Clinical coding automation with the Royal Free and Kettering General

Data scientists in the AI Lab Skunkworks team and the NHS Transformation Directorate Analytics unit are supporting this project to investigate whether the process of clinical coding (applying standard code words to health records) can be supported by artificial intelligence.

When you visit your doctor or attend hospital a lot of information is collected about you on computers, including your symptoms, tests, investigations, diagnosis, and treatments. Across the NHS, this represents a huge amount of information that could be used to help us learn how to tailor treatments more accurately for individual patients and to offer them better and safer healthcare. The challenge is that most of the information held within these records is in written form that is difficult to use.

The process of reading health records and applying standardised codes based on particular words, conditions or treatments, is called "clinical coding". The process of clinical coding is time-consuming, expensive and carries the risk of mistakes.

We are providing data science capability to a joint project with the Royal Free Hospital and Kettering General Hospital. This project aims to understand which open source models are best to support clinical coders by automating part of the clinical coding process using natural language processing (NLP) to teach computers to ‘read’ electronic health records. The aim is for the technology to summarise and suggest the standardised codes that will then be checked by clinical coders.

NLP is a branch of AI used to interpret unstructured text data, such as free-text notes.

The project was abandoned due to data access challenges.