Background: Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI’s utilization in nursing care. Objective: This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care. Methods: Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation. Results: A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan. Conclusion: This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.

Background: Mobile health (mHealth) offers significant benefits for patients with stroke, facilitating remote monitoring and personalized health care solutions beyond traditional settings. However, there is a dearth of comprehensive data, particularly qualitative insights, on the barriers to mHealth access. Understanding these barriers is crucial for devising strategies to enhance mHealth use among patients with stroke. Objective: This study aims to examine the recent literature focusing on barriers to mHealth access among patients with stroke. Methods: A systematic search of PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text was conducted for literature published between 2017 and 2023. Abstracts and full texts were independently screened based on predetermined inclusion and exclusion criteria. Data synthesis was performed using the convergent integrated analysis framework recommended by the Joanna Briggs Institute. Results: A total of 12 studies met the inclusion criteria. The majority were qualitative studies (about 42%), followed by mixed methods (25%), pilot studies (about 17%), nonrandomized controlled trials (about 8%), and observational studies (about 8%). Participants included patients with stroke, caregivers, and various health care professionals. The most common mHealth practices were home-based telerehabilitation (30%) and poststroke mHealth and telecare services (20%). Identified barriers were categorized into two primary themes: (1) at the patient level and (2) at the health provider-patient-device interaction level. The first theme includes 2 subthemes: health-related issues and patient acceptability. The second theme encompassed 3 subthemes: infrastructure challenges (including software, networking, and hardware), support system deficiencies, and time constraints. Conclusions: This systematic review underscores significant barriers to mHealth adoption among patients with stroke. Addressing these barriers in future research is imperative to ensure that mHealth solutions effectively meet patients’ needs.

Objective: To examine the impact of interdisciplinary team-based care (ITBC) on chronically ill patients and their outcomes as reported in relevant contemporary literature. Methods: In this systematic review, PubMed, MEDLINE, Web of Science, CINAHL Plus Full Text, and ten publishers were searched to identify studies published between 2019 and 2024. Titles, abstracts, and full texts were screened for eligible studies and assessed for relevance. Inclusion and exclusion criteria were implemented to ensure that only studies relevant to our objective were included. The convergent integrated analysis framework suggested by the Joanna Briggs Institute was utilized for data synthesis. Results: Ten studies were included in the systematic review. Data synthesis revealed five major themes at different levels: 1) Patient level, including themes of Patients’ Self-Improvement and Patients’ Health Outcomes; 2) Interpersonal level, including themes of Providers’ Work Performance and Shared Decision Making; and 3) Organizational level, including the theme of Healthcare Utilization. Conclusion: ITBC has a significant positive impact on chronically ill patients at multiple levels. At the patient level, it enhances self-management and health outcomes. At the interpersonal level, it improves healthcare providers’ performance and promotes shared decision-making. At the organizational level, it leads to more efficient healthcare utilization.

Background: ChatGPT by OpenAI emerged as a potential tool for researchers, aiding in various aspects of research. One such application was the identification of relevant studies in systematic reviews. However, a comprehensive comparison of the efficacy of relevant study identification between human researchers and ChatGPT has not been conducted. Objective: This study aims to compare the efficacy of ChatGPT and human researchers in identifying relevant studies on medication adherence improvement using mobile health interventions in patients with ischemic stroke during systematic reviews. Methods: This study used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four electronic databases, including CINAHL Plus with Full Text, Web of Science, PubMed, and MEDLINE, were searched to identify articles published from inception until 2023 using search terms based on MeSH (Medical Subject Headings) terms generated by human researchers versus ChatGPT. The authors independently screened the titles, abstracts, and full text of the studies identified through separate searches conducted by human researchers and ChatGPT. The comparison encompassed several aspects, including the ability to retrieve relevant studies, accuracy, efficiency, limitations, and challenges associated with each method. Results: A total of 6 articles identified through search terms generated by human researchers were included in the final analysis, of which 4 (67%) reported improvements in medication adherence after the intervention. However, 33% (2/6) of the included studies did not clearly state whether medication adherence improved after the intervention. A total of 10 studies were included based on search terms generated by ChatGPT, of which 6 (60%) overlapped with studies identified by human researchers. Regarding the impact of mobile health interventions on medication adherence, most included studies (8/10, 80%) based on search terms generated by ChatGPT reported improvements in medication adherence after the intervention. However, 20% (2/10) of the studies did not clearly state whether medication adherence improved after the intervention. The precision in accurately identifying relevant studies was higher in human researchers (0.86) than in ChatGPT (0.77). This is consistent with the percentage of relevance, where human researchers (9.8%) demonstrated a higher percentage of relevance than ChatGPT (3%). However, when considering the time required for both humans and ChatGPT to identify relevant studies, ChatGPT substantially outperformed human researchers as it took less time to identify relevant studies. Conclusions: Our comparative analysis highlighted the strengths and limitations of both approaches. Ultimately, the choice between human researchers and ChatGPT depends on the specific requirements and objectives of each review, but the collaborative synergy of both approaches holds the potential to advance evidence-based research and decision-making in the health care field.

Objective: To evaluate and synthesize evidence on the impact of educational interventions for individuals with insulin-treated type 2 diabetes mellitus (T2DM). Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, five electronic databases (Scopus, PubMed, Medline, CINAHL Plus with Full Text, and Web of Science) were systematically searched in February 2024. The search focused on studies published between 2019 and 2024 that investigated the impact of educational interventions on individuals with insulin-treated T2DM. Reference lists of the included studies were also manually searched. Titles and abstracts were screened for eligibility, and relevant full texts were assessed. Results: Out of 1,032 identified records, 11 studies met the inclusion criteria. According to the data synthesized using a convergent integrated analysis framework, five major themes have emerged: 1) Glycemic control (including subthemes improving HbA1C, decreasing postprandial plasma glucose, and decreasing fasting plasma glucose), 2) Insulin-related complications (including subthemes reducing hypoglycemic events and reducing the size of lipohypertrophy), 3) Knowledge, Attitude, and Practice (including subthemes engaging in self-management, improving insulin injection technique, improving knowledge, and improving attitude toward insulin treatment), 4) Optimal dose of insulin, and 5) Improving quality of life. Conclusion: Educational interventions are crucial for improving diabetes-related outcomes and reducing complications in individuals with insulin-treated T2DM. These interventions enhance knowledge, attitudes, and self-management practices, leading to better glycemic control and quality of life. Healthcare settings should develop and provide tailored educational programs for individuals with insulin-treated T2DM to optimize outcomes and minimize complications.