From Pilot to Platform: A Practical Guide to Implementing AI in Your Coding Workflow

Integrating a powerful AI platform into your risk adjustment process is one of the highest-leverage moves an organization can make. But successful adoption goes beyond the technology itself; it requires a thoughtful approach to change management that empowers your team and demonstrates value at every stage.

Based on our experience helping organizations go live with MedChartScan, we've developed a clear, four-step framework for a successful implementation.

Step 1: Set Clear Goals and Define Success

Before you begin, define what you want to achieve. Your goals should be specific and measurable. Are you trying to:

• Increase RAF score accuracy by a target percentage?
• Reduce chart review time per coder?
• Decrease your reliance on external contract coders?
• Improve your team's ability to find specific, high-value HCCs?

Establishing these KPIs upfront will provide a clear benchmark for measuring the success of the implementation.

Step 2: Run a Focused Pilot Program

A pilot program is the best way to prove the platform's value and build internal buy-in.

1. Select a Pilot Group: Choose a small, representative group of 3-5 coders. Include both enthusiastic early adopters and seasoned skeptics to get balanced feedback.
2. Choose a Data Set: Select a batch of recently coded charts. This allows you to directly compare the results of the AI-assisted workflow against your previous manual results.
3. Train for the New Workflow: The key is to train coders on the new mindset. They are no longer data miners; they are clinical data experts whose job is to validate AI-surfaced insights. The pilot should focus on this new, elevated role.

The results of the pilot—such as net-new HCCs found and time saved per chart—will provide the concrete data needed to justify a full-scale rollout.

Step 3: Scale with Confidence

Once the pilot has proven successful, it's time to scale.

• Phased Rollout: Onboard the rest of your team in phases. Use your pilot group members as internal champions and mentors for their peers.
• Systems Integration: Work with our team to integrate MedChartScan into your existing EHR and data systems, creating the seamless AI-assisted risk adjustment workflow that enables maximum efficiency.
• Develop New SOPs: Update your standard operating procedures to reflect the new AI-assisted, human-validated process. This ensures consistency and quality as you scale.

Step 4: Measure, Iterate, and Expand

The launch isn't the end of the journey.

• Track Your KPIs: Continuously monitor the goals you set in Step 1. Use the platform's analytics to track coder productivity, RAF accuracy, and other key metrics.
• Build Trust: As your team gets more comfortable, they will see the AI not as a black box, but as a reliable safety net that helps them do their job better.
• Expand to Proactive CDI: Once the platform is established in your coding workflow, you can leverage its powerful search and analytics capabilities to drive a more proactive CDI program.

By following a structured implementation plan, you can ensure a smooth transition that minimizes disruption, maximizes adoption, and unlocks the full potential of your investment.