Big data science: A literature review of nursing research exemplars

Bibliometric Analysis (2000-2024) of Research on Artificial Intelligence in Nursing

We conducted a bibliometrics analysis utilizing the Web of Science database, selecting 1925 articles concerning artificial intelligence (AI) in nursing. The analysis utilized the network visualization tool VOSviewer to explore global collaborations, highlighting prominent roles played by the United States, China, and Japan, as well as institutional partnerships involving Columbia University and Harvard Medical School. Keyword analysis identified prevalent themes and co-citation analysis highlighted influential journals. A notable increase in AI-related publications in nursing was observed over time, reflecting the growing interest in AI in nursing. However, high-quality clinical research and increased scientific collaboration are needed.

Big data research in nursing: A bibliometric exploration of themes and publications

AIMS

To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain.

DESIGN

Bibliometric analysis.

METHODS

The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer.

RESULTS

The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field.

CONCLUSIONS

The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field.

CLINICAL RELEVANCE

This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.

When to err is inhuman: An examination of the influence of artificial intelligence-driven nursing care on patient safety

Artificial intelligence, as a nonhuman entity, is increasingly used to inform, direct, or supplant nursing care and clinical decision-making. The boundaries between human- and nonhuman-driven nursing care are blurred with the advent of sensors, wearables, camera devices, and humanoid robots at such an accelerated pace that the critical evaluation of its influence on patient safety has not been fully assessed. Since the pivotal release of To Err is Human, patient safety is being challenged by the dynamic healthcare environment like never before, with nursing at a critical juncture to steer the course of artificial intelligence integration in clinical decision-making. This paper presents an overview of artificial intelligence and its application in healthcare and highlights the implications which affect nursing as a profession, including perspectives on nursing education and training recommendations. The legal and policy challenges which emerge when artificial intelligence influences the risk of clinical errors and safety issues are discussed.

Nurse Leader Perceptions of Data in the Veterans Health Administration: A Qualitative Evaluation

Healthcare systems and nursing leaders aim to make evidence-based nurse staffing decisions. Understanding how nurses use and perceive available data to support safe staffing can strengthen learning healthcare systems and support evidence-based practice, particularly given emerging data availability and specific nursing challenges in data usability. However, current literature offers sparse insight into the nature of data use and challenges in the inpatient nurse staffing management context. We aimed to investigate how nurse leaders experience using data to guide their inpatient staffing management decisions in the Veterans Health Administration, the largest integrated healthcare system in the United States. We conducted semistructured interviews with 27 Veterans Health Administration nurse leaders across five management levels, using a constant comparative approach for analysis. Participants primarily reported using data for quality improvement, organizational learning, and organizational monitoring and support. Challenges included data fragmentation, unavailability and unsuitability to user need, lack of knowledge about available data, and untimely reporting. Our findings suggest that prioritizing end-user experience and needs is necessary to better govern evidence-based data tools for improving nursing care. Continuous nurse leader involvement in data governance is integral to ensuring high-quality data for end-user nurses to guide their decisions impacting patient care.

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

Introduction: 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). Yet, how CIH therapies are documented in the EHR is not well understood. The purpose of this scoping review of the literature was to examine and describe research that focused on CIH therapy clinical documentation in the EHR. Methods: The authors conducted a literature search using six electronic databases: Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ovid MEDLINE, Scopus, Google Scholar, Embase, and PubMed. Predefined search terms included "informatics," "documentation," "complementary and integrative health therapies," "non-pharmacological approaches," and "electronic health records" using AND/OR statements. No restrictions were placed on publication date. The inclusion criteria were as follows: (1) Original peer-reviewed full article in English, (2) focus on CIH therapies, and (3) CIH therapy documentation practice used in the research. Results: The authors identified 1684 articles, of which 33 met the criteria for a full review. A majority of the studies were conducted in the United States (20) and hospitals (19). The most common study design was retrospective (9), and 26 studies used EHR data as a data source for analysis. Documentation practices varied widely across all studies, ranging from the feasibility of documenting integrative therapies (i.e., homeopathy) to create changes in the EHR to support documentation (i.e., flowsheet). Discussion: This scoping review identified varying EHR clinical documentation trends for CIH therapies. Pain was the most frequent reason for use of CIH therapies across all included studies and a broad range of CIH therapies were used. Data standards and templates were suggested as informatics methods to support CIH documentation. A systems approach is needed to enhance and support the current technology infrastructure that will enable consistent CIH therapy documentation in EHRs.

