Focus Area | Description |
---|---|
Data Privacy, Confidentiality and Compliances | Since it's medical data for citizens, data privacy and confidentiality is of utmost importance. It should adhere to medical compliances like HIPAA. Access tokens must have a well-defined lifecycle. Citizens should have full control to revoke access granted to any lab or hospital. |
Data Security | Well-defined security boundaries must be established for all data. Whether in transit or at rest, data should always be encrypted. Any breach or leakage could lead to the data being weaponized. |
Third-Party Data Access | As data is exposed to Vision Language Models (VLMs) and Large Language Models (LLMs), there must be clear deployment strategies and regulatory oversight. If the government intends to engage health tech startups, strict frameworks are needed to govern the use of anonymized data for detection, preparedness, and prevention of serious illnesses. |
Prediction Accuracy | Any false prediction based on medical record data can cause disaster for an individual |
Last active
June 13, 2025 02:40
-
-
Save arpitr/9ce01f0e11d1034922adaa159de57adb to your computer and use it in GitHub Desktop.
smart_ehr_considerations.md
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment