Poised to Transform: Study Reveals AI to Be Pervasive in Hospital Revenue Cycle by 2023

Artificial Intelligence (AI) will transform the way doctors, hospitals, and healthcare systems identify, collect, and manage their revenue cycle. As healthcare organizations evolve from siloed, point applications to holistic, end-to-end integration within their systems, the next three years will see a significant shift. This is according to a new study commissioned by Change Healthcare.

On the Precipice of Change

Healthcare has traditionally lagged behind other industries in its use of artificial intelligence (AI). Although the technology has made some inroads into clinical settings, with organizations applying the technology to improve efficiency, accuracy, and consistency, the revenue cycle has remained relatively uncharted territory for AI … until now.

From a healthcare-business perspective, this embrace of AI makes perfect sense. The opportunities for AI to improve revenue cycle management (RCM) are boundless. From removing administrative waste due to inefficient practices, enhancing decision support, and improving patient engagement, AI has the potential to span the revenue cycle, improve patient access, optimize the claims life cycle, guide capacity planning, and more.

But how is it being applied today? Who is using it? And where is the market heading? To answer these and other questions surrounding this transformative technology, Change Healthcare commissioned market researcher ENGINE Insights to conduct a study. Some 200 revenue cycle, IT, finance, and C-suite decision-makers were polled 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.

The results revealed that, today, hospitals and health systems are standing on the precipice of substantive change––actively seeking ways to leverage AI to address complex business problems. 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 the technology and its value varies wildly among executive management, IT, and revenue cycle leadership—and there remain concerns hindering adoption.

AI is Getting Under the RCM Hood … Gradually

The study found that nearly all U.S. hospitals (98%) 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, but AI’s application is limited and rarely spans the end-to-end revenue cycle.

Despite this traction, 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 those in IT (63%) and among nontechnical executives (48%). This could be due to the broad definition of AI in the survey or RCM leaders’ hands-on awareness of what types of technology their departments are using.

The maturity of AI applications in RCM is accelerating as well. 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.

Healthcare leaders are clearly moving in the AI direction. A full 81% of respondents have conducted a tech evaluation in the past two years, reviewing AI technology providers, solutions, or software systems specifically aimed at improving RCM processes. This suggests that they are fully onboard with applying AI to improve RCM performance.

Current Applications Are Tactical

Today, the most common applications of AI in RCM are eligibility and 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 from point solutions to a strategic and holistic approach.

So, while AI is already driving a wide range of improvements, the approach remains tactical and not holistic. Respondents indicated that, 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.

While eligibility and benefits verification (71%) and patient payment estimation (62%) will continue to be popular areas for AI in the future, there are some key functions that will also see a meaningful increase in AI use. For example, prior authorization is expected to see a 24 percentage-point increase, and payment amount/time estimation and denials prevention are expected to see an 18 percentage-point increase.

Existing RCM pain points provide opportunities for AI use. Addressing cost-to-collect, denials, and underpayments top the list of RCM pain points. Revenue cycle leaders also view A/R management and staffing as difficult, while corporate leaders have higher-than-average concerns around patient collections. AI solutions can drive improvements in all these areas.

The almost ubiquitous adoption of AI in three years will require a marked change from the current state. As of now, 36% of organizations are not using AI at all, and the maturity level of those who are using it is largely (42%) at the emerging stage. Only 12% of healthcare leaders indicate they have a fully mature program. 

Perceived Value

There are some significant variations in healthcare leaders’ perceptions of AI in RCM. While nearly eight out of 10 RCM decision-makers (78%) are satisfied with their current AI use, only 25% of corporate leaders and 46% of IT leaders are satisfied.

Perceptions on value also differ. 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.

Barriers to Adoption

Financial, security, and privacy concerns are among the most-cited issues blocking AI adoption. 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. From a tech perspective, liability and security risks are the most concerning element (61%).

If the marketplace is going to realize greater adoption, a clear ROI will be necessary to overcome doubts.

Focus, Alignment, Strategy

Like every large-scale change, there will be challenges involved in transforming the existing culture and processes, coordinating the work of multiple teams, and navigating a multifaceted implementation effort. But all these hurdles are manageable as long as an organization keeps the ultimate goals of efficient, more cost-effective, patient-centered care top of mind.

The opportunities for providers are enormous and invaluable––from improving the entire revenue cycle to better patient level-of-care prediction, clinical insights, and claims accuracy. But it is increasingly clear that this potential can only be realized if key stakeholders become aligned on AI’s capabilities, embrace a strategic AI vision, and prioritize the most impactful use cases.

By pursuing a strategic approach––establishing an AI platform and then using AI holistically versus tactically––healthcare can unleash its power across previously untapped areas like the revenue cycle and achieve what was impossible before.