Digital health interventions (DHIs) have the potential to help the growing number of chronic disease patients better manage their everyday lives. However, guidelines for the systematic development of DHIs are still scarce. The current work has, therefore, the objective to propose a framework for the design and evaluation of DHIs (DEDHI). The DEDHI framework is meant to support both researchers and practitioners alike from early conceptual DHI models to large-scale implementations of DHIs in the healthcare market.

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Health care delivery is undergoing a rapid change from traditional processes toward the use of digital health interventions and personalized medicine. This movement has been accelerated by the COVID-19 crisis as a response to the need to guarantee access to health care services while reducing the risk of contagion. Digital health scale-up is now also vital to achieve population-wide impact: it will only accomplish sustainable effects if and when deployed into regular health care delivery services. The question of how sustainable digital health scale-up can be successfully achieved has, however, not yet been sufficiently resolved. This paper identifies and discusses enablers and barriers for scaling up digital health innovations. The results discussed in this paper were gathered by scientists and representatives of public bodies as well as patient organizations at an international workshop on scaling up digital health innovations. Results are explored in the context of prior research and implications for future work in achieving large-scale implementations that will benefit the population as a whole.

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Aim Because the field of information systems (IS) research is vast and diverse, structuring it is a necessary precondition for any further analysis of artefacts. To structure research fields, taxonomies are a useful tool. Approaches aiming to develop sound taxonomies exist, but they do not focus on empirical development. We aimed to close this gap by providing the CAFE methodology, which is based on quantitative content analysis. Subject and methods Existing taxonomies are used to build a coding scheme, which is then validated on an IS project database. After describing the methodology, it is applied to develop a telemedicine taxonomy. Results The CAFE methodology consists of four steps, including applicable methods. It helps in producing quantitative data for statistical analysis to empirically ground any newly developed taxonomy. By applying the methodology, a taxonomy for telemedicine is presented, including, e.g. application types, settings or the technology involved in telemedicine initiatives. Conclusion Taxonomies can serve in identifying both components and outcomes to analyse. As such, our empirically sound methodology for deriving those is a contribution not only to evaluation research but also to the development of future successful telemedicine or other digital applications.