Work

192. Automating Real-time Data to the Surgical Secondary Events Database

Year: 2025

Type: article

Abstract: PURPOSE: Accurate recording of surgical adverse events (AEs) poses a strain on busy medical personnel. Studies have shown that documentation of AEs should not rely on self-reporting alone given the potential bias in this approach. Traditionally, our institution has relied on self-reporting and manual logging of surgical AEs in our institution’s Surgical Secondary Events (SSE) database. By integrating synoptic templates and structured notes into routine clinical documentation, we developed a novel workflow of real-time AE reporting. Starting June 2022, information entered as part of clinical documentation was mapped directly into the SSE database. The purpose of this study is to investigate the impact of this novel workflow of real-time automated data entry on the completeness and accuracy of our AE documentation in the SSE database. METHODS: A retrospective study analyzed data from October 2021 to July 2023 to identify 2 cohorts of patients: a pre-implementation and post-implementation cohort, both with a surgical AE documented in a structured note within 30 days postoperatively. Patient demographics, clinical and surgical data were recorded. Outcomes of interest included (1) completeness: whether AEs in structured notes were also documented in the SSE database and (2) accuracy of both SSE entries and structured notes with respect to the selected procedure, type and grade of complication, and management plan. The Wilcoxon rank-sum test for complex survey samples and chi-squared test with Rao & Scott’s second order correction were used to compare continuous and categorical variables, respectively. Outcomes were additionally described and compared within pre-determined subgroups by Clavien-Dindo complication grade (<3, ≥ 3) and complication type (flap related, hematoma, infection, seroma, systemic, wound and skin). RESULTS: We identified 91 patients with 108 AEs in the pre-implementation cohort and 89 patients with 107 AEs in the 30-day post-implementation cohort. The only significant difference in patient demographics between cohorts was type of surgical AE (p=0.024); pre-implementation patients had higher rates of flap related, wound and skin, and seroma complications, and post-implementation patients had higher rates of systemic and infectious complications. With respect to completeness and accuracy, 20% of pre-implementation AEs were captured in the SSE compared to 94% of post-implementation AEs (p<0.001). Of those, 45% were incorrectly documented pre-implementation as compared to 22% post-implementation (p<0.05). Our sub-group analysis demonstrated that 7% of minor AEs (grade 1 & 2) were captured in the SSE pre-implementation compared to 96% post-implementation (p<0.001). Capture of major AEs (> grade 3) also increased from 29% pre-implementation to 92% post-implementation (p<0.001). CONCLUSION: This study demonstrates the importance of an automated system for reliable and accurate reporting of surgical AEs, a process which is critical for optimal patient care and surgical outcomes. We are hopeful that our institution’s implementation of this automated system, combined with intentional user training, will serve as a standard for optimal automated AE reporting, leading to improved patient care and outcomes.

Source: Plastic & Reconstructive Surgery Global Open

DOI: 10.1097/01.gox.0001112716.86372.5c