Page 1

Mobile Trans Pilot

formation

Javed Jahangir BI Product Group Industry Business Unit

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Assumptions/Considerations for Content Pilot  Assets geared for Newer Reps  Should be developed with minimal specialized software(what are they?)  Delivery Mechanism  iPad (Many tech options)  iSpring(primarily HTML5, R?)  Non-linear story-telling presentation

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Transforming Types of Content

Architectures

Data Visualization

Storyboards/Processes

Sales Playbooks

Data Sheets

Infographics

Case Studies

Interactive Demos Positioning

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Transformational Change is Underway in U.S. Healthcare

Unsustainable Costs And our budgets simply can’t

Change Drivers

Health System Priorities

support continued inaction. U.S. HEALTHCARE COSTS ARE RISING FASTER THAN GDP 2

$

U.S. Healthcare Costs (% of GDP) GDP: $14.5T

Shifting Reimbursement Landscape

GDP: $24.4T

Regulatory Pressures

GDP: $1.0T

Evolving Care Delivery Models

Per-capita spending

$356

~$8K

Changing Science

$14K

• U.S. healthcare costs will rise f rom $2.6T in ‘10 to $4.8T in ‘21 (growing at 5.7% per year vs. 4.8% f or GDP)

• Per-capita cost, $356 in 1970, had risen to $8.4K by 2010, and is expected to reach $14K by 2021

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Managing Declining Reimbursement Evolving to different care models Driving Clinical Improvement Improving Operational Efficiency Dealing with Rapidly Evolving Competition Personalized Medicine Translational Research Consumer Engagement


These Changes Have Increased the Urgency of Answering Many Complex, Yet Longstanding Questions… “What is causing high readmissions in specific groups? (COPD, CHF, AMI, etc.) “Which treatment protocols produce better or worse outcomes? “What is the correlation between the length of acute respiratory infection and the administered doses of different antibiotics?” “How is our nurse overtime policy affecting our ICU quality measures? Patient satisfaction?” “Why are patient satisfaction ratings low in certain groups or units?

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Obstacle #1: Healthcare “Big Data” Encompasses Many Types of Data With Varying Volume, Speed, and Structural challenges Structured and Unstructured Data • EMR Records, free-text notes, images

Internal & External Sources • Research, pharmacy, govt, population, other providers

Medical Device / Machine-Generated • Blood pressure, Heart rate, glucose levels, weight, etc

Clinical • Lab test results, pharma, pathology reports, discharge summaries, clinician notes,etc.

Imaging & Diagnostics Web, Call Center, Social Media Financial & Operational • Procedures, costs, charges, payments

“Omics” • Genomics, Proteomics, Metabolomics 17Copyright © 2012, Oracle and/or its affiliates. All rights reserved.

History, Family, Demographic

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Obstacle #2: Vast Portions of Critical Information is Sitting in Unstructured Data Formats Not Addressable with Traditional Tools Traditional Sources EMR

ERP

Data Warehouse & Data Marts

Newer Sources

EMR Physician & Nurses notes

Pathology reports

Radiology reports

Histories & Physicals

Discharge summaries

Imported dictation

Home visit notes

Employee HR files

Content Systems, Files, Email

Websites

Call Center & Nurse Triage notes

Web Portal interactions

Patient/Provider emails & chats

Social Media exchanges

Patient Support Websites

3rd Party Rating/Reporting Sites

Biometric Device data

Social Media

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Oracle’s Big Data Architecture Decide Oracle Real-Time Decisions

Endeca Information Discovery

Oracle Event Processing

Cloudera Hadoop

Apache Flume

Oracle NoSQL Database

Oracle GoldenGate

Oracle R Distribution

Stream 19Copyright © 2012, Oracle and/or its affiliates. All rights reserved.

