Realising the potential of data games to keep humans in the loop for the analysis of large and complex data sets in population health sciences

Population health science requires understanding a large and increasing amount of complex data, from complete human genomes and causal networks of the aetiology of disease, to open and unstructured data about community response to rapidly changing public health challenges. Making best use of these data can have an enduring impact on millions of lives across the UK and globally. However, even with advances in machine learning it is vital that humans remain in the loop to ensure the integrity of analyses and provide the creative insights that lead to paradigm shifts in our understanding of human health. The challenge is how to present these complex data in a way that optimises decisions made by multidisciplinary teams that include scientists, clinicians, policymakers and the public. Interactive visualisation can provide a window into a complex biomedical data set or analysis, but unguided interaction can leave users lost. Rethinking these visualisations as Data Games [1; 2]—serious games that incorporate real-world data sets—is a promising way to impart an intuitive understanding of a complex data set, motivating and guiding interaction through the introduction of objectives and mechanics that support analysis and decision making[3].

Aims and objectives

Hypothesis: applying a Data Games approach to visualising complex biomedical data will enhance stakeholders’ ability to develop an internal model of the data, to generate strategies and to make better decisions.

Objectives:

1. Develop a data game with the involvement of key stakeholders in a complex biomedical data set
2. Experimentally test the impact of specific aspects of this game on the participants’ understanding of the underlying data set
3. Generalise the data game to other similarly structured data sets through machine learning approaches to procedural content generation

Methodology

The research will begin with a stakeholder workshop to identify the specific tasks that would benefit most from a supporting data game. From the workshop participants, we will recruit two panels of stakeholders to guide the game development through a combination of the Person-Based Approach for digital intervention development [4] and the Playcentric approach to game design [5] in iterative playtesting and development cycles. The panels will participate in co-production of a series of design concepts that build on the knowledge derived from the stakeholder workshop. From this testing we will select three concepts that we will take forward to develop rapid prototypes. Iterative playtesting of these prototypes will inform our selection of a single game concept to fully develop. Individual mechanics will be experimentally tested using participants recruited through the Prolific platform. The game is likely to include both digital and analogue components, with data processing and interaction supported by the digital component through procedural content generation that incorporates human co-design and playability optimisation using approaches such as multi-objective genetic algorithms, targeting both player experience and faithfulness to the underlying data set. This mixed methods approach will involve broad training in modern data science methods.

Apply for this project

This project will be based in Bristol Medical School - Population Health Sciences.

Please contact brms-pgradmin@bristol.ac.uk for further details on how to apply.

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