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Advanced Search Abstract Alberta Innovates — Health Solutions AIHS is a Canadian-based, publicly funded, not-for-profit, provincial health research and innovation organization mandated to improve health, the health system, and socioeconomic well-being of Albertans through health research and innovation.
However, measuring impact in this context is a challenge given the lack of agreement on a common approach or gold standard, diverse stakeholder interests, attribution issues, and time lags between investments and the realization of long-term impact. The purpose of this article is to: Introduction This article describes the development of a framework for measuring, assessing, and ideally optimizing the impact from Alberta Innovates — Health Solutions AIHS health research and innovation investments.
AIHS is a Canadian-based, publicly funded, not-for-profit, provincial health research, and innovation funding organization mandated to improve the health, health system, and socioeconomic well-being of Albertans through the support of health research and innovation.
AIHS is one of four Alberta Innovates organizations newly established to catalyze research and innovation within the province.
Investments in health research and innovation are substantial and funders face increasing pressure to demonstrate the impact of their investments. Building upon the work of others Buxton and Hanney ; CAHSAIHS developed an impact framework to demonstrate impact and illustrate the pathways between research investment and impact: Health research funding agencies are increasingly interested in providing valid and reliable measures of the outcomes of their investments.
Measuring outcomes in a research and innovation context is particularly challenging. For example, competing interests among affected stakeholders can result in a lack of consensus on what constitutes value and what should be measured in order to demonstrate impact.
Other challenges include problems of attribution i.
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Such inconsistencies make it difficult for funding organizations to compare or benchmark their performance against others. As Jordan argues, to build an evidence base and theory for something as complex as the research and innovation system, multiple studies, and synthesis across those studies will be required.
AIHS anticipates such efforts would be greatly facilitated by the wide-scale adoption of common theory, methods, and tools by the research and innovation funding community and its stakeholders.
To this end, AIHS set out to develop a standardized impact framework, using the guiding principles of integrating evidence-based practice and practice-based evidence.
Until recently, the evidence-based practice approach was difficult to incorporate given the lack of consensus on agreed upon methods for assessing health research.
However inthe CAHS released a report outlining a preferred framework to assess health research impact in Canada.
AIHS decided to adopt this generalized impact model that had a strong theoretical basis, thus potential to establish construct validity. This article describes the phased evolution of the AIHS impact framework.
Rather the intent of this article is to illustrate how modifications to a generalized systems-level model can meet specific organizational requirements.
History and background Given its organizational mandate to contribute to the health and socioeconomic well-being of Albertans, AIHS aims to realize impacts from its health research investments that are multi-dimensional and far reaching in nature. Indecision makers at AIHS wanted to develop an evaluation framework that could be systematically applied to assess organizational and program-level impacts.
In addition to assessing research impacts, AIHS decision makers were also interested in tracking progress towards strategic goals and objectives.
This required a framework that included concepts pertaining to organizational system dynamics, as well as social e. The following section describes how the AIHS impact framework developed and evolved to meet these organizational requirements.
First, a brief overview of the models from which the AIHS framework evolved is provided. Results from the case studies suggested that the model could be used to assess the returns of clinical and biomedical research investments within the Alberta context Buxton and Schneider The Payback model Buxton and Hanney provides a comprehensive overview of the returns that could be gained from investments in research.
The model defines impact i. Secondly, it uses an input—output logic model to depict the research activity life-cycle and the types of impact, or payback, possible from each.
The Buxton—Hanney Payback model 3 hypothesizes that returns on investment are realized when research results are translated downstream into policy and health improvements, and later into societal and economic benefits.
AIHS wanted to adopt an existing model of health research impact that was not only comprehensive but also used the best available evidence to support its implementation. From an initial search, the titles and abstracts of articles were reviewed against inclusion e.
Twelve documents peer-review articles and gray literature were included in the analysis. One study of particular relevance was Wooding et al. Overall, the Payback model had been applied in a variety of contexts including health systems, health technology assessments, basic science, and clinical research projects.
The studies included in the review were primarily retrospective case studies. Despite the common method, there was considerable variation in how the study methodology was applied and in the data sources used. Nevertheless, limitations of the model were identified and suggestions for improvement were offered by the study authors; the following were particularly relevant to the development of the AIHS impact framework: Most of the research done on the Payback model did not address the design of systematic, routine methods for evaluating research activities on an ongoing basis Buxton and Schneider To address this issue, several authors suggested using electronic data collection methods to facilitate more frequent collection and limit respondent burden Kalucy et al.
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