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Regard maps EHR data to clinical concepts, recommends diagnoses with supporting evidence, and powers documentation, revenue, HCC, and screening workflows.
Regard maps EHR data to clinical concepts, recommends diagnoses with supporting evidence, and powers documentation, revenue, HCC, and screening workflows.
Regard is designed for hospitals that need clinicians and revenue teams to find meaningful evidence across a complex electronic health record. The platform ingests and maps EHR data to clinical concepts, applies proprietary clinically validated algorithms, recommends diagnoses with supporting evidence, and feeds a “diagnosis-complete” picture into care and administrative workflows. The vendor names four primary applications: clinical notes, mid-revenue-cycle work, HCC capture, and screening. It can generate a note with diagnoses and clinical context, flag missed diagnoses before a query or denial, locate evidence supporting hierarchical condition categories, and identify patients eligible for procedures, consults, or interventions.
The homepage publishes substantial vendor-reported adoption and outcome figures: 12,993,284 recommended diagnoses accepted by clinicians, more than $50 million in revenue earned for one Arizona health system, $9.3 million in denials prevented for a North Carolina health system, and a 20% reduction in queries for a Pennsylvania regional system. A Sentara Health case study reports a 17% increase in CC/MCC capture and 4x ROI per user. These numbers are useful evaluation hypotheses, but they are not universal guarantees; each organization must validate definitions, attribution, patient mix, baseline performance, and implementation period.
Regard does not publish a standard price on the fetched homepage, and the conventional pricing route returned 404. Deployment is therefore recorded as contact-vendor enterprise pricing. Buyers should request software fees, implementation, interface development, training, support, annual increases, minimum term, and whether charges depend on beds, facilities, clinicians, encounters, or recovered revenue. A health system should compare the workflow with specialized clinical documentation products such as Abridge, Freed AI Medical Scribe, Heidi Health, and broader automation vendors such as Notable Health.
Regard’s differentiator is diagnosis intelligence across the EHR, not just ambient transcription. Mapping millions of data points and presenting evidence at the point of care can expose information hidden across notes, labs, medications, and prior encounters. One layer supporting notes, documentation integrity, risk adjustment, and screening may create more value than a single-purpose scribe. Impact analytics can help clinical and finance leaders monitor adoption and opportunity.
The risks are high because the output can influence care, documentation, coding, reimbursement, and patient prioritization. A false positive can encourage unsupported documentation; a false negative can preserve a blind spot. Published outcome claims come from selected deployments. Algorithms may behave differently by specialty, population, facility, and EHR configuration. Integration, identity, latency, alert burden, clinician trust, auditability, evidence provenance, downtime behavior, and model-change governance all matter. No MCP support was verified, and an ordinary EHR integration should not be described as MCP.
Begin with silent retrospective validation before placing recommendations in a live clinician workflow. Sample at least 1,000 encounters across medicine, surgery, ICU, emergency care, and relevant demographic groups. Have qualified reviewers measure sensitivity, precision, unsupported-diagnosis rate, evidence accuracy, and performance by subgroup and specialty. Then run a limited prospective pilot with explicit clinician acceptance and rejection controls. Track note-completion time, query rate, denial rate, CC/MCC and HCC changes, alert dismissals, clinician satisfaction, patient-safety events, and net financial impact after fees and reviewer labor.
Security and governance review should cover HIPAA business-associate terms, least-privilege EHR access, encryption, audit logs, retention, model training, subprocessors, disaster recovery, downtime, incident notification, and deletion. Establish who monitors drift, investigates disputed recommendations, and approves model changes. Regard is best suited to health systems with enough EHR complexity and governance capacity to validate it carefully; it is not a consumer diagnosis tool and should never replace clinician judgment. For adjacent document workflows, compare Azure AI Document Intelligence, and use AI agent governance guidance when defining clinical oversight.
Regard is an enterprise AI clinical insights and documentation platform for hospitals and health systems. It is strongest for full-chart review, proactive documentation, diagnosis recommendations, and revenue integrity workflows, but buyers should expect a sales-led procurement process with custom pricing and direct security review.
Generates near-complete draft clinical notes, including H&P and progress-note style documentation, using chart data before or during clinician review.
Use Case:
A hospitalist opens a patient chart and finds a pre-generated documentation draft that can be reviewed, edited, and finalized.
Reviews patient chart data to identify potentially missed diagnoses and documentation opportunities supported by chart evidence.
Use Case:
Regard flags chart evidence that may support an additional diagnosis for clinician review and documentation.
Regard is described as fitting into existing clinical workflows and using EHR chart data, though vendor-specific integration details are not public in the visible content.
Use Case:
A hospital IT and clinical leadership team validates whether Regard can fit into its current EHR workflow and documentation process.
Supports more complete clinically appropriate diagnosis documentation, including workflows related to CC/MCC capture and DRG impact.
Use Case:
A CDI or revenue integrity team evaluates whether earlier diagnosis recommendations improve documentation completeness and downstream coding accuracy.
Contact vendor
Ready to get started with Regard?
View Pricing Options →Request a demo through Regard's website, confirm supported EHR and workflow requirements, review security and compliance documentation, validate implementation scope, and model ROI using local hospital data.
Regard works with these platforms and services:
We believe in transparent reviews. Here's what Regard doesn't handle well:
Knowledge & Documents
AI-powered clinical documentation platform associated with Microsoft for Healthcare and Nuance.
Coding Agents
Clinical decision support AI that assists healthcare professionals with differential diagnosis, medical knowledge search, and evidence-based clinical reasoning.
Healthcare AI
Ada Health delivers AI-powered symptom assessment that walks users through a structured medical interview, identifies probable conditions, and recommends next steps ranging from self-care to emergency attention.
Pricing and features change often. If anything on this Regard page is wrong or missing, tell us and include a link to the source. We review every correction.