학술논문
Implementation Fidelity of Chatbot Screening for Social Needs: Acceptability, Feasibility, Appropriateness.
Document Type
Academic Journal
Author
Langevin R; Department of Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States.; Berry ABL; Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States.; Zhang J; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, United States.; Fockele CE; Department of Emergency Medicine, University of Washington School of Medicine, Seattle, Washington, United States.; Anderson L; Department of Emergency Medicine, University of Washington School of Medicine, Seattle, Washington, United States.; Hsieh D; Department of Emergency Medicine, Harbor-University of California Los Angeles Medical Center, Torrance, California, United States.; Hartzler A; Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States.; Duber HC; Department of Emergency Medicine, University of Washington School of Medicine, Seattle, Washington, United States.; Office of Health and Science, Washington State Department of Health, Seattle, Washington, United States.; Hsieh G; Department of Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States.
Source
Publisher: Thieme Country of Publication: Germany NLM ID: 101537732 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1869-0327 (Electronic) Linking ISSN: 18690327 NLM ISO Abbreviation: Appl Clin Inform Subsets: MEDLINE
Subject
Language
English
Abstract
Objectives: Patient and provider-facing screening tools for social determinants of health have been explored in a variety of contexts; however, effective screening and resource referral remain challenging, and less is known about how patients perceive chatbots as potential social needs screening tools. We investigated patient perceptions of a chatbot for social needs screening using three implementation outcome measures: acceptability, feasibility, and appropriateness.
Methods: We implemented a chatbot for social needs screening at one large public hospital emergency department (ED) and used concurrent triangulation to assess perceptions of the chatbot use for screening. A total of 350 ED visitors completed the social needs screening and rated the chatbot on implementation outcome measures, and 22 participants engaged in follow-up phone interviews.
Results: The screened participants ranged in age from 18 to 90 years old and were diverse in race/ethnicity, education, and insurance status. Participants ( n = 350) rated the chatbot as an acceptable, feasible, and appropriate way of screening. Through interviews ( n = 22), participants explained that the chatbot was a responsive, private, easy to use, efficient, and comfortable channel to report social needs in the ED, but wanted more information on data use and more support in accessing resources.
Conclusion: In this study, we deployed a chatbot for social needs screening in a real-world context and found patients perceived the chatbot to be an acceptable, feasible, and appropriate modality for social needs screening. Findings suggest that chatbots are a promising modality for social needs screening and can successfully engage a large, diverse patient population in the ED. This is significant, as it suggests that chatbots could facilitate a screening process that ultimately connects patients to care for social needs, improving health and well-being for members of vulnerable patient populations.
Competing Interests: None declared.
(Thieme. All rights reserved.)
Methods: We implemented a chatbot for social needs screening at one large public hospital emergency department (ED) and used concurrent triangulation to assess perceptions of the chatbot use for screening. A total of 350 ED visitors completed the social needs screening and rated the chatbot on implementation outcome measures, and 22 participants engaged in follow-up phone interviews.
Results: The screened participants ranged in age from 18 to 90 years old and were diverse in race/ethnicity, education, and insurance status. Participants ( n = 350) rated the chatbot as an acceptable, feasible, and appropriate way of screening. Through interviews ( n = 22), participants explained that the chatbot was a responsive, private, easy to use, efficient, and comfortable channel to report social needs in the ED, but wanted more information on data use and more support in accessing resources.
Conclusion: In this study, we deployed a chatbot for social needs screening in a real-world context and found patients perceived the chatbot to be an acceptable, feasible, and appropriate modality for social needs screening. Findings suggest that chatbots are a promising modality for social needs screening and can successfully engage a large, diverse patient population in the ED. This is significant, as it suggests that chatbots could facilitate a screening process that ultimately connects patients to care for social needs, improving health and well-being for members of vulnerable patient populations.
Competing Interests: None declared.
(Thieme. All rights reserved.)