Project Overview

Background

Data about people’s health stored in electronic health records (EHRs) can play an important role in improving the quality of patient care. Much of the information in EHRs is recorded in ordinary language without any restriction on format ('free text'), as this is the natural way in which people communicate. However, if this information were stored in a standardised, structured format, computers will also be able to process the information to help clinicians find and interpret information for better and safer decision making. This would enable EHR systems such as Epic, the system in place at UCLH since April 2019, to support clinical decision making. For instance, the system may be able to ensure that a patient is not prescribed medicine that would give them an allergic reaction.

The challenge

Free text may contain words and abbreviations which may be interpreted in more than one way, such as 'HR', which can mean 'Hour' or 'Heart Rate'. Free text may also contain negations; for example, a diagnosis may be mentioned in the text but the rest of the sentence might say that it was ruled out. Although computers can be used to interpret free text, they cannot always get it right, so clinicians will always have to check the results to ensure patient safety. Expressing information in a structured way can avoid this problem, but has a big disadvantage - it can be time-consuming for clinicians to enter the information. This can mean that information is incomplete, or clinicians are so busy on the computer that they do not have time to listen to their patients.

Meeting the need

The aim of MiADE is to develop a system to support automatic conversion of the clinician’s free text into a structured format. The clinician can check the structured data immediately, before making it a formal part of the patient’s record. The system will record a patient’s diagnoses, medications and allergies in a structured way, using NHS-endorsed clinical data standards (e.g. FIHR and SNOMED CT). It will use a technique called Natural Language Processing (NLP). NLP has been used by research teams to extract information from existing EHRs but has rarely been used to improve the way information is entered in the first place. Our NLP system will continuously learn and improve as more text is analysed and checked by clinicians.

We will first test the system in University College London Hospitals, where a new EHR system called Epic is in place. We will study how effective it is, and how clinicians and patients find it when it is used in consultations. Based on feedback, we will make improvements and install it for testing at a second site (Great Ormond Street Hospital). Our aim is for the system to be eventually rolled out to more hospitals and doctors’ surgeries across the NHS.