Big Data Solutions for Healthcare

BIGS001

Wayne Wu, Global Health Solution Architect, Intel Hubert Ding, Healthcare Solution Architect, Intel

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Agenda

• Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps

The PDF for this Session presentation is available from our Technical Session Catalog at the end of the day at: intel.com/go/idfsessionsBJ URL is on top of Session Agenda Pages in Pocket Guide

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Agenda

• Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps

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We are at an Inflection Point in Healthcare - TRENDS

Source: McKinsey Global Institute Analysis ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast

Healthcare costs are RISING

Significant % of GDP

Source: United Nations “Population Aging 2002”

25-29%

30+ %

20-24%

10-19%

0-9%

% of population over age 60

2050 WW Average Age 60+: 21%

Global AGING Average age 60+:

growing from 10% to 21% by 2050

U.S. Healthcare BIG DATA Value

$300 Billion in value/year ~ 0.7% annual productivity

growth

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We are at an Inflection Point in Healthcare - TRENDS

Source: McKinsey Global Institute Analysis ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast

Storage Growth

0

5000

10000

15000

2010 2011 2012 2013 2014 2015

Total Data Healthcare Providers (PB)

Admin

Imaging

EMR

Email

File

Non Clin Img

Research

Medical Imaging Archive Projection Case from just 1 healthcare system

Data Explosion projected to reach 35 Zetabytes by 2020, with a 44-fold increase from 2009

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Agenda

• Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps

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Big Data in Healthcare

1. Pharma/Life Sciences

3. Claims, Utilization and Fraud

2. Clinical Decision Support & Trends (includes Diagnostic

Imaging)

4. Patient Behavior/Social Networking

Where is the data coming from?

How do we create value? (examples)

1. Personalized Medicine

3. Enhanced Fraud Detection

2. Clinical Decision Support

4. Analytics for Lifestyle and Behavior-induced Diseases

McKinsey Global Institute Analysis

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Big Data Solution for Healthcare

Distributed Platform

Storage Optimization

Security and Privacy

Imaging Acceleration

New Healthcare Applications

Personalized Medicine

Clinical Decision Support Cancer Genomics

Health Info Services Primary Care Personal Health

Management Aging Society

Analytics and Visualization SQL-like Query Medical Imaging

Analytics Machine Learning

Data Processing/ Management Medical Images Medical Records Genome Data

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Agenda

• Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps

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Big Data Challenges are More than Data Size... And Require New Technologies

Lab results, billing data, images, sensors data from devices, genomics Volume

• Structured data in standard formats like HL7 • Unstructured data from dictations, transcription,

photos, images Variety

Traditional business solutions connecting to new data and analytics models for real-time value opportunities

Analyzing data from existing databases for claims, patient history, archived images, real-time data analytics for clinical decision support

Value

• Realtime rather than batch-style analysis • Data streamed in, tortured, and discarded • Making impact on the spot rather than after-the-fact

Velocity

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Agenda

• Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps

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All Eyes on Data for Value

Data Source Text-Voice-Video-Sensor

Requesting Or M2M Communication

Batch – Business Applications

Traditional Solution Environments

ERP, CRM, Batch, OLTP-DB

Edge Servers

Big Data Storage Considerations Traditional Storage Approaches

Large Analytