Journal of applied clinical medical physics · 2026
Automated linac downtime tracking across 4 radiation oncology clinics within a single hospital system utilizing statistical process control.
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Serafini Caroline E, Thomas Matthew, Yang Xiaofeng, Patel Pretesh, Roper Justin, Washington Maxine, Dhabaan Anees, Al-Hallaq Hania
Аннотация
BACKGROUND: Linear accelerator (linac) downtime disrupts clinical workflow and may delay patient treatment; however, downtime events are frequently undocumented or recorded in informal reporting systems. Data-driven approaches using routinely collected operational metrics may improve downtime surveillance and provide insight into system-level adaptation.
PURPOSE: This study aims to evaluate if statistical process control (SPC) methods can be applied to routine treatment and scheduling data from an oncology information system (OIS) to identify linac downtime events and characterize operational response across multiple radiation oncology clinics within a single hospital system.
METHODS: Daily machine-level operational data from 10 linacs across 4 clinics were analyzed over 29 months (January 2024-May 2026). Machine metrics included daily patient volume, cancellations, and operating hours. For each machine, rolling 45-day baseline means and standard deviations were computed, and SPC rules (± 3σ control limits and > 10 cancellations / day) were applied to detect downtime days. SPC-flagged events were compared with email-based downtime event logs to calculate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Downtime events were categorized by cause, and intra-hospital patient redistribution was assessed by identifying concurrent > 4σ volume increases on alternate machines.
RESULTS: Over the 29-month study period, 138 downtime events were documented in downtime event logs. SPC identified 23 additional events exhibiting operational signatures consistent with downtime. Using reported events as the reference standard, SPC achieved sensitivity 0.70, specificity 0.99, PPV 0.79, and NPV 0.99. Downtime frequently resulted in compensatory increases in patient volume on alternate machines, with 41 transfer pairs identified across three hospitals. When the reported downtime cause was specified, machine mechanical causes accounted for the largest proportion (48%) of downtime events.
CONCLUSIONS: SPC applied to routinely collected operational data provides a scalable framework for automated linac downtime detection. Integration of automated monitoring with structured reporting may improve downtime surveillance and inform proactive operational planning in radiation oncology.
PURPOSE: This study aims to evaluate if statistical process control (SPC) methods can be applied to routine treatment and scheduling data from an oncology information system (OIS) to identify linac downtime events and characterize operational response across multiple radiation oncology clinics within a single hospital system.
METHODS: Daily machine-level operational data from 10 linacs across 4 clinics were analyzed over 29 months (January 2024-May 2026). Machine metrics included daily patient volume, cancellations, and operating hours. For each machine, rolling 45-day baseline means and standard deviations were computed, and SPC rules (± 3σ control limits and > 10 cancellations / day) were applied to detect downtime days. SPC-flagged events were compared with email-based downtime event logs to calculate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Downtime events were categorized by cause, and intra-hospital patient redistribution was assessed by identifying concurrent > 4σ volume increases on alternate machines.
RESULTS: Over the 29-month study period, 138 downtime events were documented in downtime event logs. SPC identified 23 additional events exhibiting operational signatures consistent with downtime. Using reported events as the reference standard, SPC achieved sensitivity 0.70, specificity 0.99, PPV 0.79, and NPV 0.99. Downtime frequently resulted in compensatory increases in patient volume on alternate machines, with 41 transfer pairs identified across three hospitals. When the reported downtime cause was specified, machine mechanical causes accounted for the largest proportion (48%) of downtime events.
CONCLUSIONS: SPC applied to routinely collected operational data provides a scalable framework for automated linac downtime detection. Integration of automated monitoring with structured reporting may improve downtime surveillance and inform proactive operational planning in radiation oncology.