Oracle BI Foundation Suite

Oracle Big Data Connectors

Oracle Data Integrator

Oracle Database Enterprise Health Analytics Oracle Advanced Analytics Oracle Spatial & Graph

Acquire – Organize – Analyze Oracle Confidential


HHH

F r e e sca le i.M X 6 C o n t in u a H L 7 C lie n t E th e rn e t o r 3 G (Ja v a ) U SB P u ls e O x im e t e r Ja v a & Ja( Cv oa nFtXi n u o u Js a Dv aac ta ar d S t r e a m )

Local and Remote Analytics Communication

H adoop And Z ig b e e Ja v a S E

B lu e t o o t h L o w E n e r g y S u Hb a1 dGo hCo zop Rn Ft i n u a HA LT 7 -HOME CARE C o n t i n u a H L 7 U S B And W if i A P C o n t i n u a B l u e t oS eo rt vh e ar n d J a v a S E C lie n t AT-HOME U S B S u p pCARE o rt (Ja v a ) (Ja v a ) C o n t in u a H L 7 C lie n t P u ls e O x im e t e r B lo o d P r e s s u r e C u ff (Ja v a ) W e ig h t S c a le PP llaa tt ff oo(rSr mm in g le R e a d in g )

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PROVIDER( E M R ) OO rr aa ccllee HH ee aa lltt hh ccaa rr ee Data is acquired and E le c t r o n ic M e d ic a l R e c o r d C o n t i n u a B PP l u lleaat tot fof oo t hrr m amn d sent to Hadoop (E M R ) U SB Su p p o rt nHueea aaHlltLt h7h ccaa rree Data is sent Provider Set-top box or OO rr aaC cco llneet i H 3 mobile device for to Hadoop PPSllaea rttvffeoor rr mm P u ls e O x im e t e r (Ja v a ) mediating biometric data 5 B lo o d P r e s s u r e C u ff C o n t in u a H L 7 signals

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from devices 1 1E t h e r n e t o r

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devEic te h esr n e t Z ig b e e 2013 Oracle Corporation B l u e t o o t h L o w E n e r g y Confidential & Proprietary S u b 1 G h z R ZF i g b e e

B lu e t o o t h L o w E n e r g y W ifi A P Sub 1G hz RF

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2 C o n t in u a H L 7 C lie n t 12 (Ja v a ) Data sent to C o n t in u a H L 7 C lie n t Provider

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(Ja v a )

HHH

AT-HOME CARE

USB

Cloud/ Communication Network

Fl u m e

MPI Lookup Map-Reduce EDW OEP

HDFS NoSQL

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Integration with Historic EMR Patient

5

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P u ls e O x im e t e r Network ( C o n tin u o u s D a t a S t r e a m ) COMMUNICATION PROVIDER P u ls e O x im e t e r

devices

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Connector

Cloud/ 12 Communication

U S analytics B -Local W if i A P ( C o n t in u o u s D a t a S t r e a m -Real-time 2 processing C o n t in u a B lu e t o o t h a n d U SB Su p p o rt -Data correlation C o n tin u a B lu e to o th a n d -Data Set-top aggregation Provider U Sbox B S uorp p o r t At-Home Patient mobile device for P u ls e O x im e t e r -Local persistence R F P a n i c B u t t o n Care Devices B l o o d PP ur el sses uO rxei mC eu tf ef r mediating biometric data 4 signals R F P a n ic B u t t o n WB leo i og dh tP Sr ec as sl eu r e C u f f Data, ( S i n g l eW Re ei gahdt i nS cg a) l e Patients record different biometrics data-Visualize all D Data is s ent across feedback, ( S i n g l e data Re ev a ic d ie n gca ) ptures through the day, potentially capturing 100s data and Communication -Patient feedback and alerts of data points per day publishes Provid er Clo ud 2 3 1 sent to Data sent to Data sent to provider

© 2013 Oracle Corporation © 2013 Oracle Corporation Data is aggregated Patients record Confidential & biometrics Proprietary Confidential & Proprietaryand analyzed for through the day potential alerts

O E P

Server (Ja v a )

D e v ic e M a n a g e m e n t ( S in g le R e a d in g ) F ir m w a r e D o w n lo a d s

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HOSPITAL/CARE PRO

HOSPITAL/CARE PROVIDER

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Care provider alerted before back-end batch processing Data is acquired and organized for actionable

