Project description

Application Deadline: 12th November 2021.

Electronic health records (EHRs) in hospitals contain a wealth of rich, routinely collected information that have the potential to drive improvements in patient care and research. The secondary care health data space is especially heterogeneous, as it includes well-structured variables (e.g. from diagnostic and laboratory tests, manually coded data) and semi/un-structured data (e.g. images or clinical letters). While the former is often the core of healthcare data science, the latter group of unstructured data means there are important gaps in our knowledge about aspects of secondary care.

Outpatient letters and in-patient clinical notes are semi-structured free-text documents that contain key clinical information about patients, their treatments and outcomes. Whilst recorded electronically for clinical purposes, their unstructured nature and sensitive content mean that this data source is often inaccessible for secondary use to support research or service improvement. For example, identifying certain patients who needed to shield during the current pandemic relied on hospital clinical teams reviewing thousands of patient letters manually, with significant resource and time implications. For conditions managed in hospital outpatients, there is surprisingly no national system for recording diagnoses or prescribed medications: the necessary information is only available as free text in letters. Guided by relevant clinical questions, automated text-mining techniques can unlock pertinent information hidden in the massive amount of data, which in turn can assist clinical decision making.

In this project, through an interdisciplinary team linking computer science, secondary care (NHS) and epidemiology, the candidate will help develop and validate a knowledge management framework to safely unlock information stored within EHRs, test and implement text-mining methods to obtain coded data for research, integrate it with other health data and demonstrate its impact and benefits through case studies. We will focus on hospital data from Salford Royal Foundation Trust, a Global Digital Exemplar (GDE) site, to develop and validate a sharable system for extracting diagnoses, medications and other pertinent information from hospital outpatient letters and inpatient records using text mining. While we will initially focus on musculoskeletal (MSK) conditions, the project will also explore necessary transfer-learning to tailor the system for other specialities and services.

Objectives/plan:
1) To develop and validate methods for extraction and coding of key clinical information from semi-structured hospital data, including diagnoses; frequency, strength, dose and timing of drugs etc. (Years 1, 2).
2) To develop and validate methods for extracting MSK-specific information, including disease severity, activities of daily living, and quality of life (Year 2)
3) To demonstrate the value of such methods through case studies in MSK conditions and opioid safety, in collaboration with clinical researchers (Year 3).

https://www.research.manchester.ac.uk/portal/gnenadic.html
https://www.research.manchester.ac.uk/portal/will.dixon.html
https://www.research.manchester.ac.uk/portal/meghna.jani.html

Others:
UK Healthcare Text Analytics Network (Healtex): http://healtex.org/
Centre for Epidemiology Versus Arthritis: http://www.cfe.manchester.ac.uk/

UK applicants interested in this project should make direct contact with the Primary Supervisor to arrange to discuss the project further as soon as possible. International applicants (including EU nationals) must ensure they meet the academic eligibility criteria (including English Language) as outlined before contacting potential supervisors to express an interest in their project. Eligibility can be checked via the University Country Specific information page (https://www.manchester.ac.uk/study/international/country-specific-information/).

If your country is not listed you must contact the Doctoral Academy Admissions Team providing a detailed CV (to include academic qualifications - stating degree classification(s) and dates awarded) and relevant transcripts.

Following the review of your qualifications and with support from potential supervisor(s), you will be informed whether you can submit a formal online application.

To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the MRC Doctoral Training Partnership (DTP) website http://www.manchester.ac.uk/mrcdtpstudentships.

Funding will cover UK tuition fees/stipend only. The University of Manchester aims to support the most outstanding applicants from outside the UK. We are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website View Website