Bridge2AI: Bridge2AI: Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Clinical Care AI

Abstract Text There is an urgent need for infrastructure to support artificial intelligence and machine learning (AI/ML) in critical care. Developing high-resolution multi-center data sets is a critical first step towards actionable and trustworthy AI. As part of the NIH Common Fund’s Bridge2AI program, the CPatient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Clinical Care AI data generation project will meet the need of generating data for ML/AI applications aimed at characterizing acute and critical care illness, predicting complications, and measuring treatment response among patients with acute or critical illness. Through 3 pillars, the CHoRUS data generation project will addresses multiple challenges relevant for acquiring an AI-ready data set from more than 100,000 critically ill patients: 1) Data (Standards, Tool Development and Optimization, and Data Acquistion) 2) Ethics (Ethical and Trustworthy AI) and 3) People (Team Science and Skill and Workforce Development). The project’s overarching goal is to develop a publicly available, AI-ready critical care dataset, while ensuring the methods promote privacy, accountability, and clinical benefit, while promoting a new generation of AI clinicians and scientists. The dataset will also provision a holdout test set, accessible for model external validation to aid marketplace adoption of AI-developed models for implementation in acute and critical care. Drawing expertise from a comprehensive set of disciplines such as team science, law, ethics, health services, biomedical science, engineering, and scientific journal publications, this project will A) establish a legal framework for collecting data at scale, sampling to ensure comprehensive sets of patient conditions and clinical treatment strategies; B) perform community-facing ethics focus groups to determine what data is appropriate for public sharing; C) ensure that data elements feature appropriate contextual factors such as geographic distance to the nearest hospital; D) develop capabilities across a multi-center network to acquire, standardize, tokenize, store, visualize, and label data such as structured electronic health record data, tokenized unstructured electronic health record data, telemetry and EEG waveforms, imaging, and social determinants of health; E) acquire data, standardize data to the OMOP Common Data Model, transform data using approaches that limit re-identification, and label data for ; and F) cultivate expertise in the lay and scientific community to improve AI literacy nd utilization through multimodal educational approaches. To accomplish this, the project will involve extensive collaboration between centers as well as through the NIH Bridge2AI program, the NIH Bridge2AI Bridge Center, external biomedical and clinical organizations, industry, and regulatory agencies.