OBJECTIVE: Electronic health record (EHR) data are a valuable resource for population health research but lack critical information such as relationships between individuals. Emergency contacts in EHRs can be used to link family members, creating a population that is more representative of a community than traditional family cohorts. MATERIALS AND METHODS: We revised a published algorithm: relationship inference from the electronic health record (RIFTEHR). Our version, Pythonic RIFTEHR (P-RIFTEHR), identifies a patient's emergency contacts, matches them to existing patients (when available) using network graphs, checks for conflicts, and infers new relationships. P-RIFTEHR was run on December 15, 2021 in the Northwestern Medicine Electronic Data Warehouse (NMEDW) on approximately 2.95 million individuals and was validated using the existing link between children born at NM hospitals and their mothers. As proof-of-concept, we modeled the association between parent and child obesity using logistic regression. RESULTS: The P-RIFTEHR algorithm matched 1 157 454 individuals in 448 278 families. The median family size was 2, the largest was 32 persons, and 247 families spanned 4 generations or more. Validation of the mother-child pairs resulted in 95.1% sensitivity. Children were 2 times more likely to be obese if a parent is obese (OR: 2.30; 95% CI, 2.23-2.37). CONCLUSION: P-RIFTEHR can identify familiar relationships in a large, diverse population in an integrated health system. Estimates of parent-child inheritability of obesity using family structures identified by the algorithm were consistent with previously published estimates from traditional cohort studies.

We study the impact of dynamic pricing (so-called "surge pricing") on relocation decisions by Uber's driver-partners and the corresponding revenue they collected. Using a natural experiment arising from an outage in the system that produces the surge pricing heatmap for a portion of Uber's driver-partners over 10 major cities, and a difference-in-differences approach, we study the short-run effect that visibility of the surge heatmap has on 1) drivers' decisions to relocate to areas with higher or lower prices and 2) drivers' revenue. We demonstrate that the ability to see the surge heatmap has a statistically significant impact on both outcomes. Ability to see the surge heatmap explains 10%-60% of Uber drivers' self-positioning decisions, attracts drivers toward areas with higher surge prices, and increases drivers' revenue on surged trips by up to 70%. This suggests that dynamic pricing helps drivers move to where riders' demand is largest, and that the resulting reduction in spatial search friction and spatial mismatch improves waiting times and welfare for both riders and drivers.