Infante Sanchez, D, Woolley, SI, Collins, T, Pemberton, P, Veenith, T, Hume, D, Laver, K and Small, C (2017) The Quantified Outpatient - Challenges and Opportunities in 24hr Patient Monitoring. In: Informatics for Health 2017, 24-26 Apr 2017, Manchester. (Unpublished)

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1. Introduction Patient monitoring systems capable of accurate recording in the real-world, during the activities of everyday living, can provide rich objective accounts of patient well-being that have broad application in clinical decision support. Combining physiological, environmental and actigraphy sensing together with a quantified subjective patient report and activity log, provides new opportunities and new challenges in big data analysis, data mining and visual analytics. 2. Method An iterative prototyping approach together with clinical collaboration informed the design and development of a novel 24hr sensing system with broad application relevant to sleep assessment. The system design, sensor selection and visual analytic strategies were informed by literature review and pilot studies with i) clinical staff and ii) healthy participants. The sensing system comprised, i) a daytime wearable sensing unit (on-body accelerometry for Metabolic Equivalent Task, pulse, skin temperature and resistivity) and ii) two night-time sensing units (an on-body unit as per daytime but with wrist accelerometry, and a bedside unit for ambient light, temperature and sound-level). Continuous recordings were used to generate averages, minima and maxima in 1-minute, 15-minute, 1-hour and 4-hour intervals. For data mining and visual analytics, these records were combined with quantified accounts of subjective user reports and activity logs. Ten subjects (including three clinicians) tested the system for up to three consecutive days and nights and provided assessments of use and comfortability. Five clinicians were interviewed regarding system applications, barriers to use, data use and visual analytics. 3. Results Data acquisition was successful across a wide range of MET levels. System comfortability was good but with some discomfort and skin irritation arising from prolonged use of a carotid pulse sensor (selected for its robust performance compared with wristband alternatives). Electrooculography sensing for REM sleep detection was attempted but was uncomfortable and performance was unsatisfactory. Usability of the system benefitted from prolonged battery operation. Few data losses resulted from user-administration of sensors, but more resulted from a lack of prototype ruggedisation. Attempts at intuitive multivariate data visualizations, including heat maps, motion charts and clustered views, had limited success. However, the system and approach was assessed as very good for real-life application and decision support. 4. Discussion 24hr outpatient sensing has wide clinical application in rehabilitation, in the management of chronic conditions and, in pre- and post-surgical assessment. However, better detection of both low level activity and sleep is required than currently available in commercial activity monitoring devices. 5. Conclusion Multi-modal outpatient monitoring can perform robustly and with acceptable comfortability across a spectrum of activity types and levels, however, system robustness and ease-of-use are paramount to reliability, and users’ self-application of sensors requires careful attention. The new big un-delineated, multi-modal, multi-dimensional, data spaces created are unfamiliar, uncharted territories that require new understandings, guidance and training. Data mining and visual analytics provide new research insights but there are many challenges regarding their translation into clinical practice.

Item Type: Conference or Workshop Item (Poster)
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 15 Feb 2017 11:24
Last Modified: 15 Feb 2017 11:24

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