In our previous article, “The Role of AI and Automation in Revenue Cycle,” we introduced the automation continuum and the use of artificial intelligence (AI) and automation in healthcare revenue cycle management (RCM). The second installment in this series focuses on the current state of AI and automation, highlighting practical applications, the benefits, and the challenges faced by industry leaders.
The integration of AI and automation in RCM is about more than streamlining process flows; it’s fundamentally about driving healthcare efficiency every step of the way. Several areas where these technologies are making an impact include:
- Enhanced patient scheduling and registration. AI-powered tools aim to reduce patient friction and improve overall satisfaction. Advanced AI implementations, such as sophisticated chatbots, can streamline the appointment process, offering personalized interactions and comprehensive scheduling options. This not only strengthens patient relationships but also can increase operational efficiency, providing healthcare organizations with a competitive edge for improved patient experiences.
- Streamlined prior authorizations and patient access. Facilitating the exchange between payers and healthcare organizations can improve clinical workflow and patient satisfaction. While insurance and benefit verification are nearly fully automated, complete automation of the financial clearance process still faces many hurdles. Payer auth guidelines and policies are dynamic and varied, which makes full automation challenging.
- Advanced clinical documentation and medical coding. Computer-assisted coding (CAC), where codes are suggested to a skilled coder, has broad adoption in the inpatient setting. Computer-assisted professional coding (CAPC) has started to gain traction, and brings the promise of significant efficiency gains. Fully autonomous coding is beginning to show promise, particularly in high volume specialties for which large volumes of data are available (e.g., radiology, emergency medicine). Future advancements depend on access to data needed to refine the precision of large language models (LLMs) for more widespread application.
- Improved patient financial experience. Utilizing automation for streamlined collection workflows, including touchless accounts receivable (A/R) and automated denials management, has the potential to create a more frictionless patient experience.
Additionally, while areas such as clinical services and revenue integrity are further behind in adoption, there are applications and use cases to streamline these RCM processes and workflows.
Overcoming Challenges for Successful Integration
Healthcare organizations must navigate several challenges to successfully integrate AI and automation into RCM, including:
- Organizational prioritization. To create a seamless patient experience, it is essential for healthcare organizations to integrate their systems of record and eliminate data silos. The challenge lies in identifying and prioritizing the applications that will have the greatest impact. This includes applications that enhance operational efficiency, increase physician and patient satisfaction, and improve patient outcomes. However, the complexity of current technology can make it difficult to evaluate the most effective solutions. Partnering with a vendor that can provide innovative ideas and insights is crucial to implementing advanced automation through a strategic approach that aligns with organizational priorities.
- Patient friction. Decentralized scheduling and registration systems and frequent policy changes by payers can cause frustration for patients. Implementing AI and automation can help address these issues while enhancing patient engagement, loyalty, and overall satisfaction.
- Physician friction. Time-consuming documentation is a burden for physicians. Ambient documentation technologies that convert voice into text for immediate electronic health record (EHR) updates have promise in streamlining documentation processes and augmenting clinical documentation improvement (CDI) initiatives, including more effectively managing patient data for chronic conditions.
- Collaboration with Information Technology (IT). Cross-functional teams that include revenue cycle management, IT, and external partners must work together to develop and test APIs, access data lakes and fabrics, and train AI models with large datasets. It is also essential to ensure vendors are certified and comply with industry-standard protocols.
Successfully addressing these challenges when implementing AI and automation can significantly streamline operations, enhance clinical and financial outcomes, and enhance patient experiences. By effectively leveraging these innovations, health system leaders can make more informed decisions, minimize errors, and improve patient satisfaction.
Contact us for support in implementing these solutions, and watch for our final article in this series, which will explore the future state of AI and automation in healthcare revenue cycle management.
Patrice R. Wolfe