Using standardized nursing data for knowledge generation - Ward level analysis of point of care nursing documentation

BACKGROUND

Standardized nursing terminology is a prerequisite for describing nursing care processes and generating knowledge for decision-making and management. The structure of the Finnish Care Classification (FinCC) facilitates documentation of nationally agreed core nursing data: nursing diagnoses, interventions, and outcomes.

PURPOSE

To analyze the use of FinCC to assess patient care needs (nursing diagnoses), care implementations (interventions) and evaluation of the outcomes of nursing care in electronic health records.

METHODS AND MATERIALS

The descriptive study applied purposeful sampling of nursing data from nursing data repositories in three surgical wards in tertiary and secondary care hospitals. The aggregated, anonymous ward level data from a six-month period was analyzed to show distributions within frequencies and means of component, main and subcategory level use of FinCC in the three hospitals.

RESULTS

Each of the three levels of the FinCC (component, main and subcategory) were used for recording nursing care. In all hospitals, the three most used diagnosis components covered about one third of the use of all the 17 components. The five most used intervention components cover about one third of the components. The most often used components for diagnoses and interventions were Coordination of care and follow-up care, Pain Management, Activities of daily living and independence and Medication. The prevalence of different components and the main and subcategory level usage for both diagnoses and interventions varied between the hospitals.

CONCLUSION

Standardized point-of-care nursing data makes patients' daily nursing care transparent. Structured, standardized, and point-of-care nursing data can be utilized to generate new knowledge of nursing care processes and nursing care practice at ward level.

Factors influencing career success of clinical nurses in northwestern China based on Kaleidoscope Career Model: Structural equation model

AIM

To explore the relationships among self-efficacy, information literacy, social support and career success of clinical nurses and identify factors influencing clinical nurses' career success in northwestern China.

BACKGROUND

Understanding the influencing factors of career success is important for the professional development of nurses and the improvement of clinical nursing quality. Many influencing factors of career success have been identified, but there is no large-scale research on the relationships among self-efficacy, information literacy, social support and career success of clinical nurses based on Kaleidoscope Career Model. Studies examining the association of the four factors remain limited.

METHODS

A total of 3011 clinical nurses from 30 hospitals in northwestern China were selected in the cross-sectional survey, and the response rate was 94.71%. The clinical nurses completed the online self-report questionnaires including self-efficacy, information literacy, social support rating scale and career success scale. The data were analysed by SPSS23.0 statistical software using t test, analysis of variance, Pearson's correlation and multiple linear regression. Structural equation model (SEM) was used to analyse the influencing factors of career success using Mplus 8.3.

RESULTS

The career success of clinical nurses in northwestern China was at a medium level. The linear multivariate regression analysis showed that self-efficacy (β = .513), social support (β = .230), information support (β = .106), information consciousness (β = -.097), information knowledge (β = .067), information ethics (β = -.053), hospital grade (β = .118), marital status (β = -.071) and age (β = -.037) entered regression equation of clinical nurses' career success (all P < .05). SEM results showed that the career success was negatively correlated with demographic characteristics and positively correlated with social support and self-efficacy.

CONCLUSION

Demographic characteristics, self-efficacy, social support and information literacy are the influencing factors of nurses' career success, which should be considered in the process of promoting nurses' career success.

IMPLICATIONS FOR NURSING MANAGEMENT

Nursing managers need to acknowledge the significance of nurses' career success both for the realization of their own value and for the improvement of clinical nursing quality. They should encourage nurses to enhance self-efficacy and render more social support through incentive policies and foster nurses' information literacy through information technology training so as to improve their career success.

Establishing Performance Evaluation for Quality Inspection of Specialty Nurses

PURPOSE

The present study aimed to establish an index system for the performance evaluation of specialty nurses (SNs) in tertiary hospitals.

BACKGROUND

An objective index system for performance evaluation of SN has not yet been established in China.

DESIGN

A 2-round Delphi survey sought opinions from experts about the index system for SNs' performance evaluation in tertiary hospitals in China.

METHODS

Delphi survey was used to inquire approximately 20 experts from the fields of nursing management, nursing education, and clinical nursing. We determined the weight coefficient of each index of performance evaluation based on the opinion. Finally, the index of the quality evaluation was established for SN.

