The role of technology and engineering models in transforming healthcare.

IEEE reviews in biomedical engineering(2013)

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摘要
The healthcare system is in crisis due to challenges including escalating costs, the inconsistent provision of care, an aging population, and high burden of chronic disease related to health behaviors. Mitigating this crisis will require a major transformation of healthcare to be proactive, preventive, patient-centered, and evidence-based with a focus on improving quality-of-life. Information technology, networking, and biomedical engineering are likely to be essential in making this transformation possible with the help of advances, such as sensor technology, mobile computing, machine learning, etc. This paper has three themes: 1) motivation for a transformation of healthcare; 2) description of how information technology and engineering can support this transformation with the help of computational models; and 3) a technical overview of several research areas that illustrate the need for mathematical modeling approaches, ranging from sparse sampling to behavioral phenotyping and early detection. A key tenet of this paper concerns complementing prior work on patient-specific modeling and simulation by modeling neuropsychological, behavioral, and social phenomena. The resulting models, in combination with frequent or continuous measurements, are likely to be key components of health interventions to enhance health and wellbeing and the provision of healthcare.
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关键词
mobile computing technology,medical robotics,patient centered healthcare,medical information systems,health care,computational models,social sciences,healthcare system crisis,quality-of-life improvization,neurophysiology,preventive healthcare,sensor technology advances,information technology,smart home,learning (artificial intelligence),healthcare transformation motivation,neuropsychological modeling,geriatrics,behavioral modeling,healtcare inconsistent provision,pervasive computing,aging population,remote monitoring,biomedical engineering,wellbeing,medical research technical overview,healthcare escalating costs,machine learning technique,physiological models,psychology,biomedical engineering models,chronic diseases,behavioural sciences computing,social phenomena model,health enhancement,health interventions,patient care,biocybernetics,disease detection,continuous medical measurements,evidence-based healthcare,sensors,medical technology roles,patient-specific modeling and simulation,health behaviors,computational modeling,sparse sampling,behavioral phenotyping,medical networking,mathematical modeling approaches,mobile computing,patient diagnosis,activities of daily living,learning artificial intelligence,medical informatics,remote sensing technology,economics,computer simulation,robotics
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