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To explore the potential of multimodal large language models in alleviating the documentation burden on nurses while enhancing the quality and efficiency of patient care.
This position paper is informed by expert discussions and a literature review.
We extensively reviewed nursing documentation practices and advanced technologies, such as multimodal large language models. We analysed key challenges, solutions and impacts to propose a futuristic multimodal large language model-driven model for nursing documentation.
Multimodal large language models offer transformative capabilities by integrating multimodal audio, video and text data during patient encounters to dynamically update patient records in real time. This reduces manual data entry, enabling nurses to focus more on direct patient care. These systems also enhance care personalisation through predictive analytics and interoperability, which support seamless workflows and better patient outcomes. While predictive analytics could improve patient care by identifying trends and risk factors from nursing documentation, further research is required to validate its accuracy and clinical utility in real-world settings. Ethical, legal and practical challenges, including privacy concerns and biases in artificial intelligence models, require careful consideration for successful implementation.
Transitioning to multimodal large language model-driven documentation systems can significantly reduce administrative burdens, improve nurse satisfaction and enhance patient care. However, successful integration demands interdisciplinary collaboration, robust ethical frameworks and technological advancements.
Implementing multimodal large language models could alleviate professional burnout, improve nurse–patient interactions, and provide dynamic, up-to-date patient records that facilitate informed decision making. These advancements align with the goals of patient-centred care by enabling more meaningful engagement between nurses and patients.
The problem being addressed is the administrative burden of nursing documentation. We suggest that multimodal large language models minimise manual documentation, enhance patient care quality and significantly impact nurses and patients in diverse healthcare settings globally.
The study aimed to explore the recent scientific literature regarding the knowledge, attitudes and practices of informal caregivers towards supporting a person with astroke.
This study was a scoping review that followed the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Scoping Review extension) guidelines.
Searches were conducted across Medline, CINAHL, PsycINFO, EMBASE, Cochrane, SCOPUS and Web of Science from January 2009 to January 2024.
The search results from the various database sources were collated in EndNote 20 and duplicates were removed. Following the removal of duplicates, the studies were imported to Covidence and filtered based on the well-defined eligibility criteria. Three reviewers independently conducted screening and data extraction, and any conflicts were resolved through discussion.
The analysis included a total of 37 studies that focused on the knowledge, attitudes and practices related to stroke caregiving. Of these, 15 studies addressed knowledge, 24 studies examined attitudes and 33 studies looked at caregiver practices.
This scoping review finds that lack of knowledge impacts the attitudes and practices of informal stroke caregivers. With the increasing incidence of stroke and the growing number of caregivers, there is an urgent need for targeted, individualised interventions accompanied by comprehensive evaluation.
Caregivers of people with stroke are often unprepared to provide care. Further research is needed to support these individuals, ensuring improved quality of life and better health outcomes for both the caregiver and the person with stroke.
Not applicable.
Delirium is a common disorder for patients after cardiac surgery. Its manifestation and care can be examined through EHRs. The aim of this retrospective, comparative, and descriptive patient record study was to describe the documentation of delirium symptoms in the EHRs of patients who have undergone cardiac surgery and to explore how the documentation evolved between two periods (2005-2009 and 2015-2020). Randomly selected care episodes were annotated with a template, including delirium symptoms, treatment methods, and adverse events. The patients were then manually classified into two groups: nondelirious (n = 257) and possibly delirious (n = 172). The data were analyzed quantitatively and descriptively. According to the data, the documentation of symptoms such as disorientation, memory problems, motoric behavior, and disorganized thinking improved between periods. Yet, the key symptoms of delirium, inattention, and awareness were seldom documented. The professionals did not systematically document the possibility of delirium. Particularly, the way nurses recorded structural information did not facilitate an overall understanding of a patient's condition with respect to delirium. Information about delirium or proposed care was seldom documented in the discharge summaries. Advanced machine learning techniques can augment instruments that facilitate early detection, care planning, and transferring information to follow-up care. 
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