Developing Data Science Competencies Through Pre-Licensure and Post-Licensure Nursing Curricula

Abstract

Key Points In an evolving health care environment where artificial intelligence, predictive analytics, and big data are shaping care delivery, data science literacy has emerged as a critical competency for undergraduate and graduate nursing students. Embedding data science competencies into nursing curricula not only enhances students’ analytical and critical thinking skills but also strengthens their readiness to participate in interdisciplinary collaboration and health system improvement initiatives. A nursing school’s exemplar curriculum demonstrates the feasibility of integrating data science content into dense undergraduate and graduate nursing curricula without adding credits, changing plans of study, and creating scheduling and workload pressures. Embedding such content requires an incremental approach that balances foundational literacy with applied skills. Embracing data science as an integral component of nursing education underscores that the nursing profession is at the forefront of health care innovation, advancing the science and practice of nursing to better serve patients, populations, and communities in the digital age. The rapid growth of digital health data and analytic capabilities has transformed health care delivery. Data science, defined as a “set of fundamental principles that support and guide the principled extraction of information and knowledge from data”1 has emerged as a critical competency for undergraduate and graduate students.2,3 For nursing, the largest sector of the health care workforce, the integration of data science is essential. Historically, nursing education has emphasized evidence-based practice, informatics, and biostatistics. While these areas remain foundational, the rise of big data, machine learning, and artificial intelligence has expanded the knowledge, skills, and competencies required for registered nurses (RNs).4 Data science literacy enables RNs to contribute to evidence-based practice, quality improvement, predictive modeling, population health, and health equity initiatives. By leveraging data, RNs can identify trends in patient outcomes, evaluate the impact of social determinants of health, and design interventions tailored to the needs of diverse populations. The significance of data science in nursing is aligned with the American Association of Colleges of Nursing’s The Essentials: Core Competencies for Professional Nursing Education.5 The essentials “Domain 8: Informatics and Healthcare Technologies” articulates the need for entry-level professional nursing education (pre-licensure) and advanced-level nursing education (post-licensure) to demonstrate competency in informatics, data science, and health care technologies. There are 5 related competencies in domain 8, including: (1) the use of tools in the care of individuals, families, and communities; (2) gathering data, creating information, and generating knowledge; (3) using processes to deliver safe nursing care in a variety of settings; (4) supporting documentation of care; and (5) and using information and technologies ethically, legally, and professionally in care delivery.5 The AACN advanced-level data science competencies align with the National Organization of Nurse Practitioner Faculties (NONPF) Nurse Practitioner Role Competencies (2022).6 AACN Domain 8 links with NONPF’s “Nurse Practitioner (NP) Domain 8: Technology and Information Literacy.” Similarly, AACN’s The Research-Focused Doctoral Program in Nursing7 recommends the integration of data science in PhD curricula. Guided by the AACN work, Shea et al8 proposed a PhD data science curriculum organizing model. In addition, the Nursing Knowledge Big Data Science (NKBDS) working group designed a roadmap for nursing leaders to effectively use data science to generate evidence, including analyzing and applying insights from large clinical data sets in practice.9 These national initiatives reinforce that data science is a fundamental competency for professional nursing practice rather than an optional skill. The integration of data science into nursing curricula addresses several core competencies in nursing education. It equips RNs to participate in team-based, data-driven decision-making, positions them to lead in value-based care environments, and supports efforts to address inequities in care. Embedding data science into nursing education prepares graduates to engage with rapidly evolving technologies such as clinical decision support systems, predictive analytics, and digital population health platforms. It also positions RNs as leaders in data-informed health care systems and builds capacity for research, policy, and system-level interventions. Recognizing these needs, a school of nursing in the southeastern United States developed a curriculum to introduce pre-licensure and post-licensure, and doctoral students to data science concepts and applications to build foundational literacy, analytic skills, and applied competencies aligned with the AACN Essentials and national reports. Through this initiative, the school has created a model that demonstrates how data science can be integrated into nursing education to prepare the next generation of RNs to advance health, quality, and equity in a data-driven health care environment. The purpose of this paper is to describe one school’s experience in creating a data science curriculum for use across pre-licensure, post-licensure, and doctoral programs that can be replicated in other settings. CONTEXT The school launched several initiatives in recent years to begin to incorporate data science into nursing education. These include (1) creating a proprietary, searchable, big data repository with immediate access to more than 2.7 billion de-identified health