41 - From Resistance to Precision: AI's Path to Surgical Transition

Description: Implementing AI-driven analytics in surgical environments often highlights not only clinical gaps but also deep-rooted infrastructure and cultural challenges. At AdventHealth Celebration, efforts to modernize the operating room using AI technology faced immediate resistance—not from staff—but from outdated networking closets and network limitations that restricted Wi-Fi scalability. These technological constraints echo common barriers across healthcare systems that rely on secure, hardwired networks to protect patient health records, complicating the implementation of mobile, real-time data solutions.
Initial deployment involved extensive planning and workarounds to establish stable connectivity in the OR. Routing closet limitations and network instability triggered frequent service disruptions, including TCP resets during peak hours. As a result, hardware upgrades, including PoE-enabled systems and dedicated networking expansion, were necessary to continue deployment. These efforts laid the groundwork for real-time recording and analysis.
Education and training presented the next major hurdle. Staff skepticism around device placement and perceived surveillance ('big brother') slowed adoption. To overcome this, leadership emphasized the purpose: not discipline, but improvement to remove barriers. Through tailored training, feedback sessions, and visible use of data in collaborative discussions, acceptance grew. Physicians appreciated the report-outs for highlighting procedural gaps and optimization opportunities.
With stable systems and improved adoption, recordings supported the identification of systemic process delays. Specifically, delays from 'Wheels In to First Incision' and 'Close to Wheels Out' were correlated with inefficiencies in staff readiness and preoperative communication. Underestimated case durations caused a 1.5x increase in turnover time, delaying the daily schedule by over two hours.
This data laid the foundation for the next major initiative: improving on-time surgical starts. A six-month review revealed a drop in first-case on-time starts from 66.5% to 61.87%, driven by centralized Healthpass issues (incomplete H&Ps, labs, and consents), and operational delays. New interventions included a dashboard scorecard to track delays, proactive messaging to surgeons, real-time active rounding, and accountability checks. Within 90 days, the team exceeded the 76% on-time start goal in April 2025 for the first time in three years, maintaining this into the following months despite construction challenges.
Looking forward, the next iteration of AI implementation will leverage predictive dashboards to monitor live OR activity and flag inefficiencies in 'Cut to Close' and turnover workflows. These enhancements aim to further reduce intraoperative time, increase throughput, and enable surgical leaders to intervene in real time with data-backed decisions.