A Probabilistic Model for Reducing Medication Errors

BackgroundMedication errors are common, life threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings. The aim of this study was to construct a probabilistic model that can reduce medication errors by identifying uncommon or rare associations between medications and diseases.Methods and Finding(s)Association rules of mining techniques are utilized for 103.5 million prescriptions from Taiwan’s National Health Insurance database. The dataset included 204.5 million diagnoses with ICD9-CM codes and 347.7 million medications by using ATC codes. Disease-Medication (DM) and Medication-Medication (MM) associations were computed by their co-occurrence and associations’ strength were measured by the interestingness or lift values which were being referred as Q values. The DMQs and MMQs were used to develop the AOP model to predict the appropriateness of a given prescription. Validation of this model was done by comparing the results of evaluation performed by the AOP model and verified by human experts. The results showed 96% accuracy for appropriate and 45% accuracy for inappropriate prescriptions, with a sensitivity and specificity of 75.9% and 89.5%, respectively.ConclusionsWe successfully developed the AOP model as an efficient tool for automatic identification of uncommon or rare associations between disease-medication and medication-medication in prescriptions. The AOP model helps to reduce medication errors by alerting physicians, improving the patients’ safety and the overall quality of care.背景：用药差错（medication errors）频发，可危及生命、造成高额经济负担且本可预防。信息技术（information technology）与自动化系统在预防用药差错方面效率优异，因此在医疗机构中得到广泛应用。本研究旨在构建一种概率模型，通过识别药物与疾病间罕见或不常见的关联，以降低用药差错风险。 研究方法与结果：本研究针对台湾地区全民健康保险数据库中的1.035亿张处方数据，采用关联规则挖掘（association rules mining）技术展开分析。该数据集包含2.045亿条标注ICD9-CM（International Classification of Diseases, 9th Revision, Clinical Modification）编码的诊断记录，以及3.477亿条标注ATC（Anatomical Therapeutic Chemical）编码的药物使用数据。通过共现情况计算疾病-药物（Disease-Medication, DM）与药物-药物（Medication-Medication, MM）关联，并以有趣度或提升度（lift值）衡量关联强度，上述指标在本文中统称为Q值。基于DM关联与MM关联构建AOP模型，用于预测给定处方的合理性。模型验证环节通过对比AOP模型的评估结果与人类专家的验证结论完成。结果显示，该模型对合理处方的识别准确率达96%，对不合理处方的识别准确率达45%，灵敏度与特异度分别为75.9%与89.5%。 结论：本研究成功开发AOP模型，可作为高效工具自动识别处方中疾病-药物与药物-药物间的罕见或不常见关联。该模型可通过向医师发出预警，助力降低用药差错风险，提升患者安全与整体医疗照护质量。