Cloud/ response Communication Network

Fl u m e

Data sent to an devices

MPI Lookup ENTERPRISE DA TA

HDFS

NoSQL

Map-Reduce EDW OEP

Integration with Historic EMR Patient datawarehouses

4

Integration with Historic EMR Patient

Provider 5

Device captures and publishes data

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5

Data sent across cloud

12

12

Integration with Historic EMR Patient datawarehouses

Integrated data is staged for consumption by5Analytic Dashboards and Decision Systems 5 ENTERPRISE DATA

Data sent to analytical devices


Key to Success – Are New Data Discovery Tools (like Endeca,) That Allows Rapid Integration & Visualization of Disparate Types of Data Medical Notes Text

Geospacial Mapping

Environmental & Social Data Economic Data Genomics Data

Biometric Data

Diagnostic Data

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Detecting Influenza Spread

Earliest Warnings: Social Media

Google Flu Indicators

CDC Reported Cases

 Analysis of Web access logs for surveillance of influenza.

MEDINFO 1202–1206 (2004)  Infodemiology: tracking flu-related searches on the web for

syndromic surveillance. AMIA: Annual Symposium Proceedings 244–248 (2006)  Using internet searches for influenza surveillance. Clinical

Infectious Diseases 47, 1443–1448 (2008)  Detecting influenza epidemics using search engine query

data. Nature Vol 457, 19 February 2009.  Evaluation of over-the-counter pharmaceutical sales as a

possible early warning indicator of human disease. Johns Hopkins University APL Technical Digest 24, 349–353 (2003)

By processing hundreds of billions of individual searches from five years of Google web search logs, our system generates more comprehensive models for use in influenza surveillance …

More Important: 2 week earlier detection! 22Copyright © 2012, Oracle and/or its affiliates. All rights reserved.

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Detecting Influenza Spread

How Could We Use This?  Identify specific patients in your care who are most

susceptible to Flu-induced calamities

Potential Impacts:

 Insure vaccinations (specifically)  Alert Pts. & PCPs when flu actually hits community  Arrange expedited care pathways for those patients  Enact aggressive treatment protocols where appropriate  Monitor actual geographic spread

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Decreased admissions Lower Morbidity Lower Mortality Reduced Costs


Reducing Readmissions

How Can Big Data Help? Case Examples of Wins:

COMBINE:  Traditional structured care & utilization data  Unstructured data from EMR, call centers, emails  Longitudinal patient data from physician records  Regional Retail pharmacy data

 Discharge note text containing leading predictor of

CHF readmissions  Demographic/Economic data uncovering home care

coverage gaps in key patient group  Retail pharmacy feeds showing who isn’t

filling/refilling RXs

 Social Services data  Regional utilization data from payers/Medicare

 Social data mining predicting Flu spread, pinpointing

eColi outbreak source

 Etc.

We’ve only scratched the Surface so far! 24Copyright © 2012, Oracle and/or its affiliates. All rights reserved.

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Big Data Solutions For Financial Services 360 View with Customer Analytics

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Trends in Consumer Experience Using Customer Analytics to Create More Personalized CX

All interactions of each individual customer will be turned into a personalized experience:

Customers will make web / mobile their primary interaction with the bank / financial institution Those channels are already heavy 2. personalized and the customer will expect the same from the bank 3. Brands will use more differentiating content or offers to acquire and retain customers, to up-sell and cross-sell 1.

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Enterprise Inhibitors to Solving Challenges SelfService

Contact Center

Branch

Custome r Data

Custome r Data

Custome r Data

www

Custome r Data

Social Media

Smart Phone

iPad

Custome r Data

Custome r Data

Custome r Data

How to collect all valuable customer data in one source? How to connect the dots across silos of data? How to gain actionable customer intelligence and execute that? How to do this in real time? Avoid costly Integration, data handling and missed opportunities

Existing customer 360 views?

