Developing an Online Infrastructure to Enhance Model Accessibility and Validation: The Peer Models Network

'Managing values' in health economics modelling: Philosophical and practical considerations

Stakeholder involvement has been proposed as a key strategy for appropriately managing value-laden decisions or 'value judgments' in health economics modelling. Philosophers of science, however, conceive of stakeholder involvement in research in conflicting ways, and also propose alternative strategies for 'managing values' in science. Furthermore, all proposed strategies for managing values in science raise philosophical questions and practical challenges that are difficult to resolve. As a result, health economists who seek to appropriately inform value judgments in modelling must currently go without straightforward guidance. There is a need to further explore how health economists should manage value judgments in modelling, taking into account philosophical debates and contextual constraints. This paper discusses core proposals for managing values in science and identifies philosophical questions and practical challenges these proposals leave unresolved. It further considers how this could potentially inform processes to manage value judgments in health economics modelling, using examples from an ongoing modelling project called LEAP (Lifetime Exposures and Asthma Outcomes Projection). We conclude that all strategies to 'manage values' in health economics modelling have strengths and weaknesses, but are generally compatible with one another, suggesting that health economists may use a combination of strategies. Further research is needed to explore the effects of strategies to 'manage values' in health economics modelling.

Introducing Open Science in Teaching Health Economic Modelling

Open Science is gaining ground in all research fields, including health economics and outcomes research (HEOR). However, teaching Open Science is still in its infancy. This paper describes the design, implementation and evaluation of a teaching activity focusing on introducing Open Science during a Master's course during which participants have to develop a discrete event simulation. The teaching activity was organised as a series of lectures introducing different aspects of the Open Science philosophy and practices, such as good software coding practices, version control systems and reproducible research. The participants' increase in Open Science knowledge was elicited through a survey before and after the teaching innovation. After the teaching innovation, participants' knowledge of Open Science increased and they reported an improvement in Open Science-related skills, such as using a script-based statistical software, identifying and re-using open data, and collaborative script development. During the evaluation at the end of the course, the course participants mentioned that the Open Science-related content was interesting but would fit better within a course in which broader research-related content is taught. Based on this feedback, we will most likely narrow the scope of the Open-Science-related content in this course to Open Source Modelling which may better fit the scope of the course. This paper contains links to the teaching activities we developed and other resources which may be used to design teaching activities on Open Science. Herewith, we hope to inspire other teachers in including Open Science into their teaching.

Improving the usability of open health service delivery simulation models using Python and web apps

One aim of Open Science is to increase the accessibility of research. Within health services research that uses discrete-event simulation, Free and Open Source Software (FOSS), such as Python, offers a way for research teams to share their models with other researchers and NHS decision makers. Although the code for healthcare discrete-event simulation models can be shared alongside publications, it may require specialist skills to use and run. This is a disincentive to researchers adopting Free and Open Source Software and open science practices. Building on work from other health data science disciplines, we propose that web apps offer a user-friendly interface for healthcare models that increase the accessibility of research to the NHS, and researchers from other disciplines. We focus on models coded in Python deployed as streamlit web apps. To increase uptake of these methods, we provide an approach to structuring discrete-event simulation model code in Python so that models are web app ready. The method is general across discrete-event simulation Python packages, and we include code for both simpy and ciw implementations of a simple urgent care call centre model. We then provide a step-by-step tutorial for linking the model to a streamlit web app interface, to enable other health data science researchers to reproduce and implement our method.