Authors

  1. Delaney, Connie W. PhD, RN, FAAN, FACMI
  2. Pruinelli, Lisiane MS, RN
  3. Alexander, Susan DNP, ANP-BC, ADM-BC
  4. Westra, Bonnie L. PhD, RN, FAAN, FACMI

Article Content

Initiated in 2013 as a 40-member, invitation-only event, nurses, informaticians, industry and policy leaders, and others now gather annually at the University of Minnesota School of Nursing to develop and implement a national action plan for identifying, standardizing, implementing, and effectively using sharable and comparable nurse-sensitive data. The conference subsequently is open to all and grows in size annually. The 2016 conference, held on June 2 to 3, 2016, included a total of 170 participants, with 79 first-time attendees. The intent of the Big Data Conference is unique. Although participants gain new knowledge about data science topics of importance to nursing, Dr Connie White-Delaney, Dean of the University of Minnesota, believes that this conference is distinguished by its commitment to partnering and continued work among the participants. This year's conference included updates, offered by experts in the field of nursing and data science, on efforts to integrate nurse-sensitive data into the electronic/virtual fabric of health systems in ways that produce comparability to support best practices for documentation and use of the data for quality improvement, research, and business analytics.

 

Created in the style of a "think tank/summit," the conference is complimented by the establishment of 11 virtual working groups, which continue collaborating throughout the year. Think tank/summit participants and working group members, in collaboration with respective national organizations and initiatives, have produced many achievements in practice, education, research, and health policy since the first conference in 2013. This year's meeting was marked by "[horizontal ellipsis]a sense of celebration of what has been accomplished through partnership and working with established organizations,[horizontal ellipsis]to advance shareable and comparable data access and use for nursing" (email communication, C. White-Delaney, June 2016). Three themes characterized these accomplishments: sharing a framework for data gathering and sharing of nurse-sensitive data, nursing education standards that reflect the nurse's role in technology and electronic health records (EHRs), and big data science that includes nursing's leadership and contribution to transforming health and healthcare.

 

A FRAMEWORK FOR SHARABLE AND COMPARABLE NURSE-SENSITIVE DATA

The key to creating a foundation for sharable and comparable nurse-sensitive data is the use of nationally recognized data standards. The American Nurses Association (ANA) recognizes 10 terminologies and two data sets that should be used to represent nursing practice and management; yet, the use of the terminologies and data sets in practice remains limited. Nursing terminologies are knowledge models, describing the contribution of nursing to patient care and outcomes, which are essential elements of the Nursing Minimum Data Set. Conference participants collaborate on initiatives to create health policies in support of using standardized nursing terminologies. A recent example of a successful state-level initiative, created by Big Data Conference participants, includes Minnesota's reply to the need for inclusion of nursing terminologies in healthcare settings. The Minnesota Commissioner of Health, in response to the Minnesota e-Health Advisory Committee, recommended the use of nursing terminologies in every healthcare setting throughout the state.1 Extending this initiative to the national level, the ANA Board and the Office of the National Coordinator made similar recommendations:2,3

 

1. All healthcare settings should create a plan for implementing an ANA-recognized terminology supporting nursing practice within their EHRs.

 

2. Each setting type should achieve consensus on a standard terminology that best suits their needs and select that terminology for their EHRs, either individually or collectively as a group (eg, EHR user group).

 

3. Education should be available and guidance should be developed for selecting the recognized terminology that best suits the needs for a specific setting.

 

4. When exchanging a Consolidated Continuity of Care Architecture with another setting, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) and Logical Observation Identifiers Names and Codes (LOINC) should be used for the exchange of problems and care plans. The LOINC should be used for coding nursing assessments and outcomes, and SNOMED CT should be used for problems, interventions, and observation findings.2

 

5. Health information exchange between providers using the same terminology does not require conversion of the data to SNOMED CT or LOINC codes.

 

6. Development of a clinical data repository that includes multiple recognized terminologies should be based on the national recognized terminologies of International Classification of Diseases, Ninth Revision (or Tenth Revision), Current Procedural Terminology, RxNorm, SNOMED CT, and LOINC.

 

 

In addition to these recommendations, the Nursing Management Minimum Data Set has been updated and distributed through LOINC. The Nursing Management Minimum Data Set includes terms and definitions that describe the context of care in which nurses' practice contributes to effective care. The collaborative efforts of nurses in practice, EHR vendors, academics, and members of professional organizations are making progress toward establishing health policies supporting the implementation of sharable and comparable nursing data.

 

NURSING EDUCATION STANDARDS THAT REFLECT THE NURSE'S ROLE IN TECHNOLOGY AND DOCUMENTATION

Informatics knowledge and competencies needed to support the development and use of sharable and comparable nurse-sensitive data are unevenly distributed across nursing educational programs. All nurses need minimum informatics knowledge, and faculties who teach undergraduate and graduate students need to incorporate informatics knowledge and competencies into curricula. During the years, national leaders in the nursing informatics field and participants of the Big Data Conference noted that the basic American Nurses Credentialing Center certification in nursing informatics was not sufficient to assess competencies in the emerging areas of advanced informatics, data science, and data analytics. Although requirements for the inclusion of informatics essentials across prelicensure, graduate, and doctoral-level nursing education do exist, there is no clear consensus about the standardization of specific curricular content for each educational level.

