Team Name Medicine4Audit Track Health care & Life Sciences Use Case Using text analytics to “cure” labor-intensive audits and lower the burden on care personnel   Challenge Hospitals face a significant administrative burden when reporting clinical outcomes to national quality registries like DICA (Dutch Institute for Clinical Auditing). These forms are often manually filled out by medical professionals, consuming valuable time and resources.   Solution Medicine4Audit proposes an LLM-based extraction pipeline that automates the retrieval of relevant data from clinical texts to populate DICA forms. By leveraging generative AI, the team aims to reduce manual input, improve data quality, and streamline the reporting process.   Impact Efficiency Gains: Automating form completion could save hours per week for clinicians. Data Quality: Structured extraction reduces human error and improves consistency. Scalability: The solution can be adapted to other registries and hospitals. Technology SAS Viya (ML, Intelligent Decisioning, Python (Integration)) Region EMEA Team lead Eddy van der Heijde Team members Eddy van der Heijde @evdheijde  Nikki van Bommel @NikkivB  Joran Roor @snljro  Karen van der Sleen @kvds  Judith Schepers - Vinke @JudithS  Peter Schram @pjwschram  Eline Witjes-Boksem @EWitjes  Debby Vreeken @debby_vreeken  Nikki van den Heuvel @NikkiH  Social media handles *all team members' social media links here* Is your team interested in participating in an interview? Y Optional: Expand on your technology expertise     Jury Video:   Pitch Video:   ... View more