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: Post-stroke depression (PSD) is a frequent problem in stroke patients, affecting their rehabilitation process and functional outcomes. Several studies have investigated the relationship between PSD and functional outcomes, but the results have been inconsistent. Objective: This systematic review of non-experimental studies aims to investigate the prevalence of post-stroke depression and the association between post-stroke depression and functional outcomes. Method: A search of PubMed, MEDLINE, Web of Science, and CINAHL Plus with Full Text was carried out from inception until January 2024. The literature was screened using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with relevant papers included. We extracted data from non-experimental studies that examined associations between PSD and functional outcomes. The Joanna Briggs Institute for systematic reviews was used for critical appraisal. Results: Twenty-one studies met the study criteria, including sixteen cohort studies, four cross-sectional studies, and one case-control study. PSD prevalences ranged from 12.2% to 32.2% in the first two weeks, 17.9 to 35.5% in the first month, and 10.4% to 32.0% in the third month following a stroke. Functional outcomes were evaluated in four domains: degree of dependence, basic activity of daily living, instrumental activity of daily living, and physical and cognitive function. Significant associations between PSD and functional outcomes were identified after controlling potential factors such as age, comorbidities, and stroke severity. PSD had negative associations with functional outcomes in all four measure domains from one month to five years after a stroke. Depression treatment showed positive results on functional outcomes in stroke patients. Conclusion: PSD prevalence was high in the first three months after stroke. PSD is significantly associated with poor functional outcomes. PSD assessment and management should be performed on a frequent basis in the early stages of stroke to achieve the best possible functional recovery.

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.

This study aimed to explore antibiotic knowledge, antibiotic resistance knowledge, and antibiotic use among adults in Bangkok, Thailand. This is a secondary analysis of cross-sectional data generated from a sample of 161 individuals living in Bangkok. Participants completed an online self-administered questionnaire developed by the World Health Organization. Descriptive analysis, the chi-square test, and multiple logistic regression analyses were performed. The sample comprised more females (56.5%) than males (42.2%). The majority of responders (67.7%) were between the ages of 18 and 40. More than half of the respondents mistakenly believed that antibiotics could treat colds and flu (54.7% and 47.2%, respectively). About 54.7% were aware that antibiotic resistance could harm them and their families. The chi-square test results showed that the levels of education were associated with antibiotic knowledge (p = 0.012), antibiotic resistance knowledge (p < 0.001), and antibiotic use (p = 0.023). Multiple logistic regressions showed that respondents with at least a bachelor’s degree or higher had better knowledge of antibiotics. Respondents who worked in the profession had better knowledge of antibiotic resistance. Respondents with sufficient incomes were more likely to use antibiotics. Baseline data from the study will be useful in antibiotic stewardship and public health campaigns among Bangkok residents.

Objectives Antibiotic resistance poses a major global public health threat. However, research on this issue is limited, especially among nursing students. This study aims to examine knowledge of antibiotics and antibiotic resistance, antibiotic use, and eHealth literacy in Thailand. Design A cross-sectional study was conducted using an online self-administered questionnaire. The WHO Antibiotic Resistance: Multi-Country Public Awareness Survey and the eHealth Literacy Scale were used. Descriptive and multiple regression analyses were performed. Setting Thailand’s North, South, Central and Northeast between January and February 2024. Participants A total of 1180 nursing students aged 18 or older, from first to fourth year and fluent in Thai, were invited to participate. Results The participants were mostly female (89.8%), with an average age of 20.64±1.81 years. Over half of the respondents (67.7%) have used antibiotics, mistakenly believing that antibiotics could treat colds and influenza (70.3%), malaria (66.8%), measles (63.6%) and sore throats (60.9%). About 71.5% recognised the impact of antibiotic resistance on themselves and their families, but 93.1% incorrectly believed antibiotic resistance means the body resists antibiotics. Participants scored 35.92±4.21 out of 40 on eHealth literacy. In a multiple regression analysis, three factors predict knowledge of antibiotic resistance: knowledge of antibiotics (B=0.199, p<0.001, 95% CI 0.165 to 0.234), eHealth literacy (B=0.078, p<0.001, 95% CI 0.056 to 0.100) and academic year (B=0.271, p<0.001, 95% CI 0.184 to 0.358), with knowledge of antibiotics being the most impact (β=0.318). These three variables explained 23.7% of the variance in antibiotic resistance knowledge scores. Conclusions Despite high eHealth literacy, nursing students hold misconceptions about antibiotic treatable conditions and insufficient knowledge of antibiotic resistance. This highlights the need to integrate antibiotic contents into nursing curricular and enhance eHealth literacy for better access and navigate health information.