# 4.5 Digital Health Interventions and the Role of Evidence

Digital Health Interventions are unique among other health interventions in the way that evidence is generated and synthesized. An economic evaluation typically views the range of evidence for a health intervention at a single point in time in a particular context and many methodological approaches are used to reflect how health intervention parameters may change over time, context and scale. However, there are unique attributes to the evidence base for DHIs, including the way that the implementation of a digital intervention will in itself commonly generate further evidence and that the specific attributes of a digital intervention can evolve over time to adapt to context.

The distinction between efficacy (the effect observed in a controlled and ideal environment) and effectiveness (the effect in routine conditions in a specific context) is a key component of any type of economic evaluation intended to inform decision making. Evaluation of noncomplex health interventions, such as pharmaceuticals, will typically adopt a primary effect estimate from clinical trial data and then attempt to reflect any variation from the observed effect that is expected in the context of the economic evaluation. This information is also supplemented by observed data and real-world evidence (RWE) but a common objective is to utilize efficacy information to estimate effectiveness.

The regulatory environment for a particular type of health intervention is a major driver of the approach to evidence generation. The strong traditional focus of health intervention regulation on patient safety and efficacy means that evidence generation is commonly focused on whether potential benefits outweigh potential harms rather than cost effectiveness or value for money. When conducting economic evaluation for an intervention that already has regulatory approval (such as pharmaceutical), evidence required at the point of regulation will have demonstrated efficacy and safety for a particular clinical indication. In contrast, economic evaluations of non-regulated products (or where the regulatory environment does not routinely require demonstrated efficacy through clinical trial) may not have baseline evidence of impact to parameterize an analysis.

DHIs, in common with many other complex interventions, are intricately integrated into the context within which they are used. This diminishes the importance of the concept of efficacy within an economic evaluation of a DHIs and limits the applicability of the traditional evidence hierarchy. The randomized controlled trial (RCT) remains a gold standard for isolating the treatment effect of a digital or non-digital interventions, however RCTs are unlikely to be able to routinely provide the intervention effectiveness estimate required in an economic evaluation of a DHI.

However, published peer-reviewed reporting trials remains an essential element of evidence generation for DHIs and should be used where applicable. The SPIRIT-AI and CONSORT-AI statements provide clear specification for the design and reporting of protocols and trials of interventions involving Artificial Intelligence components [23] [24]. They expand on the established CONSORT and SPIRIT statements with specific components to enable the generation of evidence related to AI technologies. The expanded use of the statements will enable improved trial conduct and reporting and is likely to have a positive impact on the evidence available for use in economic evaluation.

A DHI, by its nature, generates data through its use. The data may be utilized in different ways depending on the information system in which the DHI operates, but the routine generation of data has implications for the management of evidence in an economic evaluation [7]. The data gathered will inform basic parameters such as utilization, but also enables linkages with other sources to impact on health and processes dynamically. As many DHIs include features