Implementation of an automated dashboard to monitor unplanned reoperations in a public cancer hospital in São Paulo.

Abstract

e23244 Background: Hospital Municipal Vila Santa Catarina (HMVSC), a public hospital in São Paulo affiliated with Hospital Israelita Albert Einstein, provides care to over 10,000 cancer patients and admits approximately 210 new cancer cases monthly. In 2024, over 2,000 patients underwent surgical procedures at our institution. Methods: An automated dashboard was developed using Microsoft Power BI and integrated with our Cerner Electronic Health Record (EHR) system. This dashboard consolidated data on all surgical interventions performed at HMVSC. A multidisciplinary team—including data analysts, surgeons, and nursing staff—defined key performance indicators focused on the number and rate of unplanned reoperations. Patient charts were systematically reviewed for reoperations occurring within 30 days of the index procedure, and data were uploaded to the dashboard on a regular schedule. Results: Over the course of one year, the Power BI dashboard successfully compiled data on surgical cases and flagged those requiring reoperation within 30 days. Each reoperation was individualy analyzed by a surgeon to determine if the reoperations were: (i) unplanned, (ii) planned or (iii) procedures performed on distinct surgical sites. A total of 2,039 patients underwent surgical procedures, with 105 (5.15%) requiring unplanned reoperations. While the dashboard provides an overall reoperation rate, it also allows for analyses by specific surgical teams—including Upper Gastrointestinal and Hepato-Pancreato-Biliary (HPB), Breast, Plastic, Thoracic, Colorectal, Gynecology, Orthopedic, Acute Care Service and Urology—enabling targeted investigation into potential causes of unplanned reoperations across different specialties. Although data were not updated in real time, routine uploads provided timely insights, facilitating more efficient case review, enhanced communication among clinical teams, and earlier identification of patterns associated with postoperative complications. Conclusions: The implementation of an automated dashboard using Power BI and EHR data has proven beneficial for monitoring unplanned reoperations at a high-complexity public cancer hospital. Continuous monitoring of these events is crucial to identify trends, improve surgical outcomes, and enhance patient safety. This model may be adapted by other institutions seeking to optimize perioperative care through systematic, data-driven approaches. Reoperation rates by surgical specialty. Surgical Team Patients (N) Unplanned reoperations N (%) Overall 2,039 105 (5.15) Overall (excluding acute care cases) 1,869 76 (4.07) Upper Gastrointestinal ad HPB 190 7 (3.68) Breast 143 1 (0.7) Plastic 137 4 (2.92) Thoracic 171 5 (2.92) Colorectal 266 19 (7.14) Gynecology 123 4 (3.25) Breast 143 1 (0.7) Orthopedic 88 3 (3.41) Urology 878 34 (3.87) Acute Care 221 34 (15.38)