Title: Development and testing of an intelligent system for the personalised reporting and evaluation of patient-reported data (INSPiRED).
Funder: Department of Health and Social Care

Abstract: ObjectiveTo develop an intelligent computerised system (ConcertoHealth) oriented to supporting decision-making by helping patients communicate key aspects of their health and experience of care to healthcare providers.The system will include personalised adaptive questionnaires and automated analysis of written comments relating to experience of care as well as common mental and physical health issues. It will automatically analyse information, communicate results clearly and explain what steps should be taken next.DesignThe study will consist of four complementary work packages:Work Package One Validation of computer-adaptive tests for depression, anxiety, and physical function.Computer adaptive tests (CATs) can increase the accuracy and reduce the burden of questionnaires by tailoring assessments to the individual patient: thus asking fewer but more relevant questions. Before CATs can be used, item banks' from which the CAT can withdraw' questions must be validated.I will validate item banks for mental and physical health for use in the UK using a nationally-representative sample including people with long-term conditions. The analysis will be conducted using an internationally-approved analytical procedure which brings together leading psychometric techniques including item response theory. The effect of using different psychometric models (e.g. Rasch or multidimensional item-response theory) on the length and accuracy of CATs will be evaluated.New item banks will be compared with existing legacy questionnaires. Scores from the item banks will be directly comparable with existing legacy questionnaires using the same scale.Work Package Two Develop and test machine learning algorithms to make sense of open-text reports of patient experience and outcome. Machine learning refers to a set of statistical and computational techniques where computers 'learn' to recognise patterns in data. I will use machine learning to develop algorithms to recognise patterns in open-text comments collected from a large representative sample of people in the UK, including patients with long-term conditions. Three types of algorithm will be developed to: a.) identify linguistic markers of clinical states (e.g. depression); b.) rate the emotional tone of comments (e.g. feeling isolated); and c.) identify issues which may not be brought to light using structured questionnaires alone (e.g. side-effects, safety). I will use a range of techniques including support vector machines, nave Bayesian classifiers and topic models to develop the algorithms. Supervised algorithms will be assessed using sensitivity and specificity analyses for correctly performing the tasks which they were trained to complete.Work Package Three Co-creation of guidelines with patients and clinicians to inform the development of effective feedback for patient-reported assessments.I will use participatory design to develop strategies for presenting feedback using qualitative methods. Focus groups and interviews will be conducted with 48 patients with long-term conditions and 32 healthcare professionals from primary and secondary care services. Transcripts will be analysed using iterative two-stage coding to create strategies for presenting information embedded in user experience. The acceptability and comprehensibility of these presentation strategies will be assessed using a web-based survey of 250 patients, 250 clinicians and 250 members of the public to create evidence-based guidelines. Work Package Four - Development of specialist computer system for collecting patient-reported data that includes all the tools validated in the current study.ConcertoHealth will be based on the existing computerised platform and will bring together research evidence from WP1-3 into an open-access computer-based system suitable for use across the NHS.Expected outcomesThe system will improve the patient-centeredness of care in the NHS by providing a means to highlight important issues relating to patient health and experience which can be used to improve health service delivery and organisation at the individual, local and national levels.

Lay abstract: The proposed career development Fellowship (INSPiRED) builds on a successful NIHR post-doctoral Fellowship (ATLanTiC) conducted at the Universities of Manchester and Cambridge. The goal of this award is to undertake a research programme and develop a computerised system to transform the way in which patient-reported data are collected and analysed in the NHS.Patient-reported data refers to information shared by patients about their experiences of health and care. This information makes care more patient-centred by allowing patients to communicate the issues which are most important to them. Research shows that sharing this information with healthcare professionals can bring significant benefits for patients by improving patient-doctor communication, informing decision making and increasing patient satisfaction. But there are several obstacles which must be addressed before the full potential of this information can be realised. This information can be collected using fixed-length questionnaires which can take a long time to complete and sometimes it can be difficult for doctors to understand what to do with the information they provide. Patients are sometimes asked to submit written comments, but these comments are rarely used due to a lack of resources to analyse them in detail. Intelligent computerised systems can personalise questionnaires, making them shorter, more accurate and more relevant to patients. They can also automatically arrange written patient comments to make them easier to understand.In the INSPiRED Fellowship, I will create an intelligent computerised system designed to maximise the benefits patients receive from sharing information about their health and experience. The Fellowship will consist of four complementary work packages that together aim to: 1. Allow patients to more easily share information about their health using new computer adaptive questionnaires. The burden of responding to questionnaires can be reduced using computerised systems which select the most relevant questions for each individual patient. In ATLanTiC, I have created a tool which halves the time it takes to complete a quality of life questionnaire. I will extend this system to measure common mental and physical health conditions in a standardised way across the NHS.2. Make written patient comments useable by analysing them automatically with computer-based tools which can relate written comments to patient's health and experiences of care. Written open-text comments are excellent sources of patient information because they allow patients to explain issues which are most important to them. Patient comments are routinely collected in the NHS, but they are often not used because it is difficult to read through them all. Making comments interpretable will help ensure that patients' time is not wasted and that issues relating to the quality and safety of care can be identified and resolved more effectively. In this work package, I will use machine learning (a technique where computers learn to recognise patterns in text) to automatically analyse open-text comments. I will relate these comments to important outcomes and experiences so that this information could be used to improve care and experience in the NHS. 3. Help clinicians make better use of patient-reported data by making sure the information is clearly presented and easy to understand. It is important that both patients and health service providers understand why patient-reported data are being collected, what the results mean and how this information can lead to improvements in care and experience. Research shows that quick and effective communication of scores to healthcare professionals can improve patient care significantly. I will work together with patients and healthcare professionals to find out the best way to communicate this information, so it is clearly understood.I will conduct interviews and surveys with patients living with mental and physical long-term conditions as well as healthcare professionals working in primary and secondary care. This information will be used to develop evidence-based guidelines for communicating patient-reported data in the NHS.4. Advance patient-reported data collection in the NHS by bringing the research conducted in WP1-3 together into an intelligent system which allows information to be analysed and presented automatically. I will develop a computerised system to collect and analyse information on patient experience as well as mental and physical health using personalised adaptive questionnaires and written comments. The system will help patients across the NHS securely share information about their health and experience in a way that is most likely to lead to improvements in care.The INSPiRED award will allow me to undertake further specialist training and strengthen my research collaborations to provide me with the skills to become a leader in world-class healthcare research focussed on improving patient care and experience in the NHS.

Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE