医疗保健工作人员在利用数据驱动干预来优化痴呆症预防方面的观点
Swapna Gokhale1,2, Belinda Garth1, Melinda Webb-St Mart2
1Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, Victoria, Australia.
卫生工作人员在将痴呆症预防纳入工作流程方面发现了挑战. 改善IT基础设施和员工的数字素养对于采用自动化妄想风险预测模型至关重要.
科学领域:
- 实施科学 实施科学
- 医疗保健管理的管理
- 老年医学 老年医学
背景情况:
- 预防 Delirium 在医疗保健环境中至关重要.
- 数据驱动的优化为改善妄想预防提供了潜力.
- 了解员工的观点是成功实施的关键.
研究的目的:
- 从卫生服务人员的角度确定影响妄预防实施的因素.
- 确定数据驱动狂妄症预防优化的临床采用特征和策略.
- 使用莫纳什学习健康系统 (LHS) 范式,实现妄预防的综合同化.
主要方法:
- 在澳大利亚大都会公共卫生网络中进行的定性研究.
- 针对18名临床和非临床参与者进行了有目的的抽样,这些参与者具有妄想症护理专业知识.
- 使用实施研究综合框架 (CFIR) 进行半结构面试的诱导分析.
主要成果:
- 在将妄风险识别和查整合到临床工作流程中存在挑战.
- 基础设施障碍阻碍了临床决策支持的数字化.
- 让护理人员和工作人员参与设计有效的迷妄预防优化是必不可少的.
结论:
- 影响狂妄症预防的关键因素包括IT基础设施,员工数字素养和问责制.
- 自动化妄风险预测模型需要改善医院IT和工作人员支持.
- 未来的研究应该专注于实用临床试验,以优化妄预防干预措施.
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