Selecting, Evaluating, and Translating Psychophysiological Measures into Clinical Settings: From Lab to Practice. Issue 1 (June 2018)

• Record Type: Journal Article
• Authors: Kennedy, Lauren; Lau, Nathan; Pappada, Scott; Parker, Sarah Henrickson
• Is Part Of: Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare. Volume 7:Issue 1(2018)
• Page Start: 232
• Page End: 236
• Publication Date: 2018-06
• Subjects: Human engineering -- Periodicals; Medical care -- Periodicals; 620.8
• DOI: 10.1177/2327857918071054
• Languages: English
• ISSNs: 2327-8579

Abstract

Physiological data collection methods are unobtrusive, passive, continuous, and objective. The information afforded by sensors collecting physiological data can be transformed to represent operator performance estimates and stress state visualizations in real time. This technology is conducive to healthcare settings, creating the potential to inform healthcare operators of their current performance and physiological statuses. Despite the broad and pervasive utility of sensor technology, its applications in healthcare are underutilized and misunderstood. This is likely due to the combination of a lack of understanding of the full capabilities of sensor technology, a scarcity of demonstrated uses in healthcare, and an uncertainty surrounding translation and implementation into practice. Implementing findings from providers' physiological data can be met with challenges, especially in the healthcare setting. Clinicians are most frequently concerned with patient care, and may not always recognize the importance of their own physiological state. While transitioning sensor technology from personal monitoring purposes to a data collection tool can be challenging, passive data collection via sensor technology may have significant value for learners and experienced practitioners. The goals of this paper are to: 1. raise awareness of sensor technology and its utility in clinical settings; 2. provide empirical examples of how to use sensor technology to answer basic and applied questions pertaining to clinical workflow; and 3. exemplify scalability and translatability of findings from sensor technology studies in clinical settings.