CodePerfect Auditor

Inspiration

Healthcare organizations lose significant revenue due to medical coding errors, claim denials, and compliance issues. Manual auditing is time-consuming, reactive, and unable to handle the growing complexity of clinical documentation. We wanted to build an intelligent AI system that could assist medical coders, improve coding accuracy, and make healthcare audits faster, smarter, and more transparent.

What it does

CodePerfect Auditor is an AI-powered medical coding audit platform that reads unstructured clinical notes, predicts ICD-10 and CPT codes, compares them with human-entered codes, detects discrepancies, and generates explainable audit reports. It helps hospitals reduce coding errors, improve compliance, prevent revenue leakage, and accelerate reimbursement workflows.

How we built it

We built the system using a multi-agent AI architecture.

BioBERT / ClinicalBERT were used for understanding medical text. spaCy handled medical NLP tasks like entity recognition and negation detection. PyTorch powered ICD and CPT code prediction models. XGBoost was used for confidence scoring and revenue risk detection. SHAP and LIME provided explainable AI outputs for compliance transparency. The workflow was designed as independent AI agents collaborating together for reading, coding, auditing, and evidence generation. Challenges we ran into

One of the biggest challenges was handling highly unstructured clinical documentation and understanding medical context correctly. Another challenge was ensuring explainability because healthcare systems require transparent AI decisions for compliance and auditing. We also faced difficulties in mapping complex ICD hierarchies and reducing false-positive discrepancy detection.

Accomplishments that we're proud of

We successfully designed a scalable multi-agent AI auditing workflow capable of automating medical coding validation. We integrated explainable AI into the pipeline, making the system audit-ready and trustworthy. We are proud that the solution not only improves coding accuracy but also supports human coders instead of replacing them.

What we learned

Through this project, we learned how AI can be applied in real-world healthcare workflows where accuracy, compliance, and explainability are critical. We gained hands-on experience in medical NLP, transformer models, multi-agent orchestration, and healthcare auditing systems. We also understood the importance of human-in-the-loop AI governance.

What's next for CodePerfect Auditor

We plan to integrate real-time Electronic Health Record (EHR) connectivity, improve reinforcement learning from coder feedback, and support multi-language clinical documentation. In the future, we aim to deploy CodePerfect Auditor as a cloud-based SaaS platform for hospitals and healthcare providers with advanced analytics, predictive compliance monitoring, and enterprise-scale auditing support.

Built With

• audit-logging
• clinicalbert-natural-language-processing:-spacy-machine-learning-algorithms:-xgboost-explainable-ai-tools:-shap
• compliance
• ehr/fhir-integration-apis-version-control-&-collaboration:-git
• github-monitoring-&-governance:-drift-detection
• hugging-face-transformers-medical-nlp-models:-biobert
• human-in-the-loop-validation-other-technologies:-rag-(retrieval-augmented-generation)
• icd-10-ontology-mapping
• lime-data-processing:-pandas
• multi-agent-ai-architecture
• numpy-backend-development:-fastapi-/-flask-frontend-/-dashboard:-streamlit-or-react-database:-postgresql-/-mongodb-cloud-&-deployment:-aws-/-google-cloud-platform-apis-&-integration:-rest-apis
• programming-languages:-python
• sql-ai/ml-frameworks:-pytorch