What Big Data and Data Science Mean for Schools of Nursing and Academia

Abstract

The Age of Data is upon us, promising sweeping changes in all areas of business, including healthcare. The Shaw quote reminds us that data (facts) are fundamental to change. The Fourth Paradigm: Data-Intensive Scientific Discovery (Hey et al. The fourth paradigm: data-intensive scientific discovery, 2009) builds on the importance of data. Our science is powered by advanced computing capabilities and team work. The age of data-intensive discovery encompasses the transition from hypothesis-driven to data-driven science. The convergence of statistics, computer science and physical and life sciences is a reality today. What does it mean for nursing to become more data intensive? What will it mean for the practice of nursing and how do academic institutions adjust to prepare the nurses of the future to function well in a data-rich world? The potential is great to harness the massive stores of data on biological systems as well as social determinants of health; patients could change the way that care is provided. However our history tells us it may not be an easy transition. Nursing will need a large number of data-savvy professionals who can lead the profession forward; academic nursing must change to meet this challenge. In this chapter, the critical need for workforce training will be examined and the role that schools of nursing and academia will play to prepare the workforce of the future will be described. Competencies across different types of nursing education programs will be reviewed and exemplars of innovative curricular change will be described.

“Progress is impossible without change, and those who cannot change their minds cannot change anything.”

George Bernard Shaw

Appendices

Case Study 19.1: Informatics Certification and What’s New with Big Data

• Cynthia Gadd PhD, MBA, MS, FACMI &
• Connie White Delaney PhD, RN, FAAN, FACMI 

1. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA

   Cynthia Gadd PhD, MBA, MS, FACMI

2. School of Nursing, University of Minnesota, Minneapolis, MN, USA

   Connie White Delaney PhD, RN, FAAN, FACMI

Abstract

Demands for informaticians to have knowledge of data science methodologies for database management and analytics are acute. This case study addresses the American Medical Informatics Association (AMIA) initiative to advance the preparation of informaticians and speaks to the related implications for curriculum changes. Through advanced health informatics certification these informaticians with validated advanced expertise in the specialty will support big data/data science across the health science disciplines and the healthcare systems.

Keywords

Certification • Interprofessional informatics • Accreditation • Health informatics curriculum

1.1 19.1.1 Introduction

Many forces in the healthcare industry coupled with the evolution of technologies are accelerating the promise of big data and the potential of data science to transform knowledge discovery and quality, efficiency, person-centric care, and population health. Three of the most prominent forces include:

• Rapidly rising costs and payment reform that are forcing payers and health-care providers to shift from a fee-for-service approach to a values-based, health-focused system that prioritizes patient outcomes (including rewarding providers for targeted treatments that actually work).

• Clinicians who are continuing to move towards evidence-based medicine and health, reviewing data from a multitude of sources, including genomics, to provide support for diagnostic and treatment decisions, as well as outcomes and quality measurement.

• The surge in adoption of EHRs, in the wake of HITECH and ACA, that has achieved rates of implementation equaling 76% of hospitals (Office of the National Coordinator for Health Information Technology, 2016a) and 83% of physician practices (Office of the National Coordinator for Health Information Technology, 2016b), as reported by HealthIT.gov for the most recent years available.

These forces and others demand data scientists to inform the wise use of big data to support clinicians and consumers to make better decisions—from personalized treatments to preventive care. Key healthcare applications of data science include segmenting populations to target action, genomic medicine, predictive analytics and preventive measures, patient monitoring, home devices, mobile technologies, self-motivated care, disease modeling, and enhanced EHRs. (http://www.mastersindatascience.org/industry/health-care/ Retrieved 29 Jan 2016). “Data scientists are among the most in-demand employees in the healthcare industry,” said Thomas Burroughs, Ph.D., professor and executive director of Saint Louis University Center for Health Outcomes Research (SLUCOR) “Although the healthcare system is creating an unprecedented amount of digital data today, leaders are struggling to turn ‘big data’ into usable information to improve patient care quality, patient care experience, and health system costs and efficiency. Data scientists are pivotal to transforming health, quality, and the healthcare industry.” (http://www.slu.edu/news-data-science-graduate-program. Retrieved 29 Jan 2016). The development of data science expertise among the informatics professionals within the many health informatics disciplines (e.g. dentistry, medicine, nursing, pharmacy, public health) is and will continue to be central to the transformation of the healthcare system and care of people.

