Creating AI-Enabled
All-Health Team Data Fabric
CAIDF unlocks hidden data to develop actionable, automated, multidisciplinary care team patient health summaries and insights to improve patient care and enable scientific discoveries.
What if it were possible to improve patient health just by unlocking the hidden data that already exists in medical records?
With recent advances in AI, and cross-disciplinary work of health and informatics researchers, that possibility can become reality.
Currently, in complex health situations where there are a range of clinicians all treating the same patient, a lot of data is tracked, but little of it is communicated effectively across the team. Clinicians don’t have the time to read through the detailed notes from everyone on the care team, and electronic records systems are not designed to summarize or communicate vital information between clinicians in more accessible ways.
As a result, it frequently falls on patients or their caretakers to know and convey the most important information across the care team, and that means important data can fall through the cracks. Unfortunately, the more complex the case, the more critical the data is – and the less likely it is to be understood by everyone.
Participate In A Groundbreaking Hackathon
Join other developers, designers, computer scientists, informaticians, and heatlhcare professionals. Explore a large multidisciplinary care team data set, which has never been available before.
🗓️ May 30 - May 31, 2025
📍 Crowne Plaza Chicago Hotel
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Limited spots available
What might it look like to benefit from automatically summarized care team data?
Imagine you’re new parents, and your child, Sophia, has been born prematurely and needed to spend months in the NICU after birth. During that stay, Sophia is treated by a mix of doctors, nurses, physical therapists, occupational therapists, and speech therapists, all of whom diligently log notes about what they’re observing and what Sophia needs next to grow and thrive. >
How do we make care team summarization a reality?
Right now, AI systems would not be able to do this work effectively, because they haven’t been trained on enough specific data from multiple health professions to accurately interpret the language and early indicators of all those fields. However, thanks to funding from ARPA-H, a cross-institutional group of researchers and practitioners in the fields of Health Informatics, Nursing, Medical, Physical Therapy, Speech Therapy, and Occupational Therapy are doing all the foundational work necessary to make this kind of advancement possible.