Introduction: The COVID-19 pandemic has had a major impact on the Healthcare System, changing the patterns of Emergency Department access. In fact, accesses for trauma and less severe cases decreased significantly. This decline has generally been attributed to both the effects of the lockdown, imposed by the government, and the fear of being infected by SARS-CoV-2 in the hospital.
View Article and Find Full Text PDFBackground: Increasing waiting times for elective surgery is a major concern for policymakers and healthcare staff in many countries, due to its effect on health, patient satisfaction and the perceived quality of health-care. Many organizational models to reduce surgical waiting times have been studied, but the international literature indicates that multidimensional interventions on different aspects of the surgical pathway can be more effective in reducing waiting times than interventions focused on optimizing a single aspect.
Aim: The aim of the study is to evaluate the effectiveness of a multidimensional intervention in reducing waiting times for elective surgery.
In May 2018, the non-governmental organization (NGO) began to implement an intervention to strengthen Chiulo Hospital's public health section to deliver immunization services in Mucope , Ombadja District. We aimed to evaluate the effect of this intervention. During the intervention period, actions such as staff training, improvement in the monitoring of vaccine stockpile, and the involvement of Community Health Workers were performed.
View Article and Find Full Text PDFBackground: Job quality and evaluation of workers' health have both medical and social important implications. We studied health-related quality of life (HRQL) in nurses who perform their activity in night shifts.
Methods: A cross-sectional study was conducted between October and November 2014.
Objectives: Risk adjustment is a widely used tool for health expenditure prediction and control. Early approaches for estimating health expenditure were based on patient demographic variables alone, whereas more recent models incorporate patient information, such as chronic medical conditions, clinical diagnoses, and self-reported health status. Many studies have investigated the health expenditure predictive capacity of single demographic, morbidity, or health-related quality of life measures, but the best models prove to be those that include them all.
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