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Saving lives with big data analytics that predict patient outcomes

Saving lives with big data analytics that predict patient outcomes

Insights derived from data can help healthcare providers understand
health outcomes not just for individuals but for entire groups of individuals
or populations. They can identify and predict high risk segments within a
population and help take preventive action, creating long term benefits for
patients, hospitals, governments and society at large.

To unlock the true potential of data for population health, data
from a range of disparate sources, including clinics, hospitals, pharmacies,
fitness centres and even homes and employment places, would have to be brought
together and analysed. However, traditional healthcare IT solutions tended to
be limited in scope and restricted to a particular source of data

This was the challenge being faced by Cerner Corporation (Cerner), a leader in the
healthcare IT space, whose solutions are used in over 35 countries at more than
27,000 provider facilities, such as hospitals, integrated delivery networks,
ambulatory offices, and physicians’ offices.

Cerner was expanding its historical focus on electronic
medical records (EMR) to help improve health and care across the board. To do
so, it aimed to assimilate and normalise the world's healthcare data in order
to reduce cost and increase efficiency of delivering healthcare, while
improving patient outcomes.

Mr David Edwards, Vice President and Fellow at Cerner
explained, "Our vision is to bring all of this information into a common
platform and then make sense of it — and it turns out, this is actually a very
challenging problem."

The firm accomplished this by building a comprehensive view
of population health on a Big
Data
 platform that’s powered by a Cloudera enterprise data hub (EDH).
Management tooling, scalability, performance, price, security, partner
integration, training, and support options were key criteria for the selection
of a partner.

Today, the EDH contains more than two petabytes (PB) of data
in a multi-tenant environment, supporting several hundred clients. It brings together data from an almost unlimited number of sources, and that data
can be used to build a far more complete picture of any patient, condition, or
trend. The end-result is better use of health resources.

Building the data hub

The platform ingests multiple different Electronic Medical
Records (EMRs), Health Level Seven International (HL7[1])
feeds, Health Information Exchange information, claims data, and custom
extracts from a variety of proprietary or client-owned data sources,

It uses Apache
Kafka
, a high-throughput, low-latency open-source software platform to
ingest real-time data streams. The data is then pushed back to the
appropriate data storage, HDFS
(Hadoop Distributed File System) cluster or HBase
(a noSQL database which enables random, real-time read/write access to data).

A blog post by Micah Whitacre, a senior software
architect on Cerner Corp.’s Big Data Platforms team, explains how Apache Kafka
helped Cerner overcome challenges related to scalability for the near real-time
streaming system and in streamlining data ingestion from multiple sources,
including ones outside Cerner’s data centres.

Cerner has also taken steps to ensure the security and data
integrity of its Big Data platform. A technical solution must provide a
mechanism for threat mitigation in order to be considered a viable data
management technology in the healthcare space.

Cloudera advised Cerner’s approach to encrypting data at
rest and on its Kerberos
(a network authentication protocol for client/ server applications) integra­tion.

Mr Edwards said that their real aim to get the technology
out of the way so all that users see is the value that comes from their
efforts.

"Our real aim is to get the technology out of the way
so all that users see is the value that comes from their efforts. We really
want the focus to be on the outcomes and results, not on what it takes to
deliver them. The Cloudera platform is the technology that’s driving the value
and it’s allowing us to build applications that help healthcare systems improve
how they manage the chronic conditions of their populations. We're now able to
aggregate the information, stratify it, and offer the opportunity to look at
this data in a way that has never been possible before,” he said.

Improved insights

The uniqueness of Cerner’s EDH is that it allows data to be
brought together from an almost unlimited number of sources, and that data can
be used to build a far more complete picture of any patient, condition or
trend.

The end result: better use of health resources.

Cerner’s EDH helps them understand the most significant
risks and opportunities for improvement across a population of people. Cerner
computes quality scores for manag­ing a number of chronic conditions, and
analysts can see which conditions could gain the most by improving those
scores.

For instance, Cerner can accurately determine the
probability that a person has a bloodstream infection, such as sepsis.

Sepsis is an uncontrolled inflammatory response to an
infection. It is a complex condition which is difficult for a junior doctor or
nurse to recognise. From the time sepsis first takes hold, healthcare
professionals have only the initial 6 hours after the diagnosis to deliver a
group of interventions to the patient. These interventions require close and
rapid interaction between teams in the Emergency Department, in the general
ward and in Critical Care. For an individual patient, getting the interventions
right at the right time may mean a 20-30% better chance of surviving.

Cerner developed an evidence-based algorithm, called the St.
John Sepsis agent, allowing early intervention for patients at risk, and
preventing deterioration into severe sepsis. The algorithm continuously
monitors key clinical indicators and recognises a potentially septic pattern.
The integrated system, which was created in cooperation with Methodist North
Hospital in Memphis, Tenn., gathers patient information from many sources,
helping clinicians early identify patients at risk for sepsis and providing an
accurate diagnosis and treatment.

Ryan Brush, Senior Director and Distinguished Engineer,
Cerner, said, “Our clients are reporting that the new system has actually saved
hundreds of lives by being able to predict if a patient is septic more
effectively than they could before."

Content from Cloudera
customer
success story on www.cloudera.com and
related resources.

[1] HL7
provides a comprehensive framework and related standards for the exchange,
integration, sharing, and retrieval of electronic health information that supports
clinical practice and the management, delivery and evaluation of health
services.

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