Studies in health technology and informatics
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Stud Health Technol Inform · Jun 2020
Development of a Mobile Digital Manikin to Measure Pain Location and Intensity.
Painful conditions are prevalent and substantially contribute to disability worldwide. Digital manikins are body-shaped drawings to facilitate self-reporting of pain. Some of them have been validated, but without allowing for recording of location-specific pain intensity and for use on a smartphone. ⋯ Test-retest reliability depended on the manikin's level of detail, but was generally high with most intraclass correlation coefficients âĽě0.70 and all similarity coefficients âĽě0.50. Participants found the manikin easy to use, but suggested clearer orientation (front/back, certain body locations) and would value additional feedback and diary functions. We will address these issues in the next version of the manikin before conducting a validation study.
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Research projects with humans is a highly regulated field that is currently undergoing rapid changes due to developments in eHealth and mHealth. While a patients data and samples must be thoroughly protected, they are also an invaluable source for fundamental and cutting edge research. There are processes in place to obtain a patient's consent for the use of their data and samples for research. ⋯ An Android app has been developed that brings any existing consent form to mobile devices, including the integration of the process into existing hospital IT using established data standards, such as FHIR and the ResearchStack open source framework. The app is user-tested and shown to work in a hospital setting. Lack of eIdentification and legal drawbacks were determined as the main obstacles for immediate implementation.
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This paper reports a case study on the spontaneous personalization discussions emerged from interviews with healthcare professionals when asked about their work practices and the role of information technology (IT) during consultations. We thematically analyzed the personalization elements using an existing personalization framework to provide insights on the service personalization. Our results contribute to the better design of IT solutions that can support health services' personalization.
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Stud Health Technol Inform · Jun 2020
Feasibility of Using EN 13606 Clinical Archetypes for Defining Computable Phenotypes.
Computable phenotypes are gaining importance as structured and reproducible method of using electronic health data to identify people with certain clinical conditions. A formal standard is not available for defining and formally representing phenotyping algorithms. In this paper, we have tried to build a formal representation of such phenotyping algorithm. ⋯ The EN13606 archetypes can be used to define the phenotype algorithm that basically identifies patients by a set of clinical characteristics in their records. Phenotype representations defined in EN 13606 do not satisfy all the desiderata proposed by Mo et al. and thus currently has a limited ability to define the computable phenotyping algorithms. Further work is required to make the EN13606 standard to fully support the objective.
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Stud Health Technol Inform · Jun 2020
Commercial Adoption of AI in the Healthcare Sector: An Exploratory Analysis of S&P500 Companies.
The use of Artificial Intelligence (AI) technologies within the healthcare sector is growing. However, there are differences in the speed of commercial adoption of AI across sub-sectors. ⋯ Ambulatory health care services and hospitals, as well as insurance carriers, received media coverage later, but were the quickest to take AI into commercial use. From the theory perspective our results indicate that the classical innovation diffusion theory might not fully explain these differences.