Electronic Health Reporter

By Ben Beadle-Ryby, co-founder, AKASA.

As we approach 2025, healthcare and health IT are on the cusp of transformation, driven by advancements in generative AI (GenAI).

To understand where the industry is heading, we spoke with Ben Beadle-Ryby, senior vice president and co-founder of AKASA, a leader in GenAI solutions for the revenue cycle. Ben shared his insights into the evolving role of GenAI in healthcare, the challenges health systems face, and strategies for successful adoption of these technologies.

1. How is generative AI evolving in the revenue cycle, and where do you see it heading in 2025?

GenAI is no longer theoretical in healthcare; it’s delivering measurable outcomes, though it’s in its early days. In 2025, as the technology further matures, health system adoption will follow suit, with organizations embracing use cases like prior authorization and coding. Progressive organizations, such as Cleveland Clinic and Johns Hopkins, are already optimizing operations and revenue yield by leveraging GenAI to process and interpret clinical data more accurately and efficiently.

While GenAI holds promise across the entire healthcare ecosystem, starting with financial operations makes sense due to lower risks and faster ROI. By 2025, expect health systems to rely on GenAI to optimize revenue and achieve operational excellence.

2. How have perceptions of generative AI changed?

The release of ChatGPT in late 2022 revolutionized AI perceptions. What once seemed futuristic is now accessible and actionable.

Many health systems are now exploring this technology. AKASA sponsored a survey of health leaders with HFMA that showed that more than 70 percent of healthcare organizations are actively considering the use of GenAI, with nearly 60% of organizations eyeing it for the revenue cycle.

Initially, healthcare leaders approached generative AI with appropriate caution, wary of security and compliance risks. Best practice solutions that not only have a secure, HIPAA-compliant architecture but have also found a way to tailor and fine-tune large language models (LLMs) to a given hospital or health system provide a clear path to deploying GenAI that is safe, secure, and a value-add.

Generative AI isn’t just another buzzword or a flash in the pan. Unlike older technologies like RPA or early AI, GenAI tackles nuanced, complex tasks, augmenting human expertise. The time may soon come when we cannot imagine conducting business or performing healthcare without GenAI — just as today we cannot imagine working without personal computers or the internet.

In the short term, though, and across 2025, we anticipate widespread deployment and adoption of GenAI in the healthcare revenue cycle as leaders grasp its ROI potential and ability to drive nearly 100% revenue yield. It’s no longer a question of “if” but “how fast?”

3. What are the biggest revenue cycle challenges heading into 2025, and how can GenAI address them?

Health system executives are grappling with payer challenges, subpar revenue yield, and soaring expenses — all exacerbated by workforce shortages and burnout. Generative AI offers a practical solution by automating time-consuming, complex tasks that drain resources.

For example, GenAI can analyze clinical documentation to suggest accurate codes, allowing coders to focus on review and approval rather than starting from scratch. It also tackles prior authorizations by equipping teams with precise guidance to provide payers with necessary documents or clinical answers to secure authorizations, minimizing delays, reducing reliance on clinical staff, and minimizing denials and appeals re-work. These capabilities streamline operations, ensuring organizations achieve near-perfect revenue yield while mitigating payer friction.

Additionally, GenAI democratizes institutional and clinical knowledge by embedding critical insights into AI models. This reduces dependency on specialized expertise, accelerates onboarding, and enhances productivity across teams. By addressing these top concerns, GenAI empowers health systems to optimize revenue, control costs, and alleviate workforce challenges.

4. Feedback loops connecting insights and outcomes from the business office to patient access and mid-cycle processes are critical to the revenue cycle. How else can GenAI support that goal?

Generative AI dismantles silos by creating a unified foundation across revenue cycle processes. A single AI LLM can assist with authorizations, clinical documentation, coding, and denials, leveraging the same clinical data across workflows. This shared intelligence reduces errors, accelerates processes, and fosters collaboration. Furthermore, the ability to create a closed-loop learning system that reinforces and retrains the model over time — for instance, with 835 insights or denials data — ensures continued improvement and accuracy.

In the long run, this results in fewer denials, improved coding accuracy, better reflection of quality measures, and more accurate revenue realization — all contributing to better financial outcomes.

In 2025 and beyond, this holistic approach will define successful health systems, driving both efficiency and near-perfect revenue yield.

5. What first steps should health systems take if they want to adopt generative AI?

Start by identifying key pain points where GenAI can deliver immediate ROI. Focus on areas like prior authorizations or coding, where automation can have a meaningful impact. Then, partner with experts who understand both healthcare and AI. Off-the-shelf solutions or open-source AI models may seem convenient but often fail to address healthcare’s complexities.

Tailored GenAI solutions — rigorously tested for accuracy and compliance — are essential. Choose a partner with deep domain expertise to ensure success. By taking these thoughtful steps, health systems can quickly harness GenAI’s potential, driving operational excellence and achieving improved revenue yield.

Looking Ahead

As we approach 2025, the excitement around generative AI in healthcare is palpable. With tailored applications and thoughtful implementation, health systems can harness this technology to tackle long-standing challenges in the revenue cycle and beyond. From enhancing operational efficiency to improving patient and clinician experiences, AI is reshaping the future of healthcare — and it’s an exciting time to be part of this journey.