Glia
Chloe Anderson, Mashrur Wasek, Aidan Schurr, Chris Gagnon · Team Glia
Submitted to AI Policy Hackathon at Johns Hopkins University. Sprint projects are early-stage work by participants, not Apart Research publications.
Encryption, Searchability of Anonymized Data, and Decryption of Patient Health Information to Support AI Integration in Automating Administrative Work in Healthcare Organizations.
Reviews
It is an impactful idea to streamline healthcare administration and improve patient care by developing a secure, searchable, and anonymized network for patient health information, enabling efficient data collection and federated AI-driven insights across healthcare organizations.
Like the idea, good execution on completing a proof of concept on the encryption in the notebook and generating out jsons. Just had to dock some points on the UI UX since there wasn't
Great and relevant discussion around the need for privacy-first access of healthcare data, to enable all the upcoming AI applications! The discussion on future areas of research is also thorough and interesting. What would have benefitted the project even more would have been a more in-depth technical discussion on the opportunities and limitations of the proposed encryption solution. A discussion on the already existing solutions on encryption/blockchain-based health records would also have been interesting.
Cite this project
@misc{anderson2024glia,
title = {{Glia}},
author = {Chloe Anderson and Mashrur Wasek and Aidan Schurr and Chris Gagnon},
year = {2024},
month = oct,
note = {Submitted to AI Policy Hackathon at Johns Hopkins University, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/glia}},
url = {https://apartresearch.com/sprints/projects/glia}
}More from AI Policy Hackathon at Johns Hopkins University
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