Journal Of The Econometric Society

An International Society for the Advancement of Economic
Theory in its Relation to Statistics and Mathematics

Edited by: Guido W. Imbens • Print ISSN: 0012-9682 • Online ISSN: 1468-0262

Econometrica: May, 2020, Volume 88, Issue 3

Understanding Doctor Decision Making: The Case of Depression Treatment
p. 847-878

Janet M. Currie, W. Bentley MacLeod

Treatment for depression is complex, requiring decisions that may involve trade‐offs between exploiting treatments with the highest expected value and experimenting with treatments with higher possible payoffs. Using patient claims data, we show that among skilled doctors, using a broader portfolio of drugs predicts better patient outcomes, except in cases where doctors' decisions violate loose professional guidelines. We introduce a behavioral model of decision making guided by our empirical observations. The model's novel feature is that the trade‐off between exploitation and experimentation depends on the doctor's diagnostic skill. The model predicts that higher diagnostic skill leads to greater diversity in drug choice and better matching of drugs to patients even among doctors with the same initial beliefs regarding drug effectiveness. Consistent with the finding that guideline violations predict poorer patient outcomes, simulations of the model suggest that increasing the number of possible drug choices can lower performance.

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Supplemental Material

Supplement to "Understanding Doctor Decision Making: The Case of Depression Treatment"

This dataset contains two sets of files which are described in two README files. describes the Julia and R code for the simulation results in "Understanding Doctor Decision Making: The Case of Depression Treatment". The Julia and R code contain more detailed information. Files describing the processing of the IQVIA and BCBS data on prescriptions and claims (respectively) are discussed in readme.docx.

Supplement to "Understanding Doctor Decision Making: The Case of Depression Treatment"

This online appendix contains material not found within the manuscript.