The Journal of mHealth Vol 1 Issue 3 (June 2014) | Page 37

Engaging Patients Using mHealth

Engaging Patients Using mHealth
Thought leadership article by Mark Brincat of Exco InTouch
When it comes to developing effective mHealth solutions, it is essential
to incorporate a range of proactive
methods that engage with patients, and
help to integrate the solution in to their
accepted treatment process. Maintaining
this level of engagement, is the only real
way to ensure that a solution can help to
deliver positive outcomes.
In a series of discussions Mark Brincat
of Exco InTouch considers a variety of
methods that can help solutions achieve
successful and sustained patient engagement.

ADAPTABILITY
There are no hard and fast rules as to
how mHealth adaptability is defined, but
I would suggest there are a number of
levels that help us understand the extent
to which a product might respond to
individual patient needs.
Perhaps the highest level to start with is
personalisation. This is where a patient
can put their own mark on a product
and make it feel like something that is
unique to them, perhaps including some
elements of look and feel, or methods
of interaction. Next, a patient could configure a product around their own specific needs, the product might have been
already configured with a patient’s clinical specifics by a health care professional,
but patients could additionally configure
to their own needs and priorities. Once
the patient is up and running, the product would be designed to respond to a
series of patient inputs and events, some
adhoc and some scheduled. It is here
that products are really going to differentiate themselves. These pathways are
numerous and complex and a product
must be flexible enough to respond to
different patient profiles, requirements
and progressions. Multi-level solutions
might include assessment, medications
management, lifestyle management and
informational content, all of which need
to work and adapt in sync with each
other. For example, if assessment and
medication tracking show a change in
condition, then there is a likely need to
reflect changes in lifestyle management

and informational support. Add on top
of this a patient’s state of behaviour
acceptance and you have a variable set
of parameters that a product needs to
interpret and respond to correctly.
Looking at the technology involved, this
would require a rules engine to manage
a dynamic set of interactions. At its simplest level, we can think of a rules engine
as software which uses rules that can be
applied to data to produce outcomes. It
is important that rules are only defined
where events and outcomes are sufficiently understood. In the future, expert
systems will take findings from the system and dynamically build them back
into the rules engine, so that a solution
learns and improves interventions based
on real world data. For now, we will learn
from anonymised patient data findings
and refine or build new rules and interventions into the system. Companies will
need to develop skills around the analysis
of ‘big data’, identifying signals and patterns. This will be an exciting period in
advancing our understanding of patient
populations and disease anthropology.
When patients stop taking medication
or stop proactively managing their condition generally, their change of mind
did not happen that morning, it started
weeks or months ago with a series
of smaller issues slowly stacking up.
mHealth solutions need to focus on the
multitude of issues and support patients
with a range of interactions that work in
sync with each other and respond to an
individual patient’s real world experience.
As this level of mHealth product takes
hold in the market, it will be remarkable how quickly we can advance disease
management.

BUILDING BEHAVIOUR
CHANGE INTO MHEALTH
SOLUTIONS
We have seen the successful application
of behavioural change theories in patient
services such that there is now good recognition of the ability of such methods
to encourage patients to pro X