ReviewDigital health
The user experiences of AI-based clinical decision support systems and implications for usage over time: A scoping review.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Artificial intelligence (AI)-based clinical decision support systems (AI-CDSS) have the potential to improve many facets of care, whether aimed toward unlocking new analysis methods, improving efficiency, or increasing patient safety. As AI-CDSS begins to see further real-world usage, there is an urgent need to understand how user experiences develop temporally, more so given these systems dynamic iterative nature. To explore this gap, we conducted a scoping review aiming to map user experiences with AI-CDSS, barriers, and facilitators and synthesize an overview of experiences temporally. Method: Following the scoping review methodology of Arksey and O'Malley, three databases were searched with 257 records retrieved, 16 of which met the inclusion criteria. After identifying reported experiences, we carried out a reflexive thematic analysis and "best-fit" synthesis to explore reported user experiences over time. Results: Nine overall user experience themes spanning two domains emerged from our analysis with 23 sub-themes. Themes include clinical context, clinical users, learnability, usability, trust and usefulness. Temporal mapping of experiences highlights their dynamic, interconnected nature and how these develop over time. These findings enrich the current understanding of how experiences with AI-CDSS develop over time, presenting implications for design. We additionally discuss gaps in current knowledge and opportunities for future work. Conclusions: Long-term experiences, particularly those occurring as a result of changes in model performance or as a result of iterative updates to AI-CDSS are sparsely described. Future work in this area should explore in more detail how experiences unfold over time and alongside AI-CDSS as they evolve.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.