青少年,家长和提供者对基于机器学习的临床决策支持系统的态度,以选择青少年抑郁症治疗方法
Meredith Gunlicks-Stoessel1, Yangchenchen Liu1, Catherine Parkhill1
1University of Minnesota.
Research square
|October 27, 2023
概括
青少年抑郁症的机器学习临床决策支持系统 (CDSS) 被青少年,家长和提供者积极看待. 成功实施需要解决将其整合到临床实践中的时间限制.
科学领域:
- 精神病学是一个精神病学.
- 数字健康数字健康
- 临床决策支持 临床决策支持
背景情况:
- 基于机器学习 (ML) 的临床决策支持系统 (CDSS) 在抑郁症等复杂疾病中提供了个性化治疗计划的潜力.
- 然而,基于ML的治疗选择模型在精神病学中代表性不足,并且在临床实践中没有得到广泛采用.
- 了解利益相关者的态度对于成功实施基于ML的CDSS至关重要.
研究的目的:
- 评估利益相关者对基于ML的新型青少年抑郁症CDSS (CDSS-YD) 的态度.
- 评估CDSS-YD在临床环境中的可用性和感知有用性.
- 确定实施CDSS-YD的障碍和促进者.
主要方法:
- 研究1涉及焦点小组与抑郁症青少年,家长和行为健康提供者评估对CDSS-YD的态度.
- 第二项研究涉及培训提供者在与青少年和家长的临床接触中使用CDSS-YD,随后对态度进行调查.
- 使用定性分析和调查来收集有关利益相关者的感知数据.
主要成果:
- 所有利益相关者群体都发现CDSS-YD易于理解,对个性化治疗决策有用.
- 青少年,家长和提供者在临床接触中成功使用了CDSS-YD.
- 医疗服务提供者与患者之间的关系影响了人们对可信度的看法;医疗服务提供者将诊所生产力指标视为障碍,保护时间视为促进因素.
结论:
- 该CDSS-YD显示承诺广泛接受和实用性在个性化的青少年抑郁症治疗规划.
- 解决系统层面的障碍,特别是为培训和融合分配时间,对于成功实施至关重要.
- 利益相关者参与和解决实际工作流程挑战是采用这项技术在精神卫生保健中的关键.
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