Big data
is a revolution that is under way in the healthcare sector, and it is here to
stay. Over the last decade, there’s been a remarkable increase in the supply of
information followed by years of research and development. The use of big data technologies along with advanced
analytics not only helps reduces cost but also increase patient outcomes. While
the price of treatments, diagnosis and medication become lower, the advantage
is in using predictive analytics to anticipate the outbreak of epidemic and
endemic diseases.
Health systems are now using big
data solutions to execute analytics
in multiple data streams including unstructured and structured data. Risk
models are being built with the expectation of predicting undesirable outcomes
a patient may experience. This may include negligibility, being readmitted post
discharge or even being affected with an infection while hospitalized. This
analysis can further be used to enhance patient outcomes and gain actionable healthcare insights that improve patient care.
A McKinsey report states that healthcare currently
represents 17.6% of the country’s GDP, a steady increase, more than 20 years.
Other industries are aggressively creating new
analytics tools, data application and strategies to reap higher benefits such
as predictive analysis, machine learning and graph. The healthcare industry is now looking at acute data analysis and
larger application methods to progress and collaborate with each other for
business success. As the healthcare industry experiences a drastic change in
data technology and transformation, let’s take a closer look at big data trends that are quickly changing the healthcare
industry for the better.
Individual Patient
Care
With the rise of big data,
patient focused care is steadily becoming a priority. Doctors and staff are
able to serve patients better as they can quickly analyze and locate patient
related data. The value of data is benefitting patients as well as hospitals as
it is improving the overall quality of the experience. The structured approach can
greatly reduce the net of healthcare costs while
improving patient outcomes.
Hospitals are able to provide proactive
patient care with real-time monitoring. The vital signs can be continually
monitored, analyzed and instantly alert representatives in case the patient’s
condition deteriorates. Processing these real-time events along with machine
learning algorithms gives doctors the much-needed insight to make life-saving
decision.
In a
2016 PWC survey, majority of people participating already agree that they would
be excited to experience wearable technology. 65 percent voted yes to using
wearables from doctors, 62 percent from hospitals and health insurance company
The trend of wearable technology is
quickly becoming a norm. These wearable devices and sensors allow nurses and
care givers to interact with a patient in a more convenient way. Devices can be
used to remotely monitor weight and track changes in a patient battling a heart
disease. These applications can go as far as detecting fluid medication to
check if hospitalization is required. The end result is capturing extensive
data that allows for superior patient engagement and patient care coordination
that is personalized to each individual patient needs.
Reducing Waste,
Fraud And Abuse
The spiraling healthcare costs in the United States are majorly caused by the
fraud, abuse and waste costs in the healthcare sector. Countering this notion,
big data analytics is a huge game changer. In a predictive modeling environment,
big data solutions
and Hadoop can be used to identify inaccurate claims that are systematic and
repeatable. However, a large number of the healthcare
data that is stored remains unstructured. Using machine learning algorithms,
patterns and anomalies across historical claims can be detected leading to
preventing fraudulent occurrences.
Individual data is collected from sources such as claims,
pharmacy, EMR, notes, logo, clinical and third party data which makes way for
personalized experience. Implementing big data
solutions in the data hub serves as a medium to detect fraud, waste and
abuse in the healthcare industry. For example, analytics can be used to model
the flag to certain charges and raise a red flag. This makes it easier to
prevent many fraudulent insurance claims or medical claims across the industry
and the country.
The Centers for Medicare and Medicaid Services
prevented more than $210.7 million in healthcare fraud in one year alone using
predictive analysis.
Apart from this, healthcare sector can analyze billing and patient
records to identify irregularities and notify in case of excess utilization of
services in a short notice. This is possible across healthcare services across
locations and organizations consecutively. The data is filtered clearly leaving
very little room for error in information such as identical prescriptions that
could be filed for the same patient in multiple locations.
Author Bio – Matt
Wilson – A Healthcare Expert, is working with Aegis Health Tech as senior
developer from last 5 years. He has extensive experience in patient
management system, EMR & EHR Development, Implementation and Integration.
