Heart Failure Education of African American and Hispanic/Latino Patients: Data Collection and Analysis

Itika Gupta, Barbara Di Eugenio, Devika Salunke, Andrew Boyd, Paula Allen-Meares, Carolyn Dickens, Olga Garcia

Abstract
Heart failure is a global epidemic with debilitating effects. People with heart failure need to actively participate in home self-care regimens to maintain good health. However, these regimens are not as effective as they could be and are influenced by a variety of factors. Patients from minority communities like African American (AA) and Hispanic/Latino (H/L), often have poor outcomes compared to the average Caucasian population. In this paper, we lay the groundwork to develop an interactive dialogue agent that can assist AA and H/L patients in a culturally sensitive and linguistically accurate manner with their heart health care needs. This will be achieved by extracting relevant educational concepts from the interactions between health educators and patients. Thus far we have recorded and transcribed 20 such interactions. In this paper, we describe our data collection process, thematic and initiative analysis of the interactions, and outline our future steps.
Anthology ID:
2020.nlpmc-1.6
Volume:
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
Month:
July
Year:
2020
Address:
Online
Venue:
NLPMC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–46
Language:
URL:
https://aclanthology.org/2020.nlpmc-1.6
DOI:
10.18653/v1/2020.nlpmc-1.6
Bibkey:
Cite (ACL):
Itika Gupta, Barbara Di Eugenio, Devika Salunke, Andrew Boyd, Paula Allen-Meares, Carolyn Dickens, and Olga Garcia. 2020. Heart Failure Education of African American and Hispanic/Latino Patients: Data Collection and Analysis. In Proceedings of the First Workshop on Natural Language Processing for Medical Conversations, pages 41–46, Online. Association for Computational Linguistics.
Cite (Informal):
Heart Failure Education of African American and Hispanic/Latino Patients: Data Collection and Analysis (Gupta et al., NLPMC 2020)
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