By RegardLearning Objectives
- Understand how Regard works from chart data rather than conversation
- Understand the dual payoff: safety and documentation integrity
- Evaluate diagnosis-suggestion AI as clinician support
What Is Regard?
Regard is a clinical-AI platform that works quietly inside the electronic health record. Unlike an ambient scribe that listens to a conversation, Regard reads the patient's existing chart data — labs, vitals, medications, history — and surfaces missed or under-documented diagnoses, drafts assessment-and-plan notes, and flags conditions that might otherwise go uncaptured. In effect, it reviews the chart the way a diligent colleague would, pointing out "this patient also meets criteria for X" that a busy team may not have noted.
This addresses two linked problems at once. The first is patient safety: a diagnosis present in the data but overlooked in a hectic shift. The second is documentation integrity: conditions that were real but never coded, which affects both quality measurement and appropriate reimbursement. Regard reports deployments at health systems such as Banner and Sentara, with millions of diagnoses surfaced and meaningful incremental revenue for partners. As with all diagnosis-suggestion AI, the physician reviews and confirms every suggestion — the tool's job is to make sure nothing in a complex chart is missed, not to decide on its own. It complements ambient scribes: they capture what was said; Regard reads what the data already shows.
💡Key Concept
Reading the chart, not the room: Regard analyzes existing EHR data to surface diagnoses and draft notes. It targets what a busy team might overlook in a dense chart — the diligent second reviewer.
⚠️Warning
Suggestions require confirmation. Regard proposes diagnoses and documentation; the physician reviews and confirms each one. It improves capture and safety, but it does not diagnose autonomously, and documentation must reflect genuine clinical judgment.
✅Tip
Visit Regard: withregard.com — enterprise deployment inside health-system EHRs.
Pricing
Regard sells enterprise agreements to health systems rather than published pricing; scope depends on facilities and specialties, with value often framed around diagnosis capture and documentation.
- Diagnosis-gap surfacing
- Assessment-and-plan drafting
- EHR-native workflow
- System-wide deployment
- Documentation-integrity support
- Analytics and reporting
Core Features
Diagnosis-Gap Surfacing
Reads chart data to flag diagnoses that are supported by the record but not documented, improving both safety and capture.
Assessment-and-Plan Drafting
Drafts the assessment-and-plan portion of notes from the data, which the physician reviews and finalizes.
Documentation-Integrity Support
Helps ensure real conditions are captured and coded, affecting quality measurement and reimbursement.
EHR-Native Workflow
Operates inside the electronic health record on existing data, without requiring the clinician to change how they interview patients.
Strengths
- Dual payoff — patient safety and documentation integrity together
- Works from existing data — no conversation capture required
- Complements ambient scribes — reads data, not the room
- Real deployments and impact — Banner, Sentara, millions of diagnoses surfaced
- Reduces missed diagnoses — a diligent second reviewer of the chart
Limitations and Considerations
- Suggestions need confirmation — the physician reviews and decides
- Data-quality dependence — value relies on complete, accurate charts
- Documentation must reflect judgment — capture cannot outrun clinical reality
- Integration effort — embedding into the EHR is substantial
- Not autonomous — it surfaces and drafts; it does not diagnose alone
Best Use Cases
| Use Case | Why Regard Fits | Caveat |
|---|---|---|
| Catching missed diagnoses | Reads the chart for overlooked conditions | Physician confirms each one |
| Documentation integrity | Ensures real conditions are captured | Must reflect genuine judgment |
| Complementing scribes | Reads data while scribes capture speech | Two layers to integrate |
| Quality and revenue programs | Improves capture and coding accuracy | Depends on data quality |
Key Takeaways
- Regard reads a patient's existing EHR data to surface missed or under-documented diagnoses, draft notes, and automate documentation
- It delivers a dual payoff: patient safety (overlooked diagnoses) and documentation integrity (uncaptured conditions)
- It reports deployments at Banner, Sentara, and others, surfacing millions of diagnoses with real revenue impact
- Every suggestion is reviewed and confirmed by the physician; it does not diagnose autonomously
- It complements ambient scribes — they capture what was said, Regard reads what the data already shows
