A Statistical Model-driven Surgical Case Scheduling System Improves Multiple Measures of Operative Suite Efficiency:...

We sought to determine whether a data-driven scheduling approach improves Operative Suite (OS) efficiency.

Although efficient use of the OS is a critical determinant of access to health care services, OS scheduling methodologies are simplistic and do not account for all the available characteristics of individual surgical cases.

We randomly scheduled cases in a single OS by predicting their length using either the historical mean (HM) duration of the most recent 4 years; or a regression modeling (RM) system that accounted for operative and patient characteristics. The primary endpoint was the imprecision in prediction of the end of the operative day. Secondary endpoints included measures of OS efficiency; personnel burnout captured by the Maslach Burnout Inventory; and a composite endpoint of 30-day mortality, myocardial infarction, wound infection, bleeding, amputation, or reoperation.

Two hundred and seven operative days were allocated to scheduling with either the RM or the HM methodology. Mean imprecision in predicting the end of the operative day was higher with the HM approach (30.8 vs 7.2 minutes, P = 0.024). RM was associated with higher throughput (379 vs 356 cases scheduled over the course of the study, P = 0.04). The composite rate of adverse 30-day events was similar (2.2% vs 3.2%, P = 0.44). The mean depersonalization score was higher (3.2 vs 2.0, P = 0.044), and mean personal accomplishment score was lower during HM weeks (37.5 vs 40.5, P = 0.028).

Compared to the HM scheduling approach, the proposed data-driven RM scheduling methodology improves multiple measures of OS efficiency and OS personnel satisfaction without adversely affecting clinical outcomes.