records and over 37 trillion data points from across the care continuum for use by nursing students; (2) offering 4 online modules related to data science through the school’s continuing professional development platform; (3) establishing an Artificial Intelligence/Data Science track in the PhD program; and (4) offering a Master of Science in Health Care Analytics program. While these programs provided a subset of students with opportunities in data science, integration of such core competencies was lacking across all nursing programs. The curricular effort began with feedback from students and faculty who completed the 4 data science modules and interacted with the proprietary big data repository. Feedback indicated that participants often requested access to data sets without including a specific question or purpose for their use. As a result, students would receive multiple Excel data files representing thousands of data points and not understand how to begin working with the data. Without a clear structure or guidance, it was challenging to know how to organize, clean, and analyze the files effectively. This feedback suggested a strong interest in engaging with big data, but also highlighted that students required both foundational information literacy supported by the health librarian/nurse informationist and data literacy and applied skills for using data in clinical and population health contexts, as well as clearer guidance on how to frame data-driven inquiries. Student feedback led to the development of the innovative Data Science Essentials curriculum to help students access and meaningfully analyze and apply data to their research and scholarly projects. The curriculum is appropriate for students with no prior knowledge of data science. It includes examples of research questions, analytic goals, and problem statements alongside data sets, workflows, and frameworks for managing, exploring, and applying the data. The curriculum is modular, enabling flexible integration of data science content within a single course or a series of courses. CURRICULUM The Data Science Essentials curriculum was developed to introduce nursing students across academic levels to the fundamental principles and applied practices of data science in health care. Designed as a flexible, modular sequence of online micro-learnings, the curriculum scaffolds data literacy, analytic reasoning, and applied data management skills that align with the AACN Essentials. The curriculum consists of 7 modules that together represent a continuum from foundational concepts to applied analytics. The topics and associated content for the 7 data science modules are described in Table 1. Each module develops learners’ capacity to move from clinical inquiry to data-informed action, transforming the core processes of nursing inquiry such as developing a Patient/Population/Problem, Intervention, Comparison, Outcome, and Timeframe (PICOT) question into structured, analyzable data workflows. Table 1 - Topics and content for the data science online modules Module Lessons Included Key Focus Foundations of data science in health Welcome to data science essentialsWhat is data science?Why data science matters in health practiceThe data–decision–impact loop Introduces core concepts of data science and its role in nursing and health practiceEstablishes the link between data, evidence, and decision-making through the Data–Decision–Impact cycleHighlights responsible data stewardship and its role in patient safety and quality improvement Understanding and mapping clinical workflows Introduction to workflow mappingWorkflow diagrams and swimlanesActivity: Mapping a simple workflowFrom workflow to data strategy Builds foundational understanding of how data are generated in clinical settingsConnects the development of a PICOT question to the identification of measurable variables and workflow points where data are captured. Students practice visualizing data generation using workflow diagrams and SIPOC (suppliers, inputs, processes, outputs, customer) tools Data quality and integrity What makes data “good”?Common data quality challengesEnsuring data integrity in practiceKnowledge check: Spot the data flaws Explores characteristics of “good” data and strategies for selecting and validating data sources such as the proprietary data repository built in-house, or an external source such as Medical Information Mart for Intensive Care (MIMIC) - III or other public health repositoriesEmphasizes ethical access, data governance, and documentation integrity Exploring and wrangling data Introduction to data wranglingBasic data types and structuresSorting, filtering, and cleaning dataActivity: Wrangle a sample data set Guides students through the data management lifecycle, including cleaning, organizing, and transforming dataIntroduces version control and basic wrangling in Excel or similar toolsStudents draft a simple data management plan linked to their PICOT variables Descriptive statistics made simple Why statistics matter for health professionalsUnderstanding distributions, means, and mediansVisualizing with frequency tables and histogramsPractice: Interpreting descriptive statistics Provides a practical introduction to descriptive statistics and visualizationStudents summarize data distributions and create simple charts to interpret relationships relevant to their clinical questions Introduction to health data visualization Principles of effective data visualizationChoosing the right chart for the messageVisual pitfalls and misinterpretationsActivity: Create and interpret a chart Focuses on communicating findings clearly through effective visuals and narratives. Students interpret patterns, connect them to clinical or operational meaning, and discuss implications for quality, safety, and equity. Applying it all – case study and capstone