Objectives:The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods: The article ...draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group. Results: The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions: Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.
Objectives:The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods: The article ...draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group. Results: The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions: Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.
The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare.
The article draws on the ...scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group.
The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring.
Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.
Objectives: To summarise current research that takes advantage of "Big Data" in health and biomedical informatics applications. Methods: Survey of trends in this work, and exploration of literature ...describing how large-scale structured and unstructured data sources are being used to support applications from clinical decision making and health policy, to drug design and pharmacovigilance, and further to systems biology and genetics. Results: The survey highlights ongoing development of powerful new methods for turning that large-scale, and often complex, data into information that provides new insights into human health, in a range of different areas. Consideration of this body of work identifies several important paradigm shifts that are facilitated by Big Data resources and methods: in clinical and translational research, from hypothesis-driven research to data-driven research, and in medicine, from evidence-based practice to practice-based evidence. Conclusions: The increasing scale and availability of large quantities of health data require strategies for data management, data linkage, and data integration beyond the limits of many existing information systems, and substantial effort is underway to meet those needs. As our ability to make sense of that data improves, the value of the data will continue to increase. Health systems, genetics and genomics, population and public health; all areas of biomedicine stand to benefit from Big Data and the associated technologies.
At present, most documentation forms and item catalogs in healthcare are not accessible to the public. This applies to assessment forms of routine patient care as well as case report forms (CRFs) of ...clinical and epidemiological studies. On behalf of the German chairs for Medical Informatics, Biometry and Epidemiology six recommendations to developers and users of documentation forms in healthcare were developed. Open access to medical documentation forms could substantially improve information systems in healthcare and medical research networks. Therefore these forms should be made available to the scientific community, their use should not be unduly restricted, they should be published in a sustainable way using international standards and sources of documentation forms should be referenced in scientific publications.
Background
As is well known, elderly people gradually lose the ability of self-care. The decline can be reflected in changes in their daily life behavior. A solution to assess their health status is ...to design sensor-enhanced living environments to observe their behavior, in which unobtrusive sensors are usually used. With respect to information extraction from the dataset collected by means of these kinds of sensors, unsupervised methods have to be relied on for practical application. Under the assumption that human lifestyle is associated with health status, this study intends to propose a novel approach to discover behavior patterns using unsupervised methods.
Methods
To evaluate the feasibility of this approach it was applied to datasets collected in the GAL-NATARS study. The study is part of the Lower Saxony research network Design of Environments for Aging (GAL) and conducted in subjects’ home environments. The subjects recruited in GAL-NATARS study are older people (age ≥ 70 years), who are discharged from hospital to live alone again at their homes after treatment of a femoral fracture.
Results
The change of lifestyle regularity is measured. By analyzing the correlation between the extracted information and medical assessment results of four subjects, two of them exhibited impressive association and the other two showed less association.
Conclusions
The approach may provide complementary information for health assessment; however, the dominant relationship between the change of behavior patterns and the health status has to be shown and datasets from more subjects must be collected in future studies.
Limitations
Merely environmental data were used and no wearable sensor for activity detection or vital parameter measurement is taken into account. Therefore, this cannot comprehensively reflect reality.
Operating room personnel (ORP) operating mobile image intensifier systems (C-arms) need training to produce high quality radiographs with a minimum of time and X-ray exposure. Our study aims at ...evaluating acceptance, usability and learning effect of the CBT system virtX that simulates C-arm based X-ray imaging in the context of surgical case scenarios.
Prospective, interventional study conducted during an ORP course with three groups: intervention group 1 (training on a PC using virtX), and 2 (virtX with a C-arm as input device), and a control group (training without virtX) - IV1, IV2 and CG. All participants finished training with the same exercise. Time needed to produce an image of sufficient quality was recorded and analyzed using One-Way-ANOVA and Dunnett post hoc test (alpha = .05). Acceptance and usability of virtX have been evaluated using a questionnaire.
CG members (n = 21) needed more time for the exercise than those of IV2 (n = 20): 133 +/- 55 vs. 101 +/- 37 sec. (p = .03). IV1 (n = 12) also performed better than CG (128 +/- 48 sec.), but this was not statistically significant. Seventy-nine participants returned a questionnaire (81% female, age 34 +/- 9 years, professional experience 8.3 +/- 7.6 years; 77% regularly used a C-arm). 83% considered virtX a useful addition to conventional C-arm training. 91% assessed virtual radiography as helpful for understanding C-arm operation.
Trainees experienced virtX as substantial enhancement of C-arm training. Training with virtX can reduce the time needed to perform an imaging task.
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