Dan Zeltzer

I am an assistant professor at Tel Aviv University School of Economics. I received my PhD in Economics from Princeton University. I work in Applied Microeconomics and Health Economics. My interests include physician referral networks, technology adoption, and predictive modeling in healthcare.

Other affiliations

IZA (Research Affiliate)

Center for Health and Wellbeing, Princeton University (Guest)

CV (pdf)

Research Papers

Gender Homophily in Referral Networks: Consequences for the Medicare Physician Earnings Gap, forthcoming at AEJ: Applied Economics

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 same 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.

Prediction Accuracy with Electronic Medical Records versus Administrative Claims Data (joint with Ran Balicer, Tzvi Shir, Natalie Flaks-Manov, Liran Einav, and Efrat Shadmi), Medical Care, July 2019. DOI: 10.1097/MLR.0000000000001135

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. 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. We predict 4 binary outcomes: length of stay (≥7 d), death during the index admission, 30-day readmission, and 1-year mortality. 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. 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.

Horizon Effects and Adverse Selection in Health Insurance Markets (joint with Olivier Darmouni)



blog post: https://www8.gsb.columbia.edu/articles/ideas-work/there-s-easier-obamacare-fix

We study how increasing contract length affects adverse selection in health insurance markets. While health risks are persistent, private health insurance contracts in the U.S. have short, one-year terms. Short-term, community-rated contracts allow patients to increase their coverage only after risks materialize, which leads to market unraveling. Longer contracts ameliorate adverse selection because both demand and supply exhibit horizon effects. Intuitively, longer horizon risk is less predictable, thus elevating demand for coverage and lowering equilibrium premiums. We estimate risk dynamics using data from 3.5 million U.S. health insurance claims and find that risk predictability falls significantly with horizon. Nesting these estimates in an equilibrium model of insurance markets, we find that a reform implementing two-year contracts (e.g., by lowering the frequency of open enrollment periods) would increase coverage by 12–19 percentage points from its initial level and yield average annual welfare gains of $600–$900 per person (20%–30% of the insured risk). A third of these effects is driven by insurers' response and the rest by changes in consumer expectations.

Can Targeting High-Risk Patients Reduce Readmission Rates? Evidence from Israel (with Efrat Shadmi, Tzvi Shir, Natalie Flaks-Manov, Liran Einav, and Ran Balicer)


We study a large intervention to reduce hospital readmission rates by the largest Israeli integrated healthcare system. Since 2012, the intervention flagged patients aged 65 and older with high readmission risk to providers, both upon admission and after discharge. Risk scores were based on patient-specific prior healthcare utilization. Analyzing 171,541 covered admissions during 2009–2016, we find that the intervention reduced 30-day readmission rates by 5.9% among patients aged 65–70 relative to patients aged 60–64, who were not targeted for inclusion in the intervention, and for whom no scores were calculated. The largest reduction, 12.3%, was among high-risk patients. Primary care post-discharge follow-up encounters were significantly expedited. The magnitude of the estimated effect peaked during the first two years, and it declined subsequently, after incentive payments by the Israeli Ministry of Health to organizations that reduce readmission rates were discontinued. Taken together, the evidence demonstrates that informing providers about patient risk in real time can improve care continuity and reduce hospital readmissions, and that maintaining such efforts on an ongoing basis is important to sustain their impact.

Draft available upon request

Work in Progress

Drug Diffusion through Peer Networks: The Influence of Industry Payments (joint with Lelia Agha)

To be presented at the 2019 NBER Summer Institute Healthcare Meeting

Medical drug and device companies invest over $8 billion annually in payments to physicians and hospitals; many of these payments are targeted at encouraging use of new drugs. Drug detailing efforts of pharmaceutical companies leverage peer influence within existing provider networks to broaden their reach beyond the directly targeted physicians. Using matched physician data from Medicare Part D and Open Payments, we investigate the influence of pharmaceutical payments on the prescription of new anticoagulant drugs. First, we show that pharmaceutical payments target physicians who share patients with many different providers and thus may influence a broader network of peers. Within a difference in differences framework, we find a physician’s own prescription of new anticoagulant drugs increases following a pharmaceutical payment, relative to the physician-specific baseline prescribing rate for that drug. The effect scales with the size of the payment, with large payments such as speaking and consulting fees spurring larger increases in prescribing than small payments for food. Peers of targeted physicians also increase their prescribing of the new drug after the targeted physician receives a large payment, introducing entirely new patients to the drug class. We find no evidence that drug detailing interactions lead to curtailed prescription volume for patients at high risk of dangerous side effects or low potential benefits of anticoagulation.