2.4 Interventions and evaluation
In the medical discipline, interventions can be measured through experiments – laid down procedures agreed on consensus to be followed to make discoveries, and test hypotheses. This is applicable to the implementation and use of IS given their technical nature in several settings although digital health interventions, for example, are characteristically complex with multiple components and potentially multiple goals, which give rise to evaluation methodological challenges, they can be measured through experiments. Whilst experiment does look less applicable in the management domain, it does also imply that the introduction of IS is not always a technical issue, necessitating the addressing of softer or management issues. In IS, interventions may not follow laid down procedures to measure the extent to which such systems influenced or changed identified/existing and targeted situations play similar roles. For example, IS can serve as intervention or be used to facilitate processes that improve behavioural health lifestyle through capturing relevant data (Penn, Goffe, Haste, & Moffatt, 2019). In addition to capturing data, there are other IS interventions that seek to ensuring that such data have met required quality through specific interventions. For example, interventions to improve routine health information system data quality and use for decision-making (Lemma, Janson, Persson, Wickremasinghe, & Källestål, 2020). Such an intervention can be evaluated by investigating actual system use, user needs, varying user experiences, user expertise and system user friendliness, and training. Again, in some cases, ISs are introduced to improve operations in organizations (Maguire & Ojiako, 2007). Generally, scholars have used IS Success Model as a theoretical base in a bid to understand the impact IS technologies on user behaviour and how that translates into overall improvement in services. Such improvement could be efficient resource allocation, improved hospital admission process in the context of reduced waiting time/queue to admission in the hospital. Again, user satisfaction and systems usage are by far the most widely used factors to evaluate the effectiveness of IS systems. Whilst the effectiveness of such user systems may vary depending on a range of factors including user acceptance and support as with training and change management issues, there could be the development of standardized system evaluative tools for various systems in the equivalence of medical practice guidelines established on consensus and by authoritative bodies. Similar roles could be assumed by accredited IS bodies.