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Background: Clinical artificial intelligence (AI) technologies are increasingly being introduced into hospital practice, yet evidence describing their operational integration and performance after deployment in routine clinical settings remains limited. This study examined the real-world implementation and operational integration of clinical AI within a provincial tertiary health system in China over an 18-month observation period. Methods: This retrospective longitudinal observational study used aggregated institutional data generated during routine platform deployment, including electronic medical record–linked system logs, deployment records, quality-monitoring summaries, and operational reports. The analysis focused on implementation patterns, workflow integration, selected operational indicators, and user acceptance during routine clinical use. The study evaluated implementation and operational integration rather than algorithmic accuracy, diagnostic performance, or patient-level clinical effectiveness. Results: Three AI-supported clinical pathways were included: an intelligent pre-consultation system, a multidisciplinary tumor decision-support system, and a duloxetine therapeutic drug-monitoring pathway. During the observation period, 127 clinicians across 53 specialties participated in AI-assisted clinical activities involving more than 27,000 patient encounters, 850 multidisciplinary tumor decision-support cases, and 320 therapeutic drug-monitoring episodes. Patient waiting time decreased from 18 to 13 minutes, patient satisfaction increased from 95.40% to 98.92%, consultation efficiency improved by approximately 40%, and documentation completion efficiency improved by approximately 80%. Implementation patterns differed substantially across pathways, reflecting differences in workflow position and clinical accountability rather than deployment effort alone. Conclusion: In this provincial tertiary health system, clinical AI implementation was associated with sustained operational use, cross-specialty workflow integration, and measurable changes in selected workflow indicators. These findings suggest that technical functionality alone is insufficient for sustained clinical AI use and that workflow compatibility and organizational readiness are central to routine implementation. Because this was a single-site observational study using aggregated operational data, the findings should be interpreted as implementation evidence rather than proof of clinical effectiveness. Plain Language Summary: Many hospital AI systems are tested in pilot settings but are not subsequently integrated into routine clinical operations. This study examined how a clinical AI platform was deployed in routine care at a large teaching hospital in Hebei Province, China, over an 18-month period. The study used routine hospital data, including electronic medical record–linked system logs, deployment records, quality-monitoring summaries, operational reports, and patient satisfaction information. No identifiable patient information was accessed. The analysis examined how widely the AI tools were used, which clinical services adopted them, and whether selected workflow indicators changed following routine deployment. Three AI-supported tools were included: an intelligent pre-consultation system, a multidisciplinary tumor decision-support system, and a duloxetine therapeutic drug-monitoring system. In total, 127 clinicians across 53 specialties used the platform, involving more than 27,000 patient encounters, 850 tumor decision-support cases, and 320 drug-monitoring episodes. Patient waiting time decreased from 18 to 13 minutes, patient satisfaction increased from 95.40% to 98.92%, consultation efficiency improved by approximately 40%, and documentation efficiency improved by approximately 80%. The findings suggest that clinical AI can move from pilot use into routine hospital workflows when deployment is grounded in existing clinical workflows and backed by sustained organizational support. Because this was an observational study from a single hospital, the findings should be interpreted cautiously and confirmed in other settings. Keywords: clinical artificial intelligence, real-world implementation, workflow integration, operational outcomes, implementation science
Tian et al. (Mon,) studied this question.