Leveraging Longitudinal Data to Sustain the Impact of Organ Donation Training: A Data Science Approach to Quality Monitoring.

Abstract

Background: The effectiveness of training organ donation teams in improving organ donation rates has been well established. However, studies have shown that this impact is often temporary and diminishes without ongoing support and quality control programs. This study aimed to evaluate the effects of initial training and the role of a structured quality control program in sustaining these improvements in Iran's organ procurement units (OPUs).

Method: A comprehensive training program, the Iranian OPUs Supporting System (IrOSS), was developed and implemented with the participation of 25 OPUs. Their organ donation (PMP) rates were recorded at baseline and monitored every 3 months for 6 months posttraining. Later, a quality control initiative, the Iranian OPUs Quality Control System (IrOQS), was introduced in 15 of these units. Through periodic assessments, weaknesses were identified and addressed via targeted interventions such as strategy improvements and retraining. The remaining 10 units served as a control group. PMP was reevaluated 3 and 6 months after the initial IrOQS implementation, with a final assessment 3 months after the second round. Longitudinal data were analyzed using advanced statistical techniques, including repeated-measures ANOVA, difference-in-differences (DiD), linear mixed-effects models (LMMs), trajectory analysis, and change point detection.

Results: The mean PMP across all 25 OPUs increased from 5.31 before training to 8.05 six months after training. In the intervention group, the mean PMP increased to 13.06 after the first IrOQS phase, decreased to 9.84 over the next 6 months, and increased again to 15.6 after the second IrOQS phase. In contrast, while the control group showed an initial increase after training, their PMP gradually decreased over time. Statistical analyses showed that the initial training had a significant positive effect on performance, but these improvements were not sustained without repeated interventions. Path analyses also showed that units with lower initial performance responded most positively to training but tended to regress if retrained.

Conclusion: The findings of this study suggest that initial training programs can lead to significant improvements in PMP. However, to maintain these gains, regular implementation of ongoing training programs is essential. Data science approaches and longitudinal data analysis can play a critical role in identifying performance patterns in the quality management of OPUs.