Medication regimens in older patients have been strongly associated with adverse events leading to hospitalization in ambulatory care settings. Despite a 29% hospitalization rate, to date, no research regarding medication regimens and readmission to the hospital has been completed in the home care setting. As part of a larger study evaluating predictors of readmission to the hospital from home care, descriptive analyses, chi-square tests, and t tests for independent samples were used in this secondary analysis to evaluate the Outcome and Assessment Information Set and medication records from 911 older patients admitted from the hospital to 15 home care agencies. Patients readmitted back to the hospital were older, sicker, and more cognitively impaired, and had complex medication regimens that included significant polypharmacy and inappropriate medication use. Nurses working with older adults need to be especially vigilant in monitoring medication regimens of patients to reduce opportunities for adverse drug events and subsequent hospitalization.
Determining when advanced practice registered nurse students are safe and competent for beginning-level practice is challenging. This article describes the development and testing of a capstone objective structured clinical examination designed to evaluate the practice readiness of students enrolled in the family, adult-gerontology, women's health nurse practitioner, and nurse-midwifery tracks. Lessons learned from this process and how they were used to enhance the curricula are discussed.
The purpose of this methodological study was to compare methods of developing predictive rules that are parsimonious and clinically interpretable from electronic health record (EHR) home visit data, contrasting logistic regression with three data mining classification models. We address three problems commonly encountered in EHRs: the value of including clinically important variables with little variance, handling imbalanced datasets, and ease of interpretation of the resulting predictive models. Logistic regression and three classification models using Ripper, decision trees, and Support Vector Machines were applied to a case study for one outcome of improvement in oral medication management. Predictive rules for logistic regression, Ripper, and decision trees are reported and results compared using F-measures for data mining models and area under the receiver-operating characteristic curve for all models. The rules generated by the three classification models provide potentially novel insights into mining EHRs beyond those provided by standard logistic regression, and suggest steps for further study.
Supporting gender equity for women working in geriatrics is important to the growth of geriatrics across disciplines and is critical in achieving our vision for a future in which we are all able to contribute to our communities and maintain our health, safety, and independence as we age. Discrimination can have a negative impact on public health, particularly with regard to those who care for the health of older Americans and other vulnerable older people. Women working in the field of geriatrics have experienced implicit and explicit discriminatory practices that mirror available data on the entire workforce. In this position article, we outline strategic objectives and accompanying practical recommendations for how geriatrics, as a field, can work together to achieve a future in which the rights of women are guaranteed and women in geriatrics have the opportunity to achieve their full potential. This article represents the official positions of the American Geriatrics Society. J Am Geriatr Soc 67:2447–2454, 2019
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.