This text reflects how the learning health system infrastructure is maturing and being advanced by health information exchanges (HIEs) with multiple organizations blending their data or enabling distributed computing. It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery. Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing has consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Big Data-Enabled Nursing reflects onhow health systems have developed and how electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. It provides instruction on the new opportunities for nursing and educates readers on the new skills in research methodologies that are being further enabled by new partnerships spanning all sectors.

Autorentext

Five editors are national and international experts in nursing & health informatics, representing all sectors including health systems, corporate and vendors, academia, policy, and professional associations. All invited authors are recognized experts.

Zusammenfassung
Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Health systems electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. This text reflects how the learning health system infrastructure is maturing, and being advanced by health information exchanges (HIEs) with multiple organizations blending their data, or enabling distributed computing. It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery. New opportunities for nursing and call for new skills in research methodologies are being further enabled by new partnerships spanning all sectors.

Inhalt
Big Data and Its Importance in Nursing.- Big Data Use and Its Importance in Healthcare.- A Big Data Primer.- A Closer Look at the Enabling Technologies and Knowledge Value.- Big Data in Healthcare.- Getting to Big Data: National Center Data Repository for Interprofessional Education and Collaborative Practice.- Wrestling with Big Data: How Nurse Leaders Can Engage.- Clinical and Translational Science Awards (CTSA) Extended Clinical Data Project.- Working in the New Big Data World Academic/Corporate Partnership Model.- Transformation of Research and Scholarship.- Enhancing Data Access and Utilization: Federal Datasets Relevant to Social Determinants of Health & Health Disparities Research.- Transformation of Health Care Systems.- State of the Science in Data Mining Methods.- Veteran's Administration Database (VINCI).- Kaiser-Permanente's Nursing-Focused Analytics Initiative.- Mobilizing the Nursing Workforce with Data and Analytics at the Point of Care.- The Power of Disparate Data Sources for Answering Thorny Questions in Healthcare: Five Case Examples.- What Big Data Means for Schools of Nursing and Academia.- Readiness for Big Data Science - Scholarship and Research.- Global Society & Big Data: The Future We Can Get Ready For.- Data Analytics and Visualization: The Future with Big Data.