Exploring data management content in doctoral nursing handbooks

Abstract

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.

Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research

“…Given the increasing complexity and digitization of healthcare data, nurses must have advanced data management skills to leverage AI technologies that enhance patient care, efficiency, and clinical outcomes, and to ensure alignment with best practices and adherence to professional and regulatory standards (Russell et al, 2023). These skills are also vital for nurses engaged in research and evidence-based practice because they facilitate the collection, organization, and analysis of data to advance nursing knowledge and practice (Bickford, 2015;Raszewski et al, 2021). Recent literature suggests that certain AI competencies must be incorporated into health professionals' education (Lomis et al, 2021;Russell et al, 2023): future nurses will need to be (a) evaluators who can determine the appropriateness of AI technology for specific situations and identify the information necessary for reliable results; (b) interpreters who can understand and accurately convey AI findings as well as recognize potential errors, biases, or limitations; and (c) communicators who possess the skills to articulate AI results and processes clearly to both patients, families, and other health professionals.…”

“…In nursing, NLP can extract insights from nursing notes that aid in de-identifying personal health information in clinical notes (Abu-El-Rub et al, 2022; Mitha et al, 2023). The integration of AI-powered chatbots into patient care routines is revolutionizing health care by leveraging NLP to mimic human interaction and conversation (Surve et al, 2023). This technology enables chatbots to handle various linguistic styles and dialects, potentially allowing effective patient engagement across diverse populations and benefiting those in remote or underserved regions.…”