Resilience in the Surgical Scheduling to Support Adaptive Scheduling System

Abstract

Operating Room (OR) managers frequently encounter uncertainties related to real-time scheduling, especially on the day of surgery. It is necessary to enable earlier identification of uncertainties occurring in the perioperative environment. This study aims to propose a framework for resilient surgical scheduling by identifying uncertainty factors affecting the real-time surgical scheduling through a mixed-methods study. We collected the pre- and post-surgical scheduling data for twenty days and a one-day observation data in a top-tier general university hospital in South Korea. Data were compared and analyzed for any changes related to the dimensions of uncertainty. The observations in situ of surgical scheduling were performed to confirm our findings from the quantitative data. Analysis was divided into two phases of fundamental uncertainties categorization (conceptual, technical and personal) and uncertainties leveling for effective decision-making strategies. Pre- and post-surgical scheduling data analysis showed that unconfirmed patient medical conditions and emergency cases are the main causes of frequent same-day surgery schedule changes, with derived factors that affect the scheduling pattern (time of surgery, overtime surgery, surgical procedure changes and surgery duration). The observation revealed how the OR manager controlled the unexpected events to prevent overtime surgeries. In conclusion, integrating resilience approach to identifying uncertainties and managing event changes can minimize potential risks that may compromise the surgical personnel and patients' safety, thereby promoting higher resilience in the current system. Furthermore, this strategy may improve coordination among personnel and increase surgical scheduling efficiency.

Keywords: patient safety; resilience; situation awareness; surgical scheduling; uncertainties.

Figure 1

Existing surgical scheduling monitoring process.

Figure 2

Pre-surgical scheduling list excerpted from the Order Communication System (OCS). The patient’s and surgeon’s names were masked to protect their privacy.

Figure 3

Interpretation of the schedule sheet’s parameters composed into the dimension uncertainty and clustered into Beresford Model Domain.

Figure 4

Dimension of uncertainties derived from the schedule sheet.

Figure 5

Surgery departments with highest surgery rates according to post-surgery scheduling data in this hospital.

Figure 6

Proposed framework; the Beresford model of uncertainty and Courtney uncertainty level concept has been integrated to achieve higher resilience in a mindful infrastructure.

Figure 7

Leveling the identified and categorized uncertainties as per the Courtney level of uncertainty model.