ERP

CRM

Campaign

DWH

POS Gateways

Production MES

Reporting

Analytics

Custome r Data

Custome r Data

Custome r Data

Custome r Data

Custome r Data

Custome r Data

Custome r Data

Custome r Data

Increasing data volumes

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Current Reality Back Office Systems Reporting / Analytics Debit Card Transcations Front Office Channel Systems

Credit Card Transcations Customer CRM Data

Mobile Online

Extract Transform and Load

Clickstream Data Card Merchant Data

External Data Social Data Reference Data

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Branch ATM Call Center


Back Office Systems

Reporting / Analytics

Debit Card Transcations

Oracle Endeca / Oracle Business Intelligence / Oracle R Enterprise / Oracle Exalytics

Card Merchant Data

External Data Social Data Reference Data

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Extract, Transform and Load

Clickstream Data

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Oracle Big Data Appliance Lily Enterprise

Customer Database

Content Recommendations

Cloudera / Oracle NoSQL / Big Data Connectors

Content Presentment / Disposition

Customer CRM Data

Oracle Data Integrator

Credit Card Transcations

Oracle RTD / Oracle OEP / Oracle Fusion Middleware

Solution: Real-time Personalized Offers

Front Office Channel Systems Mobile Online Branch ATM Call Center


Omni-Channel Offers with 360 View of Customer Personalized Offers to Any Channel in Real-time

 Real-time profile updates  Self-learning, closed loop model  Best-in-class modeling across structured and unstructured data  Add new dimensions in your recommendation process  One View of Customer  Deliver highly personalized offers to any channel in real-time 30Copyright © 2012, Oracle and/or its affiliates. All rights reserved.

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Channel Systems Mobile Online Branch ATM Call Center


New Wholesale Bank Initiatives Provide Wholesale Merchant Offers & Mobile Payments

 Based on Location and individual customer preferences  Millions of Customers X Thousands of Merchant Offers  Protect Payments and Drive Wholesale Deposits  Become Trustee for your Customer’s Commercial Identity

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Big Data For Remote Service Delivery

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Insert Picture Here


Oracle Remote Service Delivery Platform Enabling New Revenue Streams

Remote Connectivity Services ‘Call Home’ & ‘Call Out’ Telemetry Consumption/Utilization Metering Remote Monitoring & Diagnostics Service Alerts & Reminders

Software-Based Services Customer Self Service Device-Integrated Apps Store Embedded Software Upgrades & Patching

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New Service Offerings

Information Based Services Customer Usage Analytics Product & Services Upsell Quality & Failure Mode Analytics Warranty Analytics

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Performance-Based Service Contracts Consumption-Based Service Contracts


Remote Service Delivery Platform Oracle’s Remote Service Delivery Platform enables brand new revenue streams from innovative new services delivered through connectivity to customer assets. It also enables higher levels of customer service by being able to anticipate, identify and remedy issues proactively. Key Challenges  Infrastructure to Deliver Connected Services  Leverage Connectivity to Transform Customer Service Processes  Convert massive data volumes into Insights and Actionable Intelligence

Value: Enabling New Revenue Streams Business Benefits

Technical Benefits

 New Business Models and New Revenue

 Open event management platform

 Highly scalable – proven to scale to millions of

   

Streams Transform Customer Service through Connectivity Improved Customer Loyalty Lower Service Cost through predictive diagnostics and proactive services Comprehensive Customer Self Service Predictive models by leveraging event data: Quality, Warranty, Failure Modes, Usage Patterns

transactions per hour

 Flexible integration of events into enterprise business processes and systems

 Industry-standard data hub and data model for remote data capture

 Integrated row-level, role-based security, PCI compliance

 Integrated Advanced Analytics: Analyze

Petabytes of data using Big Data technologies

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Oracle Remote Service Delivery Platform Business Benefits  New Business Models and New     

Revenue Streams Transform Customer Service through Connectivity Improved Customer Loyalty Lower Service Cost through predictive diagnostics and proactive services Comprehensive Customer Self Service Predictive models by leveraging event data: Quality, Warranty, Failure Modes, Usage Patterns

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Oracle Remote Service Delivery Platform Technical Benefits 

Open event management platform

Highly scalable – proven to scale to millions of transactions per hour

Flexible integration of events into enterprise business processes and systems

Industry-standard data hub and data model for remote data capture

Integrated row-level, role-based security, PCI compliance

Integrated Advanced Analytics: Analyze Petabytes of data using Big Data technologies

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Oracle Remote Service Delivery Platform CRM Internet/Intranet

Remote Service Delivery Engine

Business Systems Integration

Big Data Analytics

Internet/Intranet

Visualization

Service SCM Billing Asset Management Scheduling

Service Delivery

Customer Assets 37Copyright Š 2012, Oracle and/or its affiliates. All rights reserved.

