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The healthcare industry increasingly values high-quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real-time artificial intelligence (AI)-based CDS tool.
We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note–derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians.
Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing–driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions.
This study discusses the creation of a time-series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals with HF.
This study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day-to-day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations.
Systematic reviews are considered the highest level of evidence that can help guide evidence-informed decisions in nursing practice, education, and even health policy. Systematic review publications have increased from a sporadic few in 1980s to more than 10,000 systematic reviews published every year and around 30,000 registered in prospective registries.
A cross-sectional design and a variety of data sources were triangulated to identify the journals from which systematic reviews would be evaluated for adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines and scope. Specifically, this study used the PRISMA 2020 reporting guidelines to assess the reporting of the introduction, methods, information sources and search strategy, study selection process, quality/bias assessments, and results and discussion aspects of the included systematic reviews.
Upon review of the 215 systematic reviews published in 10 top-tier journals in the field of nursing in 2019 and 2020, this study identified several opportunities to improve the reporting of systematic reviews in the context of the 2020 PRISMA statement. Areas of priority for reporting include the following key areas: (1) information sources, (2) search strategies, (3) study selection process, (4) bias reporting, (5) explicit discussion of the implications to policy, and lastly, the need for (6) prospective protocol registration.
The use of the PRISMA 2020 guidelines by authors, peer reviewers, and editors can help to ensure the transparent and detailed reporting of systematic reviews published in the nursing literature.
Systematic reviews are considered strong research evidence that can guide evidence-based practice and even clinical decision-making. This paper addresses some common methodological and process issues among systematic reviews that can guide clinicians and practitioners to be more critical in appraising research evidence that can shape nursing practice.
To explore trans men's access and use of healthcare services in Chile, based on the experiences of the trans men themselves, as well as of healthcare professionals.
A qualitative study with an ethnographic approach was carried out with 30 participants: 14 trans men and 16 healthcare professionals. Semi-structured one-on-one interviews with open-ended questions were used to collect the data. A thematic analysis was carried out with the NVivo Software.
Three main themes were identified: (1) failures in the recognition of trans identity, (2) challenges with patient-centered care, and (3) use of other (“non-trans”) health services.
The results suggest that not all transition processes are the same, individuals seek different ways; therefore, it is necessary to consider different body types and identities when planning programs and care for men in transition. Moreover, the accompaniment provided during the gender transition process should contemplate emotional and mental support.
The study outlines the need for all healthcare professionals to have training and knowledge about the transgender population, regardless of whether they are part of the teams supporting gender transition processes. The role of nurses and the contributions that can be made from nursing discipline in this research field are fundamental.
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