ArticleJournal of medical Internet research2026
Hospitalists Are Already Using AI-Why Implementation Will Determine Its Impact.
Article in Journal of medical Internet research, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: The adoption of artificial intelligence (AI) into clinical practice is accelerating, outpacing the development of organizational guidance, training, and governance. A recent study indicated that two-thirds of hospitalists are using AI, particularly large language model (LLM)-based platforms, in their clinical work. However, as with prior disruptive health technologies, adoption alone does not ensure meaningful improvement in care. Drawing on lessons from electronic health record implementation, we argue that AI's ultimate impact will be determined not by use rates, but by implementation quality and fit. Poorly implemented digital tools have been shown to increase clinician workload and burnout, despite their intended benefits. Early evidence on LLM-based diagnostic AI further underscores this risk: clinical-decision making supported by AI may be suboptimal when integration, training, and workflow design are inadequate. To provide value, AI tools must be thoughtfully embedded into clinical reasoning processes through evidence-informed training, intentional workflow design, and supportive organizational culture. As AI technologies are rapidly adopted, three priorities come into focus: training clinicians on AI inputs and interpreting outputs, applying implementation science frameworks for AI deployment in clinical environments, and establishing strategies for ongoing evaluation of the impact of AI tools over time. Implementation science frameworks offer practical guidance to assess workflow integration, training needs, infrastructure, and potential unintended consequences that can then inform adaptation of implementation strategies to enhance contextual fit. In parallel, learning health system infrastructure can enable continuous monitoring and iterative adaptation using routinely collected clinical and workflow data that reflect the value of the intervention across the quintuple aim of clinical outcomes, health equity, cost, and patient and clinician experience. AI adoption in hospital medicine is likely inevitable. Its ability to advance the quintuple aim will depend on how effectively these tools are implemented, supported, evaluated, and adapted in practice.
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.