Healthcare Usage scenario

Detecting medical billing anomalies before submission

Detecting medical billing anomalies before submission with NLP and machine learning, to reduce rejections.

Describe your project See the solution

Project overview

This scenario illustrates what we build; it is not a client reference.

This scenario shows how a network of clinics can catch billing errors before claims are sent to payers. An NLP model reads medical reports and suggests procedure codes. A machine learning model flags abnormal combinations that are likely to be rejected.

Client challenges & requirements

A network of clinics where a large share of bills is rejected by health insurers. These rejections often come from coding errors or inconsistencies.

They weigh on cash flow and force staff into long manual corrections.

Main objectives

Analyze medical reports to suggest relevant procedure codes (CCAM)

Detect anomalies and inconsistencies in billing files upstream

Reduce the claim rejection rate

Accelerate billing cycle and improve cash flow

Project solution

The solution fits into the existing billing workflow. An NLP model based on spaCy reads medical reports and extracts the key information.
The system suggests procedure codes, and a machine learning model spots abnormal combinations. The tool raises a real-time alert before each risky submission.

Indicators to track

First-submission rejection rate
Average payment delay
Time the administrative team spends on corrections
Most frequent rejection reasons

Tech stack

Serveur/Backend: Flask (Python) Gunicorn
Data Science: Pandas Scikit-learn
NLP: spaCy NLTK
Database: PostgreSQL
Infrastructure: Docker Kubernetes

Frequently asked questions

The NLP model is trained on medical reports annotated by experienced coders. It picks out procedures described in natural language and maps them to the matching CCAM codes, taking the clinical context into account.

Yes. Every rejected bill goes back into the training data, which refines the detection rules and avoids repeating the same errors.

The tool integrates through a REST API that can be connected to most billing software on the market. Alerts and suggestions appear directly in your existing workflow, without changing your processes.