Calculating Optimal Patient to Nursing Capacity: Comparative Analysis of Traditional and New Methods

Area of Science:

• Healthcare Management
• Nursing Staffing
• Patient Safety

Background:

• Traditional nurse staffing methods like patient-to-nurse (P/N) ratios and nursing hours per patient day do not account for daily fluctuations in patient needs.
• These static methods can lead to suboptimal nurse staffing, impacting patient outcomes and increasing staff burnout.

Purpose of the Study:

• To evaluate a novel dynamic bed count calculation for optimizing hourly nurse staffing levels.
• To compare the effectiveness of the dynamic bed count with traditional P/N ratios in managing patient capacity.

Main Methods:

• The Veterans Affairs Palo Alto Health Care System (VAPAHCS) piloted a dynamic bed count system, integrating P/N ratios and nursing hours per patient day for hourly calculations.
• Patient capacity was assessed across intensive care units (ICUs), medical-surgical ICUs, and acute care units from January 1, 2023, to May 25, 2023.
• Statistical analyses, including descriptive statistics and student t-tests, were used to compare the dynamic bed count with P/N ratios.

Main Results:

• The dynamic bed count provided superior temporal resolution of patient capacity compared to P/N ratios.
• On average, P/N ratios overestimated patient capacity, suggesting units might exceed optimal levels.
• For instance, acute care unit 3C showed a dynamic bed count of 21.6 versus a P/N ratio of 28.6.

Conclusions:

• The dynamic bed count offers enhanced, timely insights into staffing needs, patient acuity, and turnover.
• Implementing this method can empower healthcare departments to optimize nurse staffing and improve overall patient care.
• This approach represents a significant advancement over traditional methods for nurse staffing calculations.