@Article{jimh.2026.084876,
AUTHOR = {Bailing Zhang, Genlang Chen},
TITLE = {Knowledge–Rule–Decision: A Loosely-Coupled Architecture for Auditable High-Stakes Clinical Decision Support},
JOURNAL = {Journal of Intelligent Medicine and Healthcare},
VOLUME = {4},
YEAR = {2026},
NUMBER = {1},
PAGES = {109--124},
URL = {http://www.techscience.com/JIMH/v4n1/68053},
ISSN = {2837-634X},
ABSTRACT = {High-stakes clinical decision support (CDS) demands a property that aggregate accuracy cannot capture: a trace that a clinician who was not in the room can inspect layer by layer when the system is wrong. We argue that the way to obtain this property is to refuse to entangle the large language model (LLM) with the rest of the pipeline. We propose <sans-serif><b>KRD</b></sans-serif> <b>(Knowledge–Rule–Decision)</b>, a four-component architecture that separates fact extraction, a compile-time clinical knowledge layer in the spirit of the LLM Wiki pattern of Karpathy, a rule layer of hand-written contraindications and heuristics, and a decision interface whose <monospace>compose</monospace> method short-circuits to a rule-cited blocking response whenever any hard violation fires. We evaluate <sans-serif>KRD</sans-serif> against a pure language model, a retrieval-augmented language model, a rule-only system, and a light hybrid on a benchmark of 32 type-1 diabetes scenarios. A strict version of the unsafe-suggestion rate stratifies the five systems monotonically into four distinct tiers from 0.867 down to zero, with <sans-serif>S4</sans-serif> and <sans-serif>S5</sans-serif> tied at the floor; the full <sans-serif>KRD</sans-serif> stack and the light hybrid reach the hard-safety ceiling together; <sans-serif>KRD</sans-serif> leads the light hybrid on evidence trace completeness by 25% relative and on reviewer correction burden by 12% relative, both directionally clear and borderline significant under bootstrap intervals; and <sans-serif>KRD</sans-serif> issues 17 language model calls per benchmark pass against the light hybrid’s 32, a 47% reduction that is a direct consequence of the architectural choice to evaluate the rule layer before invoking the model. We also report honestly that the evidence gate is inert on this benchmark because every compiled concept is graded A or B, and we trace five fact-extraction failures to a single field and a single linguistic pattern. The contribution is not that <sans-serif>KRD</sans-serif> is universally optimal but that layer-wise auditability is a design discipline whose cost in this setting was lower than its critics would have predicted.},
DOI = {10.32604/jimh.2026.084876}
}