ArticleRisk management and healthcare policy2026
Clinical Artificial Intelligence Implementation in Routine Care: Real-World Operational Outcomes from a Provincial Health System in China.
Article in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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.