The healthcare industry in India is facing a complex, competitive environment driven by disruptive technology. For healthcare organizations to survive, data-driven management is needed. As payment reforms increase pressure to improve quality while lowering costs, some hospitals have adopted quality improvement methodologies from other industries, like data analytics. Experts believe healthcare providers that invest in analytics through partnerships will have success. The article outlines the types of data analytics used in healthcare - descriptive analytics of past data, predictive analytics to forecast future needs, and prescriptive analytics to provide recommendations. It also discusses challenges to implementing data-driven management and provides steps for healthcare organizations to become more data-driven.

1. Industry Trends
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Health Biz Insight August 2017 25
By: Dr. AK Khandelwal
T
he healthcare industry
in India is passing
through a highly
volatile, complex, competitive
environment fueled with
disruptive technology.
Survival of a healthcare
organisation in such a
dynamic environment requires
data-driven management.
In a rapidly-changing
environment, organisations
need innovations to optimise
their operations.
Healthcare industry
leaders strongly believe that
data driven management
shall be the main driver for
innovation, productivity and
success of an organisation in
the present environment.
These days, payers are
experimenting with bundled
payments and hospitals will
face increasing challenges
in providing better care
at a lower cost. As payers
are trying to find ways
to constrain payments,
hospitals must contend with
the steadily rising cost of
pharmaceuticals, supplies,
medical technology, and
personnel. As payment reform
gathers steam, this strategy
will become increasingly
difficult to pursue. To
improve quality and efficiency
while constraining costs, some
hospitals have adopted quality
improvement methodologies
from industries outside of
healthcare, like data driven
management.
Experts opine that
healthcare providers who
make investments in analytics,
including partnering with
analytics- focused firms, will
end up being the winners.
What is data analytic
management?
Data analytic management is
the analysis of data available
from clinical, financial and
operational process occurring
in the healthcare organisation
to identify: what has
happened? what can happen?
what can be done?
Literature mentions health
analytics is “the systematic
use of health data and related
business insights developed
through applied analytical
disciplines (e.g. statistical,
Survival of a healthcare organisation in today’s dynamic
environment requires data-driven management
Driving with Data
Poor quality data
Lack of skill
& resource
Lack of
culture
Challenges
of
Analytics use
Timely non-
availabilty of
data
Lack of
Leadership

2. Industry Trends
Health Biz Insight August 201726
contextual, quantitative,
predictive, cognitive, other
models) to drive fact-based
decision making for planning,
management, measurement
and learning”.
The types
Data analysis takes place on
three levels:
Descriptive Analytics: This
analytic uses data analysis to
provide information of the
past and answers: “What has
happened?”
It provides information like
average investigations per
day, average admission per
day, average discharge per
day, average revenue per day
etc. Needless to say, the vast
majority of statistics we use
fall into this category.
The advantage of
descriptive analytics is that
it helps us to learn from past
sales, services, cost, and their
influence on future outcomes.
Predictive Analytics: This
method uses statistical model
and forecast technique to
predict future needs. So that
we can use data to predict
how many patients will
be seen in the outpatient
department on a given day or
time of day, what percentage
of these patients shall require
admission, and how many
patients shall undergo surgery.
This information is very useful
for both providers and payors.
Prescriptive Analytics:
This method provides
direction on what actions
to take. It is a fact that
prescriptive
analytics are
relatively complex
to administer, and
most healthcare
organisations
are not yet using
them in their
daily course of
operation. However, if
implemented correctly, they
can have a significant impact
on decision making and
improve the bottom line of the
organisation.
Challenges in
implementation
Literature reveals that with
years of relying on gut feelings
and experience, hospital
administrators are adopting
data driven management
very reluctantly. According
to one study, only 4% of
organisations rely on data
driven analytics. Healthcare
organisations utilising data
driven management can be
divided in to three categories
according to their capabilities:
• Aspirational organisations
• New or limited users of
analytics
• Focused on analytics at
point-of-need
• Turn to analytics for ways
to cut costs
• Experienced organisations
• Established users of
analytics
• Seeking to grow revenue
with focus on cost
efficiencies
• Seeking to expand ability
to share information and
insights
• Transformed organisations
• Analytic use as cultural
norm
• Highest levels of analytics
prowess and experience
• Seeking targeted revenue
growth
• Feel the most pressure to do
more with analytics
Steps to becoming a
data-driven healthcare
organisation
Healthcare organisations
should ensure commitment
of all stakeholders for
Increased operational efficiency
The vantage points
Increased customer acquisition
Cost effective care

3. Industry Trends
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Health Biz Insight August 2017 27
considering data as a strategic
asset, and integrating data
as a part of their culture.
They should implement
an understanding of the
complete flow of data and
utilising data-driven insights.
Leaders should support the
integration and development
of data analytics and
build organisation culture
encouraging and appreciating
sharing of data and insights.
Companies should
invest in adoption of new
technologies and systems to
ensure continuous quality
improvement of data driven
management. Although most
organisations will take an
incremental approach to
becoming data driven, all
should begin the process
by creating an information
strategy and roadmap and by
putting in place an analytics
platform and data governance
policies that include the
following 5-S steps.
1.Sort out the data
sources: Healthcare
organisations collect data
from many individuals
at many places. This
complexity results in
poor quality data. It
is recommended that
organisations should first
sort out its data sources and
its collection mechanism.
2.Set data quality metrics
and assess and improve
the quality of proposed
sources: Wide range
of data are collected in a
healthcare organisation.
These are often not
standarised. It is essential
that quality analysis and
corrective action must exist
for all new and established
data sources. Assessments
should compare data
sources to established
data quality targets and
track improvements.
After addressing quality
issues, data must be
normalised to standardise
formats, structure. Because
normalised data will be
used for many types of
initiatives, organisations
may want to master data
types, such as location,
providers and patients,
and make them available
to all data sources. Use
of a common healthcare
data model for most
organisations will be more
useful.
3.Streamline data
integration: After an
organisation understands
how it plans to use and
analyse different data
sources, it can determine
the best platform for
integrating its data.
Literature mentions that
several factors like whether
data is structured or
unstructured, streaming or
stored historically, reports
or exploratory analysis will
be required, will decide the
platform choice. Structured
data is available from
patients’ history notes
and unstructured data
is available from emails,
doctors’ notes, test reports
etc.
4.Search analytic need:
Understanding the analytics
requirement will help an
organisation to define
priorities and determine
which visualisation and
statistics are best suited to
the task.
5.Secure and manage the
data lifecycle: Healthcare
organisations should
ensure that robust security
measures are in place to
protect their data, associated
hardware and software from
both internal and external
risks. Organisations should
ensure that at the time
of installation of system,
appropriate decisions are
taken about retention,
cost-effectiveness, reuse and
auditing of historical or new
data.
Summing up
Though data driven
management is in the nascent
stage but with increased use
of electronic medical records
by healthcare providers and
availability of skilled IT
personnel, it will provide
immense opportunity to
improve our healthcare
ecosystem.
Who
will collect data
Who
will collect data
When
data will be collected
At the time of
Investigation/Admission/
Assessment
Clinical/
Demographic
Emergency/
Admission/OT
What
data will be collected
Where
data will be collected
The advantage
of descriptive
analytics is that it
helps us to learn
from past sales,
services, cost, and
their influence on
future outcomes