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As I alluded
to in my previous blog, the potential for Big Data Analytics and Technologies
are endless in the Healthcare arena. Big data is becoming more prevalent in
every environment in this digital era, and hospitals
and health settings are no exception. They collect large amounts of data
from clinical visits, billing information, and scheduling from their customers
every day. And as private industry and the government are realizing, if we have
this data, we need to use it wisely. There have been three distinct factors
that have created this “perfect storm” which are: the federal push for electronic
health records, new reimbursement models and accountable care organizations that
need large amounts of information to be analyzed in order to more accurately
understand what occurs with patients, and new technology including implants and
mobile applications on smart phones and tablets that have increased the amount
of data available to providers and customers. Another key aspect to the influx
of data is the pressure to become evidence-based and predictive with healthcare
services.
A variety of beneficial implications of the use and analysis
of big data include:
1.
1 - Better
point-of-care decisions. Doctors will have the access to valid and relevant
data that is most current. As an example, NorthShore University Health System
in Evanston, III, has already implemented predictive models to identify which
patients are likely carriers of a dangerous microorganism, Methicillin-Resistant
Staphylococcus Aureus. By using this model, providers within the health care system
will receive alerts when a patient is admitted that meets the characteristics
of being a high risk carrier of this microorganism.
2. 2 - Reduced
re-admissions. The Electronic Medical Record (EMR) can be input into the
data warehouse and they can compute a patient’s risk of being readmitted within
a 30 day window. If the patient shows a high risk probability then that clinic
or practice should set up a follow-up visit with the patient to monitor the
situation. If a patient shows a low probability of re-admissions, then a follow
up visit would not be needed and time and money could be saved here.
3.
3 - Population
health management. Patient populations can be analyzed to discover patterns
and similarities in uncommon diseases. The implications for better, coordinated
and specialized care are endless.
4.
4 - Research
advancements. When doing research openly with large amounts of data
available, research does not become less traditional; rather, it becomes more
meaningful. It used to take around seven years for something that was
discovered to make it to the bedside of a patient. Now discoveries are more
quickly dispersed and are getting to the patients that really need it at a much
better turn around time.
5.
5 - Operational
Improvement. This gives hospitals metrics to measure their performance with
which to compare operational efficiencies. The doctors and nurses will have
motivation to achieve better results. Big Data makes performance data even
within a hospital more accessible so that efficiency and accuracy can increase.
Using Big Data in the medical
field requires the analyst to convert the data into a useful state and a more
usable format and display it with the right visualization. Anil Jain, MD, CMIO
of Explorys, a healthcare analytics company, and former senior executive
director of information technology at Cleveland Clinic says “At the end of the
day it is about making sure providers can do the right thing for the right
patient at the right time”.
Other useful links:
Awesome post with wonderful piece of information. Thanks for taking time to share this with us. Looking forward for more posts from you. Check this out: Top Healthcare Analytics Companies
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