Liu Yu-Chen, Lin Guan-Ling, Scholl Jeremiah, Hung Yi-Chun, Lin Yu-Jing, Li Yu-Chuan, Yang Hsuan-Chia
School of Nursing, College of Medicine, National Taiwan University, Taipei, Taiwan.
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
J Med Internet Res. 2025 May 27;27:e70731. doi: 10.2196/70731.
Potentially inappropriate prescribing in outpatient care contributes to adverse outcomes and health care inefficiencies. Clinical decision support systems (CDSS) offer promising solutions, but their effectiveness is often constrained by incomplete medical records.
This study aims to evaluate the effectiveness of a machine learning-based CDSS for enhancing diagnostic recommendations, which are system-suggested diagnoses, ensuring that each prescribed medication has a corresponding diagnosis documented and meets medication appropriateness.
This prospective single-arm interventional study was conducted over 1 year in the outpatient departments of a hospital. The system provided diagnostic recommendations based on machine learning algorithms trained on data from the National Health Insurance Research Database. Outcome measures included alert rates, acceptance rates of diagnostic recommendations, and variability in system performance across specialties. Descriptive and trend analyses were used to evaluate the system's effectiveness.
This study included 438,558 prescriptions from 44 physicians across 23 specialties, involving 125,000 unique patients in the outpatient departments of a regional teaching hospital. MedGuard, embedded with diagnostic recommendations, achieved an overall alert rate of 2.28% and a diagnostic recommendation acceptance rate of 56.55%. All accepted recommendations resulted in actionable changes, including prescription adjustments or the addition of missing diagnoses. Ophthalmology achieved the highest acceptance rate at 96.59%, while rheumatology, surgery, psychiatry, and infectious disease recorded acceptance rates of 0%, 0%, 24.74%, and 35%, respectively. Over the years, acceptance rates for potentially inappropriate prescriptions stabilized at 51%, despite increasing prescription volumes.
This study demonstrates the potential of embedding diagnostic recommendations into alerts within a machine learning-based clinical decision support system to improve diagnostic completeness and support safer outpatient care. Future efforts should refine alerts to align with specialty-specific workflows and validate their effectiveness in diverse clinical settings.
门诊护理中潜在的不适当处方会导致不良后果和医疗保健效率低下。临床决策支持系统（CDSS）提供了有前景的解决方案，但其有效性常常受到不完整病历的限制。
本研究旨在评估基于机器学习的CDSS在增强诊断建议方面的有效性，这些诊断建议是系统建议的诊断，确保每种开具的药物都有相应记录的诊断且符合用药合理性。
这项前瞻性单臂干预研究在一家医院的门诊部门进行了1年。该系统基于对来自国民健康保险研究数据库的数据进行训练的机器学习算法提供诊断建议。结果指标包括警报率、诊断建议接受率以及各专科系统性能的变异性。采用描述性和趋势分析来评估系统的有效性。
本研究纳入了来自23个专科的44名医生的438,558张处方，涉及一家地区教学医院门诊部门的125,000名不同患者。嵌入诊断建议的MedGuard总体警报率为2.28%，诊断建议接受率为56.55%。所有被接受的建议都带来了可采取行动的改变，包括调整处方或补充缺失的诊断。眼科的接受率最高，为96.59%，而风湿病学、外科、精神病学和传染病学的接受率分别为0%、0%、24.74%和35%。多年来，尽管处方量增加，但潜在不适当处方的接受率稳定在51%。
本研究证明了将诊断建议嵌入基于机器学习的临床决策支持系统中的警报以提高诊断完整性并支持更安全的门诊护理的潜力。未来的努力应优化警报以符合特定专科的工作流程，并在不同临床环境中验证其有效性。
