Ratings provide consumers with useful quality information, however, when ratings shift demand to highly-rated sellers, congestion might occur at the top of the quality distribution. Congestion caused by disclosure may be observed in the health care setting, where prices often cannot adjust to reflect varying quality. I study the trade-off between providing quality information for consumers and congestion using a star rating disclosure policy implemented at a large integrated health system in the United States, which requires every physician to have star ratings posted online in a standardized fashion. I identify the effects of physician star ratings on patient volume using a regression discontinuity and difference-in-discontinuity design which leverages the rounding of ratings to discrete values and the fact that I observe ratings before and after their public disclosure online. I find that an increase in a physician’s rating increases the number of new patients seen by 2.96 visits per month on a baseline of 5.48 (54% increase). I show that star ratings shift patients to physicians who more often provide medically recommended screenings, counseling, and vaccinations. However, I also show that a higher rating causes patients to wait longer for treatment. New patients wait 2.7 additional days (30.5% longer) for an additional increment of the rating scale and existing patients wait longer as well. I use these findings to compute a revealed-preference estimate of the “shadow price of a star”; I find that patients are willing to wait 3 additional days in exchange for a one standard deviation increase in physician ratings. In the absence of a price, wait times may serve as an equilibrating factor to clear the market.

A surprise medical bill is a bill from an out-of-network provider that was not expected by the patient or that came from an out-of-network provider not chosen by the patient. In 2014, 20 percent of hospital inpatient admissions that originated in the emergency department (ED), 14 percent of outpatient visits to the ED, and 9 percent of elective inpatient admissions likely led to a surprise medical bill.

A surprise medical bill is a bill from an out-of-network provider that was not expected by the patient or that came from an out-of-network provider not chosen by the patient. We investigated consumers’ hospital choices after receipt of surprise out-of-network medical bills in an elective situation, using a large national sample of medical claims for obstetric patients with two deliveries covered by employer-sponsored health insurance in 2007–14. We found that 11 percent of mothers experienced a surprise out-of-network bill with their first delivery, and this was associated with an increase of 13 percent in the odds of switching hospitals for the second delivery, compared to mothers who did not experience a surprise bill. Mothers who switched hospitals after a surprise out-of-network bill reduced their relative risk of receiving a second surprise medical bill by 56 percent, compared to mothers who did not switch after receiving their first surprise bill. These results highlight the harmful effects of surprise out-of-network bills, as patients are more likely to switch from their preferred hospital after receiving a surprise bill. Patients would benefit significantly from laws protecting them from the damaging financial consequences of surprise out-of-network bills, including those incurred in elective situations.

In 2018 New Jersey implemented a final-offer arbitration system to resolve payment disputes between insurers and out-of-network providers over surprise medical bills. Similar proposals are being considered by Congress and other states. In this article we examine how arbitration decisions compare with other relevant provider payment amounts by linking administrative data from New Jersey arbitration cases to Medicare and commercial insurance claims data. We find that decisions track closely with one of the metrics that arbitrators are shown—the eightieth percentile of provider charges—with the median decision being 5.7 times prevailing in-network rates for the same services. It is not a foregone conclusion that arbitrators will select winning offers based on proximity to this target, although our findings suggest that it is a strong anchor. The amount that providers can expect to receive through the arbitration process also affects their bargaining leverage with insurers, which could affect in-network negotiated rates more broadly. Therefore, basing arbitration decisions or a payment standard on unilaterally set provider-billed charges appears likely to increase health care costs relative to other surprise billing solutions and perversely incentivizes providers to inflate their charges over time.

Surprise medical bills occur when a patient unexpectedly or involuntarily receives care from an out-of-network provider and is billed for the amount not covered by insurance. Past studies were unable to observe whether bills for such care were sent to patients and, if so, how much patients paid directly to out-of-network providers. We used data from the Medical Expenditure Panel Survey to measure how much privately insured emergency patients paid when they likely received a surprise bill and how much physicians received in these situations. Physicians collected 65 percent of the charged amount for likely surprise bills compared with 52 percent for other cases. Patients who likely received a surprise out-of-network bill for emergency care paid physicians more than ten times as much as other emergency patients paid, on average.

Hospitals are required by law to disclose prices on their websites, yet many hospitals do not comply. We investigate the potential determinants of this noncompliance using a nationwide dataset of hospital price transparency disclosures. We examine determinants of noncompliance such as hospital market concentration, insurer concentration, and technology adoption.

I study the policy and market implications of final offer (“baseball-style”) arbitration between health insurers and doctors who dispute payments. Building off of a model by Farber (1980), I create a novel dataset of arbitration decisions and final offer bids across multiple states using Freedom-of-Information Act requests. I use the data and model to shed light on how to optimally design dispute resolution systems between two opposing parties subject to constraints imposed by legislation.