The Journal of mHealth Vol 2 Issue 1 (February 2015) | Page 10

Digital Health 2015: What Can We Expect?
Continued from page 7

ties. Expect to see many more of these
announcements going forward.
Visualise the data
Healthcare professionals and patients
can often find the raw data from devices
and monitors difficult to interpret in its
original form. Presenting data in a meaningful way that provides some form of
context, or gives data relevance, makes it
much more useful to both patients and
healthcare professionals.
Visualisations are an extremely effective method of turning source data into
something meaningful. Diagrams, 3D
models, graphs, historical charts, dashboards, alerts and indicators are all methods that can be employed to present data
in a visual format, so that it is quickly and
easily understood.
Many healthcare organisations are
already looking to develop dashboards
and platforms that can help them easily display relevant patient information
from a variety of sources. As the number
of data sources increases then the need
to efficiently process and react to analytical insights will become even more
important.
The opportunity for data visualisation across the healthcare spectrum
is vast, and is expected to continue to
grow alongside the introduction of new
devices and digital services.
Intelligent technology
There is consensus across the healthcare
community that whilst technology-led
services are going to play a significant
role in the delivery of modern healthcare,

they must be deployed in an intelligent
way. This means: simple integration into
workflows; providing the ability to scale
and increase capacity, introducing flexibility to allow agile healthcare delivery;
and, preventing duplication of services.
An algorithmic approach to medicine is
something that many are hoping to avoid,
but this is not to say that machine learning, intelligent systems, and ultimately
artificial intelligence can not have a profoundly positive impact on healthcare.
Currently many of the digital systems in
use are purely diagnostic or treatment
based. What we can expect to see over
the relatively near-future is the combination of these elements in order to create systems that can employ some level
of ‘intelligence’. By combining different technologies within one system it
becomes possible to track a patient’s
health, analyse those results, adjust treatment plans, and administer medication
or recommend the best course of action,
all from within the one platform.
This type of informed machine intervention is already used in closed-loop systems for conditions like diabetes, where
‘smart’ blood glucose monitors can analyse results instantly, calculate the necessary insulin dose required, and then communicate that information to a wearable
insulin pump, which can administer the
insulin directly into the body. This type
of system reduces the need for intervention by healthcare professionals, and significantly improves the day-to-day management of a condition like diabetes.7
Also, already in use are predictive eICU
systems that can assess vital sign data in
real-time, analyse that data, and using
advanced predictive algorithms calculate the likelihood of cardiac events well

in advance of them actually occurring.8
This gives physicians advanced warning
of adverse events and the opportunity to
take preventative action.
Systems are also in development that
will track trends and fluctuations in a
patient’s health in order to catch early
signs of heart attacks or strokes. Many
of these systems are being designed to
be wearable, which allows them to monitor people at risk of these symptoms
24-hours a day. The hope is that by using
data analytics and machine learning techniques many more systems we will be
able to move into a new era of preventative medicine.
References
1. ‘Top Health Industry Issues of 2015: Outlines of a market emerge’. PwC Health
Research Institute, 2014.
2. mHealth Show