Explainable AI-Assisted Clinical Decision Support Using Electronic Health Record Time-Series Data
Keywords:
explainable artificial intelligence, clinical decision support, electronic health records, time-series analysis, healthcare infrastructure, algorithmic fairness, model governance, predictive analyticsAbstract
The integration of artificial intelligence into clinical decision support systems holds transformative potential for modern healthcare, particularly when leveraging the rich, longitudinal data contained within electronic health records. However, the widespread adoption of such systems is critically dependent on their interpretability, as opaque model predictions undermine clinician trust and pose significant regulatory and ethical challenges. This paper presents a comprehensive analysis of explainable AI-assisted clinical decision support systems that operate on electronic health record time-series data. It moves beyond the technical specifics of individual algorithms to address the broader systemic, architectural, and governance challenges inherent in deploying these systems at scale. The discussion begins by characterizing the unique structural properties of electronic health record time-series, including irregular sampling, high dimensionality, and missing data mechanisms, and how these properties constrain model design. The paper then examines the architectural trade-offs between predictive fidelity and interpretability, contrasting deep learning frameworks with intrinsically interpretable models and post-hoc explanation methods. A substantial portion of the analysis is dedicated to the deployment infrastructure required to operationalize such systems within hospital information technology environments, focusing on data pipelines, real-time inference latency, and the sustainability of continuous model monitoring. The paper further explores the critical dimensions of fairness and robustness, demonstrating how biases embedded in historical clinical data can be amplified by opaque models, and how explainability can serve as a diagnostic tool for auditing these biases. Finally, the discussion extends to policy implications and the evolving regulatory landscape, particularly concerning model validation, accountability, and patient safety. The paper concludes that the future of AI in clinical decision support lies not in pursuing ever-greater complexity, but in architecting systems that are inherently transparent, resilient, and aligned with the socio-technical realities of clinical practice.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
