We compared EHR-based workload with reimbursement in anesthesiology and found that payments for anesthesia services are likely poorly calibrated with clinical workload, largely by not recognizing the physical and cognitive effort of caring for the sickest patients. This likely penalizes academic and safety net hospitals the most. Our method for measuring clinical workload from EHR audit log data could be used to measure the time and intensity of clinical work more objectively to better inform healthcare policy.

Physician burnout is common and has conseqeunces for the health of physicians and their patients, yet the temporal evolution and causal contributors to burnout are not well-understood. Here, we conducted a prospective study to measure the monthly evolution of burnout in intern physicians and its association with clinical workload and wrong-patient errors. Burnout was highly correlated with recent workload; interns who worked more hours and took care of more patients had more burnout. However, burnout was suprisingly elastic; interns on lighter rotations were able to recover. We think these findings have implications for intern scheduling.

Clinical work environments are filled with competing demands for clinicians' attention, resulting in attention switching between different tasks up to 150 times an hour. The consequences of such fragmented work are not well-understood, partially because measuring attention switching has traditionally been laborious observational work. We developed a novel scalable method of measuring clinician attention switching using passively collected EHR audit log data. As a case study, we applied this method to critical care clinicians. We found that ICU work was highly fragmented, and increased attention switching was associated with decreased clinician efficiency and increased errors.

20 million patients have surgery in the US every year, and ~ 1 million of those patients require life-saving blood transfusion. Presurgical preparation for transfusion is important to allow for safe and timely transfusion during surgery, but excessive preparation is unfortunately common, costly, and contributes to blood waste. In this paper, we develop a personalized surgical transfusion risk prediction model using a database of 3 million surgical patients, and show that using such a model to guide presurgical type and screen orders can potentially improve patient safety while reducing the number of unnecessary orders. Reproducible code is provided to make predictions for new patients.