干旱区生态环境知识资源中心(Arid): Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare

Arid

DOI	10.1093/jamia/ocad195
	Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare
	Zolnoori, Maryam; Sridharan, Sridevi; Zolnour, Ali; Vergez, Sasha; McDonald, Margaret, V; Kostic, Zoran; Bowles, Kathryn H.; Topaz, Maxim
通讯作者	Zolnoori, M
来源期刊	JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
ISSN	1067-5027
EISSN	1527-974X
出版年	2024
卷号	31 期号:2 页码:435-444
英文摘要	Background In the United States, over 12 000 home healthcare agencies annually serve 6+ million patients, mostly aged 65+ years with chronic conditions. One in three of these patients end up visiting emergency department (ED) or being hospitalized. Existing risk identification models based on electronic health record (EHR) data have suboptimal performance in detecting these high-risk patients.Objectives To measure the added value of integrating audio-recorded home healthcare patient-nurse verbal communication into a risk identification model built on home healthcare EHR data and clinical notes.Methods This pilot study was conducted at one of the largest not-for-profit home healthcare agencies in the United States. We audio-recorded 126 patient-nurse encounters for 47 patients, out of which 8 patients experienced ED visits and hospitalization. The risk model was developed and tested iteratively using: (1) structured data from the Outcome and Assessment Information Set, (2) clinical notes, and (3) verbal communication features. We used various natural language processing methods to model the communication between patients and nurses.Results Using a Support Vector Machine classifier, trained on the most informative features from OASIS, clinical notes, and verbal communication, we achieved an AUC-ROC = 99.68 and an F1-score = 94.12. By integrating verbal communication into the risk models, the F-1 score improved by 26%. The analysis revealed patients at high risk tended to interact more with risk-associated cues, exhibit more sadness and anxiety, and have extended periods of silence during conversation.Conclusion This innovative study underscores the immense value of incorporating patient-nurse verbal communication in enhancing risk prediction models for hospitalizations and ED visits, suggesting the need for an evolved clinical workflow that integrates routine patient-nurse verbal communication recording into the medical record.
英文关键词	home healthcare emergency department visit and hospitalization audio-recorded patient-nurse verbal communication natural language processing machine learning
类型	Article
语种	英语
收录类别	SCI-E ; SSCI
WOS记录号	WOS:001084801800001
WOS关键词	TALKING
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Health Care Sciences & Services ; Information Science & Library Science ; Medical Informatics
WOS研究方向	Computer Science ; Health Care Sciences & Services ; Information Science & Library Science ; Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404723
推荐引用方式 GB/T 7714	Zolnoori, Maryam,Sridharan, Sridevi,Zolnour, Ali,et al. Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare[J],2024,31(2):435-444.
APA	Zolnoori, Maryam.,Sridharan, Sridevi.,Zolnour, Ali.,Vergez, Sasha.,McDonald, Margaret, V.,...&Topaz, Maxim.(2024).Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare.JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION,31(2),435-444.
MLA	Zolnoori, Maryam,et al."Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare".JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 31.2(2024):435-444.