Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland (all authors); Department of Psychiatry, University of Maryland School of Medicine, Baltimore (Goldman); Department of Computer Science, Johns Hopkins University, Baltimore (Zirikly); Department of Occupational Therapy, Tufts University, Medford, Massachusetts (Marfeo); Department of Physical Therapy, University of Pittsburgh, Pittsburgh (McDonough).
Psychiatr Serv. 2023 Jan 1;74(1):56-62. doi: 10.1176/appi.ps.202200056. Epub 2022 Jun 2.
The disability determination process of the Social Security Administration's (SSA's) disability program requires assessing work-related functioning for individual claimants alleging disability due to mental impairment. This task is particularly challenging because the determination process involves the review of a large file of information, including objective medical evidence and self-reports from claimants, families, and former employers. To improve this decision-making process, SSA entered an interagency agreement with the Rehabilitation Medicine Department, Epidemiology and Biostatistics Section, in the Clinical Center of the National Institutes of Health, intending to use data science and informatics to develop decision support tools. This collaborative effort over the past decade has led to the development of the Work Disability-Functional Assessment Battery and has initiated an approach to applying natural language processing to the review of claimants' files for information on mental health functioning. This informatics research collaboration holds promise for improving the process of disability determination for individuals with mental impairments who make claims at the SSA.
社会保障管理局(SSA)残疾项目的残疾判定过程需要评估因精神障碍而声称残疾的个人申请人的与工作相关的功能。由于判定过程涉及对大量信息档案的审查,包括来自申请人、家属和前雇主的客观医学证据和自我报告,因此这项任务极具挑战性。为了改进这一决策过程,SSA 与美国国立卫生研究院临床中心的康复医学系、流行病学和生物统计学科签订了一项机构间协议,旨在利用数据科学和信息学来开发决策支持工具。在过去十年中,这项合作努力促成了工作残疾-功能评估电池的开发,并开创了一种应用自然语言处理技术来审查申请人档案中有关心理健康功能信息的方法。这种信息学研究合作有望改善 SSA 提出索赔的精神障碍个人的残疾判定过程。