A Governance and Evaluation Framework for Deterministic, Rule-Based Clinical Decision Support in Empiric Antibiotic Prescribing

TL;DR

This paper presents a structured framework for designing, governing, and evaluating deterministic clinical decision-support systems in antibiotic prescribing, emphasizing transparency, auditability, and safety in high-risk scenarios.

Contribution

It introduces a formal governance and evaluation framework that separates decision logic from rule mechanisms and focuses on behavioral alignment with explicit constraints.

Findings

Framework formalizes abstention as correct behavior when governance conditions are unmet.

Evaluation methodology uses synthetic cases to verify rule compliance.

Supports transparent and auditable decision-making in antibiotic stewardship.

Abstract

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.