1.2 19.1.2 AMIA’s Path Toward Establishing Advanced Health Informatics Certification

Parallel to this timeframe of health care transformation, health informatics has undergone another transformation in which opportunities for formal education beyond the master’s and doctoral degrees and professional recognition, such as certification, are on a meteoric rise that is commensurate with workforce demand. Certification is the process by which individuals demonstrate that they have competency in a field and that they are proficient in performance of a specific position, professional role, and/or task. The process of establishing certification within a field creates a gold standard and a clear set of expectations for the knowledge and skills that individuals should possess to be considered competent. In the field of health informatics, certification is not currently a requirement to perform certain roles and/or tasks. Rather certification is a signal of competency and experience. In time, organizations seeking to hire qualified individuals may give preference to or require certification.

AMIA has been working on the issue of certification for individuals who practice clinical and health informatics since 2005 (Shortliffe et al. 2015). AMIA’s efforts led to the medical subspecialty of clinical informatics, the American Board of Medical Specialties certification, and the AMIA Clinical Informatics Board Review Program. (Detmer and Shortliffe 2014; https://www.amia.org/clinical-informatics-board-review-course (retrieved 29 Jan 2016)).

In 2011, the AMIA Academic Forum, the membership unit within AMIA dedicated to serving the needs of post-baccalaureate biomedical and health informatics training programs, created a Task Force on Advanced Interprofessional Informatics Certification (AIIC). The Task Force issued a consensus statement in February 2012 that established three basic principles:

• A pathway to certification for individuals not eligible for the clinical subspecialty certification is critical.

• Such a pathway should focus on the core informatics content that is relevant to all professions.

• Interprofessional informatics certification should be at the graduate level, based on the same core content used for the subspecialty certification (Gardner et al. 2009), have the same rigor as the subspecialty certification process, and convey the same level of assurance of competency as the subspecialty certification.

  As part of its work, the Task Force conducted an environmental scan of the health informatics certification landscape in Sept–Oct 2011, which identified several professions for which there were no existing certifications: MD nonboard-certified and trained in biomedical informatics (e.g., with MS, PhD, or nondegree fellowship); non-MD PhD in biomedical informatics; dental informatics; medical librarianship (although there is a credentialing process); and public health informatics (although informatics is a crosscutting competency in public health certification). The professions that have existing certifications include: nursing informatics (ANCC/ANA) (although at the basic level); information science/computer science (CPHIMS/HIMSS); health information management (CCHIIM/AHIMA); and pharmacy informatics (ASHP) through accredited residency programs. The eligibility criteria for certification exams varied but graduate degrees plus experience were not unusual.

  The Task Force hypothesized that the commonalities of informatics competencies across specializations and professions are quite large relative to their differences and therefore recommended that the AIIC exam be discipline neutral. Individual specializations would be free to develop specialized designations (e.g., a certification or credential) that meet the needs of their constituencies. The AIIC and these discipline-specific designations should enhance rather than compete with each other.

  In December 2014, AMIA’s Board of Directors convened a multi-disciplinary work group to build on the effort of the AMIA Academic Forum AIIC Task Force and recommend the core content and eligibility pathways for AIIC. This AIIC Work Group includes representatives from clinical informatics research, dentistry, nutrition, nursing, osteopathy, pharmacy, public health, and radiology. To inform their deliberations, work group members solicited input from stakeholder groups within these professions and related AMIA Working Group Chairs (e.g., the Nursing Informatics WG) and over 66 individuals responded from the various disciplines. Simultaneously, AMIA staff analyzed options for establishing a trusted, professionally neutral home for developing and administering the AIIC examination. This neutral organization would establish the final core content and eligibility pathways for AIIC.

AMIA’s commitment to health informatics certification was reinforced by the following organization goals:

• Strengthen the profession of health informatics by creating a way for advanced practitioners to demonstrate their expertise.

• Establish advanced certification in health informatics that meets the needs of individuals from diverse health professions and is equivalent in rigor to the clinical informatics medical subspecialty.

• Create a clear pathway for professionals seeking advanced certification, including those who need more education and training.

• Dedicate significant resources toward the realization of the certification program launch.

In November 2015, the AMIA Board of Directors endorsed recommendations of the AIIC Work Group that:

• Defined the scope of certification and recommended the name Advanced Health Informatics Certification (AHIC) to identify the certificate.

