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Cited by 105Electronic Health Records SystemsArtificial Intelligence in Healthcare and EducationTechnology and Human Factors in Education and Health

Investigating the Heart Pump Implant Decision Process: Opportunities for Decision Support Tools to Help

Open access
Yang, Qian, Zimmerman, John, Steinfeld, Aaron, Carey, Lisa, Antaki, James F.·2016·Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/2858036.2858373PMC PMC5101017
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Abstract

Clinical decision support tools (DSTs) are computational systems that aid healthcare decision-making. While effective in labs, almost all these systems failed when they moved into clinical practice. Healthcare researchers speculated it is most likely due to a lack of user-centered HCI considerations in the design of these systems. This paper describes a field study investigating how clinicians make a heart pump implant decision with a focus on how to best integrate an intelligent DST into their work process. Our findings reveal a lack of perceived need for and trust of machine intelligence, as well as many barriers to computer use at the point of clinical decision-making. These findings suggest an alternative perspective to the traditional use models, in which clinicians engage with DSTs at the point of making a decision. We identify situations across patients' healthcare trajectories when decision supports would help, and we discuss new forms it might take in these situations.

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Crossref · 10.1145/2858036.2858373

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  • Yang et al. found that users saw little need for an ML-powered tool that performed well in laboratory tests, lacked trust in its output, and faced environmental barriers that made it difficult to use.

    “They found that users saw little need for the tool, lacked trust in its output, and faced environmental barriers that made it difficult to use.”

    From Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2021) · cites this paper as [132]

    The Discussion (page unavailable) reports that VAD clinicians perceived little need for an intelligent decision-support tool, lacked trust in its ability to help with difficult cases, and faced barriers to computer use in the clinical environment. But the paper describes a field study of potential tool use, not users testing the output of a particular ML-powered tool that had performed well in laboratory tests; laboratory success is discussed for DSTs generally. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Airdropped decision support systems often fail to embrace the richness of the clinical context.

    “Only recently have HCI researchers in these settings begun to investigate institutional contexts behind these failures [83], noting primarily that airdropped decision support systems often fail to embrace the richness of the clinical context.”

    From Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (2018) · cites this paper as [83]

  • Organisational conditions are often missed in design contexts.

    “As observed in studies of clinical decision tools, which often fail to be adopted [83], organisational conditions are often missed in design contexts.”

    From Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (2018) · cites this paper as [83]

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Research works printed but not linked: 2

  1. [17]Faiola Anthony Srinivas Preethi Karanam Yamini Chartash David Doebbeling Bradley 2014 VizCom: a novel workflow model for ICU clinical decision support CHI’14 Extended Abstracts on Human Factors in Computing Systems ACM 1705 1710
  2. [48]Yang Qian Zimmerman John Steinfeld Aaron 2015 Review of Medical Decision Support Tools: Emerging Opportunity for Interaction Design IASDR 2015 Interplay Proceedings

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Authors
Yang, Q., Zimmerman, J., Steinfeld, A., Carey, L., & Antaki, J. F.
Affiliations
  • Carnegie Mellon University (US)
  • University of Pittsburgh (US)
ORCID
Venue
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2016-05-07
Volume
2016
Pages
4477-4488
DOI
10.1145/2858036.2858373
PMC
PMC5101017
OpenAlex
W2397156396
Cited by
105
References
50
Citations by year
2026: 5 · 2025: 12 · 2024: 12 · 2023: 20 · 2022: 15 · 2021: 10 · 2020: 7 · 2019: 11 · 2018: 9 · 2017: 4
FWCI
11.12
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
NIH National Heart Lung and Blood Institute (NHLBI) (1R01HL122639-01A1)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
Keywords
clinical decision support systems, clinical decision-making, user-centered design, human-computer interaction in healthcare, barriers to computer use, clinical workflow integration, care pathways

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