Modeling Phase I Postanesthesia Care Unit Patient Transport Times Among Multiple Destinations

Area of Science:

• Anesthesiology
• Healthcare Operations Management
• Health Services Research

Background:

• Limited data exist on patient transport times from postanesthesia care units (PACUs), despite known impacts on operating room and workflow efficiency.
• Existing knowledge focuses on PACU discharge times, overlooking the resource consumption of transport duration.

Purpose of the Study:

• To quantify patient transport times from the PACU using real-world data.
• To identify factors influencing transport duration and delays.
• To evaluate methods for predicting transport workload and optimizing staffing.

Main Methods:

• Implemented Epic's Rover system to track 22,846 PACU transports over 22 months.
• Analyzed transport counts and times by destination, time of day, and day of week.
• Utilized statistical tests (χ2, Kruskal-Wallis, Shapiro-Wilk) and Bland-Altman plots to assess transport times and workload prediction accuracy.

Main Results:

• Transport times varied significantly by destination, with Phase II recovery being fastest (P ≤ .0001).
• Transport volume and total time differed across time periods and destinations (P < .0001), but weekdays were not significant.
• Approximately 50% of periods experienced delays, with significant runs of successive delays (P < .0001).
• Workload prediction based on discharge counts and average times was insufficient, differing from actual times by -1.04 to +1.05 hours per 4-hour period.

Conclusions:

• PACU transport times are destination-dependent; prioritize shorter transports, especially to Phase II PACU, when multiple patients await transport.
• Estimating transport workload solely by PACU discharge distribution is inadequate for precise staffing.
• Accurate staffing should utilize the mean and standard deviation of total transport times within specific workday periods.