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

4

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: asha@ashasaxena.com

Need Additional Information?

www.ashasaxena.com

• A Face in the Crowd: Say goodbye to anonymity

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