Analyzing and redesigning deployment pathways for internally developed artificial intelligence tools in clinical...

Artificial intelligence (AI) tools for clinical education and health research are increasingly easy to imagine and prototype, but deploying internally developed tools into legitimate institutional use remains difficult. We conducted a retrospective comparative case study of four AI tools supported by the Nursing AI Studio in one academic health and social sciences environment. The four tools each was designed to address one of the following: clinical evaluation, tailored caregiver support, clinical simulation, and statistical assistance for biomedical and social-behavioral data. We operationalized deployment burden as the organizational, technical, and coordination work required to move a locally developed functioning AI-powered web application into legitimate institutional use. Across cases, early setup steps such as Amazon Web Services access, institutional subdomain configuration, and Information Security Office review were comparatively stable when institutional knowledge and reusable assets were available. The heavier burden occurred in first-team pathway discovery, enterprise integration, and cross-institutional access. The first case included approximately two-week gaps before later requests were initiated, reflecting time spent identifying the next institutional step rather than formal review time. Later internal cases reused such pathway knowledge, but recurring integration and changing compliance requirements persisted. The cross-institutional case introduced a distinct access problem that was not solved by the internal pathway. These findings suggest that deployment burden is not a single delay but a layered process involving discovery, reuse, recurrence, and context-specific bottlenecks. Shared studio-like support mechanisms may reduce repeated rediscovery and help health and social sciences teams plan more realistically for governance, infrastructure, collaboration, and sustainability.