Hospital innovation centres increasingly make decisions concerning early development of and investment in innovative medical technologies (IMTs). At present, decisions are often made without applying a formal early assessment process to ensure selection of the most promising candidates for further development. This paper conceptualises and presents a novel model for early realistic assessment of the development of innovative medical technologies in hospitals (EARTH). The development of EARTH was based on results from a qualitative interview study exploring early assessment models in 11 organisations and a literature review of 24 models. The findings, combined with an appraisal of the models holding the most promise for hospital decision makers, led to EARTH. Eleven early assessment principles for EARTH were identified and used to create a guideline for performing and organising early assessment. The guideline consists of an analysis track and a decision track supported by three templates and five methods. In the analysis track, an impact case, a risk analysis and a “critical questioning” procedure are key elements, while in the decision track, an “evidence threshold” for “go” to usual clinical testing is essential. A model for early assessment in hospitals is proposed. EARTH (theoretically) demonstrates how a hospital can add rigour to decision making on which IMTs to pursue for further development and usual clinical testing. EARTH exhibits several desirable features relevant for early assessment, compared to traditional assessment models actually applied in hospitals. We thus believe that early assessment carries the promise of improving hospitals’ investment decisions and resource allocation during development.
|Journal||International Journal of Hospital-Based Health Technology Assessment|
|Publication status||Published - Mar 2019|
Fasterholdt, I., Kidholm, K., Yderstræde, K. B., & Pedersen, K. M. (2019). Early realistic assessment of the development of innovative medical technologies in hospitals (EARTH): a conceptual model. International Journal of Hospital-Based Health Technology Assessment , (1), 4-19. https://doi.org/10.21965/IJHBHTA.2019.001