开发一个数据模型,利用常规临床数据预测护理工作量
Dirk Hunstein1, Lena Frischen2, Madlen Fiebig3
1CEO, ePA-CC GmbH, Wiesbaden, Germany.
Studies in health technology and informatics
|August 23, 2024
概括
人工智能 (AI) 和机器学习 (ML) 可以预测护理工作量. 自理护理指数 (SPI) 有效地识别了人员需求,改善了医疗保健资源管理.
科学领域:
- 医疗保健管理的管理
- 医疗保健中的人工智能
- 护理信息学 护理信息学
背景情况:
- 在护理中有效的人力资源管理对于高质量的患者护理至关重要.
- 员工水平受到患者健康状况和护理工作量的直接影响.
- 目前用于确定人员需求的方法往往缺乏基于数据的准确性.
研究的目的:
- 开发和验证基于AI/ML的方法来预测护理工作量.
- 从常规临床数据中确定护理工作负载的关键预测因素.
- 提供数据驱动的建议,以提供最佳的护理人员配置水平.
主要方法:
- 一项涉及三家医院数据的多中心研究.
- 利用人工智能 (AI) 和机器学习 (ML) 算法.
- 确定了自护指数 (SPI),该指数来自护理评估工具 epaAC (AcuteCare 护理评估工具),作为主要预测指标.
主要成果:
- 仅SPI就解释了护理分钟 (调整后的R2) 中40%至66%的差异.
- 结合"疲劳"和"疼痛强度"等额外的预测因素,解释能力增加了高达17%.
- 建立了一个基于数据的人员控制的预测模型.
结论:
- 人工智能和机器学习模型可以根据常规数据准确预测护理工作量.
- SPI是护理工作量的一个强有力的,经过验证的预测指标.
- 这种方法为数据驱动,人工智能驱动的护理人员管理和资源分配提供了基础.
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