Balancing technology and talent in revenue cycle transformation
Executives at Northwestern Medicine and Genesis Healthcare System discuss their intentional approach to implementing AI in the revenue cycle.
A recent American Hospital Association Trailblazers report offers a compelling roadmap for health system leaders navigating the complex terrain of revenue cycle transformation.
The report, sponsored by Guidehouse, draws from real-world case studies and expert insights and emphasizes that successful transformation isn’t just about adopting artificial intelligence (AI) or automation—it’s about aligning technology with processes and people who can use it effectively.
Download the report and read our summary below of four key strategies that can help health systems drive meaningful, scalable, and sustainable revenue cycle performance improvement.
Read the full report
1. Start with strategy, not software
Before investing in AI or automation, health systems must take a step back and assess their goals for implementing the technology. The report warns against the temptation to chase quick wins with point solutions that promise instant results but lack integration. Instead, leaders should define clear goals, assess existing processes, and identify key performance indicators (KPIs) to track progress.
Assuming that AI is a “plug-and-play” solution to operational challenges is what Genesis Healthcare System Chief Strategy Officer Mike Norman calls “artificial ignorance.” His organization evaluates every AI tool against three criteria: Does it improve efficiency? Does it enhance performance? Does it align with the broader transformation strategy? If the answer isn’t yes to all three, the tool doesn’t make the cut.
“The computer will do what the human just did, and if what the human did was broken, the computer will just do more of the same—and faster.”
2. Build a platform, not a patchwork
The proliferation of AI-powered point solutions has made it easier than ever to plug in new tools. But experts caution that a piecemeal approach can lead to fragmented systems, inconsistent workflows, and poor patient experiences.
Northwestern Medicine’s transformation began with a seemingly obvious but highly challenging move: standardizing its entire enterprise on a single electronic health record (EHR) platform. This foundational step enabled consistent processes, consolidated staffing, and enterprise-wide visibility into performance. From there, the system introduced robotic process automation and AI—but only after aligning its people and processes.
“You really can’t achieve any cost savings or efficiency gains if you’re running multiple systems because you’ve got duplicative management and duplicative staffing,” said Andrew Scianimanico, the health system’s Chief Revenue Cycle Executive.
3. Balance technology with talent
AI can dramatically increase productivity—clinical appeal nurses might handle 10 claims a day, while AI can process hundreds. But technology alone isn’t the answer. Health systems must plan for how automation will affect their workforce and determine how staff can be upskilled and redeployed to fill other gaps.
Guidehouse Senior Vice President Shela Schemel emphasizes that AI should amplify human capabilities, not replace them. That means involving stakeholders from across the organization—finance, IT, clinical operations, HR—from the outset and anticipating the skill sets needed to support transformation.
“Before you deploy AI, you should evaluate your existing processes and understand what you’re trying to accomplish and how that impacts both up and downstream processes in your organization — especially within your revenue cycle.”
4. Standardize to Scale
Consistency is the key to scalability. Northwestern Medicine’s success hinged on standardizing every revenue cycle function—from patient registration to billing—across its enterprise. This not only improved internal efficiency but also enhanced the patient experience by eliminating redundant processes and confusing billing practices.
Genesis Healthcare applied the same principle to its middle revenue cycle, automating coding and documentation review only after stabilizing operations. The result? Faster, more accurate coding and improved KPIs across the board.
“Scalability is inherent to consistent workflows. Without consistent processes, scalability is next to impossible,” said Atek Pandya, Director, Revenue Cycle Automation and Engineering at Northwestern Medicine.
Dive deeper
AI has potential to revolutionize the revenue cycle—but healthcare executives must act responsibly to protect their organizations and enable success. Explore the full Trailblazers report to dive deeper into these case studies and discover how leading health systems are transforming their revenue cycle with intention, rather than impulse.