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Inpatient laboratory monitoring produces frequent blood chemistry results that must be reviewed in relation to the patient’s evolving clinical state. Although many results are statistically abnormal, only a smaller subset require urgent interpretation, escalation, or therapeutic action. Conventional rule-based critical value systems depend heavily on fixed thresholds and may generate non-actionable notifications. Pure machine-learning classifiers may detect complex patterns but can be difficult to explain, audit, or align with institutional clinical policies. This article proposes a rule-augmented artificial intelligence framework for detecting clinically significant abnormal laboratory result patterns in hospitalized adults. The framework uses established clinical logic as a structured skeleton and enriches it with sequential laboratory patterns, vital sign trends, and physician response-time feedback. The framework contains a clinical rule knowledge base, a sequential blood chemistry encoder, a vital sign fusion module, and a significance calibration layer. Together, these components would support interpretable pattern detection while allowing alert thresholds to adapt to observed clinical behavior. The proposed architecture could reduce non-actionable alerts by distinguishing isolated statistical abnormalities from evolving clinical patterns. It would also be expected to support patient-specific baselines and integrate into existing inpatient electronic health record workflows. A rule-augmented AI framework offers a pathway toward safer, smarter, and less disruptive laboratory result surveillance. Its value would depend on careful rule curation, transparent model governance, and prospective evaluation in real clinical settings.

Laboratory alert fatigue erodes the effectiveness of clinical decision support by making repeated abnormal result notifications less likely to prompt timely clinical attention. It is often recognised only after providers begin delaying, overriding, or ignoring alerts. Current alert reduction strategies are commonly based on broad thresholds or blanket suppression rules. These approaches do not explain which providers, specialties, alert types, or repeated result patterns are most susceptible to fatigue. This article proposes an interpretable machine learning model that could predict whether a provider will exhibit fatigued behaviour toward a specific laboratory alert. The model is intended to support transparent, provider-aware alert redesign rather than opaque automation. The proposed framework would use historical alert logs with features capturing alert frequency, clinical severity, provider specialty, repeated abnormal results, response time history, and override behaviour. A regularised logistic regression or gradient-boosted tree model with SHAP explanations would provide both prediction and interpretability. Conceptually, the model would generate a fatigue risk score for each provider–alert pair. It would also attribute the score to specific drivers, such as repeated low-severity results, accumulated alert burden, or recent override patterns. An interpretable fatigue prediction model could enable personalised alert suppression, escalation, or redesign before a clinically important laboratory result is missed. Such a system would support safer, more adaptive clinical decision support.