Journal of the American Medical Informatics Association : JAMIA
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The novel coronavirus disease-19 (COVID-19) pandemic has altered our economy, society, and healthcare system. While this crisis has presented the U. S. healthcare delivery system with unprecedented challenges, the pandemic has catalyzed rapid adoption of telehealth, or the entire spectrum of activities used to deliver care at a distance. ⋯ COVID-19 pandemic: (1) stay-at-home outpatient care, (2) initial COVID-19 hospital surge, and (3) postpandemic recovery. Within each of these 3 phases, we examine how people, process, and technology work together to support a successful telehealth transformation. Whether healthcare enterprises are ready or not, the new reality is that virtual care has arrived.
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J Am Med Inform Assoc · Jun 2020
Rapid design and implementation of an integrated patient self-triage and self-scheduling tool for COVID-19.
To rapidly deploy a digital patient-facing self-triage and self-scheduling tool in a large academic health system to address the COVID-19 pandemic. ⋯ Patient self-triage tools integrated into electronic health record systems have the potential to greatly improve triage efficiency and prevent unnecessary visits during the COVID-19 pandemic.
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J Am Med Inform Assoc · Jun 2020
Rapid Response to COVID-19: Health Informatics Support for Outbreak Management in an Academic Health System.
To describe the implementation of technological support important for optimizing clinical management of the COVID-19 pandemic. ⋯ The EHR is an essential tool in supporting the clinical needs of a health system managing the COVID-19 pandemic.
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J Am Med Inform Assoc · May 2020
Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.
Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been systematically studied. To bridge this gap, we developed a quantitative histopathology analytic framework to identify the types and gene expression subtypes of non-small cell lung cancer objectively. ⋯ Our study is the first to classify the transcriptomic subtypes of non-small cell lung cancer using fully automated machine learning methods. Our approach does not rely on prior pathology knowledge and can discover novel clinically relevant histopathology patterns objectively. The developed procedure is generalizable to other tumor types or diseases.
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J Am Med Inform Assoc · Mar 2020
ReviewDeep learning in clinical natural language processing: a methodical review.
This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. ⋯ Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field.