Lead Time Reduction for Cell Therapy Patients using Predictive Analytics in Quality Control

Author(s)	Ashish Patil
Country	United States
Abstract	Cell therapy is an advanced medical treatment for cancer that involves the modification of patient cells to fight cancerous cells effectively. However, ensuring quality in cell therapy is a crucial step in the treatment process, and any delay in the quality assessment phase can impact patient outcomes by increasing the lead time. Predictive analytics can play a significant role in forecasting quality timelines and prioritizing quality control tests to optimize the overall process and lead time reduction. This paper discusses the importance of quality control in cell therapy, the need for predictive analytics, a comparison of different predictive models, and a recommended approach. Additionally, the implementation of the predictive model is demonstrated using pseudo-code and its impacts are highlighted: improvement in lead time to patient being the primary impact.
Keywords	Lead time reduction (Turn Around Time: TAT Reduction), Cell Therapy, Quality Control, Predictive Analytics, Cancer Treatment, Machine Learning, Forecasting, Healthcare Optimization.
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-15
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.41902
Short DOI	https://doi.org/g9fmxp

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International Journal For Multidisciplinary Research