在提供者目录中选幽灵医生率
Jianhui Xu1, Daniel Polsky1,2
1Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Health services research
|February 5, 2026
概括
幽灵网络膨胀了医生目录的准确性. 一种新的方法发现,医疗保险优势计划中17.5%的一般和11.5%的网络特定的幽灵初级保健医生,协助目录审计.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 卫生政策 卫生政策
- 医疗信息学 医疗信息学
背景情况:
- 提供者目录对于患者在健康计划中获得护理至关重要.
- 不准确的提供者目录,包括"幽灵网络",可能导致患者困惑和护理障碍.
- 医疗保险优势 (MA) 计划依赖于准确的提供者列表来获得受益人的访问.
研究的目的:
- 为了解幽灵网络建立一个概念框架.
- 在健康计划提供者目录中引入一种用于量化幽灵医生流行率的新方法.
- 评估MA计划中幽灵初级保健医生 (PCP) 的流行情况.
主要方法:
- 将PCP分为一般的幽灵 (不适用于任何医疗保险受益者) 和特定网络的幽灵 (不适用于特定的MA网络注册者).
- 使用多个数据源,包括Ideon MA提供商目录数据,NPI注册表和OneKey数据库.
- 采用了后勤模型来区分真正不可用的医生和由于偶然而导致患者数量低的医生.
主要成果:
- 平均MA网络中17.5%的上市PCP是一般的幽灵.
- 在列出的PCP中,11.5%是网络特定的幽灵.
- 卫生维护组织网络和星级较低的网络表现出更高的幽灵PCP流行率.
结论:
- 提供者目录必须准确地反映医生对患者护理的可用性.
- 拟议的方法可以支持有针对性的网络审计,以提高目录的准确性.
- 确保目录的准确性对于有效的医疗保健提供和政策至关重要.
相关概念视频
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