Real-Time Tracking of Misdiagnosis in Electronic Health Records for Improved Predictive Modeling

Misdiagnosis, a persistent threat in healthcare, compromises patient outcomes and hinders effective treatment. Despite its prevalence, understanding and addressing misdiagnoses remains a complex challenge due to the limitations of traditional methods and fragmented data. The emergence of machine learning (ML) offers a potential game-changer, leveraging vast medical datasets to identify subtle disease signatures and personalize treatment plans with unprecedented accuracy. However, data quality remains a formidable barrier, as incompleteness, inconsistencies, and biases can distort ML models, leading to potentially detrimental misinterpretations. This paper highlights the critical role of misdiagnosis statistics in navigating this intricate landscape and creates a schema to address the problems caused by the lack of accessible misdiagnosis data.