Improving diagnosis recording for better patient care: case study in heart failure using natural language processing
The ability to record diagnosis in a detailed, accurate way is essential for both clinical care and research about healthcare. But current electronic health record (EHR) systems store much of this information in free text.
Free-text EHRs can be analysed using natural language processing (NLP) to extract information for research purposes. But for the purpose of supporting safe clinical decision making, features of patients’ diagnoses need to be organised in an agreed way, according to an information model.
Standardised diagnosis information models can help to ensure consistent care throughout the NHS and reduce the need for duplicate data collection for audit or research.
This project aims to generate an evidence base for generalisable improvements in diagnosis recording in the NHS by applying natural language processing methods, with a focus on heart failure as a clinical example.
Patient journeys will be constructed from initial symptoms to detailed diagnosis and evaluated on how well proposed information models accommodate information that currently exists only in text.
Then, the study will develop information models and recommendations for systems. The proposed models will be evaluated in pilot implementations such as a local heart failure clinic.
The overall learning from the process will be disseminated through academic publications and via clinical academic networks, aiming to engage specialist societies to develop models for recording diagnoses in their domains.