RESULTS

A total of 20 experts from 10 provinces in China reached a consensus on the tertiary indexes of the assessment model. The indexes contained first-level (4), second-level (16), and third-level (24) indicators. The 4 aspects of the performance evaluation, including clinical specialist practice assessment, nursing research, education assessment, medical cooperation recognition, and personal comprehensive ability assessment, reached consensus.

CONCLUSION

Establishing the performance evaluation for SNs aided the SNs in achieving the best clinical practice after training. The performance evaluation still needed to be continuously improved.

Exploring data management content in doctoral nursing handbooks

Objective:

While data management (DM) is an increasing responsibility of doctorally prepared nurses, little is understood about how DM education and expectations are reflected within student handbooks. The purpose of this study was to assess the inclusion of DM content within doctoral nursing student handbooks.

Methods:

A list of 346 doctoral programs was obtained from the American Association of Colleges of Nursing (AACN). Program websites were searched to locate program handbooks, which were downloaded for analysis. A textual review of 261 handbooks from 215 institutions was conducted to determine whether DM was mentioned and, if so, where the DM content was located. Statistical analysis was performed to compare the presence of DM guidance by type of institution, Carnegie Classification, and the type of doctoral program handbook.

Results:

A total of 1,382 codes were identified across data life cycle stages, most commonly in the handbooks’ project requirements section. The most frequent mention of DM was in relation to collecting and analyzing data; the least frequent related to publishing and sharing data and preservation. Significant differences in the frequency and location of codes were identified by program type and Carnegie Classification.

Conclusions:

Nursing doctoral program handbooks primarily address collecting and analyzing data during student projects. Findings suggest limited education about, and inclusion of, DM life cycle content, especially within DNP programs. Collaboration between nursing faculty and librarians and nursing and library professional organizations is needed to advance the adoption of DM best practices for preparing students in their future roles as clinicians and scholars.

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. Use of standardized terminologies is recommended for interoperable clinical data to support sharable, comparable data to enable the use of complementary and integrative health therapies and to enable research on outcomes. In this study, complementary and integrative health therapy terms were extracted from multiple sources and organized using the National Center for Complementary and Integrative Health and former National Center for Complementary and Alternative Medicine classification structures. A total of 1209 complementary and integrative health therapy terms were extracted. After removing duplicates, the final term list was generated via expert consensus. The final list included 578 terms, and these terms were mapped to Systemized Nomenclature of Medicine Clinical Terms. Of the 578, approximately half (48.1%) were found within Systemized Nomenclature of Medicine Clinical Terms. Levels of specificity of terms differed between National Center for Complementary and Integrative Health and National Center for Complementary and Alternative Medicine classification structures and Systemized Nomenclature of Medicine Clinical Terms. Future studies should focus on the terms not mapped to Systemized Nomenclature of Medicine Clinical Terms (51.9%), to formally submit terms for inclusion in Systemized Nomenclature of Medicine Clinical Terms, toward leveraging the data generated by use of these terms to determine associations among treatments and outcomes.

Health IT advances for the 21st century

ABSTRACT

The United States is working toward a value-based health care system in which reimbursement will be based on quality outcomes rather than on Current Procedural Terminology payment codes. Health data will be more easily shared, and patients will have more control of their records. Health information technology advances in the federal 21st Century Cures Act follow earlier related legislation and regulation that moved clinical care and research forward. Policy analysis of the Cures Act is presented following the three phases of the Longest model (2010): formation, implementation, and modification. With the passage of the Cures Act and promulgation of its final rules, the formation phase is complete. The implementation phase has begun. Modification may occur, based on the evaluation of key deliverables over time. Advanced practice nurses are well-suited to the use of electronic tools to share data with patients and other providers. New competencies, tools, and infrastructure are needed for advanced practice nurses to fully participate in value-based health care. Full implementation of the 21st Century Cures Act with the use of coded concepts in standardized nursing terminologies will provide an ideal foundation for strong patient-centered care, population health, and reimbursement that takes advanced nursing practice into account.

