AI-Powered Medical Record Processing

Manually extracting patient data from Electronic Health Records (EHRs) for clinical trials is slow, expensive, and error-prone. AI automates this by reading physician notes, lab reports, and discharge summaries to identify eligible patients and populate Case Report Forms (CRFs) with structured data.

• Real Example: A pharmaceutical sponsor reduced patient screening time from 3 weeks to 3 days, accelerating trial enrollment.
• ROI Impact: Cuts data abstraction costs by up to 70% and reduces protocol deviations caused by manual entry errors.

The prior authorization process is a major source of provider burnout and revenue cycle delays, often requiring staff to manually search records for specific clinical justification. AI instantly extracts relevant diagnoses, treatment histories, and test results from patient charts to auto-populate authorization forms.

• Real Example: A multi-specialty clinic reduced its average authorization submission time from 45 minutes to under 5 minutes.
• ROI Impact: Decreases administrative labor costs, accelerates reimbursement, and improves staff satisfaction by eliminating tedious data hunting.

Up to 80% of critical patient data resides in unstructured clinical notes, making it invisible to traditional analytics. AI processes these notes to create structured, queryable fields for conditions, social determinants of health (SDOH), and medication adherence.

• Real Example: A health system identified 15% more high-risk diabetic patients for its care management program by uncovering data buried in narrative notes.
• ROI Impact: Enables precise risk stratification, improves quality metric reporting (e.g., HEDIS), and supports value-based care contracts by providing a complete patient picture.

Accurate medical coding is critical for reimbursement and risk-adjusted payment models like Medicare Advantage. Manual coding is inconsistent and misses revenue. AI reads the full medical record to suggest ICD-10 and HCC codes based on documented conditions, ensuring completeness and compliance.

• Real Example: A large provider organization increased its RAF (Risk Adjustment Factor) score accuracy by 12%, capturing millions in appropriate reimbursement.
• ROI Impact: Maximizes legitimate revenue, reduces audit risk, and frees certified coders to focus on complex cases rather than routine chart reviews.

In high-acuity settings like the ED, clinicians need instant access to a patient's history. AI can generate a concise, chronological patient summary by processing years of records in seconds, highlighting active problems, medications, allergies, and recent key events.

• Real Example: An ED reduced the time physicians spent reviewing past records by 65%, allowing faster clinical decision-making.
• ROI Impact: Improves patient throughput, enhances safety by surfacing critical information (e.g., allergies), and reduces clinician cognitive load during emergencies.