Big data in modelling geographical accessibility to healthcare: a scoping review protocol

Journal article

INTRODUCTION: Research on modelling geographical accessibility to healthcare services has witnessed rapid methodological advancement and refinement. One of the contributing factors is the increasing availability of big data detailing the link between the population in need of care and the health facility such as infrastructure, travel modes and speeds, traffic congestion and the quality of road network. This has allowed more granular computation of geographic access metrics, particularly in low-and-middle income countries where data are scarce. However, there are no reviews providing a comprehensive overview of the availability and use of big data for assessing geographical accessibility to healthcare. This protocol aims to describe a methodological approach that will be used to review the existing literature on the application of big data (past or potential) in evaluating geographical accessibility to healthcare. METHODS AND ANALYSIS: To characterise the big data that can be used to model geographical accessibility to healthcare, a scoping review will be undertaken and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extensions for Scoping Reviews guidelines. We will search seven scientific databases (PubMed, Scopus, Web of Science, EBSCOhost-CINAHL, Cochrane, Embase and MEDLINE via Ovid), grey literature, reference lists of identified publications and conference proceedings. Search engines will be used to identify relevant big data services not yet used in published academic literature. All literature published in English or French will be included, regardless of publication type, geographical location or year of publication provided it describes or mentions big data that may be useful for evaluating geographical accessibility to healthcare. Study selection and data extraction will be performed independently by two researchers with a third resolving any discrepancies. Analysis will be conducted to summarise big data providers, their characteristics and their usefulness in terms of types of spatial accessibility metrics that can be derived. ETHICS AND DISSEMINATION: Formal ethical approval is not required, as primary data will not be collected in this review. Findings will be disseminated through peer-reviewed publication in a journal, conference presentation and condensed summaries for stakeholders through professional networks and social media summaries. REGISTRATION: Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/S496F.

Authors: Njogu, A; Libertini, L; Avahoundjè, EM; Grovogui, FM; Ba, OA

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BMJ Publishing Group
• Journal: BMJ Open
• Volume: 15
• Issue: 10
• Pages: e101567-e101567
• Publication date: 2025-10-21
• DOI: 10.1136/bmjopen-2025-101567
• EISSN: 2044-6055
• ISSN: 2044-6055
• Language: English