A Methodology for A Scalable, Collaborative, And Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 6; pp. 1 - 12
• Main Authors: Cohen, Raphael Y., Kovacheva, Vesela P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-06-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial Intelligence; Bioinformatics; Brain modeling; Collaboration; Data acquisition; Data models; Datasets; Delivery of Health Care; Distributed Computing; Electronic health records; Electronic Healthcare Records; Electronic medical records; Health care; Health Services Research; High resolution; Hospitals; Humans; Medical Information Systems; Medical services; Microservice architectures; Neurofibromin 2; Patients; Python; R&D; Real time; Research & development
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2023.3259395

Healthcare artificial intelligence (AI) holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tools for analysis. Collection and translation of electronic health record data, live data, and real-time high-resolution device data can be challenging and time-consuming. The development of clinically relevant AI tools requires overcoming challenges in data acquisition, scarce hospital resources, and requirements for data governance. These bottlenecks may result in resource-heavy needs and long delays in research and development of AI systems. We present a system and methodology to accelerate data acquisition, dataset development and analysis, and AI model development. We created an interactive platform that relies on a scalable microservice architecture. This system can ingest 15,000 patient records per hour, where each record represents thousands of multimodal measurements, text notes, and high-resolution data. Collectively, these records can approach a terabyte of data. The platform can further perform cohort generation and preliminary dataset analysis in 2-5 minutes. As a result, multiple users can collaborate simultaneously to iterate on datasets and models in real time. We anticipate that this approach will accelerate clinical AI model development, and, in the long run, meaningfully improve healthcare delivery.