Developing an Artificial Intelligence-Based Clinical Decision-Support System for Chest Tube Management: User Evaluations & Patient Perspectives of the Chest Tube Learning Synthesis and Evaluation Assistant (chelsea) System

Background: Chest tube management aims to balance the risks of early chest tube removal (such as postoperative complications and reinsertion) and detriments of excessive and prolonged drainage (e.g., infection, pain, and increased length of stay). The Chest tube Learning Synthesis and Evaluation Assistant (CheLSEA) is an artificial intelligence -based clinical decision support system, designed to combine, interpret, and learn from postoperative patient monitoring data to provide safe and effective recommendations for healthcare providers managing chest tube care. CheLSEA user -interface is an interactive dashboard developed to access recommendations produced by the system. The purpose of this study was to gain an understanding of healthcare professionals' perceptions, and patient's views towards an artificial intelligencebased clinical decision support system for chest tube care. An evaluation to assess the usability of the userinterface was also conducted. Methods: This mixed -methods study was conducted in three phases: (I) a survey of healthcare professionals' perceptions towards artificial intelligence -based clinical decision support system, (II) usability testing sessions with potential users of the system's user -interface, using a think -aloud approach followed by interviews with closed and open-ended questions organized in a structured worksheet, and (III) semi -structured interviews with patients to ascertain their views toward the use of artificial intelligence -based clinical decision support system in their chest tube care. Results: Survey results showed an overall positive outlook on the usefulness of CheLSEA in chest tube management and its potential to improve patient care. Healthcare professionals helped identify any challenging elements of CheLSEA's interface and provided suggestions during usability testing. Interface evaluation interviews generated major themes including visibility, understandability, usability, navigation, workflow, and usefulness. Patient interviews highlighted themes such as optimistic attitudes, implementation considerations, transparent communication with healthcare team, overall trust in the surgeon, and desirable features of artificial intelligence clinical decision support systems (AI-CDSS). Conclusions: For CheLSEA to be functional in a clinical setting, the system must have a user-friendly interface that can be integrated with users' workflow, meet clinical needs, and undergo continual usability reviews.