Single Source – Triple Flow: Pilot Trial of Structured Electronic Data Capture for Pancreatic Surgery Patients

Background: Advancements in clinical medicine depend on state-of-the-art healthcare analytics, which require patient data from electronic health record (EHR) systems and electronic data capture (EDC). We developed a federated EDC system (designated IMI-EDC) within the division of Pancreatic Surgery at Heidelberg University Hospital. This system ensures that clinical data are available for research, complies with regulatory standards, and is conducive to analysis that leverages modern and future analysis by artificial intelligence. Objective: To describe the implementation of IMI-EDC in clinical routines and compare the pilot cohort of IMI-EDC users against those using the predecessor paper-mediated system, measuring 1) patient comfort and satisfaction, 2) data quality, and 3) facilitation of workflows. Methods: A total of 2495 patients were able to submit their medical history and health-related quality of life questionnaires via the IMI-EDC. All patients using tablets were surveyed regarding their user experience. Additionally, a subset of 100 EDC submissions was compared to 100 submissions via the paper-based system with respect to data completeness, correctness, clarity, and readability. Finally, the time to transfer data to the Heidelberg Pancreas Registry (HPR) research database was compared between IMI-EDC submissions and paper-based submissions. Results: IMI-EDC was successfully implemented at our center. Approximately ¾ of patients using tablets were comfortable with the system, although there was a tendency for some older patients to experience difficulties and prefer paper. Tablet users were significantly more likely to submit completed documents than were those using paper. Specifically, 98/100 IMI-EDC users finished their documentation compared with 66/100 paper users (χ ² ₁ = 34.7, p < 0.001), with fully completed questionnaires in 70/98 IMI-EDC submissions and 24/66 paper submissions (χ ² ₁ = 23.1, p < 0.001). Data transfers to the HPR were significantly quicker for tablet submissions than for paper submissions (μ _tablet = 4.2 min, μ _paper = 9.9 min, t ₁₃ = 12.2, p < 0.001). Conclusions: IMI-EDC is satisfactory for patients and staff in terms of user experience, data completeness and workload reduction in the setting of a high-volume surgical center. The system is already expanding across the hospital and is preparing the data infrastructure for artificial intelligence systems, promising significant benefits for hospital staff, surgeons, researchers and auditors.