验证和改进调整后的临床组的未来住院和高成本预测模型,用于荷兰初级保健
Shelley-Ann M Girwar1, Marta Fiocco2,3,4, Stephen P Sutch1,5
1Department of Public Health and Primary Care, Health Campus The Hague, Leiden University Medical Center, The Hague, The Netherlands.
风险分层模型准确地预测了荷兰初级保健中的未来住院和高医疗保健成本. 将美国模型调整为本地数据显著提高了其预测准确性和临床实用性.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 随着医疗保健成本的上升,需要有效的人口健康管理策略.
- 风险分层,根据医疗记录分配分数,对于识别有未来高成本和利用风险的患者至关重要.
- 现有的预测模型,通常是基于美国的,需要对荷兰初级保健等各种医疗保健系统进行验证和适应.
研究的目的:
- 验证调整临床组 (ACG) 风险分层模型的准确性,以预测荷兰初级保健中的住院和高医疗费用.
- 根据荷兰初级保健数据的具体背景,调整和改进基于美国的预测模型.
- 评估这些模型的统计有效性和区分能力.
主要方法:
- 利用荷兰初级医疗保健数据注册,对95262名患者的培训模型和对48780名患者的验证.
- 纳入的患者数据包括年龄,性别,全科医生访问,诊断 (ICPC) 和处方药物 (ATC).
- 雇员C-统计用于歧视能力评估和校准图片用于校准评估.
主要成果:
- 调整住院模式使得C-统计数据从0.69提高到0.75.
- 调整高成本模型改善了C统计从0.78到0.85.
- 两种调整后的模型都表现出良好的区分和校准,表明预测性能得到了提高.
结论:
- 当地调整的ACG预测模型显示了改善荷兰初级保健中的风险分层的巨大潜力.
- 经过验证和调整的模型可以提高人口健康管理的效率和有效性.
- 这些发现支持使用量身定制的风险预测工具来优化医疗保健资源配置.
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