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Co-Designing an AI Chatbot to Improve Patient Experience in the Hospital: A human-centered design case study of a collaboration between a hospital, a university, and ChatGPT

  • State University of New York Binghamton University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Patient experience (PX) is an important reflection of healthcare quality and is highly related to patient health outcomes and hospital reputation of within the communities they serve. PX data reported by patients is also crucial for hospitals to improve the services they provide, however, current approaches to survey and analyze PX data have many limitations. Our team collaborated with United Health Services (UHS), a New York healthcare system, to co-design a prototype chatbot application for patients to use while in the hospital, yielding more accurate PX data, but also an opportunity for staff to respond in real-time. We discuss our human-centered design process, which entailed interviews, data mining, qualitative analysis, and the application of ChatGPT and other algorithms to recognize relevant PX complaints from natural language data. Through ongoing collaboration, we are developing a chatbot application to elicit PX feedback and allow PX experts to improve patient experience in real-time.

Original languageEnglish
Title of host publicationCHI 2024 - Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Sytems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400703317
DOIs
StatePublished - May 11 2024
Event2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024 - Hybrid, Honolulu, United States
Duration: May 11 2024May 16 2024

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024
Country/TerritoryUnited States
CityHybrid, Honolulu
Period05/11/2405/16/24

Keywords

  • ChatGPT
  • data mining
  • healthcare
  • human-centered design
  • large language model
  • natural language processing
  • patient experience
  • qualitative analysis

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