60% reduction in documentation time. 3 abnormal labs caught daily. Referral letters drafted in under 2 minutes.
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, 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 position, require months of training, and leave when they burn out or move on. Charts done without scribes are done in fragments between patients, meaning the clinician remembers less as time passes. Chart notes get thinner and less accurate as the day goes on, so the last patient of the day gets a two-line memo from a clinician running on empty. Every incomplete chart is a coding failure, a compliance exposure, and a patient-continuity risk.
Abnormal lab results are the sharpest edge of the problem. A physician orders labs, results come back three days later while the physician is buried in a full day of appointments, and someone has to notice the elevated A1c, the flagged creatinine, the borderline-high PSA — and route it correctly. When that step is manual, results fall through the cracks. Missed results are one of the most common sources of malpractice liability and delayed diagnoses.
Claire is your AI Clinical Documentation Specialist. She pre-charts patients every morning before you walk in: pulling the day's scheduled patients, medication lists, allergies, recent visit history, and chronic-disease context into a ready-to-review note. During the encounter she structures the ambient transcript into a compliant SOAP note, suggests ICD-10 and CPT codes, flags any abnormal lab against reference ranges, and drafts referral letters. After the visit, she flags abnormal results for the clinician's review before immediate action, all HIPAA-compliant with SOC 2 controls.
Each step is automated. Claire only escalates when clinical judgment is required.
Claire pulls each patient's medication list, allergies, recent lab results, last-visit notes, and active problems into a pre-visit summary highlighting what the clinician needs to review: pending labs, medication changes since last visit, overdue screenings, and chronic-disease monitor flags.
Claire reviews all incoming lab results against reference ranges and clinical-decision thresholds. Abnormal values are flagged with the specific result, reference range, change from prior values, and clinical context, then routed to the ordering provider for review.
Claire captures the clinician-patient dialogue with speaker diarization, structures the encounter into SOAP format with HPI, ROS, exam findings, assessment, and plan, and suggests ICD-10 and CPT codes based on documented language and visit complexity.
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 complexity does not support the suggested E/M level, or where an ICD-10 code lacks required specificity, so the clinician can add supporting detail before sign-off.
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 their status for completion.
Clear boundaries. Claire works autonomously within defined limits and escalates everything else.
Claire connects to the platforms you already use. No new software to learn.
Claire is deployed gradually, with measurable checkpoints at every stage.
Shadow/monitoring mode first, then a gradual rollout.
Pilot begins with two to three willing clinicians. Claire runs alongside their existing documentation for two weeks, with every note reviewed and signed by the clinician before it enters the record. Accuracy and lab-flag sensitivity are validated before rollout.
These AI employees share data and coordinate with Claire to cover your full healthcare operation.
Start with a 90-minute discovery session. We evaluate whether Claire is the right fit for your workflows and show you exactly what changes.