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Enterprise Layer Intelligence
Categories/Healthcare & Life Sciences/Regard
Regard

Regard

Founded 2017

Review electronic health records to recommend diagnoses and generate clinical documentation

Pricing model

Demo

You can use Regard to review electronic health records and identify missed patient diagnoses at the point of care. It scans complete patient charts, integrates medical history with current lab findings, and suggests accurate diagnostic codes directly within clinician workflows. The tool generates clinical notes with documented evidence to prevent billing denials and queries from coding teams. Additionally, it surfaces risk adjustment gaps through hierarchical condition category capture and flags patients needing timely medical interventions, helping hospital systems improve care quality and claim accuracy.

What Regard does

Generate structured clinical progress notesSurface supported diagnostic codes from historical patient dataIdentify Hierarchical Condition Categories across patient cohortsTrack denial prevention metrics and clinical query reductionsPrioritize patient census lists based on clinical risk indicatorsMap external EHR data points to standardized clinical conceptsScans complete electronic health record charts to surface unrecorded conditionsRecommends clinically validated diagnoses backed by chart evidenceDrafts complete clinical notes directly at the point of careFlags missed diagnoses to prevent mid-revenue cycle insurance claim denialsDetects Hierarchical Condition Category risk adjustment gapsPrioritizes patient lists based on screening needs and intervention urgencyProvides reporting dashboards to track adoption and clinical impact

Pricing breakdown

Pricing model: Book a demo

Frequently asked

EHR, Epic (App Orchard), Oracle Health / Cerner, Microsoft Nuance Dragon, SMART on FHIR, Athenahealth (unverified), Meditech (unverified)

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Regard, clinical AI, EHR integration, clinical documentation, diagnosis recommendation, medical coding, revenue cycle, HCC capture, care gaps, clinical notes, hospital AI, inpatient care, ambulatory care, electronic health record, healthcare analytics