IoT in Healthcare IT: Mega-Data Flowing

Everywhere – Get Ready Now!

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Atchison Frazer Vice President, Marketing

March 25, 2015

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The Internet of Things (IoT) increasingly impacts the everyday lives of healthcare consumers and

providers alike, providing tremendous benefits across the whole medical ecosystem.

IDC believes IoT will become a big part of the next wave of technology in healthcare, and 3rd-

Platform technologies (cloud, Big Data, mobile, and social media) will play a central role in this

transformation.

IDC also believes that the Internet of Things is fundamental to the future of healthcare. Sensors,

coupled with data analytics, cloud, and mobile technologies, provide an exciting if not challenging

opportunity to radically change our approach to disease diagnosis, treatment and prevention.

However, while healthcare providers believe IoT is emerging into the mainstream, most

professionals say they aren’t ready for the onslaught of data that inevitably comes with IoT.

IDC’s Health Insights survey from its 2014 healthcare IT summit found that while 23% of

professionals think IoT will become mainstream by 2018-19, almost 29% said they are not prepared

to handle the data implications of IoT.

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Why is IoT in healthcare perceived as such a threat to operational readiness? Here is a very simple

answer to a very complex scenario: data. The data implications of IoT in healthcare are

unprecedented: newer, larger sets of data; more granular data; data that are faster to access and

cheaper to collect. In exchange, the hope is that the data onslaught from IoT will result in improved,

more prudent decision-making while delivering more proactive and less reactive intelligent

operations.

Making sense of all that IoT data and automating a standards-based system of capturing,

monitoring, collecting and collating IoT healthcare data is a whole different challenge.

Without an automated means of converting raw IoT data into information, collecting more healthcare

data in higher volumes does not necessarily translate to supporting positive action or decision-

making. In fact, IoT data still suffer from general data concerns around trusted sources, secure

access and privacy, ease of understanding, high or low quality of data, timeliness versus

retrospective data, and whether the data present a complete picture with enough granularity to be

useful.

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An effective IoT solution for healthcare ideally must have end-to-end operationally mature elements

in place to take advantage of richer data availability including robust, holistic capture capabilities,

multi-mode transmission and integration, and not least, medical-grade software dashboards for data

visualization and decision-analysis calculations on-demand.

IDC’s vision of IoT in healthcare starts with the patient-consumer in terms of the first collection point

for IoT data, including the individuals “personal area network” or most immediate addressable

spectrum and then as a subset, the person’s “body area network,” i.e., sensors or devices

implanted, embedded or wearable that chronicle all sorts of biometrics. The mode of transmission to

an aggregation point would include the Bluetooth, cellular, Wi-Fi and ZigBee protocols.

The data aggregation points, ranging from mobile apps and devices to web portals to gateway

appliances, or even on-premise medical-cloud servers, would transmit all of that patient-consumer

data to a backend system hosting a range of services such as EMR, care management and

population health.

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The central nervous system of the IoT healthcare architecture is a very powerful analysis engine that

typically is running inside a hypervisor (virtualized infrastructure) or even a hybrid-cloud

environment. Some healthcare entities have special data processing requirements, especially in life

sciences discovery and genetic biotech development, whereby jumbo frames of data and graphics-

intensive applications for modeling potentially bog everything else down, but that also tend to be

very latency prone and susceptible to IOPS storage degradation.

These potential bottlenecks in the IoT healthcare environment can become especially acute when

remote monitoring or event telepresence HD video-medicine are introduced into the environment.

Public safety, emergency care mobility and first responders are obvious use cases for layered IoT

data stacks.

Poor performance in serving these medical applications can significantly impact end-user

experience and end-customer serviceability due to slow application responsiveness, fleeting

intermittent application jitters, and unidentifiable resource contentions. With respect to compliance,

VI monitoring tools are required to ensure network compliance auditing and real time alerting of

anomalies, including network traffic, user access, change management activities, and system errors,

as well as east-west traffic (such as VM-to-VM interactions).

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To accommodate existing data processing and integration demands, some organizations can’t avoid

the temptation to over-provision network connectivity, Internet bandwidth or storage arrays, while

others choose to deploy an intelligent data fabric to monitor all functional components of the

virtualized infrastructure.

For example, over a period of several months, a mid-size medical institution added several new

remote facilities. Their EMR system was extremely latency-sensitive, so the typical approach was to

purchase extra metro-Ethernet connections to these sites. In the past, they monitored these links

with conventional tools so they could see when utilization on the metro circuit reached a point that

would cause issues for their own end-users.

This medical organization began terminating the metro connections to the remote new sites with

NetFlow-capable firewalls, which with the right performance management system provides the flow

analysis needed to monitor the link itself or the entire remote site in real-time at a per-session basis.

Now, when they see utilization on one of these links spike, they can look at the VI performance

dashboard and find out immediately who and/or what are causing the issue.

This is good model to follow to before IoT in healthcare becomes mainstream.

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