Automating risk stratification for geriatric syndromes in the emergency department

Abstract

BackgroundGeriatric emergency department (GED) guidelines endorse screening older patients for geriatric syndromes in the ED, but there have been significant barriers to widespread implementation. The majority of screening programs require engagement of a clinician, nurse, or social worker, adding to already significant workloads at a time of record‐breaking ED patient volumes, staff shortages, and hospital boarding crises. Automated, electronic health record (EHR)‐embedded risk stratification approaches may be an alternate solution for extending the reach of the GED mission by directing human actions to a smaller subset of higher risk patients.MethodsWe define the concept of automated risk stratification and screening using existing EHR data. We discuss progress made in three potential use cases in the ED: falls, cognitive impairment, and end‐of‐life and palliative care, emphasizing the importance of linking automated screening with systems of healthcare delivery.ResultsResearch progress and operational deployment vary by use case, ranging from deployed solutions in falls screening to algorithmic validation in cognitive impairment and end‐of‐life care.ConclusionsAutomated risk stratification offers a potential solution to one of the most pressing problems in geriatric emergency care: identifying high‐risk populations of older adults most appropriate for specific GED care. Future work is needed to realize the promise of improved care with less provider burden by creating tools suitable for widespread deployment as well as best practices for their implementation and governance.

Enhancing healthcare access for an older population: The age‐friendly emergency department

“…There has been work into using existing data in the electronic health records to target fall risk/mobility screening to those most likely to have needs, thereby reducing the burden on ED nurses. 79 Additional validated fall risk tools such as the 4‐Stage Balance test take under a minute to perform and many GEDs have integrated fall risk evaluation and reduction programs successfully into their standard care. 36 …”

“…There has been work into using existing data in the electronic health records to target fall risk/mobility screening to those most likely to have needs, thereby reducing the burden on ED nurses. 79 The mobility assessment is closely tied to the fall risk assessment and prevention. Older adults make nearly 3 million ED visits for falls each year.…”

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

“…In clinical applications, machine learning algorithms are proving to be very useful ( Bao et al, 2019 ; Eichler et al, 2022 ). Machine learning (ML) (a) is a type of artificial intelligence (AI) focused on building computer systems that learn from data, (b) is a powerful tool for solving problems, streamlining various complex operations, and automating tasks, and (c) has broad applications in many areas, for example, science, engineering, industry, economics, databases, healthcare, and medicine ( Michalski et al, 2013 ; Alpaydin, 2016 ; Zhu et al, 2020 ; Sarker, 2021 ; Singh et al, 2021 ; Barton et al, 2024 ; Haimovich et al, 2024 ; Khalid et al, 2024 ). ML offers a wide range of techniques, such as decision trees, rule induction, neural networks, support vector machines (SVMs), clustering and classification methods, association rules, feature selection procedures, visualization, graphical models, or genetic algorithms; which are many more complex and use techniques well beyond traditional statistical techniques [i.e., hypothesis testing, experimental design, ANOVA, linear/logistic regression, generalized linear model (GLM), or principal component analysis (PCA)] ( Mitchel, 1997 ; Ben-David and Shalev-Shwartz, 2014 ; Marsland, 2015 ; Arnold et al, 2019 ; Bradley and Trevor, 2021 ).…”