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Authors: Henrique D. P. dos Santos 1 ; Juliana O. Damasio 1 ; Ana Helena D. P. S. Ulbrich 2 and Renata Vieira 3

Affiliations: 1 School of Technology, PUCRS, Porto Alegre, Brazil ; 2 Nossa Senhora da Conceição Hospital, Porto Alegre, Brazil ; 3 CIDEHUS, University of Évora, Portugal

Keyword(s): Electronic Health Records, Artificial Intelligence, Fall Detection, Fall Risk Prediction.

Abstract: Electronic Health Records (EHRs) have led to valuable improvements to hospital practices by integrating patient information. In fact, this data can be used to develop clinical risk prediction tools. We performed a systematic literature review with the objective of analyzing current studies that use artificial intelligence techniques in EHRs data to identify in-hospital falls. We searched several digital libraries for articles that reported on the use of EHRs and artificial intelligence techniques to identify in-hospital falls. Articles were selected by three authors of this work. We compiled information on study design, use of EHR data types, and methods. We identified 21 articles, 11 about fall risk prediction and 10 covering fall detection. EHR data shows opportunities and challenges for fall risk prediction and in-hospital fall detection. There is room for improvement in developing such studies.

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Paper citation in several formats:
Santos, H. D. P., Damasio, J. O., Ulbrich, A. H. D. P. S. and Vieira, R. (2021). Opportunities and Challenges in Fall Risk Management using EHRs and Artificial Intelligence: A Systematic Review. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 626-633. DOI: 10.5220/0010424306260633

@conference{iceis21,
author={Henrique D. P. dos Santos and Juliana O. Damasio and Ana Helena D. P. S. Ulbrich and Renata Vieira},
title={Opportunities and Challenges in Fall Risk Management using EHRs and Artificial Intelligence: A Systematic Review},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={626-633},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010424306260633},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Opportunities and Challenges in Fall Risk Management using EHRs and Artificial Intelligence: A Systematic Review
SN - 978-989-758-509-8
IS - 2184-4992
AU - Santos, H.
AU - Damasio, J.
AU - Ulbrich, A.
AU - Vieira, R.
PY - 2021
SP - 626
EP - 633
DO - 10.5220/0010424306260633
PB - SciTePress

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