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神经心理学中的技术危机

The Technology Crisis in Neuropsychology.

作者信息

Miller Justin B, Barr William B

机构信息

Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA.

NYU, School of Medicine, New York, NY, USA.

出版信息

Arch Clin Neuropsychol. 2017 Aug 1;32(5):541-554. doi: 10.1093/arclin/acx050.

Abstract

Neuropsychology has fallen reliant on outdated and labor intensive methods of data collection that are slow, highly inefficient, and expensive, and provide relatively data-poor estimates of human behavior despite rapid technological advance in most other fields of medicine. Here we present a brief historical overview of current testing practices in an effort to frame the current crisis, followed by an overview of different settings in which technology can and should be integrated. Potential benefits of laboratory based assessments, remote assessments, as well as passive and high-frequency data collection tools rooted in technology are discussed, along with several relevant examples and how these technologies might be deployed. Broader issues of data security and privacy are discussed, as well as additional considerations to be addressed within each setting. Some of the historical barriers to adoption of technology are also presented, along with a brief discussion of the remaining uncertainties. While by no means intended as a comprehensive review or prescriptive roadmap, our goal is to show that there are a tremendous number of advantages to technologically driven data collection methods, and that technology should be embraced by the field. Our predictions are that the comprehensive assessments of the future will likely entail a combination of lab-based assessments, remote assessments, and passive data capture, and leading the development of these efforts will cement the role of neuropsychology at the forefront of cognitive and behavioral science.

摘要

神经心理学一直依赖于过时且劳动强度大的数据收集方法,这些方法缓慢、效率极低且成本高昂,尽管医学的大多数其他领域都有快速的技术进步,但它们对人类行为的估计相对缺乏数据。在此,我们简要回顾当前测试实践的历史,以勾勒当前的危机,随后概述技术能够且应该融入的不同场景。我们将讨论基于实验室的评估、远程评估以及基于技术的被动和高频数据收集工具的潜在益处,并列举几个相关示例以及这些技术可能的应用方式。我们还将讨论数据安全和隐私等更广泛的问题,以及在每种场景中需要解决的其他考量因素。我们还将介绍采用技术的一些历史障碍,并简要讨论尚存的不确定性。虽然我们绝非旨在进行全面综述或给出规范性路线图,但我们的目标是表明,技术驱动的数据收集方法具有众多优势,该领域应该接纳技术。我们预测,未来的综合评估可能需要结合基于实验室的评估、远程评估和被动数据采集,引领这些工作的发展将巩固神经心理学在认知和行为科学前沿的地位。

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