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Introduction. Breast cancer management is complex, requiring personalised care from multidisciplinary teams. Research shows that there is unwarranted clinical variation in mastectomy rates between rural and metropolitan patients; that is, variation in treatment which cannot be explained by disease progression or medical necessity. This study aims to determine the clinical and nonclinical factors contributing to any unwarranted variation in breast cancer management in rural patients and to evaluate how these factors and variations relate to patient outcomes. Methods. Comprehensive data from patients who had primary breast cancer surgery from 2010 to 2014 in either a rural or metropolitan location in a single local health district was analysed (n = 686). Records were subset into two rurality groupings based on the postcode in which the patient resided, and the Modified Monash Model (MMM), an Australian system for classifying rurality. Statistical analysis was used to compare rural and metropolitan cohorts on treatments, patient characteristics, timeliness, and outcomes (recurrence and survival). Results. Rural patients had higher mastectomy rates than metropolitan patients (57% vs. 34%, p < 0.001), despite a lack of difference in clinical or demographic factors accounting for such variation. The length of time between treatment pathway stages was consistently longer amongst rural patients (p < 0.01). Rural women also had worse survival outcomes, especially amongst HER2‐positive patients who had significantly lower survival (5‐year 74% vs 82%; 10‐year 49% vs 71%, p < 0.05) than metropolitan HER2‐positive patients. Conclusion. This study reveals clinical disparities among rural breast cancer patients, that cannot be explained by demographic and clinical factors alone. Rural patients face lower rates of breast‐conserving surgery and treatment delays, attributable to systemic barriers such as limited access to specialist care, high travel costs, and suboptimal care coordination. These findings have important implications for improving equity and collaboration in delivering person‐centred breast cancer care. [ABSTRACT FROM AUTHOR]

Using a suite of artificial intelligence technologies, the current study sought to determine the prevalence of agitated behaviors in people with dementia in residential aged care facilities (RACFs) in Australia. Computerized natural language processing allowed extraction of agitation instances from the free-text nursing progress notes, a component of electronic health records in RACFs. In total, 59 observable agitated behaviors were found. No difference was found in dementia prevalence between female and male clients (44.1%), across metropolitan and regional facilities (42.1% [SD = 17.9%]), or for agitation prevalence in dementia (76.5% [SD = 18.4%]). The top 10 behaviors were resisting, wandering, speaking in excessively loud voice, pacing, restlessness, pushing, shouting, complaining, frustration, and using profane language. Four to 17 agitated behaviors coexisted in 53% of people with dementia agitation, indicating high caregiver burden in these RACFs. Improving workforce training and redesigning care models are urgent for sustainability of dementia care in RACFs. [Journal of Gerontological Nursing, 48(4), 57–64.] [ABSTRACT FROM AUTHOR]

Applying person-centered, nonpharmacological interventions to manage psychotic symptoms of dementia is promoted for health care professionals, particularly gerontological nurses, who are responsible for care of older adults in nursing homes. A knowledge graph is a graph consisting of a set of concepts that are linked together by their interrelationship and has been widely used as a formal representation of domain knowledge in health. However, there is lack of a knowledge graph for nonpharmacological treatment of psychotic symptoms in dementia. Therefore, we developed a comprehensive, human- and machine-understandable knowledge graph for this domain, named Dementia-Related Psychotic Symptom Nonpharmacological Treatment Ontology (DRPSNPTO). This graph was built by adopting the established NeOn methodology, a knowledge graph engineering method, to meet the quality standards for biomedical knowledge graphs. This intuitive graph representation of the domain knowledge sets a new direction for visualizing and computerizing gerontological knowledge to facilitate human comprehension and build intelligent aged care information systems. [Journal of Gerontological Nursing, 48(4), 49–55.] [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) is a powerful technique for extracting valuable information from unstructured electronic health records (EHRs). However, a prerequisite for NLP is the availability of high-quality annotated datasets. To date, there is a lack of effective methods to guide the research effort of manually annotating unstructured datasets, which can hinder NLP performance. Therefore, this study develops a five-step workflow for manually annotating unstructured datasets, including (1) annotator training and familiarising with the text corpus, (2) vocabulary identification, (3) annotation schema development, (4) annotation execution, and (5) result validation. This framework was then applied to annotate agitation symptoms from the unstructured EHRs of 40 Australian residential aged care facilities. The annotated corpus achieved an accuracy rate of 96%. This suggests that our proposed annotation workflow can be used in manual data processing to develop annotated training corpus for developing NLP algorithms.