• Recommended approaches for developing health informatics Core Content.

• Proposed rigorous quantitative and qualitative eligibility requirements intended to span diverse health informatics professions.

Additionally, AMIA staff identified strategies to create a certifying entity and laid the groundwork for implementing AHIC.

1.3 19.1.3 Advanced Health Informatics Certification (AHIC)

The purpose of the newly renamed Advanced Health Informatics Certification (AHIC) is to establish a clear set of expectations for the knowledge and skills that must be mastered to be proficient in a field and provide a recognized mechanism for individuals to demonstrate their proficiency and experience to potential employers. The focus of the certification is on professionals who work to improve the health of individuals and populations by applying informatics knowledge and skills to the operational aspects of information and knowledge problems that directly affect the practice of healthcare, public health, and personal wellbeing.

In 2016, the most immediate actions to insure the establishment of the AHIC were to:

• Initiate a process for finalizing Core Content that would serve as the basis for the AHIC examination;

• Publish proposed Eligibility Requirements for potential applicants of the AHIC; and

• Create a certifying entity and develop awareness and a clear value proposition for AHIC among health informatics professionals.

The certifying entity will make final decisions on Core Content, Eligibility Requirements, and other aspects of advanced health informatics certification.

Implications of AHIC for changes in health informatics education are most obvious in the need to develop Core Content to guide the creation of future AHIC examinations and in the relationship between certification of individuals and accreditation of the programs in which individuals are educated and trained. In December 2014, the Work Group initiated a high-level review of the clinical informatics subspecialty (CIS) Core Content (Gardner et al. 2009), which needs to be updated to reflect current practices, models, and technologies that were not as firmly established in 2008 when the document was developed. Prominent among these areas in need of updating is data science and its specific healthcare applications, such as predictive analytics and disease modeling. Fortunately, there are many supportive initiatives afoot, including the NIH Big Data to Knowledge (BD2K) funded programs in data science education and workforce development. (https://datascience.nih.gov/bd2k/funded-programs/enhancing-training (retrieved 29 Jan 2016). Moreover, other educational resources, such as expanding the ONC open HIT curriculum, are key. (https://projectreporter.nih.gov/project_info_description.cfm?aid=8935847&icde=0 (retrieved 29 Jan 2016).

Complementary attention to data science will be necessary during the development of a competency framework for assessing programs seeking to become accredited. While the Core Content for a certification exam and the competency framework for assessing health informatics master’s programs have distinct purposes, programs will need to consider both documents for successful accreditation and successful certification pass-rates for their graduates.

1.4 Acknowledgements

The AMIA Board of Directors, under the guidance of Gil Kuperman, Blackford Middleton, and Thomas Payne, AMIA Presidents Don Detmer, Ted Shortliffe, Kevin Fickenscher, and Douglas Fridsma, and the Academic Forum provided pivotal leadership to AMIA’s efforts to establish advanced certification for clinical and health informatics professionals. The Academic Forum Task Force on Advanced Interprofessional Informatics Certification, the Academic Forum Roles and Functions Task Force, and the Advanced Health Informatics Certification Work Group provided critical input to the AMIA Board on how to best structure AHIC. Members of the AMIA Academic Forum Task Force on Advanced Interprofessional Informatics Certification (2011–2012) and the Advanced Interprofessional Informatics Certification Work Group (2014–present) have provided critical input to the AMIA Board on how to best structure AHIC.

References

• American Medical Informatics Association. 1988. https://www.amia.org/clinical-informatics-board-review-course. Accessed 29 Jan 2016.

• Detmer DE, Shortliffe EH. Clinical informatics: prospects for a new medical subspecialty. JAMA. 2014;311(20):2067–8.

• Gardner RM, Overhage JM, Steen EB, et al. Core content for the subspecialty of clinical informatics. J Am Med Inform Assoc. 2009;16:153–7.

• Gardner RM, et al. https://datascience.nih.gov/bd2k/funded-programs/enhancing-training. Accessed 29 Jan 2016.

• http://www.mastersindatascience.org/industry/health-care/. Accessed 29 Jan 2016.

• https://projectreporter.nih.gov/project_info_description.cfm?aid=8935847&icde=0 (2014). Accessed 29 Jan 2016.

