Better quality scores, fewer headaches

Between ever-evolving payer quality programs, strict risk adjustment requirements, and the challenge of closing care gaps in a timely manner, health systems and medical groups are under pressure to deliver better population health outcomes with fewer resources. 

Yet many organizations are still relying on manual processes for chart reviews, outreach calls, and care gap scrubbing that overwhelm staff and delay progress. 

The smarter, more efficient route is automated. Key quality performance tasks can be automated in weeks and start closing gaps instantly, helping health systems provide better care.

These three proven workflows help close care gaps, streamline quality reporting, and identify risk-adjustable conditions without the administrative backlog. 

1. Chart scrubbing with care gap outreach: Close care gaps with precision and ease

Care gaps are a common drag on quality scores, but not every patient flagged as “due” truly is. This workflow begins by analyzing a registry of patients who are suspected to have overdue care gaps, like mammograms or wellness visits. 

An AI Agent confirms the patient’s true status by scanning both structured and unstructured data and clinical notes in the EHR. If the gap is already closed, the AI Agent exports the relevant evidence to the EHR and updates the record. 

If the care gap is valid, the AI Agent takes the next best action: automatically sending an SMS to the patient inviting them to schedule the appropriate appointment. 

No staff involvement is needed, and this critical workflow helps improve quality scores, reduce false positives to avoid redundant outreach, and save time on both chart review and patient outreach. 

2. Chart scrubbing for payer care gap attestation: Ensure quality credit for the care your providers are already delivering 

There’s often a disconnect between provider documentation and payer-recorded completions. Automating this workflow bridges that gap by validating open care gaps flagged by a payer and ensuring your organization gets proper credit for compliant care and exclusions. 

Using a payer-supplied registry, an AI Agent runs structured and unstructured data analysis in the EHR for each patient and care gap. It confirms whether the patient is actually overdue, or if the gap has been satisfied, excluded, or completed outside the system. 

Once verified, the required documentation is produced and formatted for submission back to the payer, enabling accurate quality program reporting for measures such as HEDIS and Stars. 

This workflow improves attestation accuracy across members, avoids manual chart audits and data abstraction, and unlocks payer incentives tied to quality performance. 

3. HCC suspecting: Surface risk-adjustable conditions before they impact your bottom line 

Many risk-adjustable conditions go uncoded simply because they’re not top-of-mind during short encounters or buried in unstructured data. This pre-visit HCC suspecting flow ensures providers have visibility into every impactful condition before the patient walks in. 

By scanning clinical history, prior documentation, and relevant diagnostic data, an AI Agent proactively flags likely HCCs for provider review. These suspected codes are embedded in the EHR workflow in time for the patient visit, allowing for in-the-moment validation and documentation. 

When combined with post-visit HCC review, this workflow supports accurate, compliant coding that protects value-based revenue, particularly in Medicare Advantage populations. It also helps improve Risk Adjustment Factor accuracy and supports clinical documentation integrity teams. 

The bottom line

Automation isn’t just about efficiency; it’s about empowering your teams to do more of the work that matters. With automation, care gap closure, quality reporting, and risk adjustment become scalable, accurate, and painless. 

Ready to reduce manual overhead and improve quality performance across the board? Let’s talk about how to get started.