Maximizing Big Data and
Analytics to Improve Performance
Asha Saxena
Founder
ACULYST, Corp
March 13, 2017
Speaker Biography
Healthcare Technology Entrepreneur
Founder & Chief Innovation Officer of ACULYST Corp., a Healthcare Analytics firm with
global headquarters in NJ. Also, served as a CEO for FUTURE TECHNOLOGIES, INC, Big
Data Analytics Consulting Firm. .
Entrepreneur in Residence & Adjunct Professor, Columbia Business School
Leads Big Data Seminars at Columbia University and is an evangelist for data analytics. Teach
Entrepreneurship and Business Analytics
Contributor, Published Author
Monthly Column, Entrepreneur Media, Beckers Hospital Review, Healthcare Business Tech, Healthcare IT
Speaker at HFMA, HIMSS, AHIMA
Computer Science Engineer and Executive Management Programs at MIT and LBS
Ms. Saxena is Certified as a Six Sigma Black Belt professional in the discipline of Operational Excellence
Maximizing Big Data and Analytics
to Improve Performance
BIG DATA
&
ANALYTICS
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BIG DATA: BUZZ WORD?
5 billion gigabytes (exabytes) of data were created from the
beginning of recorded time until 2003.
In 2011, the same amount was created every TWO DAYS.
And Now, the same amount is created every TEN MINUTES.
Source: http://money.cnn.com/gallery/technology/2012/09/10/big-data.fortune/index.html
Amount of Data…
DATA SIZE
1000 Mega
1000 Giga
1000 Tera
1000 Peta
1000 Exa
―Every day, we create 2.5 quintillion bytes of data — so
much that 90% of the data in the world today has
been created in the last two years alone.‖
WHAT IS “BIG” DATA?
Big Volume With simple (SQL) analytics
With complex (non-SQL) analytics
Big Velocity Drink from the fire hose
Big Variety Large number of diverse data sources
to integrate
BIG DATA & IOT
9
Connected Devices
DATA IS NEW OIL
―IT’S ONLY USEFUL WHEN IT’S
REFINED‖
―INFORMATION IS THE OIL OF THE 21ST
CENTURY, AND ANALYTICS IS THE
COMBUSTION ENGINE‖
VALUE
5 V’s of Big Data
Volume, Veracity, Velocity,
Variety, and Value
Banking/Marketing/IT:
Volume, Velocity, and Value
Healthcare/Life Sciences: Veracity, Variety, and Value
NEW WORLD OF HEALTHCARE
The Triple Aim in Healthcare
Improve
Population
Health
Enhance
Patient
Care
Reduce
Operational
Cost & Risk
THE SHIFT
Hospital’s revenue model has changed
OLD MODEL NEW MODEL
The more services the more fees Paid to get people healthy and keep them healthy
Keep beds full and do a lot Do only what works
NEED: DATA DRIVEN APPROACH
WHERE IS THE DATA?
How to turn BIG data
analytics to Improve
Performance?
WHERE DO WE START?
Understanding…
TYPES OF DATA
• Structured
– Ex: Relational databases, Excel sheets
• Semi-structured
– Ex: XML data, EDI, emails
• Unstructured
– Plain Text, description on a web page, body of an email, images
In the near future, Unstructured data will be more than 80% in many
organizations (source: IDC)
TYPES OF DATABASES
Flat files
Hierarchical Databases (before 1980, IBM IMS)
Relational Databases (1980 to date) SQL (Structured Query Language is used to make queries on data in a relational database)
Object oriented databases (1990s)
Non Relational Databases (NoSQL) ( 2005 onwards)
RELATIONAL DATABASE
Traditional Analytics
Data refreshed once per month
Relational Database
Preprocessed report
Big Data Analytics
Real Time Analytics
Data refreshes on demand
Distributed networks
What is the challenge?
Yellow
Pages Search
Engines
DATA WAREHOUSE
Extract,
Transform
&
Load
ANALYTICS PROJECT LIFE CYCLE
Data Provisioning • Move data from operational
systems to Data Warehouse
• Build Visualization for end users
Data Capture
• Acquire Key Data Elements
• Assure Data Quality
• Integrate Data into
operational work flow
Data Analytics
• Interpret Data
• Discover new Information/
knowledge
HEALTH: DATA ANALYTICS
• Descriptive: describes data as is
without any complex calculations or
math/statistics models. May involve
calculation of ratios, visualization
• Predictive: use information in the
data, to make a forecast, or predict
(ex: the reaction to a drug.) Usually
make use of statistical, math, or data
mining models.
• Prescriptive: when there are too
many options, a tool may help
choose or prescribe a choice. Need
health and medical knowledge in
addition to data and information
PROBLEM STATEMENT
Clinical Care
Operational Efficiencies
Better bottom line
What do we look for?
Reporting Requirements from Centers of Medicare &
Medicaid Services (CMS)
– Admissions & Readmissions
– Emergency Department Visits and Wait Times
– Mortality
– DRG Codes for Procedures
– And more….
Where to start?
Data at Hand
Improve the
Business
Prepare for
Outliers
Run the Business
Focus on…
Where are the immediate revenue opportunities?
Source: http://www.intellectualtakeout.org/library/chart-graph/operating-cost-breakdown-hospitals
USE CASES: HEALTH ANALYTICS
• Population Health
• Identify populations and individuals most at risk for future high costs,
inpatient admissions, and emergency room visits.
• 30-Day Readmission / Return Risk
• Identify inpatient encounters most at risk for 30-day readmissions or 30
day ED revisits.
• Variation Management
• Understand resource variation by disease and cost category (length of
stay, laboratory, radiology, etc...) to reduce unnecessary practice variation.
