评估医院数据中的偏见:自动预处理患者途径算法开发和验证研究
Laura Uhl1, Vincent Augusto1, Benjamin Dalmas1
1Mines Saint-Etienne Centre Ingénierie Santé, Unité Mixte de Recherche (UMR) 6158 Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Centre national de la recherche scientifique (CNRS), Saint-Etienne, France.
本研究引入了一种方法来识别和纠正患者护理途径的后勤限制,提高医院资源管理和患者护理优化数据准确性.
科学领域:
- 医疗保健管理的管理
- 过程采矿采矿采矿
- 模拟建模模的模拟模型.
背景情况:
- 优化患者护理途径对于面临资源短缺的医院至关重要.
- 后勤限制,例如床位的可用性,可能会延迟治疗并影响患者流动.
- 非计划的患者入院给维持计划住院时间表带来了独特的挑战.
研究的目的:
- 开发一个框架,用于自动检测受物流限制而不是患者需求影响的患者途径中的活动.
- 要区分医疗必要和后勤诱导的路径偏差.
主要方法:
- 一种新的方法将患者历史路径数据转化为标记和更正的数据库.
- 活动被标记为相关 (患者需求) 或无关 (后勤约束).
- 使用过程挖掘和离散事件模拟来量化该方法的影响.
主要成果:
- 该算法在识别对非计划患者途径的后勤影响方面取得了87%的准确性.
- 处理的数据导致路径变异平均减少40%.
- 模拟显示,原始数据和处理数据之间所需的床位容量存在显著差异.
结论:
- 患者途径数据是医疗需求和后勤现实的复合物.
- 识别和纠正后勤限制对于对医疗保健数据进行公正分析至关重要.
- 拟议的方法提供了一种可通用的方法,用于改善医院的患者路径分析.
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