Cohort 1 student Marceli Wac publishes a journal article in SoftwareX (Elsevier) entitled: ‘CATS: Cloud-native time-series data annotation tool for intensive care.’

Marceli had the following to say about the publication: ‘Intensive care units are complex, data-rich healthcare environments which provide substantial opportunities for applications in machine learning. While certain solutions can be derived directly from data, complex problems require additional human input provided in the form of data annotations. Due to the large size and complexities associated with healthcare data, the existing software packages for time-series data annotation are infeasible for effective use in the clinical setting and frequently require significant time commitments and technical expertise.
Our software provides a comprehensive, end-to-end solution to the time-series data annotation and proposes a novel approach for a semi-automated annotation in the cloud. It allows for conducting large-scale, asynchronous data annotation activities across multiple, geographically distributed users. The adoption of our software could benefit the wider research community by enhancing existing datasets, creating novel avenues for research that uses them and allowing for meaningful data annotation within smaller and highly specialised populations.

Produced software has the potential to be used in variety of domains and in particular, research involving the clinical time-series data. It allows the staff non-technically trained in the domain of computer science (such as clinicians) to enhance the existing datasets by creating annotations and providing additional context to data, which could be used to build novel machnie learning algorithms and apply them to the previously unexplored problems. The tool is cloud based allows for large-scale, distributed annotation, meaning that once deployed – it could be used by any group of clinicians across the world simultaneously.’

Link to Paper: https://www.softxjournal.com/article/S2352-7110(23)00289-3/fulltext#secd1e1291

Cohort 1 student Marceli publishes in JMIR Human Factors entitled ‘Design and Evaluation of an Intensive Care Unit Dashboard Built in Response to the COVID-19 Pandemic: Semistructured Interview Study’.

Marceli had the following to say about the experience: ‘Dashboards and interactive displays are becoming increasingly prevalent in most health care settings and have the potential to streamline access to information, consolidate disparate data sources and deliver new insights. Our research focuses on intensive care units (ICUs) which are heavily instrumented, critical care environments that generate vast amounts of data and frequently require individualized support for each patient. Consequently, clinicians experience a high cognitive load, which can translate to suboptimal performance. The global COVID-19 pandemic exacerbated this problem by generating a large number of additional hospitalizations, which necessitated a new tool that would help manage ICUs’ census. In a previous study, we interviewed clinicians at the University Hospitals Bristol and Weston National Health Service Foundation Trust to capture the requirements for bespoke dashboards that would alleviate this problem.

This study aims to design, implement, and evaluate an ICU dashboard to allow for monitoring of the high volume of patients in need of critical care, particularly tailored to high-demand situations, such as those seen during the COVID-19 pandemic.

Building upon the previously gathered requirements, we developed a dashboard, integrated it within the ICU of a National Health Service trust, and allowed all staff to access our tool. For evaluation purposes, participants were recruited and interviewed following a 25-day period during which they were able to use the dashboard clinically. The semistructured interviews followed a topic guide aimed at capturing the usability of the dashboard, supplemented with additional questions asked post hoc to probe themes established during the interview. Interview transcripts were analyzed using a thematic analysis framework that combined inductive and deductive approaches and integrated the Technology Acceptance Model.

A total of 10 participants with 4 different roles in the ICU (6 consultants, 2 junior doctors, 1 nurse, and 1 advanced clinical practitioner) participated in the interviews. Our analysis generated 4 key topics that prevailed across the data: our dashboard met the usability requirements of the participants and was found useful and intuitive; participants perceived that it impacted their delivery of patient care by improving the access to the information and better equipping them to do their job; the tool was used in a variety of ways and for different reasons and tasks; and there were barriers to integration of our dashboard into practice, including familiarity with existing systems, which stifled the adoption of our tool.

Our findings show that the perceived utility of the dashboard had a positive impact on the clinicians’ workflows in the ICU. Improving access to information translated into more efficient patient care and transformed some of the existing processes. The introduction of our tool was met with positive reception, but its integration during the COVID-19 pandemic limited its adoption into practice.

This project informs the future developments pertaining to the use of dashboards and interactive displays within intensive care unit settings.’

Link to Paper: https://humanfactors.jmir.org/2023/1/e49438

Cohort 1 student Romana Burgess publishes a journal article in the Journal of Non-verbal Behaviour entitled: ‘A Quantitative Evaluation of Thin Slice Sampling for Parent–Infant Interactions.’

Romana had this to say about her paper: ‘Broadly, the paper looks at whether we can use brief observations (“thin slices”) of behaviours to approximate those same behaviours over a longer period of time. The purpose of this work is to find an approach to alleviate the “coding burden”, i.e., the amount of time that researchers spend coding behaviours from observational data. In essence, behavioural coding is extremely time-intensive and laborious, and this paper both explores and quantifies the value of thin slice sampling as an alternative approach.

The analysis is based on video data of interactions between parents and their infants. These data come from two cohort studies: the Avon Longitudinal Study of Parents and Children (ALSPAC) – based in Bristol – and Grown in Wales (GiW) – based in Cardiff. Some videos were recorded in a research clinic, but most were recorded in the participants own homes.

The videos were coded in 5-minute segments for a large range of behaviours, for example, vocalisations, facial expressions, and body orientation. Then, I used Markov modelling to quantify long-term patterns and transitions between behaviours for 15 distinct thin slices of the full 5-minute interactions, and I compared measures drawn from the full sessions to those from shorter slices.

The paper identified many instances where thin slice sampling was an appropriate coding approach, although there was significant variation across behaviours. From here, I was able to quantify how long is appropriate to code for each behaviour, depending on video context and individual research objectives.

This work constitutes the first project of three that comprise my PhD thesis, and is crucial to its completion. The second project considers a different approach to alleviating the coding burden, and these two works together contribute to the third project, which looks at linking coded facial expressions during parent-infant interactions to parental mental health.’

Link to Paper: https://link.springer.com/article/10.1007/s10919-022-00420-7

Cohort 1 students Romana Burgess, Joe Mathews, Morgan Jenkinson and Bridget Ellis publish a Journal Article entitled ‘Fathers, Young Children and Technology: Changes in Device Use and Family Dynamics During the COVID-19 UK Lockdown’ in Proceedings of the ACM on Human-Computer Interaction Journal.

They had the following to say about their experience:

‘The coronavirus lockdown measures meant that families spent more time together than ever before, thanks to the shift to online schooling and working from home. By speaking with fathers during this time of stress, uncertainty, and change, we looked to understand changing perceptions of technology use and fatherhood, and we began to consider the design of father-supporting technologies to support fathers.