Virtual conversational agents versus online forms: Patient experience and preferences for health data collection

ObjectiveVirtual conversational agents, or chatbots, have emerged as a novel approach to health data collection. However, research on patient perceptions of chatbots in comparison to traditional online forms is sparse. This study aimed to compare and assess the experience of completing a health asse...

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Main Authors: Hiral Soni (Author), Julia Ivanova (Author), Hattie Wilczewski (Author), Alexandra Bailey (Author), Triton Ong (Author), Alexa Narma (Author), Brian E. Bunnell (Author), Brandon M. Welch (Author)
Formato: Livro
Publicado em: Frontiers Media S.A., 2022-10-01T00:00:00Z.
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100 1 0 |a Hiral Soni  |e author 
700 1 0 |a Julia Ivanova  |e author 
700 1 0 |a Hattie Wilczewski  |e author 
700 1 0 |a Alexandra Bailey  |e author 
700 1 0 |a Triton Ong  |e author 
700 1 0 |a Alexa Narma  |e author 
700 1 0 |a Brian E. Bunnell  |e author 
700 1 0 |a Brian E. Bunnell  |e author 
700 1 0 |a Brandon M. Welch  |e author 
700 1 0 |a Brandon M. Welch  |e author 
245 0 0 |a Virtual conversational agents versus online forms: Patient experience and preferences for health data collection 
260 |b Frontiers Media S.A.,   |c 2022-10-01T00:00:00Z. 
500 |a 2673-253X 
500 |a 10.3389/fdgth.2022.954069 
520 |a ObjectiveVirtual conversational agents, or chatbots, have emerged as a novel approach to health data collection. However, research on patient perceptions of chatbots in comparison to traditional online forms is sparse. This study aimed to compare and assess the experience of completing a health assessment using a chatbot vs. an online form.MethodsA counterbalanced, within-subject experimental design was used with participants recruited via Amazon Mechanical Turk (mTurk). Participants completed a standardized health assessment using a chatbot (i.e., Dokbot) and an online form (i.e., REDCap), each followed by usability and experience questionnaires. To address poor data quality and preserve integrity of mTurk responses, we employed a thorough data cleaning process informed by previous literature. Quantitative (descriptive and inferential statistics) and qualitative (thematic analysis and complex coding query) approaches were used for analysis.ResultsA total of 391 participants were recruited, 185 of whom were excluded, resulting in a final sample size of 206 individuals. Most participants (69.9%) preferred the chatbot over the online form. Average Net Promoter Score was higher for the chatbot (NPS = 24) than the online form (NPS = 13) at a statistically significant level. System Usability Scale scores were also higher for the chatbot (i.e. 69.7 vs. 67.7), but this difference was not statistically significant. The chatbot took longer to complete but was perceived as conversational, interactive, and intuitive. The online form received favorable comments for its familiar survey-like interface.ConclusionOur findings demonstrate that a chatbot provided superior engagement, intuitiveness, and interactivity despite increased completion time compared to online forms. Knowledge of patient preferences and barriers will inform future design and development of recommendations and best practice for chatbots for healthcare data collection. 
546 |a EN 
690 |a health data collection 
690 |a patient experience 
690 |a virtual conversational agents 
690 |a chatbots 
690 |a usability 
690 |a Medicine 
690 |a R 
690 |a Public aspects of medicine 
690 |a RA1-1270 
690 |a Electronic computers. Computer science 
690 |a QA75.5-76.95 
655 7 |a article  |2 local 
786 0 |n Frontiers in Digital Health, Vol 4 (2022) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fdgth.2022.954069/full 
787 0 |n https://doaj.org/toc/2673-253X 
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