T. Bouhafa

Publications

• Research Article   
  AI-driven radiotherapy appointment optimization: A lean management approach
  Author(s): Samia Khalfi*, T. Malih, EL M. Abiza, EL M. Sadiki, N Chenfour, Y. Aghlallou, W. Hassani, FZ Farhane, Z. Alami and T. Bouhafa
  
  Purpose: Radiotherapy is integral to cancer treatment, but inefficiencies in appointment scheduling often lead to delays, suboptimal resource utilization, and compromised care quality. Leveraging Lean Management methodologies, we identified bottlenecks and inefficiencies in the scheduling process, emphasizing the need for advanced optimization strategies. This study developed an AIdriven system to automate and optimize radiotherapy appointment scheduling, integrating patient-specific and departmental constraints. Materials and Methods: Lean Management tools, including Value Stream Mapping (VSM) and 5S analysis, identified inefficiencies in manual scheduling, such as resource misallocation and delays in urgent cases. An AI-based solution was designed with supervised machine learning algorithms to classify patient urgency and optimize schedules .. Read More»
  

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