Modular Health Kiosk for health self-assessment

Soares, Eduardo Costa Studart; Oliveira, Cristina; Maia, João Tiago Duarte; Almeida, Rafael; Coimbra, Miguel; Brandão, Pedro; Prior, Rui

doi:10.1109/iscc.2016.7543754

Cited by 8 publications

(9 citation statements)

References 5 publications

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“…The previous version was already assessed in terms of usability (Soares et al, 2016). With that in mind, the development of the new interface was made trying to keep the same flow of interaction.…”

Section: Methodsmentioning

confidence: 99%

“…The differences between the two versions will be address in the next section. (Soares et al, 2016) Little changes were made relative to the execution flow of the application in order to take advantage of the information that was collected regarding the usability. This evaluation of the application was based on observer-filled questionnaires alongside the option of keeping track of user clicks on the screen in order to evaluate the number and position of clicks per screen to see the changes to be made in the presented content.…”

Section: Related Workmentioning

confidence: 99%

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Modular Health Kiosk based on Web Technologies

Silva¹,

Brandão²,

Prior³

2017

Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies

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We describe a modular and easily reconfigurable Health Kiosk based on common, off-the-shelf Personal Health Devices and a computer with a touchscreen interface. The Kiosk is implemented in standard web technologies (JavaScript, HTML and CSS) on top of the Electron platform. It is intended to be used autonomously by the patients. It is highly modular, can easily be adapted and reconfigured by health professionals with little to no computer expertise, using a graphical interface, to adapt to different groups of patients and use cases. We document our findings, identifying problems faced throughout the development and solutions to those problems.Silva J., BrandÃčo P. and Prior R. Modular Health Kiosk based on Web Technologies.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Modular Health Kiosk based on Web Technologies

Silva¹,

Brandão²,

Prior³

2017

Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies

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“…The focus of this paper is the use of CI in healthcare as increasingly more hospitals and institutions convert from paper administration systems to digitally stored records. Searching through electronic medical records is less time consuming than doing it manually [9], [10]. In addition, the search does not only involve looking up specific details of a single patient, but searching for general patterns [18], [19].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The use of predictive models has been of particular interest to the healthcare industry [8]. Specifically since sufficient quantity of documentation has been stored digitally to form such quantities to be considered "Big Data" [9], [10]. Traditionally the use has been limited to predicting outcomes before treatment begins, to help with treatment selection or determine the risks versus benefits [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

“…However, they are closely intertwined since a successful treatment reduces the risk of relapse. This results in lower future financial burden on healthcare and means being able to effectively prioritize treatments without the loss of service [10]. Being able to adjust treatment that has already begun would contribute to achieving both goals.…”

Section: Introductionmentioning

confidence: 99%

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The use of predictive models in dynamic treatment planning

Haraldsson

Brynjolfsdottir

Woodward

et al. 2017

2017 IEEE Symposium on Computers and Communications (ISCC)

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Abstract-With the expanding load on healthcare and consequent strain on budget, the demand for tools to increase efficiency in treatments is rising. The use of prediction models throughout the treatment to identify risk factors might be a solution. In this paper we present a novel implementation of a prediction tool and the first use of a dynamic predictor in vocational rehabilitation practice. The tool is periodically updated and improved with Genetic Improvement of software. The predictor has been in use for 10 months and is evaluated on predictions made during that time by comparing them with actual treatment outcome. The results show that the predictions have been consistently accurate throughout the patients' treatment. After approximately 3 week learning phase, the predictor classified patients with 100% accuracy and precision on previously unseen data. The predictor is currently being successfully used in a complex live system where specialists have used it to make informed decisions.

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