• Office of the National Coordinator for Health Information Technology. Non-federal acute care hospital health IT adoption, Health IT Dashboard. 2016a. http://dashboard.healthit.gov/dashboards/hospital-health-it-adoption.php. Accessed Feb 2016.

• Office of the National Coordinator for Health Information Technology. Office-based physician health IT adoption. Health IT Dashboard. 2016b. http://dashboard.healthit.gov/dashboards/physician-health-it-adoption.php. Accessed Feb 2016.

• Saint Louis University. http://www.slu.edu/news-data-science-graduate-program (2015). Accessed 29 Jan 2016.

• Shortliffe EH, Detmer DE, Munger BS. Clinical informatics: emergence of a new discipline. In: Finnell JT, Dixon BE, editors. Clinical informatics study guide: text and review. New York: Springer; 2016. p. 3–21.

Case Study 19.2: Accreditation of Graduate Health Informatics Programs

• Judith J. Warren PhD, RN, FAAN, FACMI &
• J. J. Warren Ph.D., RN, FAAN, FACMI 

1. University of Kansas School of Nursing, Kansas City, KS, USA

   Judith J. Warren PhD, RN, FAAN, FACMI & J. J. Warren Ph.D., RN, FAAN, FACMI

Abstract

The role and value of accreditation of academic programs are discussed along with their relationship to professional organizations, federal regulations and practice. For a successful accreditation process to work, professional organizations must define their domains and identify standards and competencies that identify quality performance in their disciplines. Accreditation organizations must define objective standards and evidence guidelines that identify a program that provides quality education. Employers and government must insist on quality education by hiring or funding students whose programs demonstrate the achievement of accreditation. Finally, the impact of big data and data science on nursing informatics education is presented.

Keywords

Accreditation • Education • Professional organizations • Competencies

1.1 19.2.1 Introduction

Accreditation is part of the strategy that develops diverse, flexible, robust and respected higher education. The accreditation process assures and improves the quality of higher education. Organizations designed to conduct accrediting evaluations collaborate with experts to create a set of standards thus insuring the relevance of the accreditation. When a program successfully completes an accreditation review, the program is able to advertise their ability to provide appropriate instruction, student support, resources, and other services to meet the educational goals of the students (CHEA 2015). For students, accreditation is very important. The educational programs must be accredited for students to qualify for Title IV financial aid (US Department of Education 2016).

Accreditation is a quality process for programs in higher education. Accreditation assures that teaching, student achievement, curricula, academic support and other criteria meet certain levels of excellence and quality. Standards, used for this evaluation, are developed by the accrediting organizations, whose governing boards are composed of experts in the discipline being evaluated. The standards are developed and then submitted to the public for input and review. Programs submit self-studies based on these standards to the accrediting organization for evaluation. This submission is then followed with an onsite visit by site visitors who submit their evidence to the accrediting organization. Program accreditation is important for students as it provides value related to not only judging quality, but also obtaining employment, receiving student aid and transferring credits. For nursing there are two official accreditation organizations: Commission on Collegiate Nursing Education (CCNE) (CCNE 2015) and Accreditation Commission for Education in Nursing (ACEN) (ACEN 2016). For informatics there is one official accreditation organization: Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM) (CAHIIM 2015). CCNE accredits baccalaureate, masters and professional doctorate degrees in nursing. ACEN accredits diploma, associate, baccalaureate, masters and professional doctorate degrees in nursing. CAHIIM accredits masters’ degrees in health informatics and degrees in health information management. All three organizations are accredited by the Council for Higher Education Accreditations (CHEA) (CHEA 2016). CCNE is also accredited by the United States Department of Education. While CCNE and ACEN do not accredit nursing informatics specialties within the general nursing graduate programs, they do have requirements for informatics education for all students. For this reason, CAHIIM, which accredits graduate health informatics programs, is important to recognize quality informatics specialty programs in nursing.

CHEA is the primary national voice for accreditation and quality assurance to the U.S. Congress and U.S. Department of Education, as well as to the general public, opinion leaders, students and families. CHEA provides leadership for identifying and articulating emerging issues in accreditation and quality assurance. They also provide a national forum to address issues of mutual interest and concern in accreditation. CCNE, ACEN, and CAHIIM actively participate in this forum. CHEA is the only nongovernmental higher education organization in the United States that undertakes this responsibility for the public (CHEA 2015b). Encouragement is being given to accreditors to change their standards on curriculum from content-focus to outcome-focus. This new view requires programs to insure that their curricula support student achievement of the competencies specified by the professional organizations (CHEA 2016). For nursing informatics, these professional organizations are American Association of Colleges of Nursing (AACN) (AACN 2015), National league for Nursing (NLN) (NLN 2016) and American Medical Informatics Association (AMIA) (AMIA 2015).

