REVENUE CYCLE MANAGEMENT Q&A
Artificial Intelligence in Revenue Cycle Management
Regulatory and payment pressures compel healthcare finance leaders to optimize RCM solutions, what is your broad
perception of current state? As these pressures increase for hospitals and health systems, finance leaders rely primarily on traditional outsourced RCM services and specific function bolt-on solutions to augment their patient access, charging, billing, collec- tions, and follow-up systems. Eligibility and authorization solutions and traditional early out programs for self-pay encounters and col- lection agencies continue to dominate the RCM field of options available to finance leaders. Central to every hospital or health system RCM solution is a core transactional billing and collections system. These systems are tightly integrated with and rely on electronic health record systems (EHRs) as the patient care, and charge capture mechanism. With only several industry dominating EHR vendors, finance leaders face limited RCM system choices. Historically, third party bolt-on solutions are leveraged to target spe- cific areas of the revenue cycle. Sadly, this remains the state of the industry today.
How do you see the evolution towards the use of AI progressing?
Recent industry studies focused on the cur- rent state of RCM provide insight into the slow adoption of robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI). Healthcare organizations are heavily targeting areas that are key to their financial survival to include eligibility, authorization, and patient payment estima- tion where process automation is currently delivered via bolt-on solutions. In our expe- rience, far too much time is lost coordinat- ing the implementation of these solutions between disparate vendors and the core revenue cycle solutions. It is critical that industry vendors streamline access to these bolt-on solutions as a fundamental step in the evolution of AI driven solutions. Health systems with sophisticated IT shops will be best positioned to leverage advancing AI technologies while individual
hospitals and smaller organizations may find their solutions in the form of outsourced service organizations delivering these AI solutions. Emerging technologies are often expensive and require talent and expertise that is not readily available – for this reason, it is critical that organizations reflect on the best path to achieve their goals.
What are the main obstacles leveraging of AI/machine learning tools in RCM now and how can healthcare finance
leaders overcome them? While there are many obstacles to leveraging AI and other tools in revenue cycle man- agement, the primary obstacle to adoption seems to be rooted in the need for a consistent definition and understanding of AI and the availability of such technologies and tools to augment and integrate with traditional revenue cycle solutions.
Hospitals and health systems historically
rely on only a limited number of industry available RCM transaction-based systems coupled with electronic health records where charges are captured as a by-product of care delivery. These core revenue cycle solutions are costly, inflexible, and slow to evolve. They are also tightly embedded in the core application portfolio of organizations and consequently not easily replaced. Multiple data hand-offs and points of integration between disparate systems and service offerings negatively impacts RCM in healthcare. Organizations face a double- edged sword, collecting cash today while executing a sustainable future strategy. Out of necessity, financial survival routinely dom- inates revenue cycle management thinking.
Overcoming Major Obstacles
• Embrace a Consistent AI Definition • Critically Evaluate Internal Staff & Technology Capability
• Develop a Comprehensive RCM Technology Strategy & Roadmap
• Augment Core Transaction Systems as Needed with Sourced Offerings
• Avoid Locking in on Tactical Technology Solutions that may Impede Progress
www.healthitq.com Sponsored Content HealthITq 16
hcinnovationgroup.com | SEPTEMBER/OCTOBER 2021
Robecca Quammen DBA MBA FACHE CEO HealthITq & Quammen Group
What can we expect in the near future?
Over the next several years, service and technology vendors will continue to advance AI solution offerings bolstered by the ever- demanding healthcare payment system. Hospitals, health systems and providers will continue to seek solutions to the increasing burdens on their RCM operations and staff. It is likely that large service-oriented orga- nizations will be best positioned to invest in and leverage advanced AI solutions that can then be offered to hospitals and health sys- tems. Organizations should critically assess internal staffing and technology capabilities to determine how best to sustain cash col- lections vital to their survival. We know the pain points in RCM.
Disruptive technologies are on the hori- zon. Leveraging them requires educating ourselves, making sound technology and sourcing decisions, and constantly looking to the future with an open mind.
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