Pre-Charted Notes, Flagged Labs, and Referral Letters Ready Before Your First Patient
60% reduction in documentation time, 3 abnormal labs caught daily, referral letters drafted in under 2 minutes
The problem
Clinical documentation is the single largest contributor to physician burnout. The average primary care physician spends two hours on EHR documentation for every one hour of direct patient contact. After the last appointment ends, many clinicians face another 90 minutes of "pajama time" finishing charts at home. This is not a minor inconvenience -- it is a systemic crisis that drives attrition, reduces appointment availability, and directly harms patient outcomes.
The documentation burden extends beyond physicians. Medical scribes cost $30,000-$40,000 per year per provider, require months of training, and introduce their own turnover challenges. Practices without scribes rely on clinicians to navigate complex EHR templates, toggling between structured fields, free-text notes, and dropdown menus mid-conversation. The cognitive load fractures the clinician-patient relationship and increases the risk of documentation errors that cascade into billing denials and compliance exposure.
SOAP notes -- Subjective, Objective, Assessment, and Plan -- remain the gold standard for encounter documentation, yet producing them accurately under time pressure is where most documentation failures originate. Incomplete HPI narratives, missing review-of-systems elements, and vague assessment language create downstream problems: ICD-10 codes that do not support the E/M level billed, referral letters that lack clinical justification, and care gaps that slip through because the chart does not tell the full story.
Claire is your AI Clinical Documentation Specialist. She pre-charts every patient before they walk through the door -- pulling medication lists, recent lab results, visit history, and care gaps into a structured summary for the provider. During the encounter, she listens to the natural conversation, structures it into compliant SOAP format, suggests ICD-10 codes and CPT E/M levels, and flags any documentation gaps. After the visit, she drafts referral letters and catches abnormal lab results that need immediate attention. All processing is HIPAA-compliant with BAA-covered infrastructure.
How it works
How Claire works, step by step
Each step is automated. Claire only escalates when human judgment is required.
Claire pulls each patient's medication list, allergy profile, recent lab results, last visit notes, and active care gaps from the EHR. She structures a pre-visit summary highlighting what the provider needs to review: pending labs, medication changes since last visit, overdue screenings, and chronic disease metrics (last HbA1c, last BP, BMI trend)
Claire reviews all incoming lab results against reference ranges and clinical significance thresholds. Abnormal values are flagged with the specific result, reference range, change from prior value, and clinical context. Critical results (HbA1c > 9%, eGFR < 30, TSH < 0.1) trigger immediate provider notification
Claire captures clinician-patient dialogue with speaker diarization, structures the encounter into SOAP format with HPI, ROS, exam findings, assessment with ICD-10 codes, and plan with CPT E/M level suggestion. Notes are staged in the EHR for clinician review and digital signature
Claire drafts the referral letter with patient demographics, relevant clinical history, reason for referral, ICD-10 codes, current medications, recent labs, and imaging results -- formatted for the receiving specialist and ready for provider signature
Claire flags encounters where the documented language does not support the suggested E/M level or ICD-10 specificity -- for example, documenting "back pain" when M54.5 requires laterality specification, or documenting a 99214 when the HPI and exam support a 99215
Claire sends a documentation summary to the clinical team: patients pre-charted, notes completed, abnormal labs flagged, referral letters drafted, and documentation gaps identified. Unsigned notes are highlighted with time-since-completion for compliance tracking
What Claire handles vs. what stays with you
Clear boundaries. Claire works autonomously within defined limits and escalates everything else.
- ✓ Claire pulls each patient's medication list, allergy profile, recent lab resu...
- ✓ Claire reviews all incoming lab results against reference ranges and clinical...
- ✓ Claire captures clinician-patient dialogue with speaker diarization, structur...
- ✓ Claire drafts the referral letter with patient demographics, relevant clinica...
- ■ Clinicians must review and sign every note before it becomes part of the medical record -- Claire never auto-finalizes documentation
- ■ All clinical assessments, diagnoses, and treatment decisions remain exclusively with the licensed provider
- ■ Claire does not make coding recommendations that override clinician judgment -- she suggests codes based on documented language only
- ■ Patient consent for ambient listening is obtained by the practice before activation, in compliance with state and federal recording laws
- ■ Any encounter flagged with documentation-to-code mismatches is routed to the clinician, never auto-resolved
Integrations
Works inside your existing tools
Claire connects to the platforms you already use. No new software to learn.
Implementation
From zero to Claire
Claire is deployed gradually with measurable checkpoints at every stage.
- ✓ EHR API credentials and sandbox environment access (FHIR R4 preferred)
- ✓ Practice-specific documentation templates, macros, and note preferences by provider
- ✓ Specialty-specific terminology libraries (primary care, cardiology, endocrinology, etc.)
- ✓ Sample de-identified encounter notes for model calibration (minimum 200 per specialty)
- ✓ BAA execution with all data processors in the documentation pipeline
Pilot begins with a single provider and specialty, running Claire in shadow mode for two weeks where she generates notes alongside the clinician's manual documentation for accuracy comparison. After validation achieves 95%+ concordance, Claire transitions to draft mode where the clinician reviews and signs Claire-generated notes.
Your AI team
Works alongside Claire
These AI employees share data and coordinate with Claire to cover your full operation.
Deploy Claire for your healthcare operations
Start with a 90-minute discovery session. We will assess whether Claire is the right fit for your workflows and show you exactly what changes.