MediFlow AI
Human-Centred System Design for Fragmented Clinical Information Management and Report Generation
Y. Tang (TU Delft - Industrial Design Engineering)
Tilman Dingler – Mentor (TU Delft - Human-Centred Artificial Intelligence)
N. Cila – Graduation committee member (TU Delft - Human Technology Relations)
Erik Vlemmix – Graduation committee member (Accenture)
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Abstract
Clinical communication and information management are essential to healthcare, yet they remain challenged by fragmented documentation, fast-paced exchanges, data loss and burdensome administrative processes with Electronic Health Record (EHR) systems. Meanwhile, Artificial Intelligence (AI) shows strong potential to address these challenges by enhancing workflow efficiency and supporting real-time information documentation.
This graduation project, guided by TU Delft and Accenture IXID, investigates how AI can be integrated into healthcare communication processes. Literature review, comparative case studies of intensive care units (ICU) and nursing homes, and user research identified six shared pain points. These insights informed key design opportunities and criteria.
The final design proposes an AI-driven companion system, consisting of an online platform and a wearable voice-enabled pin. The pin enables privacy-conscious real-time voice capture and seamless authentication, while the platform supports transcription, classification, OCR-based digitization of handwritten notes, and AI-assisted report generation with traceable data sources. Together, they serve as a bridge between HCPs and EHR systems, reducing information loss and clerical burden.
Evaluation sessions with HCPs, patient families, and design experts confirmed the concept’s value and feasibility, while highlighting opportunities for refinement. This project contributes practice-based insights into designing AI systems that embed naturally into healthcare workflows, enhancing efficiency, reducing administrative strain, and supporting better communication outcomes.