医疗保险中的提供者归属:挑战和解决方案
Caroline S Carlin1, Roger Feldman2, Jeah Jung3
1Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, Minnesota, USA.
研究人员通过增强国家提供者标识符 (NPI) 和专业信息来改进医疗保险优势数据. 这使得医疗保险优势和传统医疗保险人群中可靠的患者归因于通常的临床医生.
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
- 卫生政策 卫生政策
背景情况:
- 医疗保险优势 (MA) 遇到数据往往缺乏完整的国家提供者标识符 (NPI) 和专业信息.
- 准确的提供者识别对于患者归属和医疗服务研究至关重要.
研究的目的:
- 为了增强MA遭遇数据与NPI和专业信息.
- 评估回顾性患者归因方法.
- 为了比较MA和传统医疗保险 (TM) 人口之间的归因性能.
主要方法:
- 丰富的MA遇到数据使用医疗保险和医疗补助服务中心 (CMS) 和公共供应商数据集.
- 应用了16种方法用于通常的临床医生归因.
- 使用2016-2022年数据对MA和TM的归因方法性能进行比较.
主要成果:
- 成功增强NPI和专业数据,到2022年覆盖99%以上的MA和TM遭遇.
- 大多数归因方法在MA和TM中表现相似.
- 建议使用分层归因方法,以保持患者与临床医生的一致性.
结论:
- 开发了方法来识别供应商的NPI,以匹配外部数据.
- 启用了在MA和TM中将患者归因于通常的护理来源.
- 促进了临床医生在医疗服务研究中使用固定效应.
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