  Healthcare 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 tooling 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
real-world AI tools requires overcoming challenges in data acquisition, scarce
hospital resources and high needs 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 backend. 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 system 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
drive real-world AI model development, and, in the long run, meaningfully
improve healthcare delivery.