Nursing competencies for use in curriculum development and accreditation evaluation are developed by AACN. These are published as “Essentials” and are freely available on their web site (AACN 2016). The “Essentials” are developed through a process of committees and consensus conferences. They represent the best thoughts about competencies for nursing practice for undergraduate and graduate students (both masters and professional doctoral degrees). Participation of administrators and faculty is essential to the process of developing these competencies.

Nursing interpretative statements for curriculum development and accreditation evaluation are developed by NLN. Standards for Accreditation have quality indicators and interpretive guidelines that guide faculty in creating their programs. The quality indicators are available on their web site (NLN 2016). These standards have been developed through group consensus of the membership.

While there are no official competencies for informatics professionals, AMIA has recently initiated a process for their development (AMIA 2016). AMIA has also become a member of CAHIIM to support accreditation of programs for this domain (AMIA 2015). AACN does not develop competencies for the specialty of nursing informatics, nor does NLN. In the same way as nurse midwives and nurse anesthetists, nurse informaticians are looking to have more than CCNE or ACEN accreditation. The addition of CAHIIM accreditation insures a strong informatics program. Nursing informatics programs are considering accreditation for their graduate programs in informatics. Accreditation discussions for nursing practice doctorates in nursing informatics have begun, as this is not yet a CAHIIM service (personal communication with the University of Minnesota School of Nursing),

CCNE, ACEN and CAHIIM also belong to and participate in the Association of Specialized and Professional Accreditors (ASPA). This organization is dedicated to enhancing quality in higher education through specialized and professional accreditation. To guide the accreditors, ASPA has developed a Code of Good Practice. This code describes best practices for member organizations for establishing relationships with their programs and institutions. CCNE, ACEN, and CAHIIM endorse and adhere these principles (ASPA 2015).

As accreditors, CCNE, ACEN, and CAHIIM track the evolution of new knowledge and professional requirements. The knowledge and ability to work with big data using data science is a new expertise desired by employers and the public. This trend has exploded due to new hardware and software capacities in handling very large amounts (petabytes) of stored and real-time data flows. The large amounts of data in electronic health records and genomic databases provide data and information to health care providers who are able to develop knowledge from these databases. New medical devices can export their data directly to electronic health records or other databases used in health care. Patients now use devices to record fitness activities, glucose monitoring, vital signs, food consumption, therapy logs, and a variety of other data. The Internet and social media have created online communities providing information and social support for patients and families. The use of the information developed in these communities is now accessible to understand the impact of disease on daily living. Analysis and use of this data is becoming critical in today’s world in identifying best practices and guiding safe, effective patient care. While specific competencies have not been developed in this field, many informatics programs are incorporating this content into their curricula. The accreditors are monitoring this trend to determine impact on standards development. Professional organizations are monitoring this new field to determine if their competencies should change or if a new competing discipline is emerging. See Fig. 19.2.1 for a depiction of the relationships between organizations.

Fig. 19.2.1

Relationships between organizations for accreditation activities

1.2 19.2.2 Accreditation Standards

CCNE, ACEN, and CAHIIM collaborate through CHEA and ASPA to develop criteria for well-defined accreditation standards that will identify how well an educational program is performing. This collaboration establishes a national education strategy that insures quality education. While each accreditor may organize their standards differently, the basic categories of what is evaluated are the same. Standards are developed for the following: the sponsoring educational institution, governance of the program, program mission and goals, program evaluation and improvement, faculty, curriculum, program director, resources, students, and compliance with fair practices. To assist programs applying for accreditation, materials and resources are available on each web site (ACEN 2016; AMIA 2015; CCNE 2015).

As faculty are responsible for curriculum, they are most interested in the standard concerning curriculum. This standard is based on a relationship with a professional organization. The professional organization represents the views and practice of the professional. The professional organization is responsible for developing the competencies required to practice. A standard on curriculum will state that the curriculum must build on the professional competencies, thus insuring student outcome-based performance. Faculty can participate in the evolution of this standard by being active in the professional organization’s competency work and by volunteering to be site visitors for the accreditor. This synergy between the profession and the accreditor insures the quality for which program accreditation was created.

