were analyzed by specialty, case urgency, robotic approach, and surgeon academic rank. Two-sample Kolmogorov-Smirnov tests were used to determine heterogeneity between distributions. A total of 37,364 operations involving 150 attending surgeons and 222 nurses were analyzed. Median SS and SC scores were 0.08 (interquartile range: 0.03–0.19) and 0.06 (interquartile range: 0.03–0.13), respectively. Higher margin SLs, senior faculty rank, elective, and robotic cases were associated with greater SS, SC, and nurse-SL scores (P<0.001). These novel measures of teaming and specialization illustrate the low levels of OR team familiarity and objectively highlight differences that necessitate a deliberate evaluation of current OR scheduling practices.
Having an interpretable dynamic length-of-stay (LOS) model can help hospital administrators and clinicians make better decisions and improve the quality of care. The widespread implementation of electronic medical record (EMR) systems has enabled hospitals to collect massive amounts of health data. However, how to integrate this deluge of data into healthcare operations remains unclear. We propose a framework grounded in established clinical knowledge to model patients’ lengths-of-stay. In particular, we impose expert knowledge when grouping raw clinical data into medically meaningful variables, which summarize patients’ health trajectories. We use dynamic predictive models to output patients’ remaining lengths-of-stay (RLOS), future discharges, and census probability distributions based on their health trajectories up to the current stay. Evaluated with large-scale EMR data, the dynamic model significantly improves predictive power over the performance of any model in previous literature and remains medically interpretable.
The Centers for Medicare and Medicaid Services (CMS) star rating methodology for publicly evaluating hospitals uses a latent variable model that is based on the pre- sumption of a single, but unobservable, hospital-specific quality factor shared across a group of performance measures. Performance measures are given higher weight if they statistically appear to be more strongly correlated with this hidden factor. We show how this approach, when applied to measures that are weakly or not correlated with each other, can effectively ignore measures and can exhibit “knife-edge” instability, so that even if hospitals improve relative to all other hospitals, they may nonetheless score lower overall because of weight shifting onto different measures than before. In contrast, we provide an approach to scoring and ranking hospitals that, under reasonable conditions, ensures that hospitals that improve relative to all other hospitals obtain higher scores, while also having the capability to autonomously adjust weights as measures are added or subtracted over time. Rather than exploit statistical correlation, we propose a conic optimization framework that offers a new integrated approach in data envelopment analysis for simultaneous efficiency analysis and performance evaluation. We develop theory that explains the behaviour of our approach, in- cluding various properties satisfied by hospital scores at optimality. Using data, we apply our approach to score and rank nearly every hospital in the United States and demonstrate the extent to which it agrees or disagrees with the existing approach to the CMS star ratings.
The Centers for Medicare and Medicaid Services (CMS) star rating methodology for publicly evaluating hospitals uses a latent variable model that is based on the pre- sumption of a single, but unobservable, hospital-specific quality factor shared across a group of performance measures. Performance measures are given higher weight if they statistically appear to be more strongly correlated with this hidden factor. We show how this approach, when applied to measures that are weakly or not correlated with each other, can effectively ignore measures and can exhibit “knife-edge” instability, so that even if hospitals improve relative to all other hospitals, they may nonetheless score lower overall because of weight shifting onto different measures than before. In contrast, we provide an approach to scoring and ranking hospitals that, under reasonable conditions, ensures that hospitals that improve relative to all other hospitals obtain higher scores, while also having the capability to autonomously adjust weights as measures are added or subtracted over time. Rather than exploit statistical correlation, we propose a conic optimization framework that offers a new integrated approach in data envelopment analysis for simultaneous efficiency analysis and performance evaluation. We develop theory that explains the behaviour of our approach, in- cluding various properties satisfied by hospital scores at optimality. Using data, we apply our approach to score and rank nearly every hospital in the United States and demonstrate the extent to which it agrees or disagrees with the existing approach to the CMS star ratings.
The effects of television advertising in the market for health insurance are of distinct interest to both firms and regulators. Regulators are concerned about firms potentially using ads to “cream skim,” or attract an advantageous risk pool, as well as the potential for firms to use misinformation to take advantage of the elderly. Firms are interested in using advertising to acquire potentially highly profitable seniors. Meanwhile, health insurance is a useful setting to study the mechanisms through which advertising could work. Using the discontinuity in advertising exposure created by the borders of television markets, this study estimates the effects of advertising on consumer choice in health insurance. Television advertising has a small effect on brand enrollments, making advertising a relatively expensive means of acquiring customers. Heterogeneous effects point to advertising being more effective in less healthy counties, which runs opposite to the concern of cream skimming. Leveraging the unilateral cessation of advertising by United-Healthcare, evidence is provided that the small advertising effect is not explained by a prisoner’s dilemma equilibrium. An analysis of longer-run effects of advertising shows that advertising effects are short lived, further decreasing the potential of advertising to create long-run value to the firm.
We estimate the benefit of life-extending medical treatments to life insurance companies. Our main insight is that life insurance companies have a direct benefit from such treatments because they lower the insurer’s liabilities by pushing the death benefit further into the future and raising future premium income. We apply this insight to immunotherapy, treatments associated with durable gains in survival rates for a growing number of cancer patients. We estimate that the life insurance sector’s aggregate benefit from FDA-approved immunotherapies is $9.8 billion a year. Such life-extending treatments are often prohibitively expensive for patients and governments alike. Exploiting this value creation, we explore various ways life insurers could improve stress-free access to treatment. We discuss potential barriers to integration and the long-run implications for the industrial organization of life and health insurance markets, as well as the broader implications for medical innovation and long-term care insurance markets.
