AI-Enhanced Operating Room Quality Model Metrics from a 2024 Single-Center Study

A prospective single-center study at Sir Run Run Shaw Hospital in Hangzhou, China, from January to December 2024. The dataset contains metrics from an AI-enhanced quality model for operating rooms, involving 347 equipment units and 156 staff members. It was created by Luping Li and published under a CC-BY-4.0 license.

Use Cases

• Predicting medical equipment failure based on IoT sensor data and maintenance protocol adherence.
• Modeling the impact of workflow optimization on operating room turnover time.
• Analyzing the relationship between staff training, technology integration scores, and productivity indices.
• Benchmarking AI model performance for quality-sensitive indicators in clinical settings.

Strengths

• Results include specific performance improvements: 37.1% reduction in equipment downtime, 25.3% increase in staff efficiency, and 33.1% decrease in room turnover time.
• Model evaluation metrics are provided, including a predictive failure algorithm accuracy of 92.8% and an AUC-ROC of 0.94.
• Multivariate analysis identifies key predictors (maintenance protocol adherence, technology integration scores) with reported beta coefficients and R-squared values.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The single-center, pre-post design may limit generalizability of findings.

Provenance

Source

figshare

Collection Method

Prospective single-center study using IoT sensors, electronic health records, and validated assessment tools.

Time Range

January 2024 to December 2024

Freshness

Last updated 2026-05-26 10:10:06; freshness should be verified.

Geography

Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China