Feasibility of Clinician-led, Organic Implementation of Software in Surgery

Introduction: Rapid advancements in software and artificial intelligence (AI) are expected to drive transformative changes in surgery and perioperative workflows. These technologies promise to automate processes, enhance decision-making and augment human capabilities in surgery. However, the integration of new workflow software in the perioperative environment presents significant challenges, primarily due to the need for stakeholder buy-in and effective change management in a traditionally siloed setting. This study aimed to assess the feasibility of a novel, bottom-up, clinician-led approach to implementing a digital huddle software within a perioperative environment.

Methods: A digital huddle software was introduced at Bellevue hospital in March 2024 using a mobile application. The software was introduced by the neurosurgery chief resident and employed personal QR codes to facilitate organic expansion of the software use. The expansion of the software included various team members, such as anesthesia, nursing management and staff.

Results: Over a six-week time period, the number of users successfully expanded from 1 to 16 without disrupting existing processes. 25 preoperative huddles were completed out of 42 eligible cases (58%). 16/25 (64%) of cases were add-on cases and 9/25 (36%) were elective cases. Core participants were neurosurgery residents/advanced practice providers, nursing staff, the head anesthesiologist, and vendors. On average, 4.4 team members viewed each huddle. The senior neurosurgery resident, the nurse manager, and the head anesthesiologist were the most frequent huddle participants (52-89%). This facilitated interdisciplinary communication, helping users address clinical questions and effectively prepare for surgery.

Conclusion : A clinician-led, organic approach to software implementation in the perioperative workflow is not only feasible but also effective in facilitating smooth adoption. This method can overcome traditional barriers to technology adoption in the clinical workflow settings, suggesting a scalable model for future software implementations in similar environments.