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Back Office


Failure and Quality Event Predictions Big Data & Advanced Analytics for Sensor Data Key Capabilities • Integrated with Connected Vehicle Platform and Remote Service Delivery Platform • Provides Information Discovery, Advanced Analytics and Deployment capabilities for Analytical models • Combine BI, real-time dashboards and Advanced Analytics Key Benefits • Architectural framework to address multiple problems • Predictive Diagnostics based on sensor data • Predictive Asset Maintenance • Engineering and Production Quality Root Cause Analysis • Combine sensor data with warranty, quality, customer data with social data for unprecedented ability to understand impact of quality on customer satisfaction 38Copyright © 2012, Oracle and/or its affiliates. All rights reserved.

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Remote Service Delivery Platform Predictive Diagnostics Data Mining Phases

Oracle Data Miner Workflow

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Big Data Architecture

Oracle Big Data Appliance

Flume

File of readings on the Linux filesystem OSCH

ODCH ODCH

HDFS

HIVE

Data Collector RSD/ODM Database Tables

User

Oracle Database with Data Miner

Hadoop/OSCH External Table

SQL Developer Data Miner UI

Endeca

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Endeca Integrator

Endeca Information Discovery


Mobile Security Architecture Mobile Device

DMZ

Mobile Interfaces

IDM Infrastructure

Access Management

Oracle SDK

Authorization

Native App

API

I AP

Web App Authentication API

OAAM Service

Security App

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Device Profile API

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Lost & Stolen Devices GPS/WIFI Location Awareness

Platform Security Services (OPSS) OPSS Service

Risk-based KBA & OTP Transactional risk analysis White & Black Lists

Directory Services REST

Device Fingerprinting & Tracking Device Registration

OAM Service OES Service

Features

Device Profile Services


DEMONSTRATION

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Predicting Service or Supply Volumes

Population by Age: 1900-2050 Source: U.S. Bureau of the Census 120,000,000 Number of Persons 60+ 100,000,000 80,000,000 60,000,000 40,000,000 20,000,000 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 Year

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Age 85+

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Age 75-84

Age 65-74

Age 60-64

Total Knee Replacements 2012 ~ 500,000

2032 ~ 3.2 M


Predicting Service or Supply Volumes

How Can Big Data Help? Better predicting key supply volumes

COMBINE:  Traditional utilization data

Potential Impacts:

 Longitudinal patient data from physician records  Regional utilization data from payers/Medicare  Regional demographics  Economic projections

Reduced Supply Costs Increased Market Share Incr. Patient Satisfaction Improved Staffing Planning service capacity & locations Facility types

 Specific adjustment overlays

Specialists General Staffing needs

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Spotlight on HR

Employee Recruitment & Retention Will Soon Take Center Stage Again! How Can Big Data Help?

Looming Trends:

Potential Impacts:

H R A n a ly tic s E m p lo y e e H R E v e n ts

Economy Slowly Improving

75% of workers unhappy in current jobs

Voluntary Termination on the Rise

Massive Bolus of Retirements Coming

Health System Demand Rising

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Reduced Turnover Improved Productivity Improved Quality Increased Patient Sat. Lower Recruiting Costs

M e tric s : H e a d c o u n t, T u r n o v e r , C o m p e n s a tio n D im e n s io n s : E v e n t T y p e , E m p lo y e e , S u p e rv is o r, O r g a n iza tio n , D a te Employee HR Events EmployeeID SupervisorID Date EventType BaseComp Bonus

OverTime

12324

506

12/3 Promotion $140K

$30K

$20/HR

12325

507

12/4 Termination $90K

$15K

$30/HR

“How can we retain our top performers?”

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