Use of instrumental variables in electronic health record-driven models.

Precision medicine presents various methodological challenges whose assessment requires the consideration of multiple factors. In particular, the data multitude in the Electronic Health Records poses interoperability issues and requires novel inference strategies. A problem, though apparently a paradox, is that highly specific treatments and a variety of outcomes may hardly match with consistent observations (i.e., large samples). Why is it the case? Owing to the heterogeneity of Electronic Health Records, models for the evaluation of treatment effects need to be selected, and in some cases, the use of instrumental variables might be necessary. We studied the recently defined person-centered treatment effects in cancer and C-section contexts from Electronic Health Record sources and identified as an instrument the distance of patients from hospitals. We present first the rationale for using such instrument and then its model implementation. While for cancer patients consideration of distance turns out to be a penalty, implying a negative effect on the probability of receiving surgery, a positive effect is instead found in C-section due to higher propensity of scheduling delivery. Overall, the estimated person-centered treatment effects reveal a high degree of heterogeneity, whose interpretation remains context-dependent. With regard to the use of instruments in light of our two case studies, our suggestion is that this process requires ad hoc variable selection for both covariates and instruments and additional testing to ensure validity.