老年员工的健康大数据国有企业的工作绩效:具有远见和诱人的挑战
Qian Zhang1,2
1School of Business Management, Universiti Utara Malaysia, Kedah, Malaysia.
Multimedia tools and applications
|June 26, 2023
概括
大健康数据分析 (BDHA) 可以通过将老年员工与合适的任务相匹配来优化老年员工的绩效,降低成本并改善组织成果. 机器学习模型分析健康数据,以更好地预测工作绩效.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 组织管理 组织管理
背景情况:
- 大型健康数据分析 (BDHA) 对现代医疗保健系统至关重要,它可以动态搜索患者记录.
- 目前的系统为健康数据分析提供了基本的接口,需要先进的解决方案.
- 了解员工健康对工作绩效的影响对于组织成功至关重要.
研究的目的:
- 探索BDHA在优化老年员工工作绩效和组织效率方面的应用.
- 研究员工健康状况,职位角色和整体绩效之间的关系.
- 用健康数据证明机器学习在预测工作绩效方面的潜力.
主要方法:
- 利用Hadoop/MapReduce和HBase来生成和管理大规模的,医院特定的健康数据集.
- 在模拟的医疗记录上测试了Apache Hadoop技术,以减少数据不一致.
- 采用机器学习原理,包括计算学习和工作任务分析,用于预测建模.
主要成果:
- BDHA能够有效地搜索患者的医疗记录,在两个小时内处理了10亿份记录.
- 分析显示,员工的健康状况,如心理健康问题,显著影响工作绩效.
- 人口统计数据显示,大多数受访者是健康的女性受访者,常见的老年疾病普遍存在.
结论:
- 使用BDHA将老年员工与适当的任务相匹配,可以提高工作绩效并降低组织成本.
- 健康数据分析,特别是机器学习,提供了一种强大的方法来预测和提高员工绩效.
- 解决心理健康问题对于维持和提高员工生产力至关重要.
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