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To develop deep learning models to predict nursing need proxies among hospitalised patients and compare their predictive efficacy to that of a traditional regression model.
This methodological study employed a cross-sectional secondary data analysis.
This study used de-identified electronic health records data from 20,855 adult patients aged 20 years or older, admitted to the general wards at a tertiary hospital. The models utilised patient information covering the preceding 2 days, comprising vital signs, biomarkers and demographic data. To create nursing need proxies, we identified the six highest-workload nursing tasks. We structured the collected data sequentially to facilitate processing via recurrent neural network (RNN) and long short-term memory (LSTM) algorithms. The STROBE checklist for cross-sectional studies was used for reporting.
Both the RNN and LSTM predicted nursing need proxies more effectively than the traditional regression model. However, upon testing the models using a sample case dataset, we observed a notable reduction in prediction accuracy during periods marked by rapid change.
The RNN and LSTM, which enhanced predictive performance for nursing needs, were developed using iterative learning processes. The RNN and LSTM demonstrated predictive capabilities superior to the traditional multiple regression model for nursing need proxies.
Applying these predictive models in clinical settings where medical care complexity and diversity are increasing could substantially mitigate the uncertainties inherent in decision-making processes.
We used de-identified electronic health record data of 20,855 adult patients about vital signs, biomarkers and nursing activities.
The authors state that they have adhered to relevant EQUATOR guidelines: STROBE statement for cross-sectional studies.
Despite widespread adoption of deep learning algorithms in various industries, their application in nursing administration for workload distribution and staffing adequacy remains limited. This study amalgamated deep learning technology to develop a predictive model to proactively forecast nursing need proxies. Our study demonstrates that both the RNN and LSTM models outperform a traditional regression model in predicting nursing need proxies. The proactive application of deep learning methods for nursing need prediction could help facilitate timely detection of changes in patient nursing demands, enabling the effective and safe nursing services.
Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical field.
Systematic review of prospective studies.
CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI-based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol.
Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five-level triage classification system. Regarding model performance, the feed-forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes.
Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health.
We have registered our review in PROSPERO (registration number: CRD 42023415232).
The systematic review aims to synthesize the literature examining the effectiveness of nurse-led remote digital support on health outcomes in adults with chronic conditions.
Adults with chronic diseases have increased rates of mortality and morbidity and use health care resources at a higher intensity than those without chronic conditions—placing strain on the patient, their caregivers and health systems. Nurse-led digital health disease self-management interventions have potential to improve outcomes for patients with chronic conditions by facilitating care in environments other that the hospital setting.
We searched PubMed/MEDLINE, Embase, PsycINFO and Cochrane Central databases from inception to 7 December 2022. We included randomized controlled trials assessing the impact of nurse-led remote digital support interventions compared to usual care on health-related outcomes in adults with chronic illness. The Cochrane risk-of-bias tool was used to assess bias in studies. Outcomes were organized into four categories: self-management, clinical outcomes, health care resource use and satisfaction with care. Results are presented narratively based on statistical significance.
Forty-four papers pertaining to 40 unique studies were included. Interventions most targeted diabetes (n = 11) and cardiovascular disease (n = 8). Websites (n = 10) and mobile applications (n = 10) were the most used digital modalities. Nurses supported patients either in response to incoming patient health data (n = 14), virtual appointment (n = 8), virtual health education (n = 5) or through a combination of these approaches (n = 13). Positive impacts of nurse-led digital chronic disease support were identified in each outcome category. Mobile applications were the most effective digital modality.
Results show that nurse-led remote digital support interventions significantly improve self-management capacity, clinical health outcomes, health care resource use and satisfaction with care. Such interventions have potential to support overall health for adults with chronic conditions in their home environments.
This study aimed to explore digital literacy among community-dwelling older adults in urban South Korea. A semistructured interview guide was developed using the Digital Competence ( 2.0 framework, which emphasizes the competencies for full digital participation in five categories: information and data literacy, communication and collaboration, content creation, safety, and problem-solving. The data were analyzed using combined inductive and deductive content analysis. Inductive analysis identified three main categories: perceived ability to use digital technology, responses to digital technology, and contextual factors. In the results of deductive analysis, participants reported varying abilities in using digital technologies for information and data literacy, communication or collaboration, and problem-solving. However, their abilities were limited in handling the safety or security of digital technology and lacked in creating digital content. Responses to digital technology contain subcategories of perception (positive or negative) and behavior (trying or avoidance). Regarding contextual factors, aging-related physical and cognitive changes were identified as barriers to digital literacy. The influence of families or peers was viewed as both a facilitator and a barrier. Our participants recognized the importance of using digital devices to keep up with the trend of digitalization, but their digital literacy was mostly limited to relatively simple levels. This scoping review aims to describe published work on the symptoms and management of long COVID conditions.
Symptoms and management of COVID-19 have focused on the acute stage. However, long-term consequences have also been observed.
A scoping review was performed based on the framework suggested by Arksey and O’Malley. We conducted a literature search to retrieve articles published from May 2020 to March 2021 in CINHAL, Cochrane library, Embase, PubMed and Web of science, including backward and forward citation tracking from the included articles. Among the 1880 articles retrieved, 34 articles met our criteria for review: 21 were related to symptom presentation and 13 to the management of long COVID.
Long COVID symptoms were described in 21 articles. Following COVID-19 treatment, hospitalised patients most frequently reported dyspnoea, followed by anosmia/ageusia, fatigue and cough, while non-hospitalised patients commonly reported cough, followed by fever and myalgia/arthralgia. Thirteen studies described management for long COVID: Focused on a multidisciplinary approach in seven articles, pulmonary rehabilitation in three articles, fatigue management in two articles and psychological therapy in one study.
People experience varied COVID-19 symptoms after treatment. However, guidelines on evidence-based, multidisciplinary management for long COVID conditions are limited in the literature. The COVID-19 pandemic may extend due to virus mutations; therefore, it is crucial to develop and disseminate evidence-based, multidisciplinary management guidelines.
A rehabilitation care plan and community healthcare plans are necessary for COVID-19 patients before discharge. Remote programmes could facilitate the monitoring and screening of people with long COVID.
The literature cites many factors that influence a nurse's decision when choosing their workplace. However, it is unclear which attributes matter the most to newly graduated nurses. The study aimed to identify the relative importance of workplace preference attributes among newly graduated nurses.
A cross-sectional study.
We conducted an online survey and data were collected in June 2022. A total of 1111 newly graduated nurses in South Korea participated. The study employed best–worst scaling to quantify the relative importance of nine workplace preferences and also included questions about participants' willingness to pay for each workplace preferences. The relationships between the relative importance of the workplace attribute and the willingness to pay were determined using a quadrant analysis.
The order according to the relative importance of workplace preferences is as follows: salary, working conditions, organizational climate, welfare program, hospital location, hospital level, hospital reputation, professional development, and the chance of promotion. The most important factor, salary, was 16.67 times more important than the least important factor, the chance of promotion, in terms of choosing workplace. In addition, working conditions and organizational climate were recognized as high economic value indicators.
Newly graduated nurses nominated better salaries, working conditions, and organizational climate as having a more important role in choosing their workplace.
The findings of this study have important implications for institutions and administrators in recruiting and retaining newly graduated nurses.
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