Validate documentation to support quality measures, clinical decision support tools, and clinical standards.
Last updated:
Reviewed by: Arkangel AI clinical review team, Clinical quality and medical AI content
Healthcare organizations face critical quality challenges that impact patient outcomes and revenue
Quality depends on documentation, yet teams can only manually review a small share of charts. Missed diagnoses, care-gap omissions, and weak documentation slip through—hurting patient outcomes, lowering HEDIS/STARS scores, and exposing providers to audit risk. The gap widens as volume grows, because adding reviewers rarely keeps pace with chart counts.
Missed diagnoses, medication errors, and overlooked screenings create poor outcomes and erode patient trust in your organization.
Payer contracts and government programs tie reimbursement to clinical quality scores, directly impacting your revenue.
Poor documentation exposes providers to audit risks, compliance violations, and contributes to clinician burnout.
Transform your quality workflow with intelligent automation that scales
AI reviews supported records made available to the configured workflow against selected quality criteria. It highlights potential care and documentation gaps for clinical validation. Coverage and turnaround depend on the inputs, integration and review process; quality scores and reimbursement are not guaranteed.
Review supported records against configured quality measures, including HEDIS/STARS where applicable.
Identify potential care gaps for clinical teams to validate and use when planning follow-up or education. Patient outcomes require clinical assessment.
Findings support human review; clinical, coding and payer decisions require validation by the responsible team.
A simple three-step process to transform your clinical quality workflow
Configure the applicable criteria and supported data sources with your implementation team. Review records available to that workflow, then route evidence and recommendations to the responsible clinicians for validation and follow-up. Coverage, processing time and feedback delivery depend on the agreed integration and review process.
Work with a dedicated engineer to configure the AI environment and train on your organizational quality criteria.
Analyze supported records with evidence and recommendations for human review. Timing depends on ingestion and workflow configuration.
Share validated findings through the configured review process. Responsible clinicians decide on follow-up and education.
How AI chart review compares with manual quality audits on coverage, feedback speed, and outcomes.
Manual audits offer contextual judgment but are limited by reviewer capacity and sampling. AI can help apply configured criteria consistently across supported records and surface findings for review. Neither approach guarantees better outcomes: clinicians must validate the findings, assess missing context and decide what action is appropriate for each patient.
| Capability | Arkangel AI | Manual quality review |
|---|---|---|
| Chart coverage | Supported records available to the workflow | Samples a small share |
| Feedback speed | Feedback after processing; timing varies by workflow | Delayed, periodic audits |
| Measure alignment | Configured criteria applied to supported inputs | Inconsistent, reviewer-dependent |
| Scalability | Cost depends on volume, plan and human review | Needs more staff as volume grows |
| Audit readiness | Traceable findings for review and audit preparation | Manual prep for each audit |
Review before submission
Identify potential documentation issues in supported records for your team to validate before submission. Denial outcomes depend on the case and payer.
Everything you need to know about chart intelligence for clinical quality
See how chart intelligence surfaces the quality gaps hiding in your records