Technology Enhancements in the Medical Review Process

While a full-fledged vision of artificial intelligence (AI) remains an elusive conquest within the healthcare industry, rapid advances in technology are showing the ability to improve workflow, enable faster decision-making and, overall, deliver a boost to medical review companies and affiliated stakeholders.

When it comes to new-age technology in healthcare, AI is still in a “science fiction” phase, says Ed Bolton, CEO and president of Nexus, a URAC-accredited independent clinical review and utilization management company based in Schertz, Texas, and immediate past president of the National Association of Independent Review Organizations (NAIRO).

While true AI – or the “digital ability of a computer or a computer-controlled robot to perform tasks commonly associated with intelligent beings,” as one definition describes it – has yet to emerge in healthcare and within the medical review field specifically, some AI-related advances have come to fruition. They could be considered sub-strata of AI – smart, agile technology that can enhance workflow but not something that is completely automated and solely machine-driven.

That rings true to Brian Rudiger, vice president of business development with ManageWare, a technology provider in the bill review, utilization review and provider network management space. Rudiger says that ManageWare seeks to augment the work of medical review companies with advanced technology that will deliver a better process.

Rudiger describes the technology that ManageWare offers in a way that drives home the distinction between the advanced technology. “We’re about creating a workflow platform that reduces paper and improves speed,” he says. “We don’t have AI built into the platform. It’s more of a validator. We’re about making the process faster.” Workflow systems like the one that ManageWare offers can also make systems more efficient and accurate.

First Step: Augmented Intelligence

Indeed, various pieces of this emerging class of technology are present in modern workflow and business solutions, Bolton says. That includes solutions that hinge on “augmented intelligence,” or the coordinated effort of technology and human beings to work together to improve processes. Within the larger umbrella of augmented intelligence are solutions like machine learning (ML) and natural language processing (NLP).

What is machine learning? According to the MIT Sloan School of Management, machine learning is a “subfield” of artificial intelligence. It “gives computers the ability to learn without explicitly being programmed.”

“The machine learning starts with data,” the MIT article states. “The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on. The more data, the better the program.”

Machine learning often veers into the space of predictive modeling, Bolton says. That makes it a suitable fit for healthcare, where healthcare organizations can use it to project clinical outcomes or deliver interventions to patients who are predicted to be at high-risk of complication due to illness, for example. Or machine learning can take a “prescriptive” function and use data to recommend actions.

Related to machine learning, natural language processing is considered a “branch” of AI, granting computers “the ability to understand text and spoken words in much the same way human beings can,” according to IBM.

This type of advanced technology has direct application within the medical review landscape, which makes sense given the depth of data that review companies contend with.

Robert Coop, Ph.D., chief AI officer with Advent Health Partners, a technology solutions provider based in Nashville, notes that his company’s CAVO platform contains natural language processing capability. The NLP system can, for instance, transform paper lab reports into condensed, itemized lists to streamline comprehension.

The technology can also quickly examine large files to assess completeness, Coop says. In a scenario where a medical review company takes on a case from a payer, the technology can discern whether any vital elements are missing. For example, the platform “can quickly surface the fact that this document has no discharge summary,” Coop says. It automatically flags files that are incomplete so that no time is wasted in a needless review.

Be Cautious About Bias

As ML and NLP solutions continue to emerge, independent review organizations should pay attention to the technology platforms that can improve their workflow and efficiency, Bolton says. But he warns against companies overselling their abilities or having blind spots to potential data flaws.

“Machine learning is valid as long as it has human oversight,” Bolton says. The issue of bias often comes up when talking about AI and AI-related solutions. If the data inputs are biased or otherwise skewed, that will affect the outputs – from analytics to predictions.

“As long as companies are aware of inherent bias,” Bolton says, “I think these are the companies that will be successful in the future.”

NAIRO is an association of URAC-accredited IROs collaborating on issues facing the rapidly changing healthcare and workers’ compensation arena. NAIRO leadership and its member-driven committees track legislative and regulatory developments at the federal and state level, advances in accreditation standards, and emerging themes such as cybersecurity, trust, compliance, and more.

NAIRO is presenting a webinar, "Technology in the Review Process," on March 29, 2023, from 12:30-4pm EST. More information will be forthcoming. Learn more about NAIRO here, and stay tuned to NAIRO’s events calendar for upcoming learning sessions.

Share this post:

Comments on "Technology Enhancements in the Medical Review Process"

Comments 0-5 of 0

Please login to comment