Big Data in the Veterans Health Administration: A Nursing Informatics Perspective

PURPOSE

This article reviews the missions of the U.S. Department of Veterans Affairs (VA) and the evolution of its electronic health record (EHR), the Veterans Health Information Systems and Technology Architecture (VistA). This system, along with its clinical graphical user interface the Computerized Patient Record System, form a key link in VA health care. A Veteran who receives healthcare through the VA can have their EHR accessed by clinicians at any VA healthcare facility across the United States and its territories. Data aggregated daily at a corporate data warehouse supports VA quality improvement and research.

ORGANIZING CONSTRUCT

Serving over 9 million Veterans, the VA is one of the largest integrated healthcare systems in the United States. It has been a leader in the development and use of healthcare informatics, EHR, and big data analytics for over 30 years. Nurses engaged in major roles in the evolution of these developments.

CONCLUSIONS

With over 500 nurses as members, the Office of Nursing Informatics' Field Alliance demonstrates the VA's continuing commitment to fostering nursing informatics. The commitment includes investment by the VA to develop nursing informaticists from among its own staff.

CLINICAL RELEVANCE

Exemplars of the impact of nursing informatics are shared. Future directions include an EHR that begins during military service and follows the Veteran into VA health care.

Pain Management in Clinical Practice Research Using Electronic Health Records

BACKGROUND

The use of electronic health record (EHR) systems encourages and facilitates the use of data for the development and surveillance of quality indicators, including pain management.

AIM

to conduct an integrative review on pain management research using data extracted from EHR in order to synthesize and analyze the following elements: pain management (assessments, interventions, and outcomes) and study results with potential clinical implications, data source, clinical sample characteristics, and method description.

DESIGN

An integrative review of the literature was undertaken to identify exemplars of scientific research studies that explore pain management using data from EHR, using Cooper's framework.

RESULTS

Our search of 1,061 records from PubMed, Scopus, and Cinahl was narrowed down to 28 eligible articles to be analyzed.

CONCLUSION

Results of this integrative review will make a critical contribution, assisting others in developing research proposals and sound research methods, as well as providing an overview of such studies over the past 10 years. Through this review it is therefore possible to guide new research on clinical pain management using EHR.

Information Model on Pain Management: An Analysis of Big Data

PURPOSE

To develop an information model to support secondary use of data using electronic health records.

DESIGN

Retrospective observational data-driven study with secondary use of data. The sample was composed of structured data from all adults admitted to clinical and surgical inpatient units of a public university hospital. Data between June 2014 and July 2019 were included, totaling approximately 51,000 unique patients.

METHODS

Six systematic steps of the Applied Healthcare Data Science Roadmap were applied.

FINDINGS

An information model on pain management was developed.

CONCLUSIONS

The data science methodology used allowed the development of information model in pain management, mapping attributes about pain management and to categorize them into assessment and reassessment, goals, interventions, and outcomes.

CLINICAL RELEVANCE

Based on the information model developed, it is possible to optimize the electronic health system and improve the quality of patient care delivery in pain management.

Identification of the Knowledge Structure of Cancer Survivors' Return to Work and Quality of Life: A Text Network Analysis

This study aimed to understand the trends in research on the quality of life of returning to work (RTW) cancer survivors using text network analysis. Titles and abstracts of each article were examined to extract terms, including "cancer survivors", "return to work", and "quality of life", which were found in 219 articles published between 1990 and June 2020. Python and Gephi software were used to analyze the data and visualize the networks. Keyword ranking was based on the frequency, degree centrality, and betweenness centrality. The keywords commonly ranked at the top included "breast", "patients", "rehabilitation", "intervention", "treatment", and "employment". Clustering results by grouping nodes with high relevance in the network led to four clusters: "participants and method", "type of research and variables", "RTW and education in adolescent and young adult cancer survivors", and "rehabilitation program". This study provided a visualized overview of the research on cancer survivors' RTW and quality of life. These findings contribute to the understanding of the flow of the knowledge structure of the existing research and suggest directions for future research.

