PhD in Visualising Complex Care Pathways in Later Life

Project Summary

Health care in later life is complex and hard to navigate. We aim to apply a combination of data visualisation, illustration, natural language processing, and process modelling to medical guidelines and patient care data, so as to visualise care pathways that are personalised to individual patients and easy to understand. Our ambition is that these interactive visualisations can help people in later life gain a deeper understanding of their trajectories of care, as well as enable discussions with specialists and family members around adapting care to their wishes, priorities and needs.

Aim

To visualise personalised care pathways to individuals in later life to help understand, explore, and discuss with doctors and family members.

Objectives

• Develop innovative methods for depicting, visualising, and discovering complex care pathways in later life, drawing on process modelling and interactive data visualisation.
• Employ Natural Language Processing methods to extract key information regarding care pathways from medical guidelines and other data, e.g., electronic health records.
• Investigate the needs of people in later life with respect to visualising and communicating complex care pathways.
• Evaluate how our novel visualisation approach can support understanding, exploration, and discussion by working closely with people in later life.

Supervisor Team

• Dr. Benjamin Bach is a Lecturer in Design Informatics and Visualization at the School of Informatics and the Institute of Design Informatics. He is leading the Visual+Interactive Data group who does research in information visualization, data-driven storytelling, immersive interfaces, and cross disciplinary visualization. Ben with help with doing research in interactive information visualization, data-driven storytelling, and immersive interfaces. Ben will help with data visualization,illustration, graphics, UI, adn HCI issues.
• Dr. Areti Manataki is a health data scientist, employing artificial intelligence methods to improve healthcare processes. She collaborates with academic and industrial partners, including NHS clinicians, social scientists and IT companies. Areti will help with the necessary background in health and health data science.
• Dr. Beatrice Alex is Chancellor’s Fellow and Turing Fellow at the University of Edinburgh and is an expert in text mining and natural language processing with applications in the healthcare sector. She leads the Edinburgh Language Technology group and co-leads the Edinburgh Clinical NLP Research Group with Dr. Honghan Wu with whom she will be leading the NLP work associated with the ACRC work package 3. Dr. Alex will support this project by providing advice on the NLP aspects of the research.
• Prof. Bruce Guthrie is Professor of General Practice and a mixed-methods health services researcher interested in multimorbidity, polypharmacy and healthcare quality/safety. He chaired the guideline development group for the NICE Multimorbidity Guideline, and is a member of the SAGE Social Care Subgroup. He is the principal investigator of the ACRC programme and will provide the clinical expertise as part of this project.

Context

This PhD is funded by the Advanced Care Research Center (ACRC) at the Usher Institite, Edinburgh. All applications will go through ACRC but we strongly encourage to get in touch with the supervisor team before applying. The ACRC runs a 1+3 program, which means that

• the first year is typically dedicated to taught courses and preparing the PhD research proposal
• the last 3 years are dedicated to the actual PhD research.

While all of the research for this project will be done at the School of Informatics and the Usher Institute, the successful candidate will graduate from the School of Engineering. The PhD candidate will also be part of the Visual+Interactive Data group and https://www.designinformatics.org and invited to join any group and reading group meetings. We strongly encourage collaborations within the group and our wide network of collaborators. The principal supervisor plans to meet weekly with the PhD candidate to assure close supervision and support.

Required skills:

• Undergraduate or postgraduate in computer science / informatics or related discipline

Relevant skills

• Strong interest in health and health data
• Experience of interdisciplinary collaborations
• Web and visualization design and development
• Good understanding of graphics design, data visualization, communication
• Basic understanding of applied natural language processing (NLP)
• Basic understanding of process modelling methods
• Human-centered design

Funding and Program Information

• The scholarship covers a stipend for 4 years with £16,500 pa.
• Successful candidates must live in the UK.
• The program is a 3+1 center for doctoral training at the University of Edinburgh
• the first year is meant to contain some taught courses, while starting to get to grips with the reserach topic
• years 2-4 are dedicated to the PhD reserach
• No teaching or other duties are required
• Applications will open soon, but talk to Ben if you are interested.
• start is Sept 2021

Application

Deadline for applications will be around late January 2021.

It is strongly advised to contact us before application with

• CV
• brief research statement (why are you interested in this, what are you planning to do).

The final decision of acceptance will be made through the CDT.

Apply here: https://www.ed.ac.uk/usher/advanced-care-research-centre/academy/how-to-apply.

Relevant References

• Wang, Z., Ritchie, J., Zhou, J., Chevalier, F. and Bach, B., 2020. Data Comics for Reporting Controlled User Studies inHuman-Computer Interaction.
• Wang, Zezhong, Harvey Dingwall, and Benjamin Bach. “Teaching data visualization and storytelling with data comic workshops.” In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1-9. 2019.
• Bach, B., Stefaner, D., Boy, J., Drucker, S., Bartram, L., Wood, J., Ciuccarelli, P., Engelhardt, Y., Koeppen, U. and Tversky, B., 2018. Narrative design patterns for data-driven storytelling. In Data-Driven Storytelling (pp. 107-133). CRC Press.
• Grando, A., Manataki, A., Furniss, S. K., Duncan, B., Solomon, A., Kaufman, D., … & Burton, M. M. (2018). Multi-method study of electronic health records workflows. In AMIA Annual Symposium Proceedings (Vol. 2018, p. 498). American Medical Informatics Association.