activity Case study walkthrough (general health scenario)Optional case study: Anesthesia practice capstone assignment overview and instructionsReflection and course wrap-up Serves as the applied capstone. Students work with de-identified data sets to answer a question derived from a PICOT framework, apply their analysis plan, and present findings in a short reflection or visualization report Each 20 to 30-minute module uses a progressive storytelling approach to situate learning within realistic clinical and public health contexts. The modules can be delivered sequentially within a dedicated data science course or embedded individually within existing courses on topics such as evidence-based practice, quality improvement, and informatics. All modules can be integrated into the learning management system as assignments. The modules include interactive activities and assessments, and assume no prior experience with data science concepts or tools, though a comfort in navigating through modern web-based content is expected. CURRICULAR INTEGRATION AND FLOW The modules follow a logical, nursing-centered progression: Ask: Frame a clinical or quality question (modules 1 to 2). Acquire: Identify and evaluate data sources (module 3). Analyze: Manage, clean, and summarize data (modules 4 to 5). Apply: Visualize and interpret findings (module 6). Assess: Reflect and communicate impact (module 7). This sequence mirrors both the evidence-based practice (EBP) cycle as well as the Data-Decision-Impact framework used in clinical analytics.10 Students learn to translate a PICOT question (eg, “In adult heart failure patients, does standardized discharge education compared to usual care reduce 30 d readmission over 6 mo?”) into operational variables, map those variables to data sources, and use descriptive analysis to produce actionable insights. Structured assignments require students to develop workflow mappings, construct a data analysis plan, and interpret preliminary findings to reinforce these competencies. Through these tasks, learners bridge clinical reasoning with data reasoning, developing the ability to move from question formulation to evidence generation, an essential competency for nurses leading data-driven quality improvement and research. The structured assignments are described in Table 2. Table 2 - Structured assignments Assignment Objective Instructions Evaluation Workflow mapping (module 2) Visualize the data-capture workflow for your project to identify hand-offs, data-entry points, and quality gaps Select a critical step of your clinical question (eg, discharge education, intra-op documentation).Outline the real-world workflow through stakeholder interview or SOP review.Create a flowchart (Swimlane or SIPOC) showing roles, tasks, data elements, and systems.Highlight 2 points where data quality may fail.Submit a PDF flowchart + 150-word summary AccuracyClarityRisk identificationProfessionalism(20 points) Data analysis plan (module 4) Draft a concise analysis plan to answer your PICOT question Use the Data Project Workbook “Data_Analysis_Plan” tab to complete: Analysis questionStatistical test/methodSoftwareVisual typeExpected outputJustify each test in one sentence and upload the tab plus a 1-page summary AlignmentAppropriatenessClarityCompleteness(25 points) Discussion: Interpreting preliminary findings (module 6) Post one unexpected preliminary finding Discuss: Possible clinical or operational explanationsHow workflow or data quality may have influenced itNext analysis or data element you would exploreRespond to 2 peers with constructive suggestions Insight depthConcept linkagePeer feedback quality(10 points) Capstone mini-lab (module 7) Demonstrate the ability to move from question formulation to evidence interpretation and translation Complete a guided analysis using a provided data set (eg, heart failure or anesthesia care)Apply prior skills such as workflow mapping, data planning, and visualization, address a real-world question, and submit a short reflection or report Alignment with questionWorkflow and data management and and clinical and and and AND learning, the Data Science Essentials curriculum and learning strategies in and Each module realistic clinical and public health that situate data science concepts within to Case such as or care help learners how data are and used in including workflow mapping, data wrangling and embedded knowledge students to apply concepts the link between analytic reasoning and the nursing These align with learning cycle and with the AACN Essentials domain 8, and data science competencies as integral to effective care. practice is the curriculum through a structured data This activity learners to evolving questions, and applications of data science to their clinical or scholarly By and the supports learning as described by students connect data analysis with clinical ethical reasoning, and systems questions are across modules to help students data concepts into their professional as evidence-based learning is through structured assignments that the evidence-based practice case and de-identified health data sets, students a PICOT question, identify relevant variables, map data workflows, and develop a basic analysis These activities guide learners in a clinical question into a measurable applying descriptive and findings within a nursing Through these tasks, students bridge clinical reasoning with data reasoning, their ability to contribute to quality improvement and research initiatives. The modular design for flexible integration across pre-licensure, post-licensure, and doctoral programs without course or faculty Each module can be or delivered as a diverse levels of data literacy learners and By and practical the curriculum analytic principles into nursing-centered and the to participate and lead data-driven decision-making across