IMPORTANCEStudies have found that female surgeons have fewer opportunities to perform highly remunerated operations, a circumstance that contributes to the sex-based pay gap in surgery. Procedures performed by surgeons are, in part, determined by the referrals they receive. In the US and Canada, most practicing physicians who provide referrals are men. Whether there are sex-based differences in surgical referrals is unknown. OBJECTIVE To examine whether physicians' referrals to surgeons are influenced by the sex of the referring physician and/or surgeon.

I assess the extent to which the gender gap in physician earnings may be driven by physicians’ preference for referring to specialists of the same gender. Analyzing administrative data on 100 million Medicare patient referrals, I provide robust evidence that doctors refer more to specialists of their own gender. I show that biased referrals are predominantly driven by physicians’ decisions rather than by endogenous sorting of physicians or patients. Because most referring doctors are male, the net impact of same-gender bias by both male and female doctors generates lower demand for female relative to male specialists, pointing to a positive externality for increased female participation in medicine. (JEL H51, I11, J16, J31, J44)

Objective: The objective of this study was to evaluate the incremental predictive power of electronic medical record (EMR) data, relative to the information available in more easily accessible and standardized insurance claims data. Data and Methods: Using both EMR and Claims data, we predicted outcomes for 118,510 patients with 144,966 hospitalizations in 8 hospitals, using widely used prediction models. We use cross-validation to prevent overfitting and tested predictive performance on separate data that were not used for model training. Main Outcomes: We predict 4 binary outcomes: length of stay (≥7 d), death during the index admission, 30-day readmission, and 1-year mortality. Results: We achieve nearly the same prediction accuracy using both EMR and claims data relative to using claims data alone in predicting 30-day readmissions [area under the receiver operating characteristic curve (AUC): 0.698 vs. 0.711; positive predictive value (PPV) at top 10% of predicted risk: 37.2% vs. 35.7%], and 1-year mortality (AUC: 0.902 vs. 0.912; PPV: 64.6% vs. 57.6%). EMR data, especially from the first 2 days of the index admission, substantially improved prediction of length of stay (AUC: 0.786 vs. 0.837; PPV: 58.9% vs. 55.5%) and inpatient mortality (AUC: 0.897 vs. 0.950; PPV: 24.3% vs. 14.0%). Results were similar for sensitivity, specificity, and negative predictive value across alternative cutoffs and for using alternative types of predictive models. Conclusion: EMR data are useful in predicting short-term outcomes. However, their incremental value for predicting longer-term outcomes is smaller. Therefore, for interventions that are based on long-term predictions, using more broadly available claims data is equally effective.

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