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Determinants of Clinical Decision Making

Paper Session

Sunday, Jan. 7, 2024 8:00 AM - 10:00 AM (CST)

Grand Hyatt, Presidio C
Hosted By: Econometric Society
  • Chair: Molly Kathleen Schnell, Northwestern University

Overconfidence and Technology Adoption in Health Care

Douglas Staiger
,
Dartmouth College

Abstract

Variation in technology adoption is a key driver of differences in productivity. Previous studies sought to explain variations in technology adoption by heterogeneity in profitability, costs of adoption, or other factors. Less is known about how adoption is affected by bias in the perceived skill to implement the technology. We develop a Bayesian framework in which the use of the technology depends on perceived skill, while the outcomes from using it depend on actual skill. We study the determinants of adoption in the case of implantable cardiac defibrillators (ICDs) for which we document large differences across hospitals in the rate of adoption between 2002-2006, and a strong reversal from 2006-2013. We find that perception bias explains two-thirds of the cross-hospital variation in ICD use. A dynamic version of the model with learning about bias predicts accurately the subsequent decline in ICD use between 2006-2013. These results suggest an important role for misperception in explaining the wide variation in the adoption of new technologies.

Patient Costs and Physicians' Information

Michael Jonathan Dickstein
,
New York University
Jihye Jeon
,
Boston University
Eduardo Morales
,
Princeton University

Abstract

In response to rising health costs, insurers have increased the share of health spending that their enrollees must pay out of pocket. We study whether physicians know the relative out-of-pocket costs patients face, and whether they respond to these demand-side incentives by altering their treatment recommendations. To distinguish physicians' knowledge about price from their sensitivity, we develop a moment inequality model that requires fewer assumptions on the physician's precise information about price. We first illustrate the value of our methodological approach in a simulated setting and then apply it in the context of physicians' prescription drug treatment choices for type 2 diabetes. Using data from privately insured patients in Oregon, we find that physicians are more price sensitive than alternative full-information empirical models might suggest. We also reject that physicians know each patient's costs. Instead, the data suggest physicians use only aggregate price information in their choice, such as a drug's average price in a year. We also identify heterogeneity in physicians' information sets by their age, gender, and past experience with diabetes management.

The Effects of Competition on Physician Prescribing

Janet Currie
,
Princeton University
Anran Li
,
Northwestern University
Molly Kathleen Schnell
,
Northwestern University

Abstract

We ask how competition influences the prescribing practices of physicians. Law changes granting nurse practitioners (NPs) the ability to prescribe controlled substances without physician collaboration or oversight generate exogenous variation in competition. In response, we find that general practice physicians (GPs) significantly increase their prescribing of controlled substances such as opioids and controlled anti-anxiety medications. GPs also increase their co-prescribing of opioids and benzodiazepines, a practice that goes against prescribing guidelines. These effects are more pronounced in areas with more NPs per GP at baseline and are concentrated in physician specialties that compete most directly with NPs. Our findings are consistent with a simple model of physician behavior in which competition for patients leads physicians to move toward the preferences of marginal patients. These results demonstrate that more competition will not always lead to improvements in patient care and can instead lead to excessive service provision.

The Effect of Fatigue and Cognitive Load on Medical Provider Decision-Making and Patient Health Outcomes

Bryan Chu
,
University of California-Berkeley
Benjamin R. Handel
,
University of California-Berkeley
Jonathan T. Kolstad
,
University of California-Berkeley
Ulrike Malmendier
,
University of California-Berkeley
Filip Matejka
,
Charles University and Academy of Science

Abstract

We leverage novel audit log data from the UCSF Emergency Department to study the role of provider fatigue and cognitive load on both provider treatment decisions and patient outcomes. The data we use have granular physician-patient level data on myriad micro-interactions, including physician notetaking, note reading, order requests, patient tests results and much more. We use these data together with the stochastic arrival of patients to the ED to estimate how physician’s workplace burden, in terms of the number of patients they treat and patient complexity, impacts subsequent care for patients that they see. We study physician heterogeneity in the impact of cognitive load and fatigue.

Discussant(s)
W. Bentley MacLeod
,
Princeton University
Eilidh Geddes
,
Northwestern University
Gautam Gowrisankaran
,
Columbia University
David Chan
,
Stanford University
JEL Classifications
  • C1 - Econometric and Statistical Methods and Methodology: General