The aim of health economic evaluation is to maximize health gains from limited resources. By definition, health economic evaluation is comparative, based on average costs and outcomes of compared interventions. Incremental costs and outcomes are used to calculate the cost-effectiveness ratio, which represents the average incremental cost per gained unit of effectiveness (i.e.: a year of life) with the evaluated intervention compared to the reference. The health economic rationale applies to all health domains. We cannot spend collective resources (health insurance) without asking ourselves about their potential alternative uses. This reasoning is useful to caregivers for understanding resources allocation decisions and healthcare recommandations. Caregivers should grab this field of expertise because they are central in this strategic reflection for defining the future French healthcare landscape.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.lpm.2016.10.014 | DOI Listing |
Am J Emerg Med
December 2024
Department of Health Policy & Organization, School of Public Health, The University of Alabama at Birmingham, Birmingham, AL, USA; Center for Outcomes and Effectiveness Research and Education, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA.
Background: Leaving before medically advised (BMA) is a significant issue in the US healthcare system, leading to adverse health outcomes and increased costs. Despite previous research, multi-year studies using up-to-date nationwide emergency department (ED) data, are limited. This study examines factors associated with leaving BMA from EDs and trends over time, before and during the COVID-19 pandemic.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Black Dog Institute, University of New South Wales, Sydney, Australia.
Background: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment methods are more effective than traditional methods such as newspapers, media, or flyers is inconsistent. Here we present insights from our experience recruiting tertiary education students for a digital mental health artificial intelligence-driven adaptive trial-Vibe Up.
View Article and Find Full Text PDFJ Trauma Nurs
January 2025
Author Affiliations: Castner Incorporated, Grand Island, NY (Dr Castner); Health Policy, Management, and Behavior, School of Public Health, University at Albany, Albany, New York (Dr Castner); Stony Brook University School of Nursing, Stony Brook, NY (Ms Zazzera); and Nursing Research and Evidence-Based Practice, Penn Medicine Lancaster General Health, Lancaster, PA (Dr Burchill).
Background: Trauma population health indicators are worsening in the United States. Nurses working in trauma care settings require specialized training for patient care. Little is known about national enumeration of nurses who hold skill-based trauma certificates.
View Article and Find Full Text PDFPLoS One
January 2025
Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading, England, United Kingdom.
Pressures on honey bee health have substantially increased both colony mortality and beekeepers' costs for hive management across Europe. Although technological advances could offer cost-effective solutions to these challenges, there is little research into the incentives and barriers to technological adoption by beekeepers in Europe. Our study is the first to investigate beekeepers' willingness to adopt the Bee Health Card, a molecular diagnostic tool developed within the PoshBee EU project which can rapidly assess bee health by monitoring molecular changes in bees.
View Article and Find Full Text PDFPLoS One
January 2025
School of Economics and Trade, Guangzhou Xinhua University, Dongguan, China.
Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!