The history, current status, and possible future of precision mental health
The history, current status, and possible future of precision mental health
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DOI：
10.1016/j.brat.2019.103506
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发表时间：
2019-12-01
影响因子：
4.1
通讯作者：
DeRubeis, Robert J.
中科院分区：
文献类型：
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作者：
DeRubeis, Robert J.
在以证据为基础的心理健康实践中，往往必须在很少或没有经验基础的情况下做出决定。一个常见的例子是，当有多个经验支持的干预措施，一个人与给定的诊断，其目的是建议治疗最有可能是有效的人。从随机临床试验中获得的数据允许识别可用于匹配患者治疗的患者特征。从历史上看，研究人员一直专注于个体调节因子，即与治疗类型在统计学上相互作用的单一变量，但这些变量很少被证明足以为治疗决策提供信息。最近，研究人员已经开始探索使用多变量算法可能改善临床决策的方法。已经确定了常见的陷阱，包括使用的方法，提供过度乐观的估计增益，可以预期从一个算法在临床环境中的应用。现在判断这些努力是否会有回报还为时过早，如果是的话，它们的使用可以在多大程度上提高精神卫生系统的效率和有效性。该领域应该继续学习和开发最强大的方法，这些方法可以产生可推广的知识，从而促进精确心理健康的目标。
In evidence-based mental health practice, decisions must often be made for which there is little or no empirical basis. A common example of this is when there are multiple empirically supported interventions for a person with a given diagnosis, where the aim is to recommend the treatment most likely to be effective for that person. Data obtained from randomized clinical trials allow for the identification of patient characteristics that could be used to match patients to treatments. Historically, researchers have focused on individual moderators, single variables that interact statistically with treatment type, but these have rarely proved powerful enough to inform treatment decisions. Recently, researchers have begun to explore ways in which the use of multivariable algorithms might improve clinical decision-making. Common pitfalls have been identified, including the use of methods that provide overoptimistic estimates of the gains that can be expected from the applications of an algorithm in a clinical setting. It is too early to tell if these efforts will pay off and, if so, how much their use can increase the efficiency and effectiveness of mental health systems. It behooves the field to continue to learn and develop the most powerful methods that can produce generalizable knowledge that will advance the aims of precision mental health.