Reasons for Caution when Evaluating Health Care Interventions Using Non-Randomised Study Designs

2004 ◽  
Vol 11 (1) ◽  
pp. 40-45 ◽  
Author(s):  
B.C. Reeves
1999 ◽  
Vol 25 (2-3) ◽  
pp. 387-402
Author(s):  
Arti K. Rai

Over the last few decades, the U.S. health care system has been the beneficiary of tremendous growth in the power and sheer quantity of useful medical technology. As a consequence, our society has, for some time, had to make cost-benefit tradeoffs in health care. The alternative—funding all health care interventions that would produce some health benefit for some patient—is not feasible, because it would effectively consume all of our resources.


PLoS Medicine ◽  
2009 ◽  
Vol 6 (7) ◽  
pp. e1000100 ◽  
Author(s):  
Alessandro Liberati ◽  
Douglas G. Altman ◽  
Jennifer Tetzlaff ◽  
Cynthia Mulrow ◽  
Peter C. Gøtzsche ◽  
...  

2011 ◽  
Vol 14 (7) ◽  
pp. A422-A423
Author(s):  
S. Chang ◽  
D. Sungher ◽  
A. Diamantopoulos

2017 ◽  
Vol 23 (1) ◽  
pp. 53 ◽  
Author(s):  
Lauren Ball ◽  
Dianne Ball ◽  
Michael Leveritt ◽  
Sumantra Ray ◽  
Clare Collins ◽  
...  

The methodological designs underpinning many primary health-care interventions are not rigorous. Logic models can be used to support intervention planning, implementation and evaluation in the primary health-care setting. Logic models provide a systematic and visual way of facilitating shared understanding of the rationale for the intervention, the planned activities, expected outcomes, evaluation strategy and required resources. This article provides guidance for primary health-care practitioners and researchers on the use of logic models for enhancing methodological rigour of interventions. The article outlines the recommended steps in developing a logic model using the ‘NutriCare’ intervention as an example. The ‘NutriCare’ intervention is based in the Australian primary health-care setting and promotes nutrition care by general practitioners and practice nurses. The recommended approach involves canvassing the views of all stakeholders who have valuable and informed opinions about the planned project. The following four targeted, iterative steps are recommended: (1) confirm situation, intervention aim and target population; (2) document expected outcomes and outputs of the intervention; (3) identify and describe assumptions, external factors and inputs; and (4) confirm intervention components. Over a period of 2 months, three primary health-care researchers and one health-services consultant led the collaborative development of the ‘NutriCare’ logic model. Primary health-care practitioners and researchers are encouraged to develop a logic model when planning interventions to maximise the methodological rigour of studies, confirm that data required to answer the question are captured and ensure that the intervention meets the project goals.


Author(s):  
Felix Holl ◽  
Jennifer Kircher ◽  
Walter J. Swoboda ◽  
Johannes Schobel

In the face of demographic change and constantly increasing health care costs, health care system decision-makers face ever greater challenges. Mobile health applications (mHealth apps) have the potential to combat this trend. However, in order to integrate mHealth apps into care structures, an evaluation of such apps is needed. In this paper, we focus on the criteria and methods of evaluating mHealth apps for cardiovascular disease and the implications for developing a widely applicable evaluation framework for mHealth interventions. Our aim is to derive substantiated patterns and starting points for future research by conducting a quasi-systematic scoping review of relevant peer-reviewed literature published in English or German between 2000 and 2021. We screened 4066 articles and identified n = 38 studies that met our inclusion criteria. The results of the data derived from these studies show that usability, motivation, and user experience were evaluated primarily using standardized questionnaires. Usage protocols and clinical outcomes were assessed primarily via laboratory diagnostics and quality-of-life questionnaires, and cost effectiveness was tested primarily based on economic measures. Based on these findings, we propose important considerations and elements for the development of a common evaluation framework for professional mHealth apps, including study designs, data collection tools, and perspectives.


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