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Your search keyword '"Clancy, Thomas R."' showing total 15 results
Start Over Author "Clancy, Thomas R." Topic big data
15 results on '"Clancy, Thomas R."'

1. Big-Data Enabled Nursing: Future Possibilities

Author

Warren, Judith J., Clancy, Thomas R., Delaney, Connie W., Weaver, Charlotte A., Delaney, Connie W., editor, Weaver, Charlotte A., editor, Warren, Judith J., editor, Clancy, Thomas R., editor, and Simpson, Roy L., editor

Published
2017
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3. Big Data Cohort Extraction to Facilitate Machine Learning to Improve Statin Treatment.

Author

Chi, Chih-Lin, Wang, Jin, Clancy, Thomas R., Robinson, Jennifer G., Tonellato, Peter J., and Adam, Terrence J.

Subjects
CONFIDENCE intervals, DATABASES, DATA warehousing, SECONDARY analysis, STATINS (Cardiovascular agents), DESCRIPTIVE statistics, DATA analytics
Abstract

Health care Big Data studies hold substantial promise for improving clinical practice. Among analytic tools, machine learning (ML) is an important approach that has been widely used by many industries for data-driven decision support. In Big Data, thousands of variables and millions of patient records are commonly encountered, but most data elements cannot be directly used to support decision making. Although many feature-selection tools can help identify relevant data, these tools are typically insufficient to determine a patient data cohort to support learning. Therefore, domain experts with nursing or clinic knowledge play critical roles in determining value criteria or the type of variables that should be included in the patient cohort to maximize project success. We demonstrate this process by extracting a patient cohort (37,506 individuals) to support our ML work (i.e., the production of a proactive strategy to prevent statin adverse events) from 130 million de-identified lives in the OptumLabs™ Data Warehouse. [ABSTRACT FROM AUTHOR]

Published
2017
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4. Nursing Knowledge.

Author

Westra, Bonnie L., Clancy, Thomas R., Sensmeier, Joyce, Warren, Judith J., Weaver, Charlotte, and Delaney, Connie W.

Subjects
NURSES, NURSING, NURSING schools, DATA analysis, LEADERS, SMARTPHONES, MOBILE apps, ELECTRONIC health records
Abstract

The integration of Big Data from electronic health records and other information systems within and across health care enterprises provides an opportunity to develop actionable predictive models that can increase the confidence of nursing leaders’ decisions to improve patient outcomes and safety and control costs. As health care shifts to the community, mobile health applications add to the Big Data available. There is an evolving national action plan that includes nursing data in Big Data science, spearheaded by the University of Minnesota School of Nursing. For the past 3 years, diverse stakeholders from practice, industry, education, research, and professional organizations have collaborated through the “Nursing Knowledge: Big Data Science” conferences to create and act on recommendations for inclusion of nursing data, integrated with patient-generated, interprofessional, and contextual data. It is critical for nursing leaders to understand the value of Big Data science and the ways to standardize data and workflow processes to take advantage of newer cutting edge analytics to support analytic methods to control costs and improve patient quality and safety. [ABSTRACT FROM AUTHOR]

Published
2015
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13. A Big Data Primer

Author

Warren, Judith J., Delaney, Connie W., editor, Weaver, Charlotte A., editor, Warren, Judith J., editor, Clancy, Thomas R., editor, and Simpson, Roy L., editor

Published
2017
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15. Why Big Data?: Why Nursing?

Author

Delaney, Connie W., Simpson, Roy L., Delaney, Connie W., editor, Weaver, Charlotte A., editor, Warren, Judith J., editor, Clancy, Thomas R., editor, and Simpson, Roy L., editor

Published
2017
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