Regard
Regard provides a clinical AI platform that reviews 100% of patient EHR data to identify and document missed diagnoses. The system ingests millions of data points, recommends diagnoses with supporting evidence, and surfaces insights across clinical notes, mid-revenue cycle, and HCC capture workflows. In a Sentara Health pilot, the platform increased CC/MCC capture by 17% with 74% voluntary adoption among hospitalists.
- Healthcare Technology
- Digital Health
- Artificial Intelligence
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Physicians typically only see 3% of the data in a patient's electronic health record, leading to missed diagnoses, sub-optimal care, and lost revenue. The sheer volume of structured and unstructured data in modern EHRs exceeds what clinicians can feasibly review, causing diagnoses to be overlooked and documentation to remain incomplete.
Solution
Regard provides a clinical AI platform that reviews 100% of patient data to identify and document missed diagnoses. The platform ingests and maps millions of data points from EHRs to clinical concepts, then applies proprietary, clinically validated algorithms to recommend diagnoses with supporting evidence. Regard surfaces this diagnostic intelligence across multiple workflows, including clinical notes, mid-revenue cycle, and HCC capture, giving clinicians a complete clinical picture at the point of care. The system adapts to existing workflows and provides impact analytics to demonstrate adoption and outcomes.
Target Audience
Primary customers are hospitals and integrated health systems, particularly hospitalists and other inpatient clinicians who need comprehensive chart review support, as well as revenue cycle and risk adjustment teams seeking to improve documentation accuracy.
Features
- Deep EHR data integration with millions of data points ingested and mapped to clinical concepts
- Proprietary diagnostic intelligence layer using clinically validated algorithms to recommend diagnoses with linked evidence
- Clinical notes workflow that formats progress notes with relevant diagnostic details ready for clinician review
- Mid-revenue cycle support that catches missed diagnoses before they become queries or denials
- HCC capture functionality that identifies new Hierarchical Condition Categories for accurate risk adjustment
- Impact analytics and reporting dashboards demonstrating adoption, impact, and opportunity across the health system
- Generative AI functionality for clinical summaries and discharge notes