RESUMEN
The multidisciplinary model (MCM) is described as one that utilizes skills and experience from practitioners belonging to various disciplines, each treating patients from a specific clinical perspective.1 The Spina Bifida Association (SBA) supports and recommends that clinical care for people with Spina Bifida (SB) be provided in specialty clinics of which the MCM is an example; that care be coordinated; and that there be a plan for transitional care.2 This paper explores the challenges the MCM faces with a transitioning and aging population in a care system that calls for a positive patient experience, engaged health care professionals, desired outcomes, with consideration of cost.
Asunto(s)
Atención a la Salud/estadística & datos numéricos , Personas con Discapacidad/psicología , Personas con Discapacidad/estadística & datos numéricos , Personal de Salud/psicología , Grupo de Atención al Paciente/normas , Disrafia Espinal/psicología , Disrafia Espinal/terapia , Adolescente , Adulto , Actitud del Personal de Salud , Niño , Preescolar , Femenino , Personal de Salud/estadística & datos numéricos , Humanos , Lactante , Masculino , Persona de Mediana Edad , Filosofía Médica , Guías de Práctica Clínica como Asunto , Estados Unidos , Adulto JovenRESUMEN
Several biases can occur when performing economic evaluations (EE) and it is important for researchers to minimize these biases, as they can significantly affect economic outcomes. This review aims, firstly, to identify biases that can occur in model-based EE and to illustrate their impact on economic outcomes and, secondly, to present a checklist for assessing the overall risk of bias in EE. Eleven biases that can occur in model-based studies were identified through a scoping review, scrutiny of systematic reviews, the authors' own experiences and discussions with experts. By combining these biases that can occur in model-based studies with biases that were identified for trial-based studies in a previous article, a 22-item checklist was developed for assessing bias in EE - the ECOBIAS checklist. This study and the ECOBIAS checklist aim to help researchers reduce biases in future EE, particularly in model-based EE. Nevertheless, further validation of the checklist is needed.
Asunto(s)
Sesgo , Lista de Verificación , Modelos Económicos , Humanos , Evaluación de Resultado en la Atención de Salud/métodos , Proyectos de InvestigaciónRESUMEN
OBJECTIVE: The objectives of this article are first to give an overview of the risks of bias in trial-based economic evaluations and, second, to identify how key sources for bias can be revealed and overcome (i.e. what bias-reducing strategies might be employed) in future trial-based economic evaluations in the field of health psychology. DESIGN: Narrative review discussing sources of bias in trial-based economic evaluations and bias-reducing strategies. RESULTS: We identified 11 biases and assigned them to a particular trial phase. A distinction is made between pre-trial biases, biases during the trial and biases that are relevant after the actual trial. All potential forms of bias are discussed in detail and strategies are shown to detect and overcome these biases. CONCLUSION: In order to avoid bias in trial-based economic evaluations, one has to be aware of all the possible forms of bias. All stakeholders have to examine trial-based economic evaluations in a rigorous and stringent manner. This article can be helpful in this examination as it gives an overview of the possible biases which researchers should take into account.