Background: The current economic context calls for rationalizing health resources that can be pursued through disinvestment from low value health technologies to invest in the best performing ones, ensuring high healthcare quality. Oncology is a field where, because of high costs of health technologies and rapid innovation, disinvestment is crucial.
Methods: On this basis, the research team investigated through a survey, based on a questionnaire, opinions and views of representatives of European countries about disinvestment, in terms of fields of application, potential advocates and barriers, specifically focusing on cancer care.
Results: A total of 17 questionnaires were filled in (response rate: 32.1%). The survey showed disinvestment is applied in several countries as a tool for containing health care expenditures and identifying obsolete technologies/ineffective interventions. Clinicians' resistance to change and industries' opposition are recognized as the most important barriers to the implementation of disinvestment policies. Potential targets of disinvestment in cancer are seen in diagnostic and therapeutic areas.
Conclusion: Despite the agreement on fields of waste and of disinvestment policies, operational methods to put disinvestment in place are lacking. Since they should rely on an inclusive assessment of the technology, Health Technology Assessment may represent a good approach.
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http://dx.doi.org/10.1093/eurpub/cky033 | DOI Listing |
GMS Health Innov Technol
November 2024
Malaysian Health Technology Assessment Section (MaHTAS), Medical Development Division, Ministry of Health, Putrajaya, Malaysia.
Disinvestment in healthcare allows for strategic reallocation of resources from low-value care to higher-value areas, particularly in promoting clinical effectiveness, improving patient outcomes, and long-term cost savings. The Malaysian Health Technology Assessment Section (MaHTAS) is investigating the incorporation of a disinvestment framework into the health technology life cycle, in accordance with the Ministry of Health Malaysia's recent healthcare transformation strategy. Several health technology assessment (HTA) reports by MaHTAS have integrated concepts of health technology reassessment, with an emphasis on effectiveness and adverse effects.
View Article and Find Full Text PDFEpidemiol Serv Saude
December 2024
Universidade Federal do Rio Grande do Sul, Departamento de Produção e Controle de Medicamentos, Rio Grande do Sul, RS, Brazil.
Objective: To analyze the recommendations for exclusion of health technologies in the Brazilian National Health System (SUS), made by the National Commission for the Incorporation of Technologies in the Brazilian National Health System (CONITEC) from 2012 to 2023, and to identify the disinvestment criteria used.
Methods: Documentary, descriptive and retrospective analysis of CONITEC reports that assessed technology exclusion requests.
Results: We identified 24 reports on 74 technologies, whereby the requests predominantly involved medications (95.
Health Res Policy Syst
December 2024
Procome Research Group, Medical Management Centre, Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.
Background: The use of low-value care (LVC) is a persistent challenge in health care. Health technology reassessment (HTR) assesses the effects of technologies currently used in the health care system to guide optimal use of these technologies. Consequently, HTR holds promises for identifying and reducing, i.
View Article and Find Full Text PDFJ Urban Health
December 2024
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Mortgage discrimination alters the distribution of investment, opportunity, and economic advantage-key contributors of health disparities. Leveraging Home Mortgage Disclosure Act data, we assessed mortgage denial risk in 380 U.S.
View Article and Find Full Text PDFHeliyon
November 2024
ADAPT Research Centre, School of Computer Science, University of Galway, Ireland.
Given the intricate nature of stock forecasting as well as the inherent risks and uncertainties, analysis of market trends is necessary to capitalize on optimal investment opportunities for profit maximization and timely disinvestment for loss minimization. In this work, we propose a deep learning model for predicting five distinct stock market trends: upward, downward, double top, rounded bottom, and rounded top. The proposed model surpasses common benchmarks, including support vector machine, random forest, and logistic regression, achieving an average accuracy of 94.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!