Healthcare Informatics & Health Information Technology (SIG Health)
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Paper Type
ERF
Paper Number
1659
Description
Sexually transmitted diseases remain a significant public health issue with substantial cost and societal implications. Despite these grave concerns, individuals suffering from these disorders do not usually seek care early due to the STDs' taboo. In the article, we try to address such individuals' care needs by developing a chatbot application using a design science research approach. The application offers a much richer experience to the users by leveraging decision tree algorithms to develop context sensitivity using the attributes of the individuals seeking information and the nature of the information sought. Further, using Google’s dialog flow and custom-built web interfaces, we build and integrate our chatbot. Subsequently, we evaluated the chatbot using simulation techniques. We explore the potential of this chatbot in providing a context-specific dialogue with patients. As future work, we intend to develop direct conversational agents with more robust conversational coherence capabilities and interconnect conversational flow with the user while maintaining solid contextual grounds.
Recommended Citation
Mulgund, Pavankumar; Sharman, Raj; THIMMANAYAKANAPALYA, Sagarika Suresh; and Agrawal, Lavlin, "Design Science Approach to developing using Chatbot for Sexually Transmitted Diseases" (2021). AMCIS 2021 Proceedings. 24.
https://aisel.aisnet.org/amcis2021/healthcare_it/sig_health/24
Design Science Approach to developing using Chatbot for Sexually Transmitted Diseases
Sexually transmitted diseases remain a significant public health issue with substantial cost and societal implications. Despite these grave concerns, individuals suffering from these disorders do not usually seek care early due to the STDs' taboo. In the article, we try to address such individuals' care needs by developing a chatbot application using a design science research approach. The application offers a much richer experience to the users by leveraging decision tree algorithms to develop context sensitivity using the attributes of the individuals seeking information and the nature of the information sought. Further, using Google’s dialog flow and custom-built web interfaces, we build and integrate our chatbot. Subsequently, we evaluated the chatbot using simulation techniques. We explore the potential of this chatbot in providing a context-specific dialogue with patients. As future work, we intend to develop direct conversational agents with more robust conversational coherence capabilities and interconnect conversational flow with the user while maintaining solid contextual grounds.
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