Multicall Memory in an AI Care Agent for Chronic Care Management Among Older Adults: Retrospective Observational Study.

Authors

Markel Sanz Ausin, Akash Chaurasia, Alex Miller, Jonathan David Agnew, Rae Lasko, Mariska Raglow-Defranco, Michelle Voisard, Saad Godil, Subhabrata Mukherjee

Published in

JMIR formative research. Volume 10. Pages e87704. Aug 21, 2026. Epub Aug 21, 2026.

Abstract

Multicall memory capabilities in AI-powered health care communication systems show promise for enhancing patient engagement, but their impact on engagement and patient satisfaction remains unclear.
This study evaluated the relationship between multicall memory usage and key patient experience metrics, including call duration and satisfaction scores, in an AI-powered health care communication system.
We conducted a retrospective analysis of 4415 AI care agent calls from 4189 patients using linear mixed-effects models to account for multiple calls per patient. The primary predictor was the number of memories used per call. Outcomes included call duration (in minutes), net promoter score, and patient satisfaction ratings. We analyzed the full dataset and relevant subsets (completed calls only and memory-using calls only) to assess the robustness of the findings.
Memory usage was significantly associated with increased call duration, with each additional memory associated with an extension of 2.47 minutes (95% CI 2.03-2.91; P<.001). This effect was consistent across sensitivity analyses, though it was attenuated in completed calls only (+0.54 min per memory; P=.004). Memory usage showed no significant association with patient satisfaction across any analysis. Given that only a small subset of calls used memories and satisfaction data were available only for completed calls, the study may have been underpowered to detect an association between memory use and net promoter score or satisfaction ratings.
Multicall memory usage is significantly associated with enhanced behavioral engagement. The findings reveal a disconnect between engagement duration and patient-reported experience, suggesting that memory optimization strategies should focus on behavioral engagement metrics while considering factors beyond usage quantity for patient satisfaction. These results provide evidence-based guidance for health care organizations implementing memory-enabled AI communication systems.

PMID:
42627706
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.

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