The effects of special nursing units in nursing homes on healthcare utilization and cost: A case-control study using propensity score matching.

BACKGROUND: Most nursing homes in South Korea lack professional nursing services, resulting in transporting residents to hospitals for mild health problems and nursing treatment needs. While the number of nursing homes has increased, the number of registered nurses working in nursing homes has declined. In 2019, the Korean Ministry of Health and Welfare and the National Health Insurance Service launched the Special Nursing Units in Nursing Homes, a pilot nurse-led model, to resolve the lack of health and nursing services in nursing homes by mandating registered nurses' minimum staffing levels and protecting their scope of practice. OBJECTIVE: This study explored the effects of the Special Nursing Units model in Nursing Homes on healthcare utilization and cost among nursing home residents. DESIGN: A comparative effectiveness research design using propensity score matching. SETTING(S): Eighteen nursing homes were selected based on the region and number of beds. PARTICIPANTS: There were 323 matched-pairs of residents from the case and control groups. METHODS: Nursing homes with more than 30 beds were recruited nationwide, with 18 nursing homes being selected based on the region and number of beds. The case group included 323 older adults receiving professional nursing services by registered nurses under the Special Nursing Units model in Nursing Homes for more than six months consecutively in 18 nursing homes between April and December 2019. We matched control participants using propensity score matching with health insurance and long-term care data. We analyzed the differences in healthcare utilization and cost changes between the case and control groups using generalized estimating equations. RESULTS: The groups were not statistically different in baseline demographic or health-related characteristics. There were 26 (8.1%) and 30 (9.3%) deaths in the case and control groups, respectively, during the six months of the model, which was not statistically different (p = .576). The case group showed statistically significant decreases in healthcare utilization and costs, including hospitalization frequency (p = .008), length of stay (p = .002), and hospitalization costs (p = .003); outpatient visit frequency (p = .003) and costs (p < .001); and home healthcare frequency (p < .001) and cost (p < .001) than the control group. CONCLUSIONS: Professional nursing services by registered nurses under the Special Nursing Units model in Nursing Homes decreased healthcare utilization and costs. A nurse-led model in nursing homes, which includes mandating the minimum staffing levels of registered nurses and protecting their scope of practice, promises to improve resident health outcomes.

Effects of registered nurse staffing levels, work environment, and education levels on adverse events in nursing homes.

This study examined the effects of nurse staffing levels, work environment, and education levels on adverse events in nursing homes. A cross-sectional study was conducted involving 216 nurses working in 62 nursing homes in South Korea, using self-reported questionnaires and data from the National Health Insurance Service of South Korea. A logistic regression model was used to investigate the effects of nurse staffing levels, work environment, and nursing education levels on the adverse events experienced by residents. An increase of one resident per nurse was significantly associated with a higher incidence of medication error, pressure ulcers and urinary tract infections. A poor work environment increased the incidence of adverse events. Compared to nurses with a bachelor's degree or higher, those with diplomas reported increased incidence rates of pressure ulcers. Improving the health outcomes of residents in nursing homes requires efforts that strengthen the nursing workforce in terms of numbers and educational level, and which improve their work environment at institutional and policy levels.

Effects of the teach-back method among cancer patients: a systematic review of the literature.

PURPOSE: This study aimed to systematically review published research on the use of the teach-back method among cancer patients and provide basic data for developing effective nursing interventions. METHODS: Using a PICOS (Population, Intervention, Comparisons, Outcomes, Study Designs) framework, we reviewed 246 studies from selected electronic databases-CINAHL, EMBASE, PubMed, PsycInfo, RISS, KISS, DBpia, NDSL, and KCI-and selected five studies for further analysis. We evaluated the reference quality using Cochrane's risk of bias and risk of bias assessment tool for non-randomized studies, following which we performed reviews and analyses. RESULTS: Five studies were selected for the final analysis, including four quasi-experimental studies and one randomized controlled experimental study. The intervention programs were provided mostly by outpatient clinics. The cancer types of the subjects were breast cancer and gastrointestinal cancer in four and one study, respectively. The number and duration of the interventions varied depending on the content. The number of outcome variables ranged from 1 to 5, depending on the study; among these, self-efficacy, symptom experience, and distress were used. Teach-back intervention programs significantly affected happiness, health literacy, anxiety about death, symptom experience, distress, and self-efficacy. CONCLUSION: This study found that teach-back interventions have positive health outcomes including happiness, uncertainty, self-efficacy, self-management behavior, symptom experience, distress, anxiety, and health literacy among cancer patients. However, it found no effects with regard to drug administration, functional measurements, or satisfaction. Future research should continuously examine the teach-back approach and assess its positive health outcomes for cancer patients.

Earthquake Detection in a Static and Dynamic Environment Using Supervised Machine Learning and a Novel Feature Extraction Method.

Detecting earthquakes using smartphones or IoT devices in real-time is an arduous and challenging task, not only because it is constrained with the hard real-time issue but also due to the similarity of earthquake signals and the non-earthquake signals (i.e., noise or other activities). Moreover, the variety of human activities also makes it more difficult when a smartphone is used as an earthquake detecting sensor. To that end, in this article, we leverage a machine learning technique with earthquake features rather than traditional seismic methods. First, we split the detection task into two categories including static environment and dynamic environment. Then, we experimentally evaluate different features and propose the most appropriate machine learning model and features for the static environment to tackle the issue of noisy components and detect earthquakes in real-time with less false alarm rates. The experimental result of the proposed model shows promising results not only on the given dataset but also on the unseen data pointing to the generalization characteristics of the model. Finally, we demonstrate that the proposed model can be also used in the dynamic environment if it is trained with different dataset.

New algorithm for the identification of intact disulfide linkages based on fragmentation characteristics in tandem mass spectra.

Identifying the sites of disulfide bonds in a protein is essential for thorough understanding of a protein's tertiary and quaternary structures and its biological functions. Disulfide linked peptides are usually identified indirectly by labeling free sulfhydryl groups with alkylating agents, followed by chemical reduction and mass spectral comparison or by detecting the expected masses of disulfide linked peptides on mass scan level. However, these approaches for determination of disulfide bonds become ambiguous when the protein is highly bridged and modified. For accurate identification of disulfide linked peptides, we present here an algorithmic solution for the analysis of tandem mass (MS/MS) spectra of disulfide bonded peptides under nonreducing condition. A new algorithm called "DBond" analyzes disulfide linked peptides based on specific features of disulfide bonds. To determine disulfide linked sites, DBond takes into account fragmentation patterns of disulfide linked peptides in nucleoside diphosphate kinase (NDPK) as a model protein, considering fragment ions including cysteine, cysteine thioaldehyde (-2 Da, C(T)), cysteine persulfide (+32 Da, C(S)) and dehydroalanine (-34 Da, C(Delta)). Using this algorithm, we successfully identified about a dozen novel disulfide bonds in a hexa EF-hand calcium binding protein secretagogin and in a methionine sulfoxide reductase. We believe that DBond, taking into account the disulfide bond fragmentation characteristics and post-translational modifications, offers a novel approach for automatic identification of unknown disulfide bonds and their sites in proteins from MS/MS spectra.