预测未来的高成本患者对护理管理干预措施的挑战
Chris Crowley1, Jennifer Perloff2, Amy Stuck3
1West Health Institute, 10350 N Torrey Pines Rd, La Jolla, CA, 92037, USA. ccrowley@westhealth.org.
BMC health services research
|September 14, 2023
概括
使用索赔数据预测未来的住院治疗是具有挑战性的. 卫生系统应该将数据分析与临床医生的见解相结合,以便更好地识别急需患者.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 数据分析数据分析数据分析.
- 预测建模预测建模
背景情况:
- 索赔数据细分用于预测未来的住院治疗.
- 了解风险细分中的精算限制对于医疗保健管理至关重要.
研究的目的:
- 为了评估索赔数据细分在医疗保险住院预测的准确性.
- 为了说明以人为中心的风险细分用于识别高利用受益人的局限性.
主要方法:
- 分析了2017-2018年100%的医疗保险受益者样本.
- 检查心力衰竭患者逐渐狭窄的细分体的住院率.
- 基于2017年数据的2018年住院率的图形表示.
主要成果:
- 较窄的患者群体显示住院率增加.
- 然而,这些部分占Medicare FFS住院总数的比例较小.
- 2018年,超过一半的跨部门和总人口的所有患者没有住院治疗.
结论:
- 索赔数据本身在预测高成本患者方面存在局限性.
- 整合临床医生的输入和"轻触"监测可以提高预测准确性.
- 使用管理和提供者指导的系统策略可能比个别患者的向更有效.
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