Background/Objectives: Robotic Process Automation (RPA) offers a potential solution for reducing the manual burden of clinical data abstraction, yet empirical evidence of its effectiveness in real-world electronic health record (EHR)-integrated cancer registries remains limited. This study aimed to evaluate the post-implementation effectiveness of RPA for cancer registry data abstraction in a tertiary hospital and to explore multidisciplinary stakeholder perceptions regarding its deployment. Methods: We implemented RPA for gastric and breast cancer registries within a production EHR system. Quantitative effectiveness was evaluated by comparing per-patient data extraction time using descriptive statistics. To ensure data integrity, all RPA-extracted outputs were entirely verified manually by researchers against source records. Qualitatively, semi-structured interviews were conducted with 14 participants and analyzed via thematic analysis based on the Promoting Action on Research Implementation in Health Services (PARiHS) framework (Evidence, Context, and Facilitation). Results: RPA was applied to 70 gastric cancer variables and 83 breast cancer variables. For the gastric cancer registry, the mean abstraction time per patient decreased by 74% (19.5 ± 3.0 to 5.1 ± 1.8 min). For the breast cancer registry, time decreased by 30% (25.4 ± 6.9 to 17.8 ± 5.5 min). Based on 2024 surgical volumes, this translates to an estimated saving of over 260 h of manual labor per year. Qualitative findings revealed that while participants recognized RPA as ideal for repetitive tasks, successful implementation was contingent on clinician cooperation and continuous output monitoring. Conclusions: RPA implementation significantly improved data abstraction efficiency in a real-world clinical research workflow. The disparity in time savings highlights that efficiency gains are contingent upon registry complexity. While formal quantitative assessments of data accuracy were not performed, RPA is a readily deployable tool for enhancing clinical data workflows when aligned with organizational readiness and robust monitoring.
Jung et al. (Tue,) studied this question.