Optimizing patient selection and scheduling for home-based chemotherapy treatment

Home-based chemotherapy treatment (HBCT) is a growing care model of home healthcare that allows patients to receive chemotherapy drug infusions from the comfort of their home. Optimization of admission planning policies presents an opportunity to ensure that resources are utilized efficiently, and patients are scheduled appropriately. This study develops a mixed integer linear programming model to provide recommendations on which HBCT-eligible patients should be treated at home versus in-clinic and when that should occur in their treatment plan. Four admission policy variations at different levels of flexibility are modeled: Baseline, Fixed-Term, Flexible-Term, and Recurrent. They are analyzed using both real patient data from a pilot program and simulated data anticipating growing demand for services. Results of the analyses show that more flexible policies improve resource utilization and reduce patient time to admission to HBCT, though this may result in increased need for organizational coordination. The Baseline policy results in significant overtime, while the other policies produce zero overtime. The Recurrent policy allows for about 11% more appointment minutes scheduled than the Fixed-Term policy and reduces patient lead time. The framework developed for this tactical-level optimization problem has been shown to be feasible for supporting data-driven decision making for admission planning.