Researchers Identify Barriers to AI Integration into Clinical Practice

Researchers from the University of Miami Miller School of Medicine, Weill Cornell Medicine, and technology innovation firm Covered By Group have identified barriers that early-stage digital health startups face to the integration and adoption of artificial intelligence (AI) in clinical practice.

These insights were gleaned as part of a study recently published in the Journal of Medical Internet Research. The study indicates that AI and other digital health technologies developed by startup companies could improve patient experience and outcomes while reducing healthcare costs if integrated into clinical care, but adoption has been slow.

“While some AI technologies have made it into the clinic, most of these have come from large conglomerates, like Google or Amazon,” said Azizi Seixas, PhD, interim chair of the Department of Informatics and Health Data Science at Miller School of Medicine and senior author on the study, in the press release. “Early-stage startup companies, which produce much of the innovation, are hardly in the mix, and we wanted to understand the barriers they face and how we might help overcome them.”

To investigate why the adoption of these technologies in clinical practice is lagging, the researchers conducted a stakeholder focus group in which they interviewed 10 early-stage digital health and healthcare AI executives to gain their perspectives on potential barriers that may play a role.

Transcripts of these interviews were queried and analyzed to find common themes that may indicate the presence of barriers to the integration, translation, and adoption of digital health technologies.

From this analysis, the research team identified four major categories of barriers: a lack of knowledge of health system technology procurement processes, challenges within those procurement processes, demanding regulatory and validation requirements, and disadvantages that early-stage digital health companies face compared to their larger counterparts.

Procurement protocols and best practices were significant issues facing digital health startups, as approaches vary across different health systems.

“Health care system procurement processes are extremely fragmented,” said the study’s first author, Iredia Olaye, PhD, who serves as CEO of Covered By Group and a researcher at Weill Cornell Medicine. “As a result, early-stage companies have more significant barriers to integrating digital health and AI into clinical practice than their later-stage technology conglomerate counterparts. The funding and regulatory processes and lack of knowledge digital health executives and health care providers bring to the process are barriers to successful integration.”

Challenges in the regulatory approval process also create hurdles that startups must contend with if they want to accelerate AI’s transition into the clinical setting. The researchers found that the cost of conducting randomized clinical trials is a major pain point, as early-stage digital health companies cannot gain regulatory approval without these trials, but run the risk of never recouping the costs associated with conducting them.

The research team recommends that the United States Food and Drug Administration (FDA), alongside other regulatory agencies, review their guidelines to ensure that public policy supports the integration of AI into clinical care.

The researchers highlighted that certain guidelines were created years before the advent of many AI and digital health technologies.

One of these guidelines is the FDA’s 510(k) clearance, which helps the agency determine whether a new medical device that a company intends to market is sufficiently similar to existing technologies and therefore streamlines the regulatory process.

This clearance pathway, the research team argued, cannot adequately evaluate advanced digital health technologies.

“It’s just not consistent,” explained Seixas. “We need the FDA to update their process to better differentiate between AI, digital health and medical devices and provide clearer guidelines for companies and health systems.”

In addition, the researchers noted that early-stage digital health companies need input from health systems and providers to spur AI innovation in healthcare.

“Tech came into health care thinking they were going to change how we do things, but many of them failed because they're just not wired to care for patients,” said Seixas. “But that is exactly our wheelhouse. To move forward, we need to add an innovation and technology infrastructure to better incorporate and implement these solutions into health care.”