On April 23, the BJC HealthCare and WashU Medicine Innovation Lab and the Institute for Informatics, Data Science and Biostatistics hosted the 7th annual Big Ideas Pitch Event. The Big Ideas competition was created to identify and support high-priority, novel projects from collaborative clinical, operational and research teams that are developing innovations in informatics and health care delivery.
Ten teams had five minutes to pitch their ideas and convince the judges why their Big Idea is worth funding. The competition was judged by leadership of BJC HealthCare and WashU Medicine.
Presenting Teams:
Towards Early Diagnosis of Autism using Computer Vision Analysis of Home Videos
PI/Project Lead – Shuo Wang, PhD | Associate Professor of Radiology, Neurosurgery, and Biomedical Engineering
Using multimodal AI models and generative techniques to identify subtle autism spectrum disorder (ASD) behaviors
AI-Driven Predictive Models for Precision Anticoagulation in Pediatric ECMO
PI/Project Lead – Ahmed Said, MD, PhD | Assistant Professor of Pediatrics, Division of Pediatric Critical Care Medicine
Leveraging high-resolution patient and ECMO-specific data to improve real-time decision-making
Leveraging Large Language Models (LLM) to Summarize Clinical Information for Radiologists
PI/Project Co-Leads – Govind Mattay, MD, MBA, Karan Jani, MD, Yasasvi Tadavarthi, MD | Diagnostic Radiology Residents
Using LLM to efficiently summarize relevant clinical history for radiologists from the Electronic Medical Record (EMR)
AI-Augmented ECG Interpretation to Improve Clinical Decision-Making in Wide Complex Tachycardia
PI/Project Lead – Adam May, MD | Associate Professor, Division of Cardiology
Assessing the impact of novel AI-ECG technology on clinician interpretation, decision-making, and workflow efficiency
Teaching Trauma Informed-Care: An AI-based Simulation of Sickle Cell Disease in the ED
PI/Project Lead – Robbie Paulsen, MD | Associate Professor of Emergency Medicine
Using AI-based simulation to enhance Trauma-Informed Care principles designed to foster trust and improve clinical outcomes for SCD patients who seek emergency care
Extraction of Clinical Variables using LLM: Automated Harnessing of the EHR to drive QI in Surgery
PI/Project Lead – Dominic Sanford, MD, FACS | Assistant Professor, Liver, Pancreas & Gastrointestinal Surgery
Using LLM to improve the extraction of clinical data from the Electronic Health Record (EHR) to augment the efficiency and scalability of surgical complication reporting
Real-Time Quantitative Blood Loss Measurement for Early Hemorrhage Detection with a Novel Wearable Device
PI/Project Lead – Christine O’Brien, PhD | Assistant Professor of Biomedical Engineering
Leveraging wearables to monitor real-time quantitative blood loss to improve hemorrhage detection and patient outcomes
AI-Powered Video-Based Ergonomic Coaching Platform for Surgeons
PI/Project Lead – Peinan Zhao, PhD | Assistant Professor, Obstetrics & Gynecology
Using advanced multi-camera depth sensing and deep learning models to provide unobtrusive, real-time feedback, addressing surgical ergonomics without disrupting workflow
Clinical Trials API
PI/Project Co-Leads – Christopher Abraham, MD, MSACI, Associate Professor of Radiation Oncology | Matthew Schmidt, PhD, Assistant Professor of Radiation Oncology
Leveraging AI to address critical inefficiencies in radiation oncology by streamlining workflow and enhancing data-driven treatment optimization
NAM-2 (1) Leveraging ML and Wearable Device Activity Patterns to Improve Prehabilitation and Surgical Outcomes
PI/Project Lead – Braxton Goodnight, BS | Biological Engineering, MD Candidate
Using existing machine learning algorithms to predict high-risk patient subgroups for prehabilitation intervention, mitigating adverse postoperative outcomes
Winning teams can be awarded up to $50,000 to fund their innovative projects. Stay tuned for our winning team announcement in early May!
