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

Area of Science:

• Oncology Nursing
• Healthcare Operations Management
• Health Services Research

Background:

• Outpatient oncology infusion centers (OICs) utilize acuity systems for staffing and patient assignments.
• Existing OIC acuity measurements are inconsistent and lack objective operational validation.
• No studies have compared different acuity models for their impact on objective operational metrics.

Purpose of the Study:

• To evaluate three acuity models against patient volume and hours in predicting OIC nurse staffing needs.
• To assess the impact of acuity models on objective metrics like missed nurse lunch breaks and patient wait times.
• To identify a superior acuity model for OIC operations.

Main Methods:

• Developed classification machine learning models to analyze acuity data.
• Compared the predictive power of three acuity models with patient hours and patient visits.
• Evaluated objective operational outcomes including nurse breaks and patient wait times.

Main Results:

• No statistically significant improvement was observed in predicting nurse staffing needs using acuity models.
• Acuity models did not demonstrate significant impact on reducing missed nurse lunch breaks.
• Patient wait times were not significantly improved by the implementation of tested acuity models.

Conclusions:

• Current acuity models do not offer statistically significant advantages over simpler metrics for OIC nurse staffing.
• Objective operational benefits, such as reduced wait times and improved break adherence, were not achieved with the studied acuity models.
• Further investigation into refined acuity methodologies or alternative operational strategies is warranted for OICs.