Health system execs on board with generative AI, but few have a plan in place

Health system executives say they’ve been convinced by generative AI and its potential to shoulder some of healthcare’s biggest burdens, yet very few have a plan in place to make it happen, Bain & Company reported in an Aug. 7 survey brief.

In a poll of 94 health system leaders conducted by the consulting firm, three-quarters of respondents said they believe the increasingly trendy technology “has reached a turning point in its ability to reshape the industry,” Bain partners Eric Berger and Margaret Dries wrote. “However, only 6% have an established generative AI strategy.”

Health systems are primed for a new tool like generative AI to make an impact, the consultants wrote. Providers are coming off a tough year financially with issues like labor shortages, inflation and burnout hammering their organizations.

As such, it’s little surprise that use cases targeting administrative burden and operational efficiency were at the top of executives’ shortlists.

Per the report, system leaders most frequently cited charge capture and reconciliation (39 respondents), structuring and analysis of patient data (37 respondents) and workflow optimization and automation (36 respondents) as the highest generative AI priority for their organization within the next 12 months.

These were closely followed by generative AI-based clinical decision support tools (35 respondents), predictive analytics and risk stratification (31 respondents), telehealth and remote patient monitoring (29 respondents) and administrative call centers (29 respondents).

That second bracket of use cases rose in executives’ esteem when anticipating where the technology would be within two to five years. For this extended window, the leaders most often pointed to predictive analytics and risk stratification (44 respondents), clinical decision support tools (41 respondents) and diagnostics and treatment recommendations (37 respondents) as the highest generative AI priorities for their organization.

Respondents also shed some light on why their organizations may be dragging their feet on the new technology. Per the poll, health system leaders viewed resource constraints (46 respondents), lack of technical expertise (46 respondents) and regulatory and legal considerations (33 respondents) as the greatest barriers to implementation within their organizations.

Of note, the executives less frequently cited ethical concerns (18 respondents), unclear benefits (20 respondents) and clinical risk (28 respondents) as roadblocks to adoption and rollout.

“Even when organizations can overcome these hurdles, one major challenge remains: focus and prioritization,” Berger and Dries wrote. “In many boardrooms, executives are debating overwhelming lists of potential generative AI investments, only to deem them incomplete or outdated given the dizzying pace of innovation. These protracted debates are a waste of precious organizational energy—and time.”

To get the ball rolling on generative AI in healthcare, the consultants recommended health system CEOs and chief financial officers prioritize pilots of lower-risk applications with narrow, internal focuses.

“Some [applications], like call center and chatbot support, can improve the patient experience. However, given the current challenges around regulation and compliance, the most successful early initiatives are likely to be internally focused, such as billing or scheduling,” they wrote.

They also advised that system leaders decide early on whether they want to buy, partner or build out their generative AI tools. Starting early will also allow systems to funnel cost savings and organizational experience from early rollouts into more sophisticated use cases, such as those with clinical roles, a few years down the line when the technology matures.

“We believe the next generation of leading healthcare companies will start today, with highly focused, low-risk use cases that boost productivity and cost efficiency,” the consultants wrote. “Over the next three to nine months, these companies will improve margins and learn how to implement a generative AI strategy, building up the funds and experience needed to invest in a more transformative vision.”

Generative AI has been a key focus of health industry discussion and dealmaking over the last few months.

Electronic health record maker Epic recently kicked off a collaboration with Microsoft-owned Nuance to integrate a ChatGPT-powered app into its software platform and already has systems like Stanford Health Care, UC San Diego Health, UNC Health and UW Health in line to be early adopters. Duke Health also tapped Microsoft as its partner to explore new AI-based tools for clinical care and administrative efficiency, while the University of Kansas Health System enlisted medical transcription startup Abridge for cost-effective, AI-generated summaries of provider-patient conversations.

Payers could also stand to benefit from automating call center interactions, pre-authorization, claims denials and appeals—though, much like the provider space, such rollouts still carry their fair share of risks.