EURO 2024 Copenhagen
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997. Using large routine health service datasets for modelling

Invited abstract in session MC-17: Simulation models in healthcare, stream OR in Health Services (ORAHS).

Monday, 12:30-14:00
Room: 40 (building: 116)

Authors (first author is the speaker)

1. Sally Brailsford
Southampton Business School, University of Southampton
2. Tracey England
University of Southampton

Abstract

In the past lack of data was often cited as a major challenge in healthcare modelling, but in recent years large electronic datasets have become increasingly available for use in research. This talk discusses the advantages and disadvantages of using such datasets, in particular routine health service data primarily collected for other purposes, compared with collecting prospective data or using the clinical literature. These ‘pros and cons’ are illustrated by three case studies. The first dates back to the 1990s and concerns screening for diabetic retinopathy; the other two are connected, much more recent, and concern care for older people. All three projects involved large multi-disciplinary teams of researchers. The first project involved the development of a discrete-event simulation model, parameterised using data from the literature. The two related projects involved the development of system dynamics simulation models, both using large routine health service datasets to estimate the model parameters. While access to such data might seem to be a luxury, compared with the difficulties of obtaining data for the diabetic retinopathy model, deriving the required information from the data was not always straightforward.

Keywords

Status: accepted

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