Opportunities and Challenges in Fall Risk Management using EHRs and Artificial Intelligence: a Systematic Review

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.