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Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Chatbot for Social Need Screening and Resource Sharing With Vulnerable Families: Iterative Design and Evaluation

Emre Sezgin1, A Baki Kocaballi2, Millie Dolce1

  • 1Nationwide Children's Hospital, Columbus, OH, United States.

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Summary

A new chatbot, DAPHNE, screens for social needs and connects families to resources. User-centered design improved usability, though concerns about resource accuracy and EHR integration remain.

Keywords:
chatbotconversational agentdigital healthevaluationfeasibilityimplementationiterative designprimary caresocial determinants of healthsocial needsusability

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Area of Science:

  • Healthcare Technology
  • Social Determinants of Health
  • Human-Computer Interaction

Background:

  • Unmet social needs significantly impact health outcomes.
  • Screening for social needs is common in healthcare, but resource linkage is often poor.
  • Technology-based solutions can overcome structural barriers to addressing social needs.

Purpose of the Study:

  • To present the design and evaluation of DAPHNE (Dialog-Based Assistant Platform for Healthcare and Needs Ecosystem).
  • To assess DAPHNE's effectiveness in screening social needs and linking patients to resources.

Main Methods:

  • A three-stage approach: end-user survey, iterative design with stakeholders, and feasibility/usability assessment.
  • Involved surveys with low-income households (n=201), stakeholder sessions (n=10), and usability testing with community health workers (n=4) and social workers (n=9).
  • Utilized descriptive statistics, chi-square tests, content analysis, and thematic analysis.

Main Results:

  • Younger, employed individuals were more likely to use a chatbot for social needs.
  • Stakeholders emphasized provider-technology collaboration, inclusive design, and user education.
  • The chatbot met expectations for usability (System Usability Scale score=72/100), but concerns about resource accuracy and EHR integration were noted.

Conclusions:

  • Chatbots can offer personalized support for identifying and meeting social needs.
  • User-centered, iterative design is crucial for developing effective social needs chatbots.
  • Future research should focus on chatbot efficacy, cost-effectiveness, and scalability.