As discussed in our previous articles, deploying agentic artificial intelligence (AI) can reduce denial rates, improve claim accuracy, and accelerate collections. The success of automation technology depends on a phased approach to implementation founded on commitment to continuous improvement through robust feedback loops and effective change management that avoids common pitfalls.
Tracking Key Metrics
Measuring performance and refining workflows are essential to the long-term success of agentic AI. By analyzing outcomes, including what worked, what failed, and why, healthcare organizations can refine agent rules, retrain models, and identify new automation opportunities. Additionally, regularly updating training data ensures alignment with payer rules, workflows, and regulations.
To evaluate the impact and effectiveness of the digital workforce, healthcare organizations should track both traditional revenue cycle management (RCM) metrics, such as appeal success rates, denial rates, and time-to-collect, and new AI-specific key performance indicators (KPIs). These include:
- Task adherence: Did the agent fulfill the user’s intent and complete the assigned task accurately?
- Accuracy: Percentage of tasks completed without human intervention, including correct code selection and valid tool utilization.
- Feedback performance: Measure how well agents learn from outcomes to refine workflows and improve decision-making.
- Traditional RCM metrics: Appeal success rate, denial rate, time-to-collect, cost-to-collect, and dollars recovered or protected.
Driving Adoption Through Change Management
Adoption hinges on early stakeholder engagement, clear communication, and incremental rollouts. Training equips teams for evolving responsibilities such as quality assurance, exception handling, and oversight.
Common Pitfalls to Avoid
Avoiding these common pitfalls is also critical to ensure the successful deployment of agentic AI, which include:
- Weak feedback loops: Without performance feedback, digital agents cannot improve.
- Over-automation: Attempting complex workflows too early can undermine the ability of digital agents to deliver a rapid return on investment (ROI). Start with high-impact, low-complexity cases.
- Siloed data: Digital agents require comprehensive and accessible data to operate effectively. Incomplete or inaccessible data limits outcomes. Ensure data is clean, connected, and AI-ready.
Best Practices for Long-Term Success
To maximize the impact of agentic AI, follow these best practices:
- Engage stakeholders early: Include operations, IT, and compliance teams from the beginning to drive buy-in.
- Incremental roll out: Pilot small initiatives, demonstrate wins, then scale.
- Teams training: Prepare human agents for evolving roles that encompass new responsibilities in quality assurance, exception handling, and oversight.
- Link initiatives to ROI: Tie automation efforts to measurable financial outcomes to sustain buy-in and adoption.
Agentic automation is a powerful, evolving innovation in healthcare RCM. Success requires embracing exploration, learning from missteps, and refining approaches based on real-world feedback. Healthcare organizations can unlock the full potential of agentic automation and create a future-ready infrastructure by focusing on practical, attainable solutions and building a collaborative hybrid intelligence model.
Download the white paper, A Healthcare Leader’s Guide to Building a Digital RCM Workforce, for more on best practices for evaluating performance, refining workflows, and driving continuous improvement in healthcare RCM.
