Overdiagnosis occurs when abnormalities meeting diagnostic criteria would remain asymptomatic if undiagnosed. Cases initially identified through digital diagnostic tools but later recognised as overdiagnosis are referred to as ‘digital overdiagnosis’. Data-driven frameworks to quantify and mitigate overdiagnosis remain limited. This study introduces a framework that integrates clinical trajectories to train a machine learning (ML)-based disease classifier, enabling the quantification and mitigation of digital overdiagnosis, using Heparin-Induced Thrombocytopenia (HIT) as a case study.

A pre-existing HIT classifier identified HIT-positive and HIT-negative cases, with ground truth based on HIT diagnostic criteria. Clinical trajectories for True Positive (TP) and True Negative (TN) patients were clustered using a novel process-models-based approach. Overdiagnosis was detected when TP cases clustered with predominantly TN cases. The classifier was then retrained with an ‘updated label’ integrating both HIT criteria and the concordant trajectory, to reduce overdiagnosis while maintaining accuracy.

7.2% of TP cases were identified as overdiagnosed. Retraining with the updated labels successfully reclassified 89.5% of overdiagnosed cases as TN, with only a minimal reduction in performance (MCC decreased by 0.03, positive likelihood ratio decreased by 0.49, and negative likelihood ratio increased by 0.05). Clinical outcomes—length of stay, thrombotic events, and mortality—differed significantly between non-overdiagnosed and overdiagnosed cases, and between non-overdiagnosed and TN cases, but not between overdiagnosed and TN cases, confirming that overdiagnosed patients resemble TN patients.

Incorporating clinical trajectories into ML-based diagnosis enables the quantification of digital overdiagnosis. This approach could refine ML algorithms by prompting a reassessment of criteria-based disease labels in supervised learning.