In partnership with IU Health, the largest health system in Indiana with 16 hospitals, we jointly developed a suite of advanced data and decision analytics to support a novel internal travel nursing program. This work addresses a long-standing gap in healthcare between state-of-art data decision support analytics and operational processes. Four months after implementation of our integrated machine learning and optimization tool demonstrated 5% lower understaffing and annualized savings of $900K.

Given the increase in the demand for temporary healthcare workers and their additional cost burden, we propose a two-stage stochastic optimization framework to inform recruitment decision making for a provider facing a period of highly uncertain demand. The optimal recruitment decisions are analytically characterized under a general setting. A case study is conducted to illustrate the application of our framework in an inpatient ward. We also show potential savings from adoption of our model.

This paper develops a queueing game model to investigate the impact of virtual triage in the acute care setting. We find that, when virtual triage excessively recommends emergency (primary) care, it could bring about a decrease in ED (GP) visits. Another finding is for arbitrary self-triage accuracy, the adoption of informative virtual triage can worsen system performance. To unlock the operational benefits, we characterize the optimal virtual triage accuracy subjective to the ROC curve.