Title: Natural language processing for real-time data capture in electronic health records to improve clinical care and operational efficiency
Funder: Department of Health and Social Care

Abstract: Background: Much of the information in electronic health records (EHRs) is in the form of unstructured text, making it difficult to retrieve for clinical decisions, and limiting its utility for clinical decision support, audit and research. Natural language processing (NLP) can be used to convert unstructured text into structured data, but the semantic nuances of human language mean that this process is prone to error, and requires clinician validation before the information can safely be used for clinical decision making. Entering structured data directly into EHRs can be onerous with conventional user interfaces, and is often done incompletely. Aim: To develop and clinically evaluate a system to support automatic conversion of clinicians text into structured data at the point of care, allowing immediate validation. Project plan: Existing open source NLP algorithms will be adapted to develop a new NLP system optimised for use at the point of care. It will convert diagnoses, medications and allergies into structured formats that conform to NHS-endorsed clinical data standards. The NLP system will be tested in a simulation environment, and then installed in University College London Hospitals (UCLH) and integrated with UCLH s Epic EHR, allowing structured data items extracted from newly-created text to be displayed for immediate validation before they are committed to the record. Qualitative and quantitative evaluations will be carried out with clinicians using the software in real consultations. The NLP system will be further improved based on user input and will be installed for testing at a second NHS site (Great Ormond Street Hospital). Timelines: By 12 months: NLP system and approvals ready for clinical evaluation. By 24 months: NLP system accepted for widespread roll-out in UCLH. Anticipated Impact and Dissemination: Open source NLP software to assist real-time structured clinical recording, and a strategy for wider, sustainable deployment across the NHS.

Lay abstract: Data stored in electronic health records (EHRs) can play an important role in improving the quality of patient care in the NHS. Much of the information in EHRs is recorded in ordinary language without any restriction on format (&ldquo;free text&rdquo;), 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 to support clinical decision making, such as ensuring that a patient is not prescribed a medicine they are allergic to. Free text may contain words and abbreviations which may be interpreted in more than one way, such as &ldquo;HR&rdquo;, which can mean &ldquo;Hour&rdquo; or &ldquo;Heart Rate&rdquo;. 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. The aim of this project 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. 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 a simulation environment, and then install it for testing at 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.

Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: GENERIC HEALTH RELEVANCE