Craig Garthwaite, John Graves, Tal Gross, Zeynal Karaca, Victoria Marone, and Matthew J. Notowidigdo study the effect of the Affordable Care Act Medicaid expansion on hospital services, with a focus on the geographic variations of its impact, finding that it increased Medicaid visits, decreased uninsured visits, and lead the uninsured to consume more hospital services overall— primarily through outpatient visits to the ED for deferrable conditions. Notably, the authors found significant heterogeneity across Medicaid-expansion states with some experiencing large changes in utilization and others seeing little change.
The U.S. health care system uses commercial algorithms to guide health decisions. Obermeyer et al. find evidence of racial bias in one widely used algorithm, such that Black patients assigned the same level of risk by the algorithm are sicker than White patients (see the Perspective by Benjamin). The authors estimated that this racial bias reduces the number of Black patients identified for extra care by more than half. Bias occurs because the algorithm uses health costs as a proxy for health needs. Less money is spent on Black patients who have the same level of need, and the algorithm thus falsely concludes that Black patients are healthier than equally sick White patients. Reformulating the algorithm so that it no longer uses costs as a proxy for needs eliminates the racial bias in predicting who needs extra care.
During the push to pass the Affordable Care Act, President Barack Obama often described the “crushing cost of health care” that was causing millions of Americans to “live every day just one accident or illness away from bankruptcy” and repeatedly stated that the high cost of health care “causes a bankruptcy in America every 30 seconds.” Stories of illnesses and injuries with financial consequences so severe that they caused households to file for bankruptcy were used as a major argument in support of the 2010 Affordable Care Act. And in 2014, Senators Elizabeth Warren (D-MA) and Sheldon Whitehouse (D-RI) cited medical bills as “the leading cause of personal bankruptcy” when introducing the Medical Bankruptcy Fairness Act, which would have made the bankruptcy process more forgiving for “medically distressed debtors.” But it turns out that the existing evidence for “medical bankruptcies” suffers from a basic statistical fallacy; when we eliminated this problem, we found compelling evidence of the existence of medical bankruptcies but discovered that medical expenses cause many fewer bankruptcies than has been claimed.
We use an event study approach to examine the economic consequences of hospital admissions for adults in two datasets: survey data from the Health and Retirement Study, and hospitaliza- tion data linked to credit reports. For non-elderly adults with health insurance, hospital admis- sions increase out-of-pocket medical spending, unpaid medical bills and bankruptcy, and reduce earnings, income, access to credit and consumer borrowing. The earnings decline is substantial compared to the out-of-pocket spending increase, and is minimally insured prior to age-eligibility for Social Security Retirement Income. Relative to the insured non-elderly, the uninsured non- elderly experience much larger increases in unpaid medical bills and bankruptcy rates following a hospital admission. Hospital admissions trigger less than 5 percent of all bankruptcies.
American hospitals are required to provide emergency medical care to the uninsured. We use previously confidential hospital financial data to study the resulting uncompensated care, medical care for which no payment is received. Using both panel-data methods and case studies, we find that each additional uninsured person costs hospitals approximately $800 each year. Increases in the uninsured population also lower hospital profit mar- gins, suggesting that hospitals do not pass along all uncompensated care costs to other parties such as hospital employees or privately insured patients. A hospital’s uncompensated care costs also increase when a neighboring hospital closes.
Health insurance confers benefits to the previously uninsured, including improvements in health, reductions in out-of-pocket spending, and reduced medical debt. But because the nomi- nally uninsured pay only a small share of their medical expenses, health insurance also provides substantial transfers to non-recipient parties who would otherwise bear the costs of providing uncompensated care to the uninsured. The prevalence of uncompensated care helps explain the limited take-up of heavily-subsidized public health insurance and the evidence that many recipients value formal health insurance at substantially less than the cost to insurers of pro- viding that coverage. The distributional implications of public subsidies for health insurance depend critically on the ultimate economic incidence of the transfers they deliver to providers of uncompensated care.
Transparency of quality in the healthcare sector primarily aims to facilitate patients' care decisions, however, it also provides useful information to competing healthcare providers. We study how competitors respond to increased transparency about rivals' quality by exploiting a regulatory change that initiated disclosure about the quality of all kidney dialysis facilities in the United States. We show that competitors are 27% more likely to open new facilities near low-quality incumbents after the transparency program is implemented. We also show that the effect of transparency on competition is restricted to states without licensing requirements that create barriers to entry. Evidence from patient referrals indicates that the new transparency regime increases the sensitivity of demand to quality and that the increase in competition is costly to low-quality incumbents, as they lose 31% of their new patient referrals—equivalent to a $3.74 million loss of a facility's annual revenue—to higher-quality entrants. Finally, increased competition from rivals leads to improved patient outcomes by reducing hospitalizations, and incentivizes incumbents' investments in patient care through an immediate increase in nurse practitioners and social workers.
Our faculty and their research regularly appear in the media. See highlights below.
Listen to Professor Dan Adelman's insights on measuring healthcare outcomes in this podcast episode of the Business of Medicine Series on ENT in a Nutshell, hosted by Dr. Ashley Nassiri.
Below are some examples of data used by our faculty.
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