Automated analysis of qualitative data using AI for patient safety

Do you want to learn how to use AI to analyse qualitative patient experiences (text) and improve patient safety? Do you want to pioneer innovative new approaches to understanding quality and safety in healthcare?<br /> <br /> The NIHR Yorkshire and Humber Patient Safety Research Collaborative (PSRC) is inviting applications for a full-time fully-funded PhD studentship, as part of its exciting interdisciplinary ‘Safety Intelligence’ research theme. This studentship is at the intersection between social and organisational psychology (understanding human behaviour in context) and qualitative and quantitative methodology (analysing soft textual data for insights). It will appeal to candidates from a broad range of backgrounds including health services research, data science and organisational psychology/behaviour. The successful candidate does not need to be an expert in all of these domains, but, they do need to demonstrate a willingness to engage with literature from these domains. Expertise in analysing textual data (qualitative or quantitative) is desirable, and willingness to learn how to use AI to analyse text is essential. Comprehensive training and support will be provided.

<p>The successful candidate will join a vibrant research team that is improving patient safety by reducing unintended harm in health care organisations. The research will address the challenges of (1) how to detect novel and emerging risks in unstructured patient experience feedback (eg patient narratives about their care) and (2) how to report findings and insights into healthcare institutions to improve patient safety. Traditionally this process has relied upon manual analysis of patient narratives, and the research aims to automate this process, scaling up and speeding up organisational learning. The studentship is an opportunity to: join a large and active research team; contribute to research articles with real-world impact; become an expert in using novel AI methods to analyse unstructured text; and improve patient safety.</p> <p>Potential research questions include: what is safety critical information within patient narratives? Can this information be reliably identified and extracted using automated processes? What typologies and coding frames exist for analysing these narratives? Can large language models (eg ChatGPT, Claude) accurately apply these coding frames? How can the insights identified within patient narratives feed-forward into patient improvement? How should quantitative insights (the evidence for change) be balanced with qualitative insights (what should change) to drive organisational learning?</p> <p>The studentship will be based in the School of Psychology at the University of Leeds (with links to the Leeds Institute for Data Analytics) and will work into the Patient Safety Research Collaborative (PSRC) located at the Bradford Institute for Health Research at Bradford teaching Hospitals NHS Trust. The student will join a multidisciplinary team of researchers in Patient Safety Intelligence, including expertise in patient safety research (specifically safety intelligence), organisational behaviour, safety science and artificial intelligence in health. The team comprises:</p> <ul> <li>Dr Jonathan Benn (School of Psychology, University of Leeds, and project lead)</li> <li>Prof. Alex Gillespie (Department of Psychological and Behavioural Science, London School of Economics)</li> <li>Prof. Carl Macrae (Nottingham University Business School)</li> <li>Dr Luke Budworth (Senior Research Data Analyst, Yorkshire and Humber PSRC)</li> </ul> <p>Yorkshire and Humber PSRC PhD students will become NIHR trainees and you will benefit from a range of training support and resources to develop your knowledge and health research skills. You will also be embedded within the Yorkshire Quality and Safety Research Group, which is a friendly and dynamic group of researchers conducting high-quality, rigorous and applied health services research.</p> <h5>References:</h5> <ul> <li>Fairie P, Zhang Z, D'Souza AG, Walsh T, Quan H, Santana MJ. Categorising patient concerns using natural language processing techniques. BMJ Health Care Inform. 2021 Jun;28(1):e100274. doi: 10.1136/bmjhci-2020-100274. PMID: 34193519; PMCID: PMC8246286.</li> <li>Fong, Allan MS. Realizing the Power of Text Mining and Natural Language Processing for Analyzing Patient Safety Event Narratives: The Challenges and Path Forward. Journal of Patient Safety 17(8):p e834-e836, December 2021. | DOI: 10.1097/PTS.0000000000000837</li> <li>Gillespie A, Reader TW. Online patient feedback as a safety valve: An automated language analysis of unnoticed and unresolved safety incidents. Risk Anal. 2022 Aug 9. doi: 10.1111/risa.14002. Epub ahead of print. PMID: 35945156.</li> <li>Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA. 2011 Aug 24;306(8):848-55. doi: 10.1001/jama.2011.1204. PMID: 21862746.</li> <li>Tabaie A, Sengupta S, Pruitt ZM, Fong A. A natural language processing approach to categorise contributing factors from patient safety event reports. BMJ Health Care Inform. 2023 May;30(1):e100731. doi: 10.1136/bmjhci-2022-100731. PMID: 37257922; PMCID: PMC10254979.</li> <li>Wong A, Plasek JM, Montecalvo SP, Zhou L. Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges. Pharmacotherapy. 2018 Aug;38(8):822-841. doi: 10.1002/phar.2151. Epub 2018 Jul 22. PMID: 29884988.</li> <li>Young IJB, Luz S, Lone N. A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis. Int J Med Inform. 2019 Dec;132:103971. doi: 10.1016/j.ijmedinf.2019.103971. Epub 2019 Oct 5. PMID: 31630063.</li> </ul>

<p>To apply for this scholarship opportunity applicants should complete an <a href="https://medicinehealth.leeds.ac.uk/faculty-graduate-school/doc/apply-2">online application form</a> and attach the following documentation to support their application. </p> <ul> <li>A full academic CV</li> <li>Degree certificate and transcripts of marks</li> <li>Evidence that you meet the University's minimum English language requirements (if applicable)</li> </ul> <p>To help us identify that you are applying for this scholarship project please ensure you provide the following information on your application form;</p> <ul> <li>Select PhD in Psychology as your programme of study</li> <li>Give the full project title and name the supervisors listed in this advert</li> <li>For source of funding please state you are applying for a NIHR PSRC Scholarship</li> </ul> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

Applicants to this scholarship should normally have (or be about to obtain) an Undergraduate degree of 2:1 or above (or international equivalent) in a relevant subject area. A Master’s degree is desirable, but not essential (for example: a Master’s degree in computer/data science, or a health/medicine-related area such as epidemiology, public health, psychology or medical research). Additionally, experience with programming and in relevant areas of data science, such as machine learning and language models, would be considered an advantage. Strong verbal and written communication skills are required for effective interdisciplinary collaboration and engagement with a broad range of research stakeholders including patients and the public. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Admissions Team prior to making an application.<br /> <br /> Other conditions:<br /> • Due to limited funding, we can only consider applicants for this position who are eligible for UK fee status.<br /> • Applicants must not have already been awarded or be currently studying for a doctoral degree.

Candidates whose first language is not English must provide evidence that their English language is sufficient to meet the specific demands of their study. The minimum English language entry requirement for postgraduate research study in the School of Medicine is an IELTS of 6.5 overall with at least 6.0 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<p>This opportunity is funded by the National Institute of Health Research (NIHR). The scholarship will attract an annual tax-free stipend of £19,237 for year one, and this will increase each year for up to 3 years subject to satisfactory progress. Academic fees will also be paid at the UK fee rate. This scholarship opportunity is for Full-time study (duration: 3 years). The award will be made for one year in the first instance and renewable for a further period of up to two years, subject to satisfactory academic progress.<br />  </p>

<p>For informal enquiries regarding this project please contact Dr Jonathan Benn: <a href="mailto:J.Benn2@leeds.ac.uk">J.Benn2@leeds.ac.uk</a></p> <p>For further information about the admissions process please contact the Admissions Team at: <a href="mailto:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</a></p>