The Byzantine Role of Big Data Application in Nursing Science: A Systematic Review

Big data have the potential to determine enhanced decision-making process and to personalize the approach of delivering care when applied in nursing science. So far, the literature on this topic is still not synthesized for the period between 2014 and 2018. Thus, this systematic review aimed to identify and synthesize the most recent evidence on big data application in nursing research. The systematic search was undertaken for the evidence published from January 2014 to May 2018, and the outputs were formatted using the PRISMA Flow Diagram, whereas the quality appraisal was addressed by recommendations consistent with the Critical Appraisal Skills Program. Twelve studies on big data in nursing were included and divided into two themes: the majority of the studies aimed to determine prediction assessment, while only four studies were related to the impact of big data applications to support clinical practice. This review tracks the recent state of knowledge on big data applications in nursing science, revealing the potential for nursing engagement in big data science, even if currently limited to some fields. Big data applications in nursing might have a tremendous potential impact, but are currently underused in research and clinical practice.

A survey of current practices in data management education in nursing doctoral programs

BACKGROUND

The inclusion of data management instruction within nursing doctoral curricula has not been systematically examined.

PURPOSE

The purpose of this study is to determine the extent of data management education within nursing doctoral programs.

METHOD

Separate surveys were created for DNP (332) and PhD (138) program directors. Survey questions were based on the stages of the UK Data Service Research Data Lifecycle.

RESULTS

One hundred and four nursing doctoral program directors responded, a 22% response rate. Sixty-seven (64%) were from DNP programs while 37 (35%) were from PhD programs. Although program directors reported that they were teaching stages of the research data lifecycle, data management is mostly being taught through individual mentoring or a single lecture within a required course, and that students' project data were not being preserved.

CONCLUSIONS

Nursing doctoral programs need to develop consistent data management education, build an awareness of data policies, and clarify student project data sharing and ownership.

Secondary Data Analysis in Nursing Research: A Contemporary Discussion

This editorial provides an overview of secondary data analysis in nursing science and its application in a range of contemporary research. The practice of undertaking secondary analysis of qualitative and quantitative data is also discussed, along with the benefits, risks and limitations of this analytical method.

Opioid use disorder research and the Council for the Advancement of Nursing Science priority areas

BACKGROUND

Chronic diseases, such as opioid use disorder (OUD) require a multifaceted scientific approach to address their evolving complexity. The Council for the Advancement of Nursing Science's (Council) four nursing science priority areas (precision health; global health, determinants of health, and big data/data analytics) were established to provide a framework to address current complex health problems.

PURPOSE

To examine OUD research through the nursing science priority areas and evaluate the appropriateness of the priority areas as a framework for research on complex health conditions.

METHOD

OUD was used as an exemplar to explore the relevance of the nursing science priorities for future research.

FINDINGS

Research in the four priority areas is advancing knowledge in OUD identification, prevention, and treatment. Intersection of OUD research population focus and methodological approach was identified among the priority areas.

DISCUSSION

The Council priorities provide a relevant framework for nurse scientists to address complex health problems like OUD.

Nursing research priorities based on CINAHL database: A scoping review

Aim

To analyse nursing research based on the CINAHL database to identify research priorities for nursing.

Design

A scoping literature review was conducted. The CINAHL Plus (EBSCO) Full Text was searched between 2012-2018.

Methods

Out of 1522 original publications, 91 fulfilled the inclusion criteria. The Joanna Briggs Institute critical appraisal tools were applied. Data were analysed by a thematic analysis method.

Results

A strong emphasis should be put on development and evaluation of nursing theories and, in addition, randomized controlled trial studies, meta-synthesis, experimental and intervention studies are needed in nursing research. Development of competencies and skills in the nursing profession ought to be studied more extensively and research should be focused on variety fields of nursing practice.

Agent-Based Modeling: A Method for Investigating Challenging Research Problems

BACKGROUND

For all our successes, many urgent health problems persist, and although some of these problems may be explored with established research methods, others remain uniquely challenging to investigate-maybe even impossible to study in the real world because of practical and pragmatic obstacles inherent to the nature of the research question.

OBJECTIVES

The purpose of this review article is to introduce agent-based modeling (ABM) and simulation and demonstrate its value and potential as a novel research method applied in nursing science.

METHODS

An introduction to ABM and simulation is described. Examples of current research literature on the subject are provided. A case study example of community nursing and opioid dependence is presented.

RESULTS

The use of ABM and simulation in human health research has increased dramatically over the past decade, and meaningful research is now commonly found published widely in respected, peer-reviewed journals. Absent from this list is innovative ABM and simulation research published by nurse researchers in nursing-specific journals.

DISCUSSION

ABM and simulation is a powerful method with tremendous potential in nursing research. It is vital that nursing embrace and adopt innovative and advanced research methods if we are to remain a progressive voice in health research, practice, and policy.

