@shubhsinghnft I just love it when Shankaran said team is living, breathing, eating, sleeping chandrayaan for last 4 years to complete this. This is the kind of effort which is key to every project.
@shubhsinghnft@harrywalia019@ScribeHow It's not completely autonomous. It required human touch to add some context to the documentation but all time that is consumed in creating base structure was saved.
@shubhsinghnft@harrywalia019@ScribeHow Check your email. The documentation you needed for epharmacy, due to time crunch, we found this tool and created all that within minutes.
@shubhsinghnft@kunikhanna I agree. And not just that, where there is digitisation to some extent, the systems are independent from each other. Data is sitting in silos. Interoperability is not there even in developed countries.
@shubhsinghnft Actually based on my experience, Amazon's textract is better than vision. It gives more control to you on different format of extraction and what to do with the extracted information.
Yeah, patient files which are low quality or not maintained, we are not there yet for OCR.
@shubhsinghnft It can read but out of context. I think the main issue we will have is making the sense of the scanned data.
Also handwriting is not the only issue, the state of the physical files they are in, old, bad paper and so, also posses a challenge
@shubhsinghnft We have tried Tesseract, Amazon's Textract, google vision AI. All of them gave good results. It can handle hand written notes very well.
@shubhsinghnft
@shubhsinghnft To harness AI's potential, we must prioritize efforts to digitize medical records and create interoperable systems. I think with the E-health TT platform, that's a step in the right direction.