增强索赔数据,以改善医院死亡率风险调整
Michael Pine1, Harmon S Jordan, Anne Elixhauser
1Michael Pine and Associates Inc, Chicago, Ill, USA. mpine@aol.com
JAMA
|January 4, 2007
概括
在入院代码和实验室数据上添加现有数据显著改善了医院对死亡率的风险调整模型. 这提高了医院质量测量和公共报告的准确性.
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
- 健康结果的健康结果.
背景情况:
- 准确的风险调整对于在公共报告和绩效支付计划中评估医院绩效至关重要.
- 现有的风险调整方程需要足够的临床细节来准确测量医院的质量.
研究的目的:
- 评估将入院时存在的 (POA) 代码和数值实验室数据纳入行政索赔数据对风险调整后医院死亡率的影响.
- 确定不同类型数据的附加值,以提高医院风险调整模型的准确性.
主要方法:
- 对住院患者死亡率的风险调整方程进行了比较分析,使用了宾夕法尼亚州188家医院 (2000年7月-2003年6月) 的数据.
- 通过顺序添加临床数据,包括行政数据,POA代码和实验室值来开发模型,以评估使用c统计数据的歧视力.
- 该研究侧重于特定的疾病 (急性心肌梗塞,充血性心力衰竭,脑血管事故,胃肠道出血,肺炎) 和程序 (腹腔大动脉瘤修复,冠状动脉旁路移植手术,切术).
主要成果:
- 基线管理模型实现了0.79 (0.02) 的平均c统计.
- 包括POA代码和数值实验室数据显著改善了风险调整,产生分别为0.84 (0.01) 和0.86 (0.01) 的平均c统计数据.
- 进一步添加复杂的临床数据导致了适度的改善,平均c统计值为0.88 (0.01).
结论:
- 招生代码和数值实验室值的存在大大提高了管理数据库对风险调整的预测能力.
- 虽然有价值,复杂的临床发现的二次抽象与POA代码和实验室数据相比提供了最小的额外预测益处.
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