Thomas Godfrey

Thomas Godfrey

Ph.D. student at King's College London

Publications

Supporting Emergency Department Risk Mitigation with a Modular and Reusable Agent-Based Simulation Infrastructure

Published in 2023 Winter Simulation Conference, 2023

For emergency departments (EDs) to maintain sustainable care of patients, hospital management must continually explore potential interventions to clinical practice. Agent-based modelling (ABM) can be a valuable tool to support this planning in a controlled environment. Existing approaches to ABM development are best suited for one-off models. However, conditions in EDs can change frequently, making the use of one-off models infeasible. Decision-makers must be able to trust simulations appropriately for them to be effective in intervention exploration. Domain-specific modelling languages (DSMLs) can address these challenges by offering a reusable library of appropriately-abstract, domain-familiar, modelling concepts across case studies and automatic translation of these concepts into executable models. In this paper we present a DSML to support repeated modelling exercises in the ED domain and illustrate the use and reuse of this DSML across two concrete case studies in London-based NHS emergency departments.

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A Methodology for DSML-assisted Participatory Agent-Based Enterprise Modelling

Published in Practice of Enterprise Modelling 2022, 2022

Participatory agent-based modelling (ABM) can help bring the benefits of simulation to domain users by actively involving stakeholders in the development process. Collaboration in enterprise modelling can improve the model developer’s understanding of the domain and therefore improve the effectiveness of domain analysis. Where many agent-oriented methodologies focus on the development of one-off models, domain-specific modelling languages (DSML) can improve the re-use of concepts identified in domain analysis across multiple case studies and expose modelling concepts in domain-appropriate terms, increasing model accessibility. To realise the benefits of DSMLs we need to understand how DSML development can be incorporated into typical agent-based modelling. In this paper we discuss existing methodologies for ABM development and DSML development, and we discuss the benefits merging the two can bring. We present a methodology for DSML-assisted participatory agent-based modelling, and support the methodology with a case study—a modelling exercise conducted in collaboration with a hospital emergency department on the topic of infection control for COVID-19 and Influenza.

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Domain-Specific Modelling Languages for Participatory Agent-Based Modelling in Healthcare

Published in 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), 2021

Hospitals need to look into operational interventions to keep up with increasing patient numbers, and the threats of infectious diseases. Changes such as new ward layouts, staff allocations, testing schemes, admission pathways, etc., need to be considered to improve patient flow and safety. Agent-Based Modelling (ABM) has promising potential for simulating these types of interventions in hospital settings without interfering with real-world operations. While this approach to simulation has seen great academic support, the acceptance of these simulations in practice is still relatively low. Typical software development processes, and the use of general-purpose programming languages, make simulation implementations largely inaccessible to their intended users. Explaining model behaviour becomes challenging, and domain users are unable to modify or develop their own models without handing over to software developers. We propose that participatory modelling, aided by a suite of domain-specific modelling languages can help resolve these issues. We outline a set of DSMLs to involve users directly in model development with the aim of improving implementation comprehensibility, improving model development times, and improving overall simulation acceptance in a healthcare setting.

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