An Infusion Scheduling Decision Support Tool to Manage Resource Utilization.

Abstract

Background and objectives: As demand for infusion therapy increases due to an aging population and growing rates of chronic illness, effective resource management in scheduling becomes more critical to serve patients in ambulatory infusion centers. The wide range of treatment types, lengths, and nursing requirements, as well as capacity constraints, makes infusion scheduling challenging to effectively manage patient flow and resource utilization. Despite efforts to improve scheduling efficiency, the lack of practice implication and feasibility remains. This study aims to develop a tool that delivers data- and algorithm-driven recommendations for appointment scheduling decisions to improve resource management.

Methods: We introduce a real-time decision support tool that simultaneously accounts for (1) existing nursing workload and space utilization and (2) the nursing time and infusion chairs required for upcoming patients. The tool interface is presented, and the functions of each element are described. The appointment time selection algorithm was developed to provide schedulers with options for scheduling, constrained by nursing and chair availability. The tool also provides schedulers with the flexibility to deviate from recommendations and receive dynamic feedback on patient volume and nursing needs based on their decisions.

Results: The implementation results show a significant reduction in distributing nurses' workload in terms of time mismatching between patients' needs and nursing availability by 45.5%, P < .001, while the chair utilization remained consistent. The feedback from both the scheduling team and nurses was positive, which provided strong evidence for how this tool addressed and targeted their scheduling needs. In addition, key practice behaviors and implementation challenges that impact operations efficiency are discussed for further improvement.

Conclusions: The proposed tool offers a streamlined, interactive, and adaptable scheduling approach for better resource utilization and ultimately reduces the time schedulers spend on making manual decisions.