Assessment of the Impact of Home-Based Hospitalization on Health Outcomes: An Observational Study.

INTRODUCTION: In Portugal, evidence of clinical outcomes within home-based hospitalization programs remains limited. Despite the adoption of homebased hospitalization services, it is still unclear whether these services represent an effective way to manage patients compared with inpatient hospital care. Therefore, the aim of this study was to evaluate the outcomes of home-based hospitalization compared with conventional hospitalization in a group of patients with a primary diagnosis of infectious, cardiovascular, oncological, or 'other' diseases. METHODS: An observational retrospective study using anonymized administrative data to investigate the outcomes of home-based hospitalization (n = 209) and conventional hospitalization (n = 192) for 401 Portuguese patients admitted to CUF hospitals (Tejo, Cascais, Sintra, Descobertas, and the Unidade de Hospitalização Domiciliária CUF Lisboa). Data on demographics and clinical outcomes, including Barthel index, Braden scale, Morse scale, mortality, and length of hospital stay, were collected. The statistical analysis included comparison tests and logistic regression. RESULTS: The study found no statistically significant differences between patients' admission and discharge for the Barthel index, Braden scale, and Morse scale scores, for both conventional and home-based hospitalizations. In addition, no statistically significant differences were found in the length of stay between conventional and home-based hospitalization, although patients diagnosed with infectious diseases had a longer stay than patients with other conditions. Although the mortality rate was higher in home-based hospitalization compared to conventional hospitalization, the mortality risk index (higher in home-based hospitalization) assessed at admission was a more important predictor of death than the type of hospitalization. CONCLUSION: The study found that there were no significant differences in outcomes between conventional and home-based hospitalization. Home-based hospitalization was found to be a valuable aspect of patient- and family-centered care. However, it is noteworthy that patients with infectious diseases experienced longer hospital stays.

Machine Learning Approaches for the Frailty Screening: A Narrative Review.

Frailty characterizes a state of impairments that increases the risk of adverse health outcomes such as physical limitation, lower quality of life, and premature death. Frailty prevention, early screening, and management of potential existing conditions are essential and impact the elderly population positively and on society. Advanced machine learning (ML) processing methods are one of healthcare's fastest developing scientific and technical areas. Although research studies are being conducted in a controlled environment, their translation into the real world (clinical setting, which is often dynamic) is challenging. This paper presents a narrative review of the procedures for the frailty screening applied to the innovative tools, focusing on indicators and ML approaches. It results in six selected studies. Support vector machine was the most often used ML method. These methods apparently can identify several risk factors to predict pre-frail or frailty. Even so, there are some limitations (e.g., quality data), but they have enormous potential to detect frailty early.