Administrators are responsible for creating a program that is responsive to society’s demand for well-educated graduates. As knowledge, innovation, and technology evolve, the need for adding new content becomes a challenge: how to add this new material in an already full curriculum. This dilemma is well described in a classic text, The Saber-Tooth Curriculum by Abner Peddiwell (1939). This is a fable about a faculty who taught the fighting of saber-tooth tigers long after the tigers were extinct, because tradition demanded the content. Today’s educators use program evaluation, an accreditation standard, to determine how well their program is meeting societal needs and how the curriculum should evolve. Program improvement, another accreditation standard, focuses on monitoring trends and professional practices to create needed improvements in the curriculum. The excitement about big data and data science is not only being monitored by academic programs but also by professional organizations and accrediting organizations. ACEN, AACN and AMIA have workshops and presentations at their conferences and provide forums for professionals seeking jobs in these new fields of big data and data science. As they explore these new fields, discussions concerning competencies are beginning. As the competencies are developed and added to the official view, then CCNE, ACEN, and CAHIIM, who are also monitoring these new fields, will begin to revise accreditation guidance by pointing to these new competencies in the curriculum standards. Thus, the relationship between practice, education, and accreditation is enriched.

Another standard concerning faculty specifies the credentials needed by them to insure that a quality education is provided. Formal education achievement, licensure, and certification are the common components of credentialing. Since big data and data science are new and academic offerings and programs in these areas are just now emerging, a further method of evaluation of faculty competence in these fields must be added—evidence of lifelong learning. Faculty development programs need to be in place to support learning in regard to big data and data science. These programs need to supplement faculty reading and discussions. Evidence of interacting with experts in these fields, whether they are at conferences or online modalities, needs to be documented. Faculty development is probably the greatest challenge for administration. Learning is a labor-intensive process requiring time and resources. Faculty time and desire for mastery of new content and skills need to be encouraged and rewarded. Including learning experiences to the curriculum for big data and data science will require an investment in faculty.

Having sufficient resources is the next standard to be considered in the accreditation standards. What is the evidence for resources for teaching and learning in big data and data science? In the Big Data Primer (See Chap. 3) we learned that the “Vs—volume, velocity, and variety” defined big data. So to teach big data, a program needs to have data sources that meet these criteria for student learning. Not only are very large data sets needed but also the hardware (appropriate data architecture, servers, input devices, and output device) and software (database management systems for big data, programming languages, and data analytics) to support learning to engage in big data analysis need to be available. As previously mentioned in this book, new partnerships with industry or the government may need to be forged to provide access to these resources. Data science requires faculty who are knowledgeable in this domain: expertise in statistics or epidemiology is not sufficient. As with big data, resources for data science include software able to assist the student with data visualization, data wrangling, analytics, decision support, and business intelligence. These resources are in addition to resources normally evaluated in accreditation activities. No longer do we need only libraries and skill laboratories (as in the days of old), we need access to the above resources and partnerships. We need high-speed access to the Internet for online education from academia and industry. These requirements require more funding than schools of nursing are used to having. Plus, this funding is about data and analytics, not funding to support learning about patient care as in the past. Strategy will be required to create the value proposition for this budget at a time when all other programs at a university are being challenged with big data and data science needs in their disciplines. The future of nursing will be in learning to use big data to improve patient care and outcomes.

1.3 19.2.3 Recommendations for Future Accreditation Requirements

Since big data and data science are new fields in nursing and informatics, determining the quality of programs offering instruction is in its infancy. There is a developmental process that needs to occur to put accreditation standards in place. First the question of whether big data and data science is new or a fad must be answered. Companies are partnering with academia to meet their needs for data scientists (Bengfort 2016). Over the last few years Gartner has moved big data from its place in the peak of inflated expectations to the middle of the trough of disillusionment on the HypeCycle for Technology curve, thus indicating beginning adoption (Gartner 2016). They list big data and data analytics as two of the five major trends to watch. Evidence of healthcare organizations using big data approaches is reflected in the new positions for data scientists. The National Institutes of Health has a new center for Big Data to Knowledge (BD2K) and has awarded grant money in this domain (NIH 2015). AACN, AMIA, and NLN have papers and workshops at their conferences. Faculty are beginning to develop skills and expertise. The next hurdle is to modify existing competencies to include this knowledge and skill. This process usually takes one to two years. Then AACN, NLN, and AMIA will notify CCNE, ACEN, and CAHIIM of the new competencies. Once CCNE, ACEN, and CAHIIM receive the new competencies, programs will be given another one to two years to come into compliance.

