26-27 Jan | FREE virtual study fair | REGISTER NOW 26-27 Jan | FREE virtual study fair | REGISTER NOW
University College London Featured PhD Programmes
University of Southampton Featured PhD Programmes
University of Sheffield Featured PhD Programmes

Precision Medicine DTP – What’s next? Using data visualization to make sense of personalised paediatric critical care pathways

   School of Informatics

Edinburgh United Kingdom Health Informatics

About the Project


Paediatric critical care (PCC) is a highly valued specialist service that saves infants’ and children’s lives. However, having a child needing critical care is a stressful event for parents because their child suddenly requires different technologies within paediatric intensive care units (PICU) to survive. This often results in persistent post-traumatic stress syndrome in parents even after the child has recovered from critical illness [1].

One common reason PICU is such a stressful environment for parents is because PCC patients follow complex pathways, from the moment they are admitted to the PICU to the time that they are discharged. These pathways are different for each patient and depending on their severity of illness, they may require different levels of organ support. Parents find it particularly hard to make sense of and navigate these complex pathways, causing them stress and anxiety. Regardless of the level of organ support a PCC patient requires, the technologies used in PICU routinely generate large volumes of data (e.g. bedside physiological data including heart rate, blood pressure, mechanical ventilator data, etc.) which inform the design of personalized care pathways. However, communicating this complex specialist information in a meaningful way to parents without using medical jargons is particularly hard in the traditional verbal consultation method. Some clinicians will attempt to draw simplified diagrams to explain but this is dependent on the clinicians’ ability to draw and communicate via their rudimentary drawings.

In this proposal, we aim to apply data comics and other techniques from data visualization and data-driven storytelling to provide for an effective communication between clinicians and parents. Data comics is a way of communicating data and data visualizations, pioneered by Bach [2, https://datacomics.github.io]. Data comics combine methods from data visualization and data literacy [3], and they have been found an effective communication medium [4] for a wide range of audiences. We will build on this work and explore further forms of communication such as physical flapbooks and the central question how to generate these personalized stories from the patient data.


To support clinician-parent communication, our aims with this proposal are as follows:

1. Identify personalized PCC pathways and associated health indicators through the analysis of clinical and physiological data.

2. Develop novel visualizations of personalized PCC pathways for a non-expert audience, which capture key information about the patient’s status and describe the next steps in their care. This will involve user-centered design methods such as interviews and focus groups. Our prototypes will be functional softwareapplications that generate comics, flapbooks, etc.

3. Evaluate the usefulness of the visualizations, in terms of support for doctorparent communication and parents’ understanding of PCC plan for their child. This will also involve user-centered design methods and it will include the participation of clinicians and parents.

Improved communication and understanding of their child’s condition and progress on PICU may ultimately help to reduce parental anxiety and emotional distress. This is an important outcome in modern family-orientated PCC delivery.

Training Outcomes

We provide a unique opportunity for the student to develop and apply their quantitative and interdisciplinary skills to a variety of data types and sources. Specifically:

Digital Excellence

• Understanding of the data collection environment in critical care units.

• Experience with data linkage technologies and platforms.

• Producing state-of-the-art and novel data visualization techniques.

• Programming software for visualizations and the personalization thereof.


• Data science skills, including processing, exploration, visualization and analysis of data.

• Analyzing clinical time-series data, routinely collected


• Domain knowledge in paediatric critical care pathophysiology.

• Development of precision medicine approaches to improve paediatric critical care.

• Data visualization design and communication skills, human-centered design and evaluation methods.

About the Programme

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.


Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:


Funding Notes

Start: September 2022

Qualifications criteria: Applicants applying for an MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area. The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,609 (UKRI rate 2021/22).

Full eligibility details are available: View Website

Enquiries regarding programme:


[1] Bronner, M.B., Peek, N., Knoester, H., Bos, A.P., Last, B.F. and Grootenhuis, M.A., 2010. Course and predictors of posttraumatic stress disorder in parents after pediatric intensive care treatment of their child. Journal of pediatric psychology, 35(9), pp.966-974.
[2] Bach, B., Wang, Z., Farinella, M., Murray-Rust, D. and Henry Riche, N., 2018, April. Design patterns for data comics. In Proceedings of the 2018 chi conference on human factors in computing systems (pp. 1-12).
[3] Wang, Z., Sundin, L., Murray-Rust, D. and Bach, B., 2020, April. Cheat sheets for data visualization techniques. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
[4] Wang, Z., Wang, S., Farinella, M., Murray-Rust, D., Henry Riche, N. and Bach, B., 2019, May. Comparing effectiveness and engagement of data comics and infographics. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-12)

Email Now

Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs