ArticleLearning health systems2026
From Challenge to Studio: A Prototype Frontline AI Innovation Mechanism to Address the AI Embedding Deficit in Healthcare and Educational Institutions.
Article in Learning health systems, 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
Introduction: Health systems are expanding access to artificial intelligence (AI), yet frontline integration into teaching, clinical workflows, and research practice remains uneven. We frame this gap as the "AI embedding deficit," reflecting limited organizational pathways that enable frontline professionals to translate local needs into legitimate, supported, and sustainable AI-enabled tools and workflows. This Experience Report describes a prototype intervention mechanism implemented at an academic nursing school: a rapid-cycle Nursing-AI Challenge followed by a year-round Nursing-AI Studio (NAIS) designed to stabilize and scale embedding. Methods: We implemented the Nursing-AI Challenge as a structured, time-bounded mechanism to solicit real-world challenges from nursing faculty, translate and curate submissions through an interdisciplinary facilitation role, match challenges to AI trainees and mentors, and support rapid proposal development culminating in a public showcase. We then developed NAIS to address continuity and embedding barriers revealed in the Challenge, incorporating continuous intake, environmental scanning, supported co-development, year-round coordination, formalized interdisciplinary partnerships, and a strategic memorandum of understanding with the Chief Information Officer's office to support legitimate integration pathways. Results: In the inaugural Challenge cycle (Spring 2025), nursing faculty identified 17 challenges spanning education, clinical practice, and research, and students in AI-related fields submitted eight proposals, followed by team formation, co-design of the AI prototypes, and a public showcase event. Implementation reflections identified key sustainability barriers including semester-bounded participation, summer momentum gaps, and intellectual property ambiguity. NAIS was established (Fall 2025 onward) to mitigate these barriers and support ongoing embedding. In early Spring 2026, NAIS launched "Teaching with AI," a clinical faculty innovation initiative piloting an AI-powered teaching tool fully deployed within the university's information technology infrastructure. Conclusions: A Challenge-to-Studio pathway may function as a practical prototype mechanism for learning health systems seeking to reduce embedding barriers and convert decentralized frontline innovation into durable, governable, and evaluable practice change.
Indexed as
Identifiers
What Socratic 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 Socratic graph.