The following recommendations are made concerning accreditation, big data and data science:

Competency Work

• Develop competencies specific to big data and data science

• Determine which competencies are for all students and which are for nursing data scientists

• Link big data, as big data requires a team approach, to interprofessional education

Implementation Work

• Provide for faculty development and experience

• Develop partnerships that share resources for big data and data science work

• Reward faculty leadership in developing these programs

• Identify forms of faculty scholarship in big data and data science

Accreditation Work

• Identify evidence for determining faculty competence

• Produce guidance for accreditation application preparation

• Train site visitors to evaluate evidence of quality education in big data and data science programs

1.4 19.2.4 Conclusion

As big data and data science are gathering more impact in health care and education, expect to see more employment opportunities in these areas. Students will want to receive quality education for employment. Employers will want to know that potential employees have achieved a quality education. Professional organizations will develop competencies. Accreditors will include big data and data science within their scopes of work. All of this will occur at a rapid rate. Begin now to get ready for the future.

References

• Accreditation Commission for Education in Nursing. http://www.acenursing.org. Accessed 1 Feb 2016.

• American Association of Colleges of Nursing. http://www.aacn.nche.edu. Accessed 28 Dec 2015.

• American Association of Colleges of Nursing. The Essentials Series. http://www.aacn.nche.edu/education-resources/essential-series. Accessed 5 Jan 2016.

• American Medical Informatics Association. http://www.amia.org. Accessed 28 Dec 2015.

• American Medical Informatics Association. Health informatics accreditation committee. https://www.amia.org/health-informatics-accreditation-committee. Accessed 5 Jan 2016.

• American Medical Informatics Association. AMIA Joins CAHIIM. https://www.amia.org/news-and-publications/press-release/amia-joins-cahiim-leadinformatics-program-accreditation. Accessed 28 Dec 2015.

• Association of Specialized and Professional Accreditors. Code of good practice. http://www.aspa-usa.org/code-of-good-practice. Accessed 28 Dec 2015.

• Bengfort J. Big Data 101: How higher ed is teaching data science. 2013. http://www.edtechmagazine.com/higher/article/2013/10/big-data-101-how-higher-ed-teaching-data-science Accessed 11 Jan 2016.

• Commission on Accreditation for Health Informatics and Information Management Education. http://www.cahiim.org. Accessed 28 Dec 2015.

• Commission on Collegiate Nursing Education. http://www.aacn.nche.edu/ccne-accreditation. Accessed 28 Dec 2015.

• Council for Higher Education Accreditation. Value of accreditation. http://www.chea.org/pdf/Value%20of%20US%20Accreditation%2006.29.2010_buttons.pdf. Accessed 28 Dec 2015.

• Council for Higher Education Accreditation. http://www.chea.org/pdf/chea-at-a-glance_2015.pdf. Accessed 28 Dec 2015.

• Council for Higher Education Accreditation. http://www.chea.org/accreditation_toolkit/accreditation_toolkit.pdf. Accessed 15 Jan 2016.

• Council for Higher Educations (2015). Recognized accrediting organizations. http://www.chea.org/pdf/CHEA_USDE_AllAccred.pdf#search=“recognition”. Accessed 1 Feb 2016.

• Gartner. Gartner’s Hype Cycles for 2015: Five megatrends shift the computing landscape. https://www.gartner.com/doc/3111522?ref=SiteSearch&sthkw=hype%20cycle%20big%20data&fnl=search&srcId=1-3478922254. Accessed 5 Jan 2016.

• National Institutes of Health. Big data to knowledge. https://datascience.nih.gov/bd2k. Accessed 28 Dec 2015.

• National League for Nursing Accreditation Commission. http://www.nln.org/accreditation-services/proposed-standards-for-accreditation. Accessed 1 Feb 2016.

• Peddiwell A. The saber-tooth curriculum. New York: McGraw-Hill; 1939.

• US Department of Education, Office of Student Aid. https://studentaid.ed.gov/sa/home. Accessed 4 Feb 2016.