0728 4a Show-Me Summer Conference_Big Data


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7/24/2016

Making a Big Impact with Big Data: Analytics Applications in Health Care Presented by: Jeremy Clopton, CPA, CFE, ACDA, CIDA Director – Big Data & Analytics, Digital Forensics

Success in Applying Analytics = Becoming an Insight-Driven Organization

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Today’s Topics Building a Foundation Emerging Technologies Applications in Health Care Analytics Framework Closing Thoughts

Building a Foundation

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Definition of Big Data:

Lots of option – pick your favorite.

More important: You have data (lots of it) and need to be using it.

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Definition of Analytics: …analysis methods designed to extract useful information for answering strategic questions...

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But wait, there’s more!

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Data Universe Created in Last 2 Years - 90%

Total Information Awareness

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Data Volumes are Increasing

Source: https://nsa.gov1.info/utah-data-center/udc-photo.html

What are you doing to harness | leverage | capture | use your data?

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Emerging Technologies

Tools

Standard Analysis (Excel, Access) Data Analytics (ACL, IDEA, Arbutus) Data Visualization (Tableau, Analysts’ Notebook) Artificial Intelligence (Machine Learning, Social Media, Sentiment)

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Structured Data Analytics

Visual Analytics

Techniques

Unstructured Data Analytics

Relationship Mapping

New & Developing Technologies • Textual Analytics • Machine learning  Supervised  Unsupervised

• Advanced analytics  Regression  Predictive

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Network Relationship Mapping Emotion Detection

Named Entity Extraction

Textual Analytics Social Media Extraction

Predictive Coding Topic Mapping

Machine Learning

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Machine Learning • Supervised  Give examples and answers, machine finds more like it.

• Unsupervised  Give data, machine finds the patterns, and applies its own rules.

Machine Learning: Clustering

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Advanced Analytics: Outlier Detection

Advanced Analytics: Logistic Regression

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Advanced Analytics: Correlation

How are you applying emerging technologies to everyday problems?

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Applications in Health Care

Application ideas to become insight-driven.

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Profitability Analysis

Business Problem • Hospital guarantees revenue to specialty practice  Procedure A = $42  Procedure B = $52  Procedure C = $75

• Over time, percent of guarantee by hospital increases • What is the cause?  Option 1: Poor collection efforts/collections  Option 2: Change in payor mix

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Payment Percentages by Payor Legend Blue = payor group 1 Red = payor group 2

Payor Mix Trend Legend Blue = payor group 1 Red = payor group 2

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What led to the problem?

Payment Reform

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Success in payment reform requires insights into performance metrics.

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$40,000

160

$35,000

140

$30,000

120

$25,000

100

$20,000

80

$15,000

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$10,000

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$5,000

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Patient Volume by Age

Medicare’s Episode Payments

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$0 0-60

61-65

66-70

71-75

76-80

81-85

85-90

91-95

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Fraud Risk Management

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The Fraud Triangle Perceived pressure facing individual

Perceived opportunity to commit fraud

Fraud Person’s rationalization or integrity

Emotions: Tone Detection and Sentiment

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Emotions: Tone Detection and Sentiment Anger Frustration Anxiety/Nervous Tension Vague/evasive Conspiratorial Sadness Intimacy

Positive Negative

Fraud Example

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Reputational Risk Management

Reputational Risk Management Objectives: • Identify issues & respond before they become crises • Proactive approach Approach: • Monitor trends & changes in patterns

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Reputational Risk Monitoring - Metrics • Overall sentiment trend

• Nature of activity

• Key emotional drivers

• Location of activity & influencers

• Influencers • Influencer relationships • Proliferation of activity

When

Where

What

Who

Why

Reputation Data

How

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Application Framework

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The Three V’s for Identifying Opportunities 1. Viable  The problem is suited to the available tools

2. Valuable  Is it worth doing?

3. Vital  Technology is key to success

Strategic Question

Procedures

Analyze

Objectives

Data

Manage

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Procedure Development

Ad Hoc Individual Automated Individual Automated Groups Continuous Analytics

Closing Thoughts for the Day

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What’s the Focus? • Not designed as intrusion of privacy • Not reading everyone’s email • Looking for signals • Patterns are key

Challenges to Overcome • Policies around data ownership & use • Legal implications • Ethical considerations

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New Mindset • Communications are used to transact business • Corporates assets are used to transact business • Transacting business = business transaction

THANK YOU! FOR MORE INFORMATION Jeremy Clopton, CPA, CFE, ACDA, CIDA Director | BKD, LLP Practice Leader – Big Data & Analytics, Digital Forensics E: W: T: L:

[email protected] http://bkd.com/bigdata @JeremyClopton http://www.linkedin.com/in/jeremyclopton

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