Haroon Tharwat - FINTR: Optimization-Based Integration of HIS and RTLS Data for Nursing Task Retrieval

Process mining offers great potential to support the improvement of healthcare processes, but the Hospital Information Systems (HIS) data that is often used suffers from data quality issues such as delayed or missing task entries. Real-Time Location Systems (RTLS) provide an alternative data source as they capture staff and equipment positions over time, providing more accurate timestamps, but do not contain semantic information on the tasks performed. While there are clear synergies between both data sources and both have been considered in isolation in process mining research, a method to systematically integrate them to obtain an enhanced input for process mining is missing. This paper introduces FINTR (Footprint-based Integration for Nursing Task Retrieval), a semi automated method that integrates HIS and RTLS data via task footprints (templates that encode how each nursing task type should appear across both data sources) to provide an enhanced foundation for process mining in healthcare. Preliminary evaluation on synthetic data shows that FINTR reconstructs nursing tasks in a noise-free setting and reveals how its performance evolves when increasing levels of a common type of noise in real-world settings are present.