Detecting Data Deviation in Electronic Health Records
Kaiping Zheng, Horng-Ruey Chua, Beng Chin Ooi
Data deviation in electronic health records (EHR) refers to discrepancies between recorded entries and a patient’s actual physiological state, indicating a decline in EHR data fidelity. Such deviation can result from pre-analytical variability, documentation errors, or unvalidated data sources. Effectively detecting data deviation is clinically valuable for identifying erroneous records, excluding them from downstream clinical workflows, and informing corrective actions. Despite its importance and practical relevance, this problem remains largely under-explored in existing research. To bridge this gap, we propose a bi-level knowledge distillation approach centered on a task-agnostic formulation of EHR data fidelity as an intrinsic measure of data reliability. Our approach performs layered knowledge distillation in two stages: from a computation-intensive, task-specific data Shapley oracle to a neural oracle for individual tasks, and then to a unified EHR data fidelity predictor. This design enables the integration of task-specific insights into a holistic assessment of a patient’s EHR data fidelity from a multi-task perspective. By tracking the outputs of this learned predictor, we detect potential data deviation in EHR data. Experiments on both our hospital’s proprietary dataset and the public MIMIC-III dataset consistently validate the effectiveness of our approach in detecting data deviation in EHR data. Case studies further demonstrate its practical value in identifying clinically meaningful data deviation.