Automating risk stratification for geriatric syndromes in the emergency department

被引:7
作者
Haimovich, Adrian D. [1 ]
Shah, Manish N. [2 ]
Southerland, Lauren T. [3 ]
Hwang, Ula [4 ,5 ]
Patterson, Brian W. [2 ,6 ,7 ]
机构
[1] Beth Israel Deaconess Med Ctr, Dept Emergency Med, Boston, MA USA
[2] Univ Wisconsin, Sch Med & Publ Hlth, BerbeeWalsh Dept Emergency Med, Madison, WI USA
[3] Ohio State Univ, Wexner Med Ctr, Dept Emergency Med, Columbus, OH USA
[4] James J Peters VAMC, Geriatr Res Educ & Clin Ctr, Bronx, NY USA
[5] Yale Sch Med, Dept Emergency Med, New Haven, CT USA
[6] Univ Wisconsin Madison, Dept Ind & Syst Engn, Dept Biostat & Med Informat, Madison, WI USA
[7] 300 Univ Bay Dr,Suite 310,Mailcode 9123, Madison, WI 53705 USA
基金
美国医疗保健研究与质量局;
关键词
artificial intelligence; automation; emergency medicine; machine learning; screening; COGNITIVE IMPAIRMENT; ELDERLY EMERGENCY; PREVENTIVE-CARE; OLDER-ADULTS; FALL RISK; MEDICAL PATIENTS; SCREENING TOOL; MENTAL STATUS; DELIRIUM; HEALTH;
D O I
10.1111/jgs.18594
中图分类号
R592 [老年病学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 100203 ;
摘要
Background: Geriatric 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.Methods: We 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.Results: Research 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.Conclusions: Automated 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.
引用
收藏
页码:258 / 267
页数:10
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