AMBULATORY PATIENT RISK
MANAGEMENT
Low
Risk
Medium
Risk
High
Risk
Ambulatory based care managers assess
real time population risk scores,
including patient risks for costs,
admission, ED visit, disease, and
mortality.
The practice sets thresholds for each risk
category to flag “high” risk patients.
Care managers proactively reach out to
high risk patients to provide education
and manage care gaps.
UT SOUTHWESTERN
• 30-Day Readmission / Return Risk
• Identify inpatient encounters most at risk for 30-day readmissions or 30 day ED revisits.
• Reduce preventable, avoidable readmissions
• Real-time EHR data analytics helped cut readmissions by five percent by drawing on nearly 30 data elements included in the patient’s chart.
• Analytics can be used in real-time to automatically identify and target patients at the highest risk of readmission early in their initial hospitalization when there is a lot that can be done to improve and coordinate their care, so they will do well when they leave the hospital
SOUTH TEXAS MEDICAL ASSOCIATES
• Is using Advanced Analytics on clinical data to identify causes
of Readmission and developed comprehensive treatment and
intervention plans.
• In just 6 months, SETMA reduced readmissions by 22%.
• Source: http://www-01.ibm.com/software/analytics/healthcare/
UNIVERSITY OF CALIFORNIA DAVIS
• Hospital quality and patient safety in the ICU
• Routinely collect EHR data as the fodder for
ANALYTICS that gives clinicians an early
warning about sepsis, which has a 40 percent
mortality rate and is difficult to detect until it’s
too late.
TORONTO HOSPITAL: NEONATAL CARE
• Using various sensors, heart beat and other data streams are
collected, analyzed and correlated in real time to detect
infections. An IBM Research developed analytics system was
able to detect infection 24 hours before it became clinically
apparent to the doctors.
• In neonatal care, advancing treating by even an hour can be
life saving.
• Source: http://www-01.ibm.com/software/analytics/healthcare
Patient History Patient Risk of Event
or Outcome
Risk Model Development
1000s of Patient Features
• Age
• Gender
• Geography
• Income
• Education
• Race
• Diagnoses
• Procedures
• Chronic conditions
• Visit and admission history
• Outpatient medications
• Vital signs
• Lab orders and results
• Radiology orders
• Social characteristics
• Behavioral characteristics
Multivariate Statistical Modeling –
Decision Tree Analysis
Machine Learning
Available Risk Models
Population Risk Models
(predicts future 12 months)
• Predicted future cost
• Risk of inpatient admission
• Risk of emergency dept (ED) visit
• Risk of diabetes
• Risk of stroke
• Risk of AMI
• Risk of hypertension
• Risk of mortality
Event Based Risk Models
(predicts future 30 days)
• Risk of 30 day readmission
• Risk of 30 day ED re-visit
PREDICTIVE RISK MODEL
WHO IS ADOPTING?
Health Systems
Fee for Service Community Hospitals
ACOs
Medical Group with Insurance Product
State Medicaid Program
Federally Qualified Health Centers
Example 1:
• Quality: Monitoring Readmission Rates and Root Causes
Challenge
Inability to proactively identify high risk patients with complications
Inability to identify root causes of 30 day re-admissions by patient
• Implications Patients who are re-admitted tend to have lower patient satisfaction scores
Penalties for not meeting 30 day re-admission rate targets lowers revenue and increases costs, thus
lowering profitability
Quality: Monitoring Readmission
Rates and Root Causes
• Solution – Leverage root cause analysis on patients re-admitted within 30 days of initial discharge
– Analysis of patient history and risk enables product line managers to proactively change processes
and protocols, to lower re-admission rates
• Customer Benefits
– Patient Readmission rates reduced 25%
– Penalties of over $12MM avoided annually
Readmissions
Re-admission counts and
percentage by coverage
type, with drill downs
into root causes
Analysis of Readmission
Patterns by Day of Week
and Month, as basis for
remediation
Example 2:
Staffing: Manage Labor Cost Drivers
• Challenge
– Inability to analyze labor data rapidly based on ERP system reports
alone
– Inability to identify trends and variance in staffing and labor costs over
time - that contributes over 60% of all expenses
• Implications
– Unscheduled time off leads to avoidable overtime and cost overruns
– Overtime lowers employee satisfaction and morale
Staffing: Manage Labor Cost Drivers
• Solution
– Labor Cost dashboards drive actionable insights and correlations
between unscheduled time off and overtime utilization, with cost
implications
– Department heads can easily identify outliers and repeat violators of
paid time off policies, for appropriate action
• Customer Benefits
– Overtime reduced 15%, saving $ 20mm annually
– Employee Satisfaction scores improved on most Nursing Units
Staffing: Manage Labor Cost Drivers
Ability to visualize
staffing and labor
expenses by any time
horizon, for remediation
Analysis to correlate
unscheduled time off
against overtime by
employee
ANALYTICS
Questions
Tools Data
ANALYTICS
WHERE TO START?
Step 4: Adoption
Training Usability
Step 3: Implement
Timeline Results
Step 2: Technology
Build vs Buy Data Integration
Step 1: Strategy
Executive Engagement Defining Goals
FIVE TAKEAWAYS
Focus on the biggest and highest value opportunities
Within each opportunity, start with questions, not data
Embed insights to drive actions and deliver value
Keep existing capabilities while adding a new ones
Use an information agenda to plan for the future
Success looks like…
Call or email me:
Asha Saxena Email: [email protected]
Need Additional Information?
www.ashasaxena.com
• A Face in the Crowd: Say goodbye to anonymity
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