Scope the decision
Choose one repeated document bottleneck, one primary input, and one accountable output.
Evidence-linked coding QA for RCM audit teams
KamiBridge turns medical documents into source-linked coding and documentation checks. Reviewers see the exact page and sentence, stay in control of every decision, and evaluate the workflow without sending patient data.
Use the free coding audit evidence checklist · No email requiredReview case
Synthetic chart · 4 pages
Assessment and plan reviewed with patient.
Type 2 diabetes mellitus without complications.
Continue current medication and monitoring.
No acute symptoms reported at this visit.
Primary diagnosis
Type 2 diabetes mellitus
Page 2 · Assessment
Suggested ICD-10-CM
E11.9
Evidence found
Documentation check
Ready for human review
3 of 3 rules passed
Designed around operational review
Choose one repeated document bottleneck, one primary input, and one accountable output.
Define the safe test set, acceptance thresholds, and expensive failure modes before implementation.
Deliver source-linked results, review controls, exceptions, and a clear production recommendation.
Focused solutions
Our first engagement focuses on one measurable bottleneck: finding and checking the source evidence behind coding and documentation exceptions.
Link candidate ICD-10-CM concepts and documentation exceptions to the exact source language so a qualified reviewer can inspect the basis.
Support the same review queue by identifying missing signatures, dates, orders, pages, or required fields.
Return approved results as review-ready JSON, CSV, or a defined FHIR resource while preserving source coordinates.
Synthetic workflow demo
Walk through one chart-to-code example from ingestion to human approval. The example is synthetic and shows the operating boundaries we propose for an evaluation.
Interactive guided sample
Synthetic data only · self-paced or auto-play
Choose Play demo or select a step to explore the sample.
Assessment and plan reviewed with patient.
Type 2 diabetes mellitus without complications.
Continue current medication and monitoring.
No acute symptoms reported at this visit.
Step 1 · Ingest
Pages are normalized and assigned stable source coordinates before any suggestion is generated.
Visible result
Input: synthetic PDF · 4 pages
Delivery architecture
The model is only one part of the system. A dependable workflow also needs document normalization, explicit rules, evidence mapping, exception states, review controls, and usable export.
PDF, fax, image, or structured source
Pages, fields, sections, and metadata
Models plus explicit workflow rules
Evidence, confidence, and exceptions
JSON, CSV, FHIR, or customer environment
Agree what “correct” means and which errors carry the highest operational cost.
Compare field quality, coverage, exception rate, and human review time.
Document hosting, access, retention, deletion, and approval responsibility before production.
Delivery experience
Our team has delivered medical-document, electronic-signature, and clinical workflow software for dozens of hospital customers in China since 2016.
For international exploration, we use synthetic or appropriately de-identified examples and do not publish customer names, results, or patient information without permission. Detailed references can be discussed during a qualified evaluation.
10-day fixed-scope pilot
Design-partner pricing
From $1,500
Days 1–2
One workflow, safe sample set, success metrics and failure costs.
Days 3–6
A working review flow for one input and one agreed output.
Days 7–9
Field-level results, coverage, exceptions and human review time.
Day 10
A live walkthrough, findings report and clear production recommendation.
Engineering partnership
We combine medical-document workflow knowledge with rapid software delivery. The engagement stays narrow enough to evaluate, but the implementation is designed with production constraints in view.
We start from reviewers, queues, exceptions, and downstream decisions—not a generic AI feature list.
Important outputs keep a visible connection to source text, page context, and reviewer action.
Pilot outputs include failure findings, deployment considerations, and a clear next-step recommendation.
Data approach
The first conversation and demonstration require no patient data. If a real-data pilot is justified, data flow, retention, hosting and responsibilities are defined before access.
Start with synthetic or appropriately de-identified samples.
Run the workflow in a customer-controlled environment when required.
Important outputs remain reviewable and approval-gated.
Agree what is stored, for how long, and how it is deleted.
Questions before a pilot
No. Discovery and the first demonstration use synthetic data. A pilot can also begin with appropriately de-identified samples or run in your environment.
No. The pilot supports qualified reviewers with evidence-linked suggestions and checks. It does not replace professional judgment or automatically submit claims.
One workflow, one principal input format, one agreed output, a review interface, evaluation results, and a production recommendation.
Integration is evaluated during discovery. CSV, JSON and a defined FHIR resource are common pilot outputs; production integrations are scoped separately.
Workflow review
Tell us what happens today. We will respond with the next useful question and, if there is a fit, a proposed evaluation plan.
Prefer email? doudou@kamibridge.com
No patient data required
No platform replacement pitch
A focused answer, not a generic AI deck