数字化转变社区精神卫生保健:算法集成劳动力的现实经验教训
N Bidargaddi1, B Patrickson1, J Strobel2
1Flinders University, College of Medicine and Public Health, Flinders Health & Medical Research Institute, Digital Health Research Lab, Adelaide Australia.
算法化的劳动力可以通过自动化监督任务来支持心理健康专业人员,解决社区心理健康服务中的劳动力短缺和倦怠问题. 研究突出了关键的实施过程和有效整合的潜在挑战.
科学领域:
- 心理健康服务研究 心理健康服务研究
- 医疗信息学 医疗信息学
- 人与计算机的交互
背景情况:
- 社区精神卫生服务面临着严重的劳动力短缺和工作流程效率低下.
- 电子健康记录的信息过载有助于临床医生疲劳和疲劳.
- 算法工作人员提供了一个潜在的解决方案,以增强人类的专业知识,并协助监督任务.
研究的目的:
- 审查从澳大利亚社区心理健康服务中对算法劳动力的五年实施研究项目的经验教训.
- 为了说明算法劳动力实施的基础过程:设计,验证,整合和优化.
- 讨论关于以人为中心,算法与人类对齐以及组织后果的见解.
主要方法:
- 五年 (2018-22) 实施研究经验的选择性审查.
- 对澳大利亚两个社区心理健康服务 (农村和城市) 进行的研究分析.
- 通过四个基本流程回顾实施过程.
主要成果:
- 实施涉及算法设计,概念验证,工作流集成和优化.
- 获得了关于服务预期的人为中心性的见解.
- 确定了潜在的算法-人类错位和意想不到的工作负载/责任后果.
结论:
- 算法工作人员可以支持社区心理健康服务,但需要仔细实施.
- 解决以人为中心和潜在的错位是成功整合的关键.
- 组织必须考虑对临床医生的工作量和问责制的影响.
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