Why Healthcare Is Behind in AI, and How the Industry Can Catch Up

 

Artificial intelligence (AI) and machine learning have proven their worth in numerous industries—social media platforms that are perfectly curated to your tastes, the ability to shop online for clothes, groceries, and even real estate and cars (not to mention cars that drive themselves). The healthcare industry however, lags behind others. In this post, we’ll discuss why this happened, how automation solutions can help process and surface insights from the masses of data flooding the healthcare system, and what the future will look like for patients and plans alike when healthcare catches up and embraces automation.

 

Why healthcare is behind when it comes to AI

There’s an understandable extreme level of caution around embedding automation in healthcare systems and technology; lives are on the line, and if there were ever an industry where it’s critical that humans make major decisions, healthcare is it. That being said, many of the decision-makers in healthcare lack an in-depth understanding of the current capabilities of these kinds of tools, the use cases for them (many of which are administrative rather than clinical), or the mechanisms put in place to ensure humans remain in control of patient care.

 A holistic view of a patient’s health is just out of reach in the absence of tools that make data processing efficient.

A second reason AI hasn’t achieved deep penetration in healthcare is the state of the industry’s technology. It wasn’t too long ago that hospitals housed huge document storage rooms and hired file clerks to sort, alphabetize, and distribute medical documents into physical patient folders. Although electronic health records (EHRs) are now the standard, every hospital has customized its installation, making it difficult for these systems (even those from the same manufacturer) to “talk” to one another. There are many examples of technology not standardized across the industry. The typical national payer, for instance, uses up to 15 technology tools and platforms to meet the needs of its members. But interoperability is an issue—only a few of these systems can communicate with each other.

Further complicating the picture, is the very nature of healthcare data. There is not one standard way of recording and translating data between healthcare institutions or corporations, or even systems within the same corporation. Because of that, it makes it very challenging for an automation algorithm to predict and understand errors in the data (…but not impossible, as we’ll elaborate on below). It’s much easier to leverage automation for Uber, DoorDash, or Amazon, because the data is generated by machines, and therefore inherently controlled and clean. The humans who run healthcare are anything but standard, on the other hand. Each has their own way of understanding and organizing data points (language, phrasing, punctuation, emojis, and shorthand). It takes incredibly sophisticated algorithms to process an Excel spreadsheet created by a person.

 

How automation solutions can process and surface insights from the masses of data flooding the healthcare system

Given all these barriers—particularly the “messy data” issue—some question whether it’s even possible to successfully leverage AI and machine learning in healthcare. The answer is a resounding, “Yes.” As tech platforms intended to advance care continue to proliferate, so do the data they generate. The problem in healthcare today isn’t a lack of data; it’s actually the inverse. There’s so much data that neither administrators nor clinicians can successfully process all of it and extract value. A holistic view of a patient’s health is just out of reach in the absence of tools that make data processing efficient.

A smart solution like veda’s can step in as a “Rosetta stone” to translate this messy data and process it in just hours and with 98% accuracy.

Luckily, in the past few years, automation algorithms have become more sophisticated, with a “next generation” of solutions that are capable of parsing the messy, human-generated data that permeates healthcare now emerging. There are almost endless use cases for putting such sophisticated solutions to use, but one that’s very easy to understand is using AI to make the search for in-network care simpler for patients.

Health plans are constantly receiving updates from providers in their networks, such as where they are located, who has joined or left a practice, and more. Currently, most plans have staff manually inputting these updates from Excel spreadsheets into their unique systems. As a result, updates take up to six weeks to show in the patient-facing portals, and the accuracy of the entries can be as low as 60%, despite payors’ best efforts.

A smart solution like veda’s can step in as a “Rosetta stone” to translate this messy data and process it in just hours and with 98% accuracy. Veda’s AI understands human-generated data points, in all their diversity, and makes it possible for healthcare organizations to exchange data seamlessly. The provider directory use case is just one of many ways that automation can be used to organize and cleanse data, making it possible to extract insights that previously remained locked.

 

A future where healthcare catches up and patients benefit

The pandemic created a huge influx of patient data that overwhelmed healthcare organizations, creating the final push that many needed to finally test the automated solutions they had been wary of for so long. The outcomes of these “tests” conducted out of pure necessity were overwhelmingly positive; patients were receiving the care they needed in a more timely manner, reduced administrative costs and errors, and health plan readiness for compliance with the provision of the No Surprises Act that requires them to make provider directory updates in just 48 hours starting January 1, 2022.

What do we have to look forward to in the future as more and more healthcare organizations adopt automation? We’ll continue to see the $1 trillion annual administrative spend in healthcare go down. We’ll continue to see patients accessing care more easily. And best of all, we’ll see more resources dedicated to what really matters to all stakeholders in the system—patient care.

Veda’s AI understands human-generated data points, in all their diversity, and makes it possible for healthcare organizations to exchange data seamlessly.

Veda’s AI automation solution helps health plans leverage machine learning to process data efficiently and effectively, so you can continuously maintain compliance and improve ROI. Schedule a demo to see what veda can do for you.