Change Healthcare Research: AI to Become Widespread in Hospital Revenue Cycle by 2023

AI use accelerating as early adopters reap benefits, but not everyone is on board

Artificial Intelligence (AI) will transform the way doctors, hospitals, and healthcare systems identify, collect, and manage their revenue cycle over the next three years as healthcare organizations evolve from day-to-day use to strategic integration within their systems, according to a new study by Change Healthcare. Two-thirds of hospital and health system executives report using AI in some revenue cycle capacity and nearly all expect to be using it within three years. However, familiarity with AI and its impact varies wildly among executives, IT, and revenue cycle leadership—and there are budgetary, security, privacy, and accuracy concerns complicating AI adoption.

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These findings and more are detailed in Poised to Transform: AI in the Revenue Cycle, a national study of 200 revenue cycle, IT, finance, and C-suite decision makers commissioned by Change Healthcare and conducted by market researcher ENGINE Insights. The study polled healthcare executives to understand their knowledge of and familiarity with AI, discover areas for improvement, and learn how the technology is used now and will be used in the future.

“AI is primed to transform revenue cycle management for those providers who understand how to use it strategically,” said Luyuan Fang, PhD, chief AI officer at Change Healthcare. “Providers that close the gaps revealed by this research will be well-positioned to reap financial, operational, and clinical gains from the technology—including improving the end-to-end revenue cycle, claims accuracy, denial reduction, clinical insights, level-of-care prediction, and more. But this potential can only be realized when executive stakeholders are aligned on strategic deployment of AI and how to measure success.”

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The research focused on healthcare executives with decision-making authority from the executive, financial, revenue cycle management, and IT departments at U.S. hospitals and health systems. Key research findings include:

• Nearly all U.S. hospitals plan to be using AI pervasively across the revenue cycle within three years. About two-thirds of all respondents (65%) report that they now use AI in RCM (compared to 89% of those in RCM roles), but AI’s application is limited, and rarely spans the end-to-end revenue cycle. While only 12% of respondents consider their AI implementations to be mature today, 35% expect their implementations to be “early mainstream/fully mature” by 2023. By 2023, 98% of healthcare leaders anticipate using AI in RCM, and 81% have conducted a tech evaluation, reviewing AI technology providers, solutions, or software systems aimed at improving RCM processes.

• Stark gaps in opinion are hindering healthcare from fully capitalizing on the transformative power of AI. Reported usage of AI in RCM is much higher among those in revenue cycle roles (89%) than IT (63%) and non-technical executives (48%).Those in RCM roles (78%) are satisfied with their current use of AI, compared to just 46% of IT leaders and 25% of non-technical executive and financial respondents. An overwhelming majority (86%) of those in RCM roles see value in using AI in RCM compared to 52% of IT and 44% of executive and financial decision makers. This disparity points to the need for RCM leaders to better communicate AI’s effectiveness at improving financial outcomes and the ROI of their AI investments.

• AI is driving a wide range of improvements, but the approach is tactical and not end-to-end. Among the two-thirds of hospitals currently using AI in the revenue cycle, driving patient and payer payments (83%) and cash flow (80%) are the most-cited improvements. The most common applications are eligibility/benefits verification (72%) and patient payment estimation (64%). By 2023, respondents expect prior authorization (68%) and payment amount/timing estimation (62%) to emerge as leading applications. While these functions may receive more attention than others, providers anticipate an overall increase in AI use across revenue cycle functions, indicating an evolution toward a strategic end-to-end approach.

• Financial, security, and privacy concerns block AI adoption and dampen success factors. Budgetary concerns are the leading barriers to initiating AI in RCM and full AI integration. Three quarters (76%) of non-technical executives cited budgetary concerns as the primary obstacle to full AI integration. A majority of providers (56%) report liability, risk, and privacy concerns. Staffing (50%), lack of trust in the information provided (45%),and infrastructure challenges (43%) are also barriers to fully integrating AI, demonstrating some key pain points organizations will have to work through before fully maturing their AI strategy.

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