Health Informatics and Visualization

会議の名前
CHI 2022
Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients
要旨

Electronic health records in critical care medicine offer unprecedented opportunities for clinical reasoning and decision making. Paradoxically, these data-rich environments have also resulted in clinical decision support systems (CDSSs) that fit poorly into clinical contexts, and increase health workers cognitive load. In this paper, we introduce a novel approach to designing CDSSs that are embedded in clinical workflows, by presenting problem-based curated data views tailored for problem-driven discovery, team communication, and situational awareness. We describe the design and evaluation of one such CDSS, In-Sight, that embodies our approach and addresses the clinical problem of monitoring critically ill pediatric patients. Our work is the result of a co-design process, further informed by empirical data collected through formal usability testing, focus groups, and a simulation study with domain experts. We discuss the potential and limitations of our approach, and share lessons learned in our iterative co-design process.

受賞
Honorable Mention
著者
Minfan Zhang
University of Toronto, Toronto, Ontario, Canada
Daniel Ehrmann
Hospital for Sick Children, Toronto, Ontario, Canada
Mjaye Mazwi
Hospital for Sick Children, Toronto, Ontario, Canada
Danny Eytan
Hospital for Sick Children, Toronto, Ontario, Canada
Marzyeh Ghassemi
MIT, Cambridge, Massachusetts, United States
Fanny Chevalier
University of Toronto, Toronto, Ontario, Canada
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501887

動画
Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students' Mental Health
要旨

Reflecting on stress-related data is critical in addressing one’s mental health. Personal Informatics (PI) systems augmented by algorithms and sensors have become popular ways to help users collect and reflect on data about stress. While prediction algorithms in the PI systems are mainly for diagnostic purposes, few studies examine how the explainability of algorithmic prediction can support user-driven self-insight. To this end, we developed MindScope, an algorithm-assisted stress management system that determines user stress levels and explains how the stress level was computed based on the user's everyday activities captured by a smartphone. In a 25-day field study conducted with 36 college students, the prediction and explanation supported self-reflection, a process to re-establish preconceptions about stress by identifying stress patterns and recalling past stress levels and patterns that led to coping planning. We discuss the implications of exploiting prediction algorithms that facilitate user-driven retrospection in PI systems.

著者
Taewan Kim
KAIST, Daejeon, Korea, Republic of
Haesoo Kim
KAIST, Daejeon, Korea, Republic of
Ha Yeon Lee
Seoul National University, Seoul, Korea, Republic of
Hwarang Goh
Inha University, Incheon, Korea, Republic of
Shakhboz Abdigapporov
Inha University, Michuhol-gu, Incheon, Korea, Republic of
Mingon Jeong
Hanyang University, Seoul, Korea, Republic of
Hyunsung Cho
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Kyungsik Han
Hanyang University, Seoul, Korea, Republic of
Youngtae Noh
KENTECH, Naju-si, Jeollanam-do, Korea, Republic of
Sung-Ju Lee
KAIST, Daejeon, Korea, Republic of
Hwajung Hong
KAIST, Deajeon, Korea, Republic of
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517701

動画
Exploring Perceptions of Cross-Sectoral Data Sharing with People with Parkinson’s
要旨

In interdisciplinary spaces such as digital health, datasets that are complex to collect, require specialist facilities, and/or are collected with specific populations have value in a range of different sectors. In this study we collected a simulated free-living dataset, in a smart home, with 12 participants (six people with Parkinson’s, six carers). We explored their initial perceptions of the sensors through interviews and then conducted two data exploration workshops, wherein we showed participants the collected data and discussed their views on how this data, and other data relating to their Parkinson’s symptoms, might be shared across different sectors. We provide recommendations around how participants might be better engaged in considering data sharing in the early stages of research, and guidance for how research might be configured to allow for more informed data sharing practices in the future.

受賞
Honorable Mention
著者
Roisin McNaney
Monash University, Melbourne, Australia
Catherine Morgan
University of Bristol, Bristol, United Kingdom
Pranav Kulkarni
Monash University, Melbourne, VIC, Australia
Julio Vega
University of Pittsburgh, Pittsburgh, Pennsylvania, United States
Farnoosh Heidarivincheh
University of Bristol, Bristol, United Kingdom
Ryan McConville
University of Bristol, Bristol, United Kingdom
Alan Whone
University of Bristol, Bristol, United Kingdom
Mickey Kim
University of Bristol, Bristol, United Kingdom
Reuben Kirkham
Monash University, Melbourne, Australia
Ian Craddock
University of Bristol, Bristol, United Kingdom
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501984

動画
Personal Dream Informatics: A Self-Information Systems Model of Dream Engagement
要旨

We present the research area of personal dream informatics: studying the self-information systems that support dream engagement and communication between the dreaming self and the wakeful self. Through a survey study of 281 individuals primarily recruited from an online community dedicated to dreaming, we develop a dream-information systems view of dreaming and dream tracking as a type of self-information system. While dream-information systems are characterized by diverse tracking processes, motivations, and outcomes, they are universally constrained by the ephemeral dreamset - the short period of time between waking up and rapid memory loss of dream experiences. By developing a system dynamics model of dreaming we highlight feedback loops that serve as high leverage points for technology designers, and suggest a variety of design considerations for crafting technology that best supports dream recall, dream tracking, and dreamwork for nightmare relief and personal development.

著者
Michael Jeffrey Daniel. Hoefer
University of Colorado Boulder, Boulder, Colorado, United States
Bryce E. Schumacher
University of Colorado Boulder, Boulder, Colorado, United States
Stephen Voida
University of Colorado Boulder, Boulder, Colorado, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517669

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