Introduction: As budget constraints become more and more visible, there is growing recognition for greater transparency and greater stakeholders' engagement in the pharmaceuticals' pric-ing&reimbursement (P&R) decision making. New frameworks of drugs' value assessments are searched for. Among them, the multi-criteria decision analysis (MCDA) receives more and more attention. In 2014, ISPOR established Task Force to provide methodological recommendations for MCDA utilization in the health care decision making. Still, there is not so much knowledge about the real life experience with MCDA's adaptation to the P&R processes.
Areas Covered: A systematic literature review was performed to understand the rationale for MCDA adaptation, methodology used as well as its impact on P&R outcomes.
Expert Commentary: In total 102 hits were found through the search of databases, out of which 18 publications were selected. Although limited in scope, the review highlighted how MCDA can im-prove the decision making processes not only regarding pricing & reimbursement but also contribute to the the risk benefit assessment as well as optimization of treatment outcomes. Still none of re-viewed studies did report how MCDA results actually impacted the real life settings.
Background: There is widespread enthusiasm for scaling interventions to strengthen health systems. However, little is known about the scalability of such interventions in Africa. In this study, we seek to assess the scalability of interventions for improving the functionality of health systems in Africa, as a key to large-scale implementation strategy of interventions with potential for impact.
Background: Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a reference tool for healthcare-related information for healthcare providers and trainees. The objective of this study was to assess the consistency, quality, and accuracy of the responses generated by ChatGPT on healthcare-related inquiries.
Methods: A total of 18 open-ended questions including six questions in three defined clinical areas (2 each to address "what", "why", and "how", respectively) were submitted to ChatGPT v3.
Background: Malawi ranks 142 out of 170 countries on the UN's Gender Inequality Index (GII). Women and men in Malawi have unequal access to and control over resources. Previous research has primarily examined gender roles and norms from a women's perspective, but few studies have investigated men's attitudes and behaviors regarding gender equality.
Department of Obstetric Nursing, West China Second University Hospital, Sichuan University, No. 1416, Chenglonglu Avenue, Chengdu, Sichuan, Jinjiang District, 610066, China.
Background: Breastfeeding is the optimal source of nutrition for infants and young children, essential for their healthy growth and development. However, a gap in cohort studies tracking breastfeeding up to six months postpartum may lead caregivers to miss critical intervention opportunities.
Methods: This study conducted a three-wave prospective cohort analysis to examine maternal breastfeeding trajectories within the first six months postpartum and to develop risk prediction models for each period using advanced machine learning algorithms.
Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases.
