Abstract
Background: It is important to determine the relative value for money of health innovations when allocating limited healthcare resources. Implementation strategies require and consume healthcare resources yet are often excluded from published economic evaluations. This paper reports on the development of a pragmatic implementation costing instrument to assist with the planning, delivery, and evaluation of digital health implementation strategies.
Methods: A modified e-Delphi process was adopted to develop an implementation costing instrument. Purposive sampling was used to recruit a panel of experts in implementation science, health economic evaluations and/or digital health from the academic, government, clinical or health service sectors. In each round, participants were sent an electronic questionnaire and a prototype of the implementation costing instrument. The prototype in the initial round was informed from a literature review and qualitative interview findings. The prototype was updated iteratively between rounds in response to the panel’s feedback. In subsequent rounds, participants also received the anonymous results of items that did not reach consensus in the previous round. Termination occurred once consensus was reached on integral questions (those pertaining specifically to the instrument design) or when three rounds were completed, to prevent sample fatigue. Consensus was defined as at least 75% of experts in agreement for any item.
Results: Consensus was reached on the core components and design of the instrument from a panel of twelve experts in implementation science, health economic evaluations and/or digital health. Areas where consensus was not reached included users’ level of implementation science knowledge, specificity of the tool to digital health and accessibility via digital formats.
Conclusions: Cost-IS is a pragmatic data collection instrument designed to estimate the costs of implementation strategies and activities. Further piloting of Cost-IS is required to establish its feasibility and generalisability.