Swiss Pharmaceutical Company

Developed a machine learning model that predicts the complexity of coding/tagging a stay given the clinical documents to improve billing correctness

Challenge

• Correct tagging of patient stay needed for accurate insurance processing
• Incorrectly tagged documents can be under-reimbursed or rejected
• Some documents are very complex to tag/classify, and others are routine
• Need to know if a document is somehow “off” (i.e. anomalous) from a content perspective

Solution

• Created data pipelines for clean aggregation of data
• Built application to automatically classify the “complexity” of documents, so they would be handled by the correct expert
• Same application created knowledge to enable working on anomaly detection

Business impact

• NLP model accuracy comparable with human expert coders
• Faster tagging for billing purposes
• More efficient workload distribution of coding tasks based on complexity and code skill
• Better decision support and minimised manual errors