Using conversational agents to explain medication instructions to older adults

Renato F.L. Azevedo, Dan Morrow, James Graumlich, Ann Willemsen-Dunlap, Mark Hasegawa-Johnson, Thomas S. Huang, Kuangxiao Gu, Suma Bhat, Tarek Sakakini, Victor Sadauskas, Donald J. Halpin

Research output: Contribution to journalArticlepeer-review


In an effort to guide the development of a computer agent (CA)-based adviser system that presents patient-centered language to older adults (e.g., medication instructions in portal environments or smartphone apps), we evaluated 360 older and younger adults' responses to medication information delivered by a set of CAs. We assessed patient memory for medication information, their affective responses to the information, their perception of the CA's teaching effectiveness and expressiveness, and their perceived level of similarity with each CA. Each participant saw CAs varying in appearance and levels of realism (Photo-realistic vs Cartoon vs Emoji, as control condition). To investigate the impact of affective cues on patients, we varied CA message framing, with effects described either as gains of taking or losses of not taking the medication. Our results corroborate the idea that CAs can produce a significant effect on older adults' learning in part by engendering social responses.

Original languageEnglish (US)
Pages (from-to)185-194
Number of pages10
JournalAMIA Annual Symposium Proceedings
StatePublished - 2018

ASJC Scopus subject areas

  • General Medicine


Dive into the research topics of 'Using conversational agents to explain medication instructions to older adults'. Together they form a unique fingerprint.

Cite this