Using language analysis on patient experience feedback
Research type
Research Study
Full title
Listen, Learn & Improve: Using language analysis to interpret and act on written patient experience feedback for near real-time patient benefit
IRAS ID
234218
Contact name
Ruth Nicholson
Contact email
Sponsor organisation
Imperial College London
Duration of Study in the UK
1 years, 2 months, 2 days
Research summary
This project will be conducted in three key phases. The first phase will make use of natural language processing (NLP) along with machine learning (ML) to extract themes from free-text field data provided by patient comments in patient experience surveys. Although these free-text (unstructured) fields contain rich data in terms of experience, the data is often under-utilised due to the effort required to manually review the content. NLP provides a tool for translating human language into coded data so that it can be analysed using traditional and more advanced techniques. Using ML on this coded data will provide a source of data to further inform business intelligence and quality improvement based on direct patient feedback to help prevent future negative patient experience.
Given that utilisation of NLP is a relatively new technique in clinical research it is important that the accuracy of results is directly validated. The second phase will explore views of healthcare staff about the usefulness of NLP, how the data should be presented and discuss quality improvement ideas from the existing data. The third phase will be a focus group involving patients, their family and/or carers, to determine patients opinions of providing feedback using free-text. Only those that would have access to the identifiable data as standard will do so before it is anonymised
REC name
North East - Tyne & Wear South Research Ethics Committee
REC reference
17/NE/0306
Date of REC Opinion
14 Sep 2017
REC opinion
Favourable Opinion