Cardiovascular Nursing Science Priorities: A Statement From the American Heart Association Council on Cardiovascular and Stroke Nursing

BACKGROUND

The American Heart Association's (AHA) Council on Cardiovascular and Stroke Nursing (CVSN) plays a critical role in advancing the mission of the AHA in the discovery of new scientific knowledge. The aim was to identify priority research topics that would promote and improve cardiovascular (CV) health, provide direction for the education of future nurse scientists, and serve as a resource and catalyst for federal and organizational funding priorities.

METHODS

A Qualtrics survey, which included 3 questions about priorities for CVSN nurse researchers, was sent to the CVSN Leadership Committee and all CVSN Fellows of the AHA (n = 208). Responses to the questions were reviewed for word repetitions, patterns, and concepts and were then organized into thematic areas. The thematic areas were reviewed within small groups at the November (2016) in-person CVSN leadership meeting.

RESULTS

Seventy-three surveys were completed. Five thematic areas were identified and included (1) developing and testing interventions, (2) assessment and monitoring, (3) precision CV nursing care, (4) translational and implementation science, and (5) big data. Topic areas noted were stroke, research methods, prevention of stroke and CV disease, self-management, and care and health disparities.

CONCLUSION

Five thematic areas and 24 topic areas were identified as priorities for CV nursing research. These findings can provide a guide for CV nurse scientists and for federal and foundational funders to use in developing funding initiatives. We believe additional research and discovery in these thematic areas will help reduce the rising global burden of CV disease.

Research Agenda of the Oncology Nursing Society: 2019-2022

PROBLEM STATEMENT

To define the Oncology Nursing Society Research Agenda for 2019-2022.

DESIGN

Multimethod, consensus-building approach by members of the Research Agenda Project Team.

DATA SOURCES

Expert opinion, literature review, surveys, interviews, focus groups, town hall, and review of research priorities from other cancer care organizations and funding agencies.

ANALYSIS

Content analysis and descriptive statistics were used to synthesize research priority themes that emerged.

FINDINGS

Three priority areas for scientific development were identified.

IMPLICATIONS FOR NURSING

The Research Agenda can be used to focus oncology nurses' research, scholarship, leadership, and health policy efforts to advance quality cancer care, inform research funding priorities, and align initiatives and resources across the ONS enterprise.

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. The purpose of this article is to explain a knowledge discovery and data mining approach that was employed to discover knowledge about hospital-acquired catheter-associated urinary tract infections from multiple data sources, including electronic health records and nurse staffing data. Three different machine learning techniques are described: decision trees, logistic regression, and support vector machines. The decision tree model created rules to interpret relationships among associated factors of hospital-acquired catheter-associated urinary tract infections. The logistic regression model showed what factors were related to a higher risk of hospital-acquired catheter-associated urinary tract infections. The support vector machines model was included to compare performance with the other two interpretable models. This article introduces the examples of cutting-edge machine learning approaches that will advance secondary use of electronic health records and integration of multiple data sources as well as provide evidence necessary to guide nursing professionals in practice.

Ripe for Disruption? Adopting Nurse-Led Data Science and Artificial Intelligence to Predict and Reduce Hospital-Acquired Outcomes in the Learning Health System

Nurse leaders are dually responsible for resource stewardship and the delivery of high-quality care. However, methods to identify patient risk for hospital-acquired conditions are often outdated and crude. Although hospitals and health systems have begun to use data science and artificial intelligence in physician-led projects, these innovative methods have not seen adoption in nursing. We propose the Petri dish model, a theoretical hybrid model, which combines population ecology theory and human factors theory to explain the cost/benefit dynamics influencing the slow adoption of data science for hospital-based nursing. The proliferation of nurse-led data science in health systems may be facing several barriers: a scarcity of doctorally prepared nurse scientists with expertise in data science; internal structural inertia; an unaligned national "precision health" strategy; and a federal reimbursement landscape, which constrains-but does not negate the hard dollar business case. Nurse executives have several options: deferring adoption, outsourcing services, and investing in internal infrastructure to develop and implement risk models. The latter offers the best performing models. Progress in nurse-led data science work has been sluggish. Balanced partnerships with physician experts and organizational stakeholders are needed, as is a balanced PhD-DNP research-practice collaboration model.