health care settings. Complete of each assignment are provided in Table 2. AND The Data Science Essentials curriculum was designed using technologies. are delivered through the learning management system interactive knowledge and reflection Each module short interactive and embedded activities created in learners to engage a aligned with existing tools and technologies used at the through as well as to of the learning into practical students engage with realistic data and analytic While the data is a proprietary health care data public or data set such as the Medical Information Mart for Intensive Care National Health and or for data be to similar learning Through guided case students use this repository and data sets to variables related to quality, outcomes, and social determinants of This supports the development of competencies in ethical data data and the of data quality and Data and visualization are using was for its and across health care Students learn to and data sets, basic descriptive and generate tables and The Data Project a students through the analytic from a question to variables, selecting and This structured analytic reasoning principles of data and learning is supported through tools that construct and diagrams to how data are and on across and These activities the in clinical and quality improvement these technologies and By integrating data tools within an learning the curriculum that students develop data literacy using the digital in and research settings. and the design developed a guide that enables faculty to the Data Science Essentials curriculum from an exemplar course within the can the content as a course or modules or assignments into existing courses. The exemplar course includes standardized assignment and interactive can data sets, and reflection to align with their course learning across programs. This approach supports flexible programs to incorporate data science content within curricula or to an learning experience for students. The modular supports for learning, such as an or online an for students or to data science courses across programs where modules be For all 7 modules can be integrated into the evidence-based practice the students can learn that data the for evidence, that the 2 concepts are are not the Through and applied students understand that data represent or that are and and that no or interpretation on its in data is and applied to support or a specific or In this students how data science the tools and to information into evidence that can policy, practice, and in health and nursing contexts. students can apply content from all 7 modules to and evaluate their scholarly projects. the data science curriculum is and be The plan is designed to feedback from students and faculty to evaluate the and of the online modules and to identify areas for Students evaluate the curriculum through can report in using data for and in data applying analytic reasoning to clinical and the role of data in advancing evidence and quality and and to engage in data-driven initiatives. on with of and of the module The plan also includes for faculty to identify support needs and for students and faculty to The integration of data science into nursing education both an and a for of As the exemplar curriculum align with the AACN Essentials and role competencies to prepare RNs with competencies that are critical for practice, research, and Data science literacy enables RNs to contribute to data-driven decision-making and for ethical and use of technologies. and of the curriculum several data science content into nursing curricula requires an incremental approach that balances foundational literacy with applied skills, in prior Students from with basic concepts and analysis of data faculty development is have the for with data science to translation to students. a role in the data science curriculum is delivered in a and innovative is that it is to content within dense nursing curricula without adding credits, changing plans of study, and creating scheduling and workload pressures. for nursing may be finding and to the to create and the need for to The is that nursing data science as a core competency across all levels of education. Without RNs for health care where artificial intelligence, predictive analytics, and big data are shaping care delivery. data science also addresses as RNs in responsible and ethical data practices are to in for populations, and that data-driven not health the integration of data science into nursing curricula supports the in advancing health care quality, safety, and equity. By and of nursing can to build a national framework that graduate is to in a health care This exemplar how integrating data science into nursing education is both and By with the AACN Essentials and to needs, this is a model for RNs to in a health care The integration of data science into nursing education a critical step advancing the capacity to engage in decision-making and to lead within data-driven health As health care to through rapid innovation, the ability to access, and apply data has fundamental to and care. Embedding data science competencies into nursing curricula not only enhances students’ analytical and critical thinking skills but also strengthens their readiness to participate in interdisciplinary collaboration and health system improvement initiatives. this and academic leaders the of data science principles across all levels of nursing education. This includes developing faculty curricular innovation, and that align with competencies in informatics, analytics, and data By data science as an integral component of nursing education, the profession can at the forefront of health care innovation, advancing both the science and practice of nursing to better serve patients, populations, and communities in the digital age.

Ähnliche Arbeiten