Smartphone-enabled video-observed versus directly observed treatment for tuberculosis: a multicentre, analyst-blinded, randomised, controlled superiority trial

by Alistair Story, PhD; Robert W Aldridge, PhD; Catherine M Smith, PhD; Elizabeth Garber, MSc; Joe Hall, MSc; Gloria Ferenando, MSc; et al.

Published in The Lancet, 21 February 2019. DOI: https://doi.org/10.1016/S0140-6736(18)32993-3

 

 

Directly observed treatment (DOT) has been the standard of care for tuberculosis since the early 1990s, but it is inconvenient for patients and service providers. Video-observed therapy (VOT) has been conditionally recommended by WHO as an alternative to DOT. Researchers tested whether levels of treatment observation were improved with VOT.

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High-performance medicine: the convergence of human and artificial intelligence

by Eric J. Topol

Published in Nature Medicine, 07 January 2019. 

 

 

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.

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Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis

by Jia Zhang, MSc;  Laura Tüshaus, PhD;  Néstor Nuño Martínez, MSc;  Monica Moreo, MSc;  Hector Verastegui, Lic;  Stella M Hartinger, PhD;  Daniel Mäusezahl, PhD;  Walter Karlen, Prof Dr 

Published in JMR Publications: The Leading eHealth Publisher, 14 December 2018

 

 

Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks.

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What is an appropriate level of evidence for a digital health intervention?

by Felix Greaves, Indra Joshi, Mark Campbell, Samantha Roberts, Neelam Patel, John Powell

Published in The Lancet, 10 December 2018. DOI: https://doi.org/10.1016/S0140-6736(18)33129-5

 

 

Harnessing new digital technologies to support the delivery of health services centred around the needs of patients has been embraced by the National Health Service (NHS) in England. Digital technologies—eg, apps, wearables, and software algorithms—have the potential to support a technology-enabled health system in which care interactions are moved away from formal settings and citizens are encouraged to manage their own health and illness. The scalability and often low marginal cost of digital interventions suggest they might deliver cost benefits to stretched services facing the demands of ageing populations living longer with higher levels of chronic disease. At the same time, a publicly funded health system has both financial and moral reasons to spend money conscientiously and judiciously to provide evidence-based effective care for its citizens.  Article access can be found here.  
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Clinical Decision Support in the Era of Artificial Intelligence

by Edward H. Shortliffe, MD, PhD and Martin J. Sepulveda, MD, ScD

Published in JAMA, 05 November 2018. doi:10.1001/jama.2018.17163

 

This Viewpoint article by Shortliffe and Sepulveda is "focused on the subset of decision support systems that are designed to be used interactively by clinicians as they seek to reach decisions, regardless of the underlying analytic methodology that they incorporate." Through this publication, explore the authors' take on clinical decision support systems (CDSSs) and how they have the potential to be used effectively in healthcare settings:

 

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