Blum-Riese Professor of Biostatistics, University of Chicago, Chicago, IL, USA.
Department of Family Medicine, University of Colorado, Aurora, CO, USA.
Curr Psychiatry Rep. 2019 Jul 1;21(8):67. doi: 10.1007/s11920-019-1053-9.
We review recent literature on the adaptive assessment of complex mental health disorders and provide a detailed comparison of classical test theory and adaptive testing based on multidimensional item response theory.
Adaptive tests for a wide variety of mental health traits (e.g., depression, anxiety, mania, substance misuse, suicidality) are now available in a cloud-based environment. These tests have been validated in a variety of settings against lengthy structured clinical interviews with excellent results and even higher reliability than fixed-length tests. Applications include screening and assessments in emergency departments, psychiatric and primary care clinics, student health clinics, perinatal medicine clinics, child welfare settings, and the judicial system. The future of mental health measurement will be based on automated screening and assessments. Adaptive tests will provide increased precision of measurement and decreased burden of measurement. Integration into the electronic health record is important and now easily accomplished.
我们回顾了关于复杂精神障碍适应性评估的最新文献，并基于多维项目反应理论详细比较了经典测试理论和适应性测试。
现在已经可以在基于云的环境中对各种精神健康特征（例如，抑郁、焦虑、躁狂、物质滥用、自杀意念）进行适应性测试。这些测试已经在各种环境中针对冗长的结构化临床访谈进行了验证，结果非常出色，甚至比固定长度的测试具有更高的可靠性。应用包括急诊科、精神病和初级保健诊所、学生健康诊所、围产期医学诊所、儿童福利机构和司法系统的筛查和评估。未来的精神健康测量将基于自动化的筛查和评估。适应性测试将提供更高的测量精度和更低的测量负担。与电子健康记录的集成非常重要，现在已经可以轻松实现。
