为抗生素经验性用药设计可审计的规则系统,确保决策透明且保守。
A Governance and Evaluation Framework for Deterministic, Rule-Based Clinical Decision Support in Empiric Antibiotic Prescribing
- 用确定性规则分离决策逻辑与推荐权限,保证输入一致输出一致。
- 设定明确不推荐场景,系统在不符合条件时自动放弃推荐。
- 通过预设模拟病例验证系统行为符合规则,而非临床效果好坏。
高风险临床情境下的经验性抗生素处方常面临信息不全的问题,不当覆盖或过度升级可能危及安全并损害抗菌药物管理。尽管已有临床决策支持系统尝试介入,但多数缺乏明确的治理与评估机制,无法界定作用范围、拒绝条件、推荐许可及系统预期行为。本文提出一种确定性规则系统的治理与评估框架,强调行为一致性以保障透明性与可审计性。框架将治理作为核心设计要素,将临床决策逻辑与是否发出建议的规则机制相分离。明确包含拒绝机制、确定性守则及排除规则等核心构建。评估方法采用一组预设的合成临床案例,其行为预期由规则驱动。验证重点在于系统行为是否与规则一致,而非临床疗效、预测准确率或结果优化。当治理条件未满足时,系统选择不推荐被视为正确且有意的结果。该框架为经验性抗生素处方中的确定性决策支持系统提供了一种可复现的规范、治理与检验路径,尤其适用于对透明度、可审计性和保守性要求高的场景。
原文摘要 · Abstract (English)
Empiric antibiotic prescribing in high-risk clinical contexts often requires decision making under conditions of incomplete information, where inappropriate coverage or unjustified escalation may compromise safety and antimicrobial stewardship. While clinical decision-support systems have been proposed to assist in this process, many approaches lack explicit governance and evaluation mechanisms defining scope, abstention conditions, recommendation permissibility, and expected system behavior. This work specifies a governance and evaluation framework for deterministic clinical decision-support systems operating under explicitly constrained scope. Deterministic behavior is adopted to ensure that identical inputs yield identical outputs, supporting transparency, auditability, and conservative decision support in high-risk prescribing contexts. The framework treats governance as a first-class design component, separating clinical decision logic from rule-based mechanisms that determine whether a recommendation may be issued. Explicit abstention, deterministic stewardship constraints, and exclusion rules are formalized as core constructs. The framework defines an evaluation methodology utilizing a fixed set of synthetic, mechanism-driven clinical cases with predefined expected behavior. This validation process focuses on behavioral alignment with specified rules rather than clinical effectiveness, predictive accuracy, or outcome optimization. Within this protocol, abstention is treated as a correct and intended outcome when governance conditions are not satisfied. The proposed framework provides a reproducible approach for specifying, governing, and inspecting deterministic clinical decision-support systems in empiric antibiotic prescribing contexts where transparency, auditability, and conservative behavior are prioritized.
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