Economic value of using electronic transporter applications for post-anesthesia care unit staffing decisions rather than manual logging of transport durations

Abstract

Background

Recently, we used data from Epic's Rover Transport application to analyze patient discharges from a phase I post-anesthesia care unit (PACU). We perform a retrospective cohort study to analyze how and when to use transporter log data to appropriately balance Type I and Type II error rates in the context of PACU transporter staffing decision-making.

Methods

The Rover Transport app was used to track all PACU transports from July 2022 through April 2024, totaling 22,846 across 461 workdays. The total hours of PACU transport follow a trapezoidal pattern, with load increasing through the morning (08:00–11:59), plateauing in the afternoon and evening (12:00–15:59 and 16:00–19:59), and then decreasing at night (20:00–23:59). Each transporter spends approximately one hour per four-hour period transporting. To inform transporter staffing decisions from these data, pairwise comparisons were generated between each workday's “light” periods (08:00–11:59 vs 20:00–23:59) and “busy” periods (12:00–15:59 vs 16:00–19:59). The probability distribution of these pairwise comparisons were compared with normal distributions using Shapiro-Wilk tests and standardized normal probability plots. Then, for repeated statistical power analyses to guide PACU transporter staffing, Type II errors were considered at least as costly as Type I errors. Setting α = β = 0.05, we determined how many days of data were required to differentiate between the “light” periods (08:00–11:59 vs 20:00–23:59) and the “busy” periods (12:00–15:59 vs 16:00–19:59), using the minimum actionable difference of one hour per four-hour period.

Results

Both pairwise comparisons were normally distributed (Shapiro-Wilk W > 0.99). At α=β=0.05, proper differentiation of hours of PACU transport workload between four-hour periods required total transport data from at least 18 out of every 100 workdays for the “light” four-hour periods, or 44 out of every 100 workdays for the “busy” four-hour periods. Relaxing the combined error rate to 0.15 reduced the day requirements to 36 and 15. Restricting the combined error rate to 0.02 required 80 and 32 days of data for comparison between “busy” and “light” four-hour periods respectively.

Conclusions

The number of days of data needed for statistically powerful comparisons between four-hour period workloads are prohibitively large for manual collection. Therefore, hospitals not yet using the transport tracking capabilities in their electronic medical records for PACU transports will benefit from using them, even if only for the improved staffing decisions the data allows.