What's next? Using data visualization to make sense of personalised paediatric critical care pathways

Paediatric critical care (PCC) is a highly specialist service that saves infants' and children's lives. However, having a child needing critical care is a stressful event for parents, as 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. These pathways are different for each patient and depending on their severity of illness, they 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 care a PCC patient requires, the technologies used in PICU routinely generate large volumes of data (e.g. heart rate, blood pressure, 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 project, 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 are 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 and the central question how to generate these personalized stories from the patient data.
Aims
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 software-applications that generate comics, etc.
3. Evaluate the usefulness of the visualizations, in terms of support for doctor-parent 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 help to reduce parental anxiety and emotional distress, an important factor in modern family-orientated PCC delivery.

References:
[1] Bronner, M.B. et al., 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. et al, 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. et al, 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. et al, 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).