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Powering Big Data for Nursing Through Partnership
Ellen M Harper1, Sara Parkerson
1Cerner Corporation, Kansas City, Missouri (Dr Harper); and Philips, Baltimore, Maryland (Ms Parkerson).
Nursing Administration Quarterly
|September 5, 2015
Summary
This study identified key principles, barriers, and challenges for integrating nurse-sensitive data into big data sets. The goal is to improve healthcare quality and patient outcomes using big data in nursing.
Area of Science:
- Healthcare Informatics
- Nursing Science
- Data Science
Background:
- The Healthcare Information and Management Systems Society supported the Big Data Principles Workgroup.
- The Workgroup aimed to address the Triple Aim challenge in healthcare.
- Previous efforts lacked comprehensive guidelines for big data in nursing.
Purpose of the Study:
- To identify essential principles for utilizing big data in nursing.
- To determine barriers and challenges in incorporating nurse-sensitive data into big data sets.
- To develop a framework for improving care quality and outcomes through big data.
Main Methods:
- Convened a Workgroup comprising experts in big data and nursing.
- Reviewed existing literature and best practices related to big data in healthcare.
- Facilitated discussions to identify and categorize principles, barriers, and challenges.
Main Results:
- Established a set of "Guiding Principles for Big Data in Nursing."
- Identified significant barriers including data standardization, interoperability, and privacy concerns.
- Highlighted challenges in data analysis and the translation of findings into clinical practice.
Conclusions:
- Big data offers significant potential for enhancing the quality of care and patient outcomes in nursing.
- Adherence to guiding principles is crucial for effective and ethical big data utilization.
- Overcoming identified barriers is essential for successful integration of nurse-sensitive data into big data initiatives.