Health system execs bullish on generative AI, but still lack strategy

The past few years have been tough for health system bottom lines, with the COVID-19 public health emergency, critical staffing and workforce shortages, inflation and other financial pressures combining to strain budgets to the breaking points. More than 50% of U.S. hospitals reported a negative margin to close out 2022, according to a new report from Bain & Company.

But C-suite leaders see reason for optimism in the years ahead, the survey shows, thanks to the cost saving potential of fast-evolving generative AI technologies.

Health system execs are particularly excited about AI and automation’s potential to streamline financial and operational processes, tackle administrative inefficiencies and reduce clinician burnout. They see big opportunities for improving workflows and clinical documentation, managing and analyzing data and more in the year ahead.

Further out, in the next two to five years, hospital leaders say they’re planning more AI-powered initiatives around predictive analytics, decision support, guided treatment insights and more, according to the report.

The potential is especially attainable given that the cost to train AI and machine learning models has decreased exponentially, according to Bain: “Down 1,000-fold since 2017,” it promises an “arsenal of new productivity-enhancing tools at a low investment.”

But while the report shows 75% of C-suite leaders are excited that generative AI has “reached a turning point in its ability to reshape the industry,” it also finds that just 6% of health systems polled have an established generative AI strategy.

Still, that number is expected to grow as more and more provider organizations get serious about harnessing the potential of generative AI and automation to help address some long-standing clinical, financial and operational challenges.

Among the top health system priorities for generative AI applications, according to the report:

  • Charge capture and reconciliation

  • Structuring and analysis of patient data

  • Workflow optimization and automation

  • Clinical decision support

  • Predictive analytics and risk stratification

  • Telehealth and remote patient monitoring

  • Call centers for administrative purposes

  • Diagnostics and treatment recommendations

  • Provider and patient workflows, including payer interactions

  • Suggestions for care coordination and health system navigation

  • Treatment and therapy recommendations for providers

  • Call center for clinical questions

  • Summary of clinical literature and research mining

  • Assistance for patient financial counseling and Q&A

  • Procurement and contract management

  • Referral support and routing for providers and patients

  • Drug discovery and clinical trial design

Still, Bain & Company researchers warn that there are significant hurdles ahead as health systems race to implement these fast-advancing technologies.

“Solutions to the greatest hurdles aren’t yet keeping up with the rapid technology development,” they say. “Resource and cost constraints, a lack of expertise, and regulatory and legal considerations are the largest barriers to implementing generative AI, according to executives.

“Even when organizations can overcome these hurdles, one major challenge remains: focus and prioritization,” researchers add. “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.”

As health systems work to develop “pragmatic” strategies for generative AI adoption, Bain suggests that hospital execs keep four guiding principles top-of-mind. They should, as researchers write in the report:

  1. Pilot low-risk applications with a narrow focus first. When gaining experience with currently available technology, companies are testing and learning their way to minimum viable products in low-risk, repeatable use cases. These quick wins are typically in areas where they already have the right data, can create tight guardrails, and see a strong potential return on investment.

  2. Decide to buy, partner, or build. CEOs will need to think about how to invest in different use cases based on availability of third-party technology and importance of the initiative.

  3. Funnel cost savings and experience into bigger bets. As the technology matures and the value becomes clear, companies that generate savings, accumulate experience, and build organizational buy-in today will be best positioned for the next wave of more sophisticated, transformative use cases.

  4. Remember AI isn’t a strategy unto itself. To build a true competitive advantage, top CEOs and CFOs are selective and discerning, ensuring that every AI initiative reinforces and enables their overarching goals.

“Providers and payers are looking for profit opportunities while also doubling down on employee morale, clinical care, and patient experience,” said Eric Berger, a partner in Bain’s Healthcare & Life Sciences practice, in a statement. “Many recognize the potential AI offers to boost productivity, yet they are acutely aware of the uncertainties around evolving technology.

“This uncertainty cuts both ways,” he added. “While there is hype, there is also opportunity. Leading companies are taking this technology shift seriously and getting started with highly focused, low stakes use cases with some near-term ROI while building up the experience and confidence needed to invest in a more transformative vision.”

Mike Miliard is executive editor of Healthcare IT News
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Healthcare IT News is a HIMSS publication.

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This post originally appeared on TechToday.

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