HPNA 2019-2022 Research Agenda: Development and Rationale

Building on the strong work of previous research agendas (2009-2012, 2012-2015, 2015-2018), the Hospice and Palliative Nurses Association Research Advisory Council developed the 2019-2022 Research Agenda in consultation with Hospice and Palliative Nurses Association (HPNA) membership and assessment of major trends in palliative nursing. The HPNA Research Advisory Council identified 5 priority areas and asked subject experts in each area to summarize the state of the science, identify critical gaps, and provide recommendations for future research. This document expands the executive summary published on the HPNA website (www.advancingexpertcare.org/hpna/) and provides supporting evidence for the 2019-2022 recommendations. The 5 priority areas are as follows: (1) pediatric hospice and palliative nursing research; (2) family caregiving; (3) interprofessional education and collaborative practice; (4) big data science, precision health, and nursing informatics; and (5) implementation science.

Models of collaboration and dissemination for nursing informatics innovations in the 21st century

BACKGROUND

Nursing informatics innovations are constantly adapting to a rapidly changing health care environment.

PURPOSE

This study aims to present the lessons learned from 4 nursing informatics projects and rationale for development decisions to inform future informatics innovations.

METHODS

Using a comparative cross-case analysis, four case studies of informatics projects led by nurse scientists were described and analyzed through the lens of the Informatics Research Organizing Model which was modified to include policy and interoperability contexts.

FINDINGS

The comparison analysis examined dynamic relationships between processes and constructs in nursing informatics interventions and also highlighted the scientific, intellectual property, technical, and policy challenges encountered among the four case studies.

DISCUSSION

The analysis provided implications for future intervention development and implementation in consideration of multiple contexts for nursing informatics innovations.

Turning Health Systems Data Into Actionable Information

Health systems produce vast amounts of complex, multidimensional data. Health systems nurse leaders, informaticians, and nurse researchers must partner to turn these data into actionable information to drive quality clinical outcomes. The authors review health systems in the era of big data, identify opportunities for health systems-nursing research partnerships, and introduce emerging approaches to data science education in nursing.

A crosswalk between the Omaha System and guiding undergraduate public health nursing education documents

The Omaha System is the hallmark evidence-based clinical information management system used in nursing education, research, and practice. Multiple education documents guide public health workforce preparation. This qualitative study identified similarities and gaps between the Omaha System and seven guiding documents commonly used by nurse educators. A crosswalk design was employed. The setting was virtually based using online technology. Recommendations are for public health nurse educators to update their teaching practices using evidence-based approaches.

Reducing Emergency Room Visits and In-Hospitalizations by Implementing Best Practice for Transitional Care Using Innovative Technology and Big Data

BACKGROUND

Efforts to improve care transitions require coordination across the healthcare continuum and interventions that enhance communication between acute and community settings.

AIMS

To improve post-discharge utilization value using technology to identify high-risk individuals who might benefit from rapid nurse outreach to assess social and behavioral determinants of health with the goal of reducing inpatient and emergency department visits.

METHODS

The project employed a before and after comparison of the intervention site with similar primary care practice sites using population-level Medicaid claims data. The intervention targeted discharged persons with preexisting chronic disease and delivered a care transition alert to a nurse care coordinator for immediate telephonic outreach. The nurse assessed social determinants of health and incorporated problems into the EHR to share across settings. The project evaluated health outcomes and the value of nursing care on existing electronic claims data to compare utilization in the years before and during the intervention using negative binomial regression to account for rare events such as inpatient visits.

RESULTS

Avoiding readmissions and emergency visits, and increasing timely outpatient visits improved the individual's experience of care and the work life of healthcare providers, while reducing per capita costs (Quadruple Aim). In the intervention practice, the nurse care coordinator demonstrated the value of nursing care by reducing inpatient (25%) and emergency (35%) visits, and increasing outpatient visits (27%). The estimated value of avoided encounters over the secular Medicaid trend was $664 per adult with chronic disease, generating $71,289 in revenue from additional outpatient visits.

LINKING EVIDENCE TO ACTION

Using health information exchange to deliver appropriate and timely evidence-based clinical decision support in the form of care transition alerts and assessment of social determinants of health, in conjunction with data science methods, demonstrates the value of nursing care and resulted in achieving the Quadruple Aim.