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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.