摘要
提出了在医疗质量持续改进策略中使用数据挖掘技术对医疗质量指标进行分析,特别是使用决策树分析方法对某医院8405例出院病人进行了数据挖掘分析,得出影响病人死亡率的几个重要因素。这些因素分别为:住院天数、疾病种类、所在科室以及年龄,并以决策树图形方式揭示了这些因素与病人死亡率的关系。同时,为了分析和监控医院医疗指标的变化趋势,我们开发了医疗质量决策支持系统,包括医疗质量持续改进的指导原则和方法。将来计划把支持全院医疗质量持续改进(CQI)的其他医疗质量指标纳入到软件系统中,并将质量决策支持系统与医院HIS医生工作站软件进行集成。
This study presents an analysis of healthcare quality indicatiors using data mining for developing quality improvement strategies.Specifically, improtant factors influencing the inpatient mortality were identified using a decision tree method for data mining based on 8 405 patient who were discharged from the hospital.Important factors for the inpatient mortality were length of stay, disease classes, discharge departments, and age groups.The optimum range of target group in inpatient healthcare quality indicatiors were identified from the gains chart.In addition, a Decision Support System （DSS） was developed to analyze and monitor trends of quality indicatiors.Guidelines and tutorial for quality improvement activities were also include in the system.In the future, other quality indicators should be analyzed to effectively support a hospital-wide Continuous Quality Inprovement （CQI） activity and the DSS should be well integrated with Hospital Information System （HIS） to support concurrent review.
出处
《中国医院管理》
北大核心
2006年第4期22-24,共3页
Chinese Hospital Management
基金
上海科委基金项目:医疗机构成本控制及智能决策分析系统(编号:其他-170)
关键词
医疗质量改进
数据挖掘
决策支持系统
continuous quality improvement, data mining, decision Support System
