Measuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation Article Swipe
Gabriele Marini
,
Benjamin Tag
,
Jorge Gonçalves
,
Eduardo Velloso
,
Raja Jurdak
,
Daniel Capurro
,
Clare McCarthy
,
William Shearer
,
Vassilis Kostakos
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.2196/19874
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.2196/19874
Background The use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings. Such a streamlined data-driven approach can help in increasing the uptime of operating rooms and more broadly provide an improved understanding of facility utilization. Objective The aim of this study is to measure the accuracy of the system and algorithmically calculate measures of mobility and occupancy. Methods We developed a Bluetooth low energy, proximity-based localization system and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. In addition, we collected ground-truth data and used them to validate system performance and accuracy. A number of analyses were conducted to estimate how people move in the hospital and where they spend their time. Results Using ground-truth data, we estimated the accuracy of our system to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms in the hospital occupied by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering, we showed that the system could differentiate between staff and patients without training. Conclusions Analysis of longitudinal, location-based data can offer rich operational insights into hospital efficiency. In particular, they allow quick and consistent assessment of new strategies and protocols and provide a quantitative way to measure their effectiveness.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/19874
- https://mhealth.jmir.org/2020/10/e19874/PDF
- OA Status
- gold
- Cited By
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- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3085410529Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/19874Digital Object Identifier
- Title
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Measuring Mobility and Room Occupancy in Clinical Settings: System Development and ImplementationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-09-14Full publication date if available
- Authors
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Gabriele Marini, Benjamin Tag, Jorge Gonçalves, Eduardo Velloso, Raja Jurdak, Daniel Capurro, Clare McCarthy, William Shearer, Vassilis KostakosList of authors in order
- Landing page
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https://doi.org/10.2196/19874Publisher landing page
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https://mhealth.jmir.org/2020/10/e19874/PDFDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://mhealth.jmir.org/2020/10/e19874/PDFDirect OA link when available
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Occupancy, Computer science, Environmental science, Engineering, Architectural engineeringTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 3, 2024: 1, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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