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Regard AI

AI diagnostic assistant for inpatient physicians

4.3(198 reviews)
paidEnterprise hospital pricing. Per-provider or per-bed licensing. Contact for quote.View full pricing →

Visit Regard AI

https://regard.com

About Regard AI

Regard is an AI diagnostic assistant for hospitalists and inpatient medicine physicians. It analyzes patient data in the EHR (labs, vitals, notes, imaging reports) and surfaces potential diagnoses the treating team may have missed — acting as a comprehensive, always-on second opinion that improves diagnostic accuracy and documentation completeness.

Key Features

Real-time diagnostic suggestion from EHR data
Problem list completion and ICD-10 code suggestions
Lab and vital trend analysis
Risk stratification alerts
EHR integration (Epic, Cerner)
HIPAA-compliant cloud infrastructure
Hospitalist-specific clinical workflow
Quality metric improvement tracking

Regard AI Pros & Cons

Pros

  • +Catches diagnoses physicians miss due to cognitive load
  • +Improves diagnostic accuracy and documentation completeness
  • +Helps avoid underdiagnosis that affects quality scores and revenue
  • +Integrates into EHR without disrupting physician workflow
  • +Peer-reviewed evidence of diagnostic and revenue improvement

⚠️ Cons

  • Hospital-level implementation — not individual-physician pricing
  • Suggestions require physician review — no autonomous diagnosis
  • May surface too many suggestions in complex patients (alert fatigue risk)
  • EHR integration complexity varies by institution

Who Is Regard AI Best For?

👤Hospitalist groups and inpatient medicine departments
👤Hospital quality teams improving diagnosis-related group (DRG) accuracy
👤Health systems reducing diagnostic errors
👤CMOs seeking to improve clinical documentation and coding revenue

Regard AI Use Cases

💡Preventing Missed Diagnoses

A hospitalist caring for a complex patient with 12 active problems uses Regard, which surfaces 'possible anemia of chronic kidney disease' based on trending lab values the physician hadn't connected. The diagnosis is confirmed, added to the problem list, and treatment is adjusted — improving patient care and ensuring accurate DRG coding.

💡Hospital Revenue Integrity

A hospital CFO deploys Regard to improve clinical documentation completeness across the hospitalist group. Regard surfaces undocumented diagnoses that, when added to the medical record, upgrade DRG codes. The hospital recovers $3.2M in legitimate reimbursement within 12 months without changing care — just documentation accuracy.

💡Quality Metric Improvement

A hospital quality team uses Regard to reduce hospital-acquired condition diagnoses by ensuring clinical documentation captures present-on-admission (POA) diagnoses that would otherwise be missed. Accurate POA documentation improves the hospital's quality metrics and prevents inappropriate penalties.

Tags

healthcarediagnosisclinical aihospitalistehrdiagnostic supportmedical ai
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