AI-assisted accuracy
Ranked medical code suggestions with confidence scores and pilot model review for complex encounters.
- Multi-model analysis pipeline
- Confidence-graded suggestions
- Manual code override support
MediCoder turns clinical documentation into reviewed, standardized data — ICD-10-CM / ICD-11 / ICD-10-PCS codes, medications mapped to the Japanese drug master (YJ), structured facts such as switch reasons and disease scores, and cleansing dictionaries for hospital terms — every result tied to source evidence and an audit trail.
Acute on chronic systolic CHF
Acute kidney injury, unspec.
Type 2 DM w/ hyperglycemia
Pneumonia, organism unspec.
ICD-10-CM, ICD-11, ICD-10-PCS, ICD-9-CM and the JP drug master (YJ)
Evidence, model version, and reviewer on every decision
Pilot report, suggestions, and final codes side by side
Coder, supervisor, and admin roles with PAT support
MediCoder is designed around how enterprise coding departments operate — from document intake through final code assignment, with supervision and audit built in.
Ranked medical code suggestions with confidence scores and pilot model review for complex encounters.
Every code ties back to a span in the source document, with reasoning coders can verify in seconds.
Document libraries, bulk workflows, and governance controls designed for hospital-scale teams.
Every capability supports accuracy, throughput, and traceability — the priorities of enterprise revenue cycle and CDI teams.
Pilot report, AI suggestions, and final codes in one view — no tab switching during review.
Folders, tags, extraction status, and re-attach workflows for high-volume chart processing.
Map hospital drug and disease names to the JP masters — exact, modifier-stripped, then AI — with bulk review and dictionary export.
Track analysis jobs, processing status, and case progress across your coding queue.
Append-only audit history with role guards and reviewer attribution on every action.
Role-based permissions, token-based API access, and configurable confidence policies.
A structured four-step process that keeps coders, supervisors, and compliance aligned on every case.
Upload clinical notes, operative reports, and labs. Text extraction runs automatically.
Bundle documents into a case with encounter context, payer, and coding scope.
Run AI analysis. Monitor job status and review ranked suggestions as they arrive.
Accept, reject, or flag codes. Add manual entries. Lock the case with full audit trail.
Evidence spans in the source document drive every suggestion. Coders see the reasoning, confidence, and source quote — not a black box.
Every accepted code links back to its evidence span, model version, configuration snapshot, and reviewer. Enterprise teams get the audit depth they need for internal QA and external review.
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