Clinical Documentation Intelligence
A human-supervised AI review system for documentation quality, time-based billing integrity, coding support, and explainable compliance findings.
01 / The problem
What needs to change
Clinical notes can contain incomplete justification, inconsistent time details, or weak support for billed services, creating avoidable review work and compliance risk.
02 / The approach
A focused path through the complexity
The system separates structured extraction from compliance reasoning, combines model output with deterministic time and documentation checks, and requires traceable evidence for every finding.
03 / Key capabilities
What the system is designed to do
- API and spreadsheet-based assessment ingestion
- Two-stage extraction and compliance reasoning pipeline
- Deterministic time, code-fit, and documentation checks
- Confidence thresholds, citations, and human review actions
- Reanalysis, audit logs, metrics, search, and administration
04 / Architecture
Technology and system shape
- Queue-oriented Azure Functions processing pipeline
- Tenant-partitioned document storage model
- Versioned prompts, schemas, and configurable rule sets
- Static review dashboard with live API and local development modes
- Role-aware administration and callback notifications
05 / Results
What the repository demonstrates
- The prototype implements ingestion, analysis, evidence, review, reanalysis, administration, and operational metrics as one coherent workflow.
- Low-confidence or weakly supported output is surfaced for human attention instead of being treated as an automated decision.
06 / Lessons
What the work teaches
- Deterministic rules and model reasoning are complementary when each owns the part it can explain best.
- Confidence is useful only when it changes workflow behavior and directs human review.
07 / Screenshots
Approved visuals
No public screenshots yet
Add only approved screenshots to the project record.