Machine Learning, Healthcare, Artificial Intelligence, Nursing, Predictive Analytics

KenSci Represents to Improve Patient Care Through Sharable, Comparable Nursing Data

Written by Whende Carroll, MSN RN BC, Director, Nursing Informatics, KenSci on 28 Jun 2018

The sixth annual Nursing Knowledge: Big Data Science Conference brought together more than 150 professionals from academia, practice, research, information technology, health systems and standards organizations from across the country to learn how they use big data to solve issues in nursing care delivery. The University of Minnesota — Twin Cities campus hosted the event.

This conference and proceedings initiative — the National Action Plan for advancing nursing knowledge — has a vision for better health outcomes resulting from the standardization and integration of the information nurses gather in EHRs and other information systems, which is increasingly the source of insights and evidence used to prevent, diagnose, treat and evaluate health conditions. In this work, the addition of contextual data, including environmental, geographical, behavioral, imaging, and other features and variables will lead to breakthroughs for the health of individuals, families, communities, and populations.

Each year nursing workgroups, who meet regularly throughout the year, comprised of conference-goers convene to discuss the work they have accomplished between conferences to develop and enhance multiple aspects of the National Action Plan for advancing nursing knowledge. Conference participants share a goal of achieving health improvements and efficiencies that will come from ensuring that nursing data is captured in EHRs and other health technology sources and that the data is available in shareable and comparable formats supporting useful, actionable insights by clinicians, researchers, policymakers, and patients.

A pre-conference session workshop was held for nurses and healthcare professionals, including me, to do hands-on data science, using Python to run models to predict patients at risk for opioid overdose. Almost all participants had never done this type of data interrogation before. It was a well-orchestrated session that allowed non-scientists to dip their toes into the work of machine learning and prediction.

The 2018 Conference Workgroups (these are a sample) have specific objectives, summarized below -

· Care Coordination

Identify essential concepts to support care coordination through the development of use cases for simple and complex care coordination during for transitions, sharing use cases with HL7 to support standards development.

· Clinical Data Analytics

Demonstrate the value of sharable and comparable nurse-sensitive data to support practice and translational research for transforming healthcare and improving patient quality and safety.

· Nursing Informatics Education

Ensure that graduate level nurses and faculty are being exposed to and can demonstrate the competencies needed to lead Big Data science activities to benefit nurses, patients, and consumers.

· Nursing Value

Address the issue of how to measure nursing value and develop new techniques that will provide real-time metrics to monitor quality, costs, performance, effectiveness, and efficiency of nursing care.

· Transforming Nursing Documentation

Explore ways to decrease EHR documentation burden and use information at the right time in the clinician workflow to support evidence-based and personalized care, instituting the IOM’s recommendations to “accelerate the integration of best clinical knowledge into care decisions.”

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My workgroup’s, Engage and Equip Nurses in Health IT Policy, focus for the next year is to collaborate with the American Nurses Association (ANA) and National Council of State Boards of Nursing (NCSBN) to develop a nurse identifier for use in vendor and other HIT systems to measure nursing quality and safety and further nursing research.

The Nursing Big Data Science initiative is exciting work for nurses, the highest number of practicing clinicians in our country. The emergence of this movement to take an applied approach to manipulate data speaks to nurses want and need to understand it for enhanced care delivery better and using it for research and helping teach it in nursing academic space. I look forward to hearing about the accomplishments of workgroups at next year’s 2019 NKBDS conference.

If you are interested in learning more about the work nurses are doing with Big Data, and have come directly from this initiative, check out this text authored and edited by annual conference leaders: Big Data-Enabled Nursing: Education Research and Practice

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