Embarking on a cultural transformation within a school of nursing.

Schools of Nursing across the country are encountering fiscal, programmatic and leadership challenges exacerbated by chaos and fragmentation in health care systems. This article focuses on the transformation journey of the School of Nursing at the University of Minnesota highlighting the complex context of higher education, challenges faced, and strategies executed that focused on significant and sustained culture change. Recommendations are offered to enable all schools to embark on their own transformative journeys.

Measuring the Impact of Primary Care Team Composition on Patient Activation Utilizing Electronic Health Record Big Data Analytics.

Purpose: Team-based care has been linked to key outcomes associated with the Quadruple Aim and a key driver of high-value patient-centered care. Use of the electronic health record (EHR) and machine learning have significant potential to overcome previous barriers to studying the impact of teams, including delays in accessing data to improve teamwork and optimize patient outcomes.

Methods: This study utilized a large EHR dataset (n=316,542) from an urban health system to explore the relationship between team composition and patient activation, a key driver of patient engagement.

A Systematic Review of Nurses' Perceptions of Electronic Health Record Usability Based on the Human Factor Goals of Satisfaction, Performance, and Safety.

The poor usability of electronic health records contributes to increased nurses' workload, workarounds, and potential threats to patient safety. Understanding nurses' perceptions of electronic health record usability and incorporating human factors engineering principles are essential for improving electronic health records and aligning them with nursing workflows. This review aimed to synthesize studies focused on nurses' perceived electronic health record usability and categorize the findings in alignment with three human factor goals: satisfaction, performance, and safety.

Future advancement of health care through standardized nursing terminologies: reflections from a Friends of the National Library of Medicine workshop honoring Virginia K. Saba.

Objective: To honor the legacy of nursing informatics pioneer and visionary, Dr. Virginia Saba, the Friends of the National Library of Medicine convened a group of international experts to reflect on Dr. Saba's contributions to nursing standardized nursing terminologies.

Using data-driven analytics and ecological systems theory to identify risk and protective factors for school absenteeism among secondary students.

Chronic absenteeism is an administrative term defining extreme failure for students to be present at school, which can have devastating long-term impacts on students. Although numerous prior studies have investigated associated variables and interventions, there are few studies that utilize both theory-driven and data-informed approaches to investigate absenteeism. The current study applied data-driven machine learning techniques, grounded in "The Kids and Teens at School" (KiTeS) theoretical framework, to student-level data (N = 121,005) to identify risk and protective variables that are highly associated with school absences.

Documentation of Complementary and Integrative Health Therapies in the Electronic Health Record: A Scoping Review.

Complementary and integrative health (CIH) therapies refers to massage therapy, acupuncture, aromatherapy, and guided imagery. These therapies have gained increased attention in recent years, particularly for their potential to help manage chronic pain and other conditions. National organizations not only recommend the use of CIH therapies but also the documentation of these therapies within electronic health records (EHRs).

Evaluating Systemized Nomenclature of Medicine Clinical Terms Coverage of Complementary and Integrative Health Therapy Approaches Used Within Integrative Nursing, Health, and Medicine.

The use of complementary and integrative health therapy strategies for a wide variety of health conditions is increasing and is rapidly becoming mainstream. However, little is known about how or if complementary and integrative health therapies are represented in the EHR. Standardized terminologies provide an organizing structure for health information that enable EHR representation and support shareable and comparable data; which may contribute to increased understanding of which therapies are being used for whom and for what purposes.

An Applied Healthcare Data Science Roadmap for Nursing Leaders: A Workshop Development, Conceptualization, and Application.

Nurse leaders working with large volumes of interdisciplinary healthcare data are in need of advanced guidance for conducting analytics to improve population outcomes. This article reports the development of a roadmap to help nursing leaders use data science principles and tools to inform decision-making, thus supporting research and approaches in clinical practice that improve healthcare for all. A consensus-building and iterative process was utilized based on the Cross-Industry Standard Process for Data Mining approach to big data science.

National Center for Interprofessional Practice and Education IPE core data set and information exchange for knowledge generation.

Since 2012, the National Center for Interprofessional Practice and Education has worked with over 70 sites implementing over 100 interprofessional education and collaborative practice (IPECP) programs in the United States (U.S.).

Knowledge Discovery With Machine Learning for Hospital-Acquired Catheter-Associated Urinary Tract Infections.

Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics.

Factors Associated With Healthcare-Acquired Catheter-Associated Urinary Tract Infections: Analysis Using Multiple Data Sources and Data Mining Techniques.

Purpose: The purpose of this study was to identify factors associated with healthcare-acquired catheter-associated urinary tract infections (HA-CAUTIs) using multiple data sources and data mining techniques.

Subjects And Setting: Three data sets were integrated for analysis: electronic health record data from a university hospital in the Midwestern United States was combined with staffing and environmental data from the hospital's National Database of Nursing Quality Indicators and a list of patients with HA-CAUTIs.

Methods: Three data mining techniques were used for identification of factors associated with HA-CAUTI: decision trees, logistic regression, and support vector machines.

Delay Within the 3-Hour Surviving Sepsis Campaign Guideline on Mortality for Patients With Severe Sepsis and Septic Shock.

Objectives: To specify when delays of specific 3-hour bundle Surviving Sepsis Campaign guideline recommendations applied to severe sepsis or septic shock become harmful and impact mortality.

Design: Retrospective cohort study.

Setting: One health system composed of six hospitals and 45 clinics in a Midwest state from January 01, 2011, to July 31, 2015.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts.

Big data science: A literature review of nursing research exemplars.

Background: Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge.

Purpose: The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals.

Methods: A literature review of studies published between 2009 and 2015.

A Data Mining Approach to Determine Sepsis Guideline Impact on Inpatient Mortality and Complications.

Sepsis incidents have doubled from 2000 through 2008, and hospitalizations for these diagnoses have increased by 70%. The use of the Surviving Sepsis Campaign (SSC) guidelines can lead to earlier diagnosis and treatment; however, the effectiveness of the SSC guidelines in preventing complications for this population is unclear. The overall purpose of this study was to apply SSC guideline recommendations to EHR data for patients with severe sepsis or septic shock and determine guideline compliance as well as its impact on inpatient mortality and sepsis complications.