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Designing Support for Sensemaking in Multimodal, Multi-model Mental Health Assessments

Ademaj, Gemza LU ; Zhang, Xinyuan ; Abbasi, Ahmed ; Sarker, Saonee and Sarker, Suprateek (2025) Forty-Sixth International Conference on Information Systems (CIS) In ICIS 2025 Proceedings p.1-9
Abstract
Mental health assessments increasingly rely on remote formats that produce rich but complex multimodal data in the form of text, audio, and video, processed through multiple machine learning models. While these environments offer new opportunities for insight, they also pose significant challenges for effective clinical sensemaking. This study introduces a dashboard designed to address these challenges by enabling practitioners to explore behavioral data from multiple angles while mitigating overreliance on model accuracy metrics. Grounded in the Design Science Research, the dashboard design is informed by an integration of Integrative Sensemaking Theory and Signal Detection Theory. The research contributes a set of design requirements for... (More)
Mental health assessments increasingly rely on remote formats that produce rich but complex multimodal data in the form of text, audio, and video, processed through multiple machine learning models. While these environments offer new opportunities for insight, they also pose significant challenges for effective clinical sensemaking. This study introduces a dashboard designed to address these challenges by enabling practitioners to explore behavioral data from multiple angles while mitigating overreliance on model accuracy metrics. Grounded in the Design Science Research, the dashboard design is informed by an integration of Integrative Sensemaking Theory and Signal Detection Theory. The research contributes a set of design requirements for supporting sensemaking in multimodal, multi-model contexts, instantiates them in a dashboard artifact, and proposes an evaluation with practitioners using clinically grounded sensemaking tasks. This work advances computational design by offering theoretical and practical insights for advancing the integration of these complex data in mental healthcare context. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
ICIS 2025 Proceedings : IS in Healthcare - IS in Healthcare
series title
ICIS 2025 Proceedings
issue
15
pages
1 - 9
conference name
Forty-Sixth International Conference on Information Systems (CIS)
conference location
Nashville, United States
conference dates
2025-12-14 - 2025-12-17
ISSN
3067-0896
language
English
LU publication?
yes
id
cc31d593-3683-4b94-80d3-aa9620a5c5b8
alternative location
https://aisel.aisnet.org/icis2025/is_health/ishealthcare/15/
date added to LUP
2025-12-15 11:12:27
date last changed
2025-12-15 15:33:56
@inproceedings{cc31d593-3683-4b94-80d3-aa9620a5c5b8,
  abstract     = {{Mental health assessments increasingly rely on remote formats that produce rich but complex multimodal data in the form of text, audio, and video, processed through multiple machine learning models. While these environments offer new opportunities for insight, they also pose significant challenges for effective clinical sensemaking. This study introduces a dashboard designed to address these challenges by enabling practitioners to explore behavioral data from multiple angles while mitigating overreliance on model accuracy metrics. Grounded in the Design Science Research, the dashboard design is informed by an integration of Integrative Sensemaking Theory and Signal Detection Theory. The research contributes a set of design requirements for supporting sensemaking in multimodal, multi-model contexts, instantiates them in a dashboard artifact, and proposes an evaluation with practitioners using clinically grounded sensemaking tasks. This work advances computational design by offering theoretical and practical insights for advancing the integration of these complex data in mental healthcare context.}},
  author       = {{Ademaj, Gemza and Zhang, Xinyuan and Abbasi, Ahmed and Sarker, Saonee and Sarker, Suprateek}},
  booktitle    = {{ICIS 2025 Proceedings : IS in Healthcare}},
  issn         = {{3067-0896}},
  language     = {{eng}},
  number       = {{15}},
  pages        = {{1--9}},
  series       = {{ICIS 2025 Proceedings}},
  title        = {{Designing Support for Sensemaking in Multimodal, Multi-model Mental Health Assessments}},
  url          = {{https://aisel.aisnet.org/icis2025/is_health/ishealthcare/15/}},
  year         = {{2025}},
}