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Optimization of radiotherapy infrastructure in cancer treatment facilities: A comprehensive review of strategies for treatment efficiency and patient safety
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S. Lu   , Q. Zhan , L. Zhang , J. Cui |
| Department of Infrastructure, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, 250014, China , jhri911@163.com |
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Abstract: (16 Views) |
Background: Optimizing radiotherapy infrastructure is essential for delivering safe and effective cancer treatments while minimizing costs and improving sustainability. Materials and Methods: A narrative review was conducted using PubMed, Scopus, and Web of Science (2015–2024) with the terms “radiotherapy,” “infrastructure,” “energy efficiency,” and “patient safety.” Inclusion criteria focused on studies addressing energy systems, treatment planning technologies, and infrastructure optimization in cancer facilities. A case study of the Xinhua Cancer Center (Shandong, China) was also analyzed, including renewable energy integration, shielding layouts, and AI-driven maintenance strategies. Results: The literature review identified key strategies such as advanced electrical layouts, real-time dosimetry, AI-assisted planning, and integration of renewable energy. The Xinhua Cancer Center case demonstrated that solar (78,336 kWh/day), wind (1,071 kWh/day), and battery storage (200 kWh/day) provided 87.2% of energy needs, reducing treatment downtime to 12.6%. Ergonomic layouts decreased procedural errors by 17% and improved treatment readiness by 12%. Predictive maintenance lowered equipment downtime by 30% and improved treatment accuracy by 25%. Challenges included retrofitting costs (USD 80–250/m²) and regulatory constraints. Conclusion: Optimized radiotherapy infrastructure can substantially enhance treatment efficiency and patient safety. Future directions include AI-based treatment algorithms, hybrid energy storage, smart grid integration, and block chain-secured patient records. |
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| Keywords: Radiotherapy, Health Facility Infrastructure, Energy Efficiency, Patient Safety, Neoplasms, Artificial Intelligence. |
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Full-Text [PDF 730 kb]
(3 Downloads)
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Type of Study: Review article |
Subject:
Radiation Biology
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