ArticleAdvances in simulation (London, England)2026
A function-based framework for AI-amplified, data-driven healthcare simulation research.
Article in Advances in simulation (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Healthcare simulation research increasingly generates rich, multimodal datasets, yet substantial portion of these data remain underutilized due to analytic, technical, and resource constraints. As artificial intelligence becomes more accessible, there is a risk that adoption in simulation research is driven by novelty rather than by clearly articulated analytic needs. This article proposes a function-based framework that conceptualizes artificial intelligence as a research amplifier for data-driven simulation-based research. Rather than organizing approaches by algorithm family or tool type, the framework groups AI use according to its function in the research workflow across four domains: (1) Data Processing and Integration: transforming raw traces into synchronized, research-ready datasets; (2) Comparative and Predictive Analytics: benchmarking performance and modeling learning trajectories or outcomes; (3) Behavioral and Interaction Analysis: decoding individual and team behaviors from observational traces; and (4) Automation and Acceleration: streamlining research activities such as transcription, coding support, and structured summarization through automated or semi-automated workflows. Five practical steps illustrate how the framework can be embedded in research planning: clarifying the analytic task, mapping it to functional domains, specifying data representations and integration needs, designing the human–AI division of labor, and planning evaluation and reporting. Ethical, trust, and accountability considerations are discussed across functional domains, emphasizing that distinct risks and safeguards emerge across different AI-enabled research activities. By centering function and purpose, this framework aims to help simulation researchers make explicit, intentional decisions about when and how AI meaningfully extends analytic capacity.
Indexed as
Identifiers
What OpenQuestion holds
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.