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Can AI help solve America’s medical records problem?

by trpliquidation
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Can AI help solve America's medical records problem?

If you don’t work in a medical office, you might be wondering, “what problem with medical records?”

However, if you’ve ever asked a hospital to send medical records to another practice or healthcare facility, you may have an idea of ​​what the country’s medical professionals are dealing with.

The Old Machine: Faxing in Medical Offices

Essentially, medical offices are unable to send each other information in a direct and straightforward manner. There is a physical reminder of this in the back of many offices: they make noise and spit out paper documents.

That’s right: it’s a fax machine.

To many first-time visitors, it may seem strange that doctor’s offices in the 21st century rely on a technology that has been replaced in most other industries.

But there’s another issue that makes this a unique challenge for the medical community: HIPAA’s strict privacy standards mean that systems like Docusign or various types of PDFs may not be secure enough to send via email. And if there is no secure digital channel, then it’s back to the fax.

This Vox article goes into detail about the reasons for the ubiquity of the fax machine in American medicine (you can also read this article on SentinelOne to understand the implications of HIPAA).

Those who pay attention will remember how the HHS HITECH Act included “meaningful use” standards established by the Obama administration that encouraged the adoption of medical health records across the board.

The federal agency was adept at getting offices to adopt EHRs and EHRs. However, the government has not planned for the interoperability problem, and many medical software systems do not talk to each other, so people have to take information from one system and route it to another.

The potential of artificial intelligence models

Let’s find a new way to possibly solve this problem.

Theoretically, if AI systems can process data and make it change from any format to another, they should be able to bridge the gap between different medical software systems and thereby automate almost all the labor-intensive processes that are now being performed. done by clerks.

A article by Andreessen Horowitz talks about solving the “cluttered inbox problem” by synchronizing different types of unstructured or ill-structured data into a lean, mean identification engine, so to speak.

A few diagrams provide suggestions for building this kind of architecture, leading to a native-AI system of record.

“Being able to replace human labor with LLMs essentially puts you one step ahead of all existing downstream software systems,” writes David Haber. “The ability to synthesize unstructured speech or text data and systematically extract relevant information gives you the right to initiate (and own) other downstream workflows.

That idea of ​​downstream systems is relevant. Getting things in order at the top creates a tighter funnel, a better way to distribute information.

Ultimately, using the power of LLMs, we can finally move past some of the data formatting that has plagued medical records for decades, despite the digital switchover years ago.

But that is certainly not the only use case for AI in this way. From finance to law and beyond, business leaders are revolutionizing business processes by cleaning up data systems in these ways. Let’s continue to look at what companies are doing to reinvent their office practices. The old fax may soon be a thing of the past.

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