Using Acuity to Predict Oncology Infusion Center Daily Nurse Staffing and Outcomes

Background: Outpatient oncology infusion centers (OICs) use acuity to quantify the complexity and intensity of care to improve staffing levels and equitable patient assignments. OIC interviews revealed inconsistent measurement of acuity and a mixture of use cases. No publications measured objective operational benefits beyond surveyed nurse satisfaction or compared different models of acuity.

Objectives: This study assessed three acuity models across multiple centers to determine whether acuity was superior to patient volumes or patient hours in predicting the number of nurses needed to care for scheduled patients in an OIC, as well as the effect on objective metrics of missed nurse lunch breaks and patient wait times. A secondary end point was used to identify a superior model.

Methods: Classification machine learning models were built to assess the predictive value of three acuity models compared to patient hours and patient visits.

Findings: None of the tested acuity models were found to have statistically significant improvement to the prediction of needed OIC nurse staffing, patient wait times, or missed nurse lunch breaks.