The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index.
Investigating the Heart Pump Implant Decision Process: Opportunities for Decision Support Tools to Help
Open accessAbstract
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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“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]
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“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]
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“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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- 10.1037/0022-3514.71.2.315 · DOI
- Qian Yang , John Zimmerman , and Aaron Steinfeld . 2015 . Review of Medical Decision Support Tools : Emerging Opportunity for Interaction Design . In IASDR 2015 Interplay Proceedings. Qian Yang, John Zimmerman, and Aaron Steinfeld. 2015. Review of Medical Decision Support Tools : Emerging Opportunity for Interaction Design. In IASDR 2015 Interplay Proceedings.
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Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300830 · Cited by 777
Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design
Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376301 · Cited by 622
“First, we draw on our many years of experience in designing a wide range of AI systems, from simple adaptive user interfaces [58, 12], to large-scale crowd-sourced transportation systems [59]; from clinical decision supports [57, 55] to natural language productivity tools [52, 60, 16].”
BackgroundSelf-citationExpanding Explainability: Towards Social Transparency in AI systems
Ehsan, U., Liao, Q. V., Muller, M., Riedl, M. O., & Weisz, J. D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445188 · Cited by 538
Veale, M., Van Kleek, M., & Binns, R. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174014 · Cited by 490
“ose bought arXiv:1802.01029v1 [cs.CY] 3 Feb 2018 from vendors, and are facing immediate, value laden challenges [14]. As observed in studies of clinical decision tools, which often fail to be adopted [83], organisational conditions are often missed in design contexts. Indeed fields studying technologies in practice hav…”
BackgroundHuman-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making
Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., Stumpe, M. C., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300234 · Cited by 405
Metadata
- Authors
- Yang, Q., Zimmerman, J., Steinfeld, A., Carey, L., & Antaki, J. F.
- Affiliations
- Carnegie Mellon University (US)
- University of Pittsburgh (US)
- Venue
- Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2016-05-07
- Volume
- 2016
- Pages
- 4477-4488
- 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
- green
- www.ncbi.nlm.nih.gov
- 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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