Subject search terms: Medical records--Data processing; Keyword search terms: Electronic health records, Electronic medical records, Nursing documentation.
Edge-Of-Things in Personalized Healthcare Support Systems: Cognitive Data Science in Sustainable Computing by
Edge-of-Things in Personalized Healthcare Support Systems discusses and explores state-of-the-art technology developments in storage and sharing of personal healthcare records in a secure manner that is globally distributed to incorporate best healthcare practices. The book presents research into the identification of specialization and expertise among healthcare professionals, the sharing of records over the cloud, access controls and rights of shared documents, document privacy, as well as edge computing techniques which help to identify causes and develop treatments for human disease. The book aims to advance personal healthcare, medical diagnosis, and treatment by applying IoT, cloud, and edge computing technologies in association with effective data analytics.
Leveraging Data Science for Global Health by
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure - and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources - including news media, social media, Google Trends, and Google Street View - can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Pragmatic Randomized Clinical Trials: Using Primary Data Collection & Electronic Health Records by
Pragmatic Randomized Clinical Trials Using Primary Data Collection and Electronic Health Records addresses the practical aspects and challenges of the design, implementation, and dissemination of pragmatic randomized trials, also sometimes referred to as practical or hybrid randomized trials. While less restrictive and more generalizable than traditional randomized controlled trials, such trials have specific challenges which are addressed in this book. The book contains chapters encompassing common designs along with advantages and limitations of such designs, analytic aspects in planning trials and estimating sample size, and how to use patient partners to help design and operationalize pragmatic randomized trials. Pragmatic trials conducted using primary data collection and trials embedded in electronic health records - including electronic medical records and administrative insurance claims - are addressed. This comprehensive resource is valuable not only for pharmacoepidemiologists, biostatisticians and clinical researchers, but also across the biomedical field for those who are interested in applying pragmatic randomized clinical trials in their research.
Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics by
The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.