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This unique collection of chapters from world experts on person-centered outcome (PCO) measures addresses the following critical questions: Can individual experiences be represented in measurements that do not reduce unique differences to meaningless uniformity? How person-centric are PCO measures? Are PCO measurements capable of delivering the kind of quality assured quantification required for high-stakes decision making? Are PCO measures likely to support improved health care delivery? Have pivotal clinical studies failed to deliver treatments for diseases because of shortcomings in the PCO measures used? Are these shortcomings primarily matters of precision and meaningfulness? Or is the lack of common languages for communicating outcomes also debilitating to quality improvement, research, and the health care economy? Three key issues form an urgent basis for further investigation. First, the numbers generated by PCO measures are increasingly used as the central dependent variables upon which high stakes decisions are made. The rising profile of PCO measures places new demands for higher quality information from scale and test construction, evaluation, selection, and interpretation. Second, PCO measurement science has well-established lessons to be learned from those who have built and established the science over many decades. Finally, the goal in making a PCO measurement is to inform outcome management. As such, it is vitally important that key stakeholders understand that, over the last half century, developments in psychometrics have refocused measurement on illuminating clinically important individual differences in the context of widely reproduced patterns of variation in health and functioning, comparable scale values for quality improvement, and practical explanatory models. This book’s audience includes anyone interested in person-centered care, including healthcare researchers and practitioners, policy makers, pharmaceutical industry representatives, clinicians, patient advocates, and metrologists. This is an open access book.
Mensuration & systems of measurement --- Medical equipment & techniques --- Psychological testing & measurement --- Medical administration & management --- patient-centered outcomes --- psychometrics --- health measurement --- social measurement --- clinical outcome assessments --- patient reported outcomes
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Patient-centered health care --- Medical informatics --- Medicine, Comparative --- Medical care --- Federal aid to medical care research --- Trusts and trustees --- Evaluation. --- Research --- Patient-Centered Outcomes Research Institute (U.S.) --- United States. --- Appropriations and expenditures.
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Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards,
Medicine --- digital transformation --- rapid prototyping --- augmented and virtual reality (AR/VR) --- artificial intelligence (AI) --- machine learning (ML) --- personalized dental medicine --- tele-health --- patient-centered outcomes --- integrated care, medical–dental integration, simulation model, dental research --- oral medicine --- oral healthcare --- dentistry --- gerodontology --- elderly patient --- big data --- Big Data --- digital dentistry --- oral health --- ethical issues --- dental education --- augmented reality (AR) --- virtual reality (VR) --- artificial intelligence --- AI --- machine learning --- ML --- cone beam computed tomography (CBCT) --- intraoral scanning --- facial scanning --- healthcare cost --- medical healthcare cost --- dental healthcare cost --- zero-inflated model --- neural network
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Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards,
digital transformation --- rapid prototyping --- augmented and virtual reality (AR/VR) --- artificial intelligence (AI) --- machine learning (ML) --- personalized dental medicine --- tele-health --- patient-centered outcomes --- integrated care, medical–dental integration, simulation model, dental research --- oral medicine --- oral healthcare --- dentistry --- gerodontology --- elderly patient --- big data --- Big Data --- digital dentistry --- oral health --- ethical issues --- dental education --- augmented reality (AR) --- virtual reality (VR) --- artificial intelligence --- AI --- machine learning --- ML --- cone beam computed tomography (CBCT) --- intraoral scanning --- facial scanning --- healthcare cost --- medical healthcare cost --- dental healthcare cost --- zero-inflated model --- neural network
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Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards,
Medicine --- digital transformation --- rapid prototyping --- augmented and virtual reality (AR/VR) --- artificial intelligence (AI) --- machine learning (ML) --- personalized dental medicine --- tele-health --- patient-centered outcomes --- integrated care, medical–dental integration, simulation model, dental research --- oral medicine --- oral healthcare --- dentistry --- gerodontology --- elderly patient --- big data --- Big Data --- digital dentistry --- oral health --- ethical issues --- dental education --- augmented reality (AR) --- virtual reality (VR) --- artificial intelligence --- AI --- machine learning --- ML --- cone beam computed tomography (CBCT) --- intraoral scanning --- facial scanning --- healthcare cost --- medical healthcare cost --- dental healthcare cost --- zero-inflated model --- neural network --- digital transformation --- rapid prototyping --- augmented and virtual reality (AR/VR) --- artificial intelligence (AI) --- machine learning (ML) --- personalized dental medicine --- tele-health --- patient-centered outcomes --- integrated care, medical–dental integration, simulation model, dental research --- oral medicine --- oral healthcare --- dentistry --- gerodontology --- elderly patient --- big data --- Big Data --- digital dentistry --- oral health --- ethical issues --- dental education --- augmented reality (AR) --- virtual reality (VR) --- artificial intelligence --- AI --- machine learning --- ML --- cone beam computed tomography (CBCT) --- intraoral scanning --- facial scanning --- healthcare cost --- medical healthcare cost --- dental healthcare cost --- zero-inflated model --- neural network
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