用机器学习预测血常规结果，减少住院患者重复检验15%。
SmartAlert: Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Lab Utilization Reduction
- 基于机器学习预测血常规稳定结果，智能提示是否需重复检验。
- 干预组52小时内血常规检测次数下降至1.54次，降幅15%（p<0.01）。
- 适合医疗管理者与临床系统开发者参考，关注高效安全的临床决策支持。
重复进行可能无临床价值的实验室检测是常见现象，增加患者负担和医疗成本。教育与反馈效果有限，通用检验限制和电子提醒又可能影响正常诊疗。本文介绍并评估了集成于电子病历的机器学习驱动临床决策支持系统SmartAlert，该系统通过预测血常规结果的稳定性，减少不必要的重复检测。本案例研究描述了在两家医院八个急症病房开展的随机对照试点中，针对9270例入院患者从2024年8月15日至2025年3月15日的实施过程、挑战与经验教训。结果显示，显示SmartAlert后52小时内血常规检测次数显著减少（1.54次对比1.82次，p<0.01），相对降低15%，且未对次要安全指标造成负面影响。实施经验包括：概率模型在临床中的解释、利益相关方对模型行为的共识定义、复杂模型部署的治理流程、用户界面设计考量、与临床运营优先级对齐，以及来自终端用户的定性反馈价值。结论表明，经过周密实施与治理流程支持的机器学习驱动的临床决策支持系统，可提供精准指导，安全减少住院患者重复实验室检测。
原文摘要 · Abstract (English)
Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts impede appropriate clinical care. We introduce and evaluate SmartAlert, a machine learning (ML)-driven clinical decision support (CDS) system integrated into the electronic health record that predicts stable laboratory results to reduce unnecessary repeat testing. This case study describes the implementation process, challenges, and lessons learned from deploying SmartAlert targeting complete blood count (CBC) utilization in a randomized controlled pilot across 9270 admissions in eight acute care units across two hospitals between August 15, 2024, and March 15, 2025. Results show significant decrease in number of CBC results within 52 hours of SmartAlert display (1.54 vs 1.82, p <0.01) without adverse effect on secondary safety outcomes, representing a 15% relative reduction in repetitive testing. Implementation lessons learned include interpretation of probabilistic model predictions in clinical contexts, stakeholder engagement to define acceptable model behavior, governance processes for deploying a complex model in a clinical environment, user interface design considerations, alignment with clinical operational priorities, and the value of qualitative feedback from end users. In conclusion, a machine learning-driven CDS system backed by a deliberate implementation and governance process can provide precision guidance on inpatient laboratory testing to safely reduce unnecessary repetitive testing.
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