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Statistical Process Control Charts for Monitoring Next-Generation Sequencing and Bioinformatics Turnaround in
Sneha Rajiv Jain1, Wilson Sim1, Cheng Han Ng1
1Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Frontiers in Oncology
|October 11, 2021
Summary
Statistical process control methods like CUSUM and EWMA were used to analyze learning curves in precision oncology. Both next-generation sequencing (NGS) and bioinformatics processes showed reduced turnaround times (TAT) with experience.
Area of Science:
- Precision oncology and molecular diagnostics.
- Laboratory process improvement and quality management.
Background:
- Precision oncology relies on next-generation sequencing (NGS) and bioinformatics for targeted therapies.
- Laboratory turnaround time (TAT) is a critical metric for laboratory performance.
Purpose of the Study:
- To apply statistical process control (SPC) methods to analyze learning curves in NGS and bioinformatics.
- To evaluate the impact of experience on laboratory turnaround time (TAT) in precision medicine.
Main Methods:
- Regression models (robust linear, negative binomial) analyzed TAT trends.
- CUSUM and EWMA charts were employed to monitor TAT.
- Statistical analyses were performed using Stata v16.0.
Main Results:
- Significant TAT reductions were observed with increasing experience for both NGS and bioinformatics.
- EWMA and CUSUM charts showed generally consistent TAT trends.
- Bioinformatics learning curve was shorter (54 cases) than NGS (85 cases).
Conclusions:
- Characterizing TAT in NGS and bioinformatics enhances data timeliness in precision oncology.
- Early identification of process issues can improve cancer care delivery.
- SPC methods provide valuable insights into laboratory process optimization.
Keywords:
bioinformaticscomputational biologynext generation sequencingprecision medicineprecision oncologyMore Related Videos
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