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.