Med Deck

Med Deck

Med Deck is a Machine-learning and Blockchain powered Medical excellence for validating the Electronic Health Record and storing the EHRs in IPFS

Med Deck

Med Deck

Med Deck is a Machine-learning and Blockchain powered Medical excellence for validating the Electronic Health Record and storing the EHRs in IPFS

The problem Med Deck solves

👩🏼‍⚕️If an EHR is not updated immediately, as soon as new information is gleaned, this could lead to subsequent errors in diagnosis, treatment, and health outcomes.

👩🏼‍⚕️It's time-consuming where the Clinical Research Associates are spending their valuable time to cross-check the EDC data against the data present in EHR to ensure its correctness.

👩🏼‍⚕️An essential part of a strong EHR is the ability to have an information technology team available to solve technical problems immediately so that patient care interruptions are minimized.

👩🏼‍⚕️All computerized systems are vulnerable to attacks by hackers, and EHR systems are not immune. The consequences of private medical information getting into the wrong hands could be dire.

👩🏼‍⚕️Disruption of work-flow

Challenges we ran into

🩸Working with Streamlit for the first time was a challenging task ahead. Although we faced some roadblocks while integrating Streamlit with IPFS, we are glad that we were able to provide server side rendering and create a single page application.

🩸Storing the patient records in the encrypted form and decrypting the file and maintaining privacy of the records was a huge challenge on our way. We are glad that we were able to store the data in the encrypted format in the IPFS and retrieve it from IPFS using the stored hash value.

🩸We are glad that we were able to integrate data visualization and display weekly reports for the admin.To add to that,we also learnt grievances can be in the form of review,complaint and suggestion and how to handle them.

🩸At the beginning of the hackathon,we always thought of easing out the admin's role and avoid the time consuming jobs done in this stream.We are happy that we accomplished what we planned for

Tracks Applied (1)

HEALTHCARE

Proposed Solution Validation of Electronic Health Records with Random Forest Classifier to ensure data correctness Track...Read More

Discussion