 

Since the initial conference in 2013, summit participants and working group members worked to address the needs and challenges of standardized informatics content in nursing curricula. Big Data conference activities focused on building and leveraging standards for prelicensure faculty and nurse educators with beginning experience in informatics teaching in graduate nursing programs. An additional focus of the conference has turned to creating resources and a framework for faculty members to ensure adequate graduate-level education in nursing informatics competencies.

 

Major actions and achievements include the following:

 

1. Development of the Nursing Informatics Deep Dive Program, a collaborative effort of AACN, the University of Minnesota School of Nursing, and the University of Maryland School of Nursing. Materials that were developed at the Deep Dive Workshop continue to be offered by the University of Minnesota School of Nursing, featuring numerous sample assignments, links to informatics standards and professional Web sites, and instructional videos on a variety of subjects such as EHRs, standardized nursing languages, workflow, consumer informatics, telehealth, and other key emerging areas (https://www.nursing.umn.edu/outreach/nursing-informatics-education-and-resources). These materials are freely available on the University of Minnesota School of Nursing Web site to support nursing faculty teaching informatics.

 

2. Multiple in-person conferences and Webinars were conducted to share informatics materials for faculty in prelicensure nursing programs.

 

3. Participants collaborated with the American Medical Informatics Association's initiative to create board certification for physicians in clinical informatics and with the Commission on Accreditation for Health Informatics and Information Management Education to create advanced informatics certification and accreditation for nurses and other health professionals. These efforts can be leveraged to foster interprofessional education and preparation for advanced informatics certification and accreditation.

 

 

BIG DATA SCIENCE THAT INCLUDES NURSING'S LEADERSHIP AND CONTRIBUTION TO TRANSFORMING HEALTH AND HEALTHCARE

Groundbreaking big data and data science research is emerging from a shared framework for data collection and empowerment by continued support of nursing education standards that reinforce and advance nurses' role in technology and data definition, documentation, and use. The interprofessional process of care coordination and capture of data across the care continuum has been clarified and enhanced by nurse scientists involved in big data and data science research. Many of these nurse scientists have been annual participants and leaders of the Big Data Conference since its inception in 2013. Efforts to disseminate findings from this research increase annually. The March 2017 issue of the Western Journal of Nursing Research will be devoted to Big Data Science.

 

Other examples include monthly Webinars held in partnership with the National Institute of Nursing Research-Nursing Informatics Subgroup on topics such as the following:

 

* National Institutes of Health Common Data Elements (Jerry Sheehan)

 

* Informatics Initiatives Empowering Nursing (Judith Warren)

 

* PCORI and Other Collaboratives Supporting Nursing Research (Rachel Richesson)

 

* Improving Access and Utilization of Data Sources to Support Research and Programs Intended to Eliminate Disparities (Rosaly Correa de Araujo)

 

* Optum Labs: Big Data Empowering Nursing Research (Thomas Clancy)

 

* Advancing Patient Centered Outcomes Research: Implications for Research Methods and Standard Terminology (Robin Newhouse).

 

 

The inclusion of nursing-specific data in EHRs is critical to the development of big data science. Consequently, the validation of EHR flow sheet information models to incorporate interprofessional common data models is necessary to support efforts in nursing research. Current validation efforts include six industry partners (Allina, Cedars Sinai, Duke, Kaiser Permanente, MediComp, and the New York Visiting Nursing Service). Extension of the Clinical and Translational Science Award and Patient Centered Outcomes Research Institute common data models continues and is expected to enhance consortia partnerships for amassing nursing-sensitive large data set research capacity.

 

Many additional topics were discussed at the 2016 Big Data Conference in both podium presentations and working groups. The need for continued support of the American Academy of Nursing policies on social determinants of health, and the identification of specific gaps describing how nursing's engagement and dissemination of these policies using informatics concepts, was of particular interest at this year's conference. The development of a nursing value model designed to produce objective measures of nursing value by measuring the intensity of nursing care and cost at the level of the patient, and across healthcare settings, was also presented. Ongoing efforts to define the nursing value model include collection of user histories, data definitions, and a pilot study that is in progress.

 

The many accomplishments stemming from annual participation and growth of the Big Data Conference have been disseminated through multiple sources such as Webinars, national presentations, and peer-reviewed publications and by professional, industry, and policy partnerships. Working together, nurses can make a difference in the design, implementation, and sharing of best practices that will transform health informatics and healthcare. The University of Minnesota School of Nursing is committed to continuing its support of the annual Nursing Knowledge: Big Data Science Conference, as long as there is interest and passion among nurses for attending this "[horizontal ellipsis]unique, 'think-tank' conference with work continuing throughout the year" (email communication, Bonnie Westra, June 2016).

 

References

 

1. Minnesota Department of Health. Recommendations regarding the use of standard nursing terminologies in Minnesota. 2014. http://www.health.state.mn.us/e-health/standards/index.html. [Context Link]

 

2. American Nurses Association. Position statement: inclusion of recognized terminologies supporting nursing practice within electronic health records and other health information technology solutions. 2015. http://www.nursingworld.org/MainMenuCategories/Policy-Advocacy/Positions-and-Res. [Context Link]

 

3. Office of the National Coordinator. 2016 interoperability standards advisory. 2016. https://www.healthit.gov/standards-advisory/2016. [Context Link]