Assessing hospital environment conditions is necessary for healthcare providers and patients to coordinate safe care. The aims of this research included: a) identifying patterns in hospital visit feedback transcripts regarding bathroom doors and lights in the hospital room and b) interpreting the results to make recommendations for more enabling clinical environments. The methods used by the research team included organizing transcript data, assigning codes, and conducting an interrater reliability test to assess codebook efficacy. Finally, working with maternal and infant mortality experts, recommendations for the hospital were developed. We identified four possible interventions to address barriers: a) implement low-height, dimmable lighting along the base of the patient room, b) provide personal lights, such as penlights, to staff for nighttime assessments, c) install and improve on existing grab bars in patient room bathrooms and d) replace the standard patient room bathroom door with a different kind of auditory/visual privacy barrier.
All newborns experience low blood glucose levels when they first initiate carbohydrate metabolism. Some levels remain low, with potential seizures and severe brain injury. Predicting newborns at higher risk is clinically useful because newborns can have their blood sugar raised with breastfeeding, donor milk, formula, or oral dextrose gels. Additionally, informing parents of this higher risk can enhance shared decision-making in the first 48 hours after birth. To address this, we propose three predictive models using binary logistic regression for newborns receiving treatment with oral dextrose gels for hypoglycemia. The first is a parsimonious model, where a high-risk newborn's first blood glucose value is highly predictive of requiring an oral dextrose gel treatment. The second model can be used earlier in the clinical workflow. It is based on the most predictive variables that are also electronically available for all newborns and do not change much in the electronic health record. The third model explores the most predictive variables based on a conceptual model of factors associated with health disparities. These three models are informed from insights gleaned by an exploratory analysis of alternative outcome measures, variables, and threshold cutoffs using a standard heuristic of greedily finding the highest average difference for records on both sides of partitions. We discuss how the dynamics of when data are available during a hospital stay in the postnatal care unit for all patients impact the selection of useful variables for electronically-based decision support. We plan to modify handouts for postnatal care nurses that detail treatment guidance and support shared decision-making. We plan to embed stratified guidance, recommended scripts for high and low-risk cohorts, orientation materials for float and junior nurses, and patient-facing educational materials.
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.