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<p>Dental chairs are essential for sustaining clinical care, yet their reliability in academic environments has rarely been quantified. This retrospective study, conducted across multiple clinics at Al-Andalus University for Medical Sciences (Tartous, Syria), integrated weekly usage logs, coded failure records, and equipment lifespan data to examine the relationship between workload and malfunction. Analysis revealed that prosthodontic and restorative clinics experienced the highest workloads and the greatest number of failures. Saliva ejectors, delivery systems, and control units accounted for most recurrent incidents, with a small subset of fault types responsible for the majority of breakdowns. The relationship between chair lifespan and fault frequency was generally weak, suggesting that age alone poorly predicts malfunction. Predictive modeling with random forest and linear regression demonstrated limited accuracy (R² ≈ 0.15) but revealed that case mix was a stronger driver of workload variation than calendar-related factors. Overall, the findings indicate that failure risk is shaped primarily by clinical specialization and workload intensity. Adapting maintenance strategies to actual usage and case profiles—rather than applying uniform schedules—may reduce downtime and improve patient flow. Expanding datasets and integrating real-time operational data could further strengthen predictive maintenance in academic dental settings.</p>

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Retrospective analysis of dental chair failures and usage patterns in academic dental clinics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

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  • Saleh Massoud,
  • Ebrahim Ismaiel,
  • Ayman Al Kayal

摘要

Dental chairs are essential for sustaining clinical care, yet their reliability in academic environments has rarely been quantified. This retrospective study, conducted across multiple clinics at Al-Andalus University for Medical Sciences (Tartous, Syria), integrated weekly usage logs, coded failure records, and equipment lifespan data to examine the relationship between workload and malfunction. Analysis revealed that prosthodontic and restorative clinics experienced the highest workloads and the greatest number of failures. Saliva ejectors, delivery systems, and control units accounted for most recurrent incidents, with a small subset of fault types responsible for the majority of breakdowns. The relationship between chair lifespan and fault frequency was generally weak, suggesting that age alone poorly predicts malfunction. Predictive modeling with random forest and linear regression demonstrated limited accuracy (R² ≈ 0.15) but revealed that case mix was a stronger driver of workload variation than calendar-related factors. Overall, the findings indicate that failure risk is shaped primarily by clinical specialization and workload intensity. Adapting maintenance strategies to actual usage and case profiles—rather than applying uniform schedules—may reduce downtime and improve patient flow. Expanding datasets and integrating real-time operational data could further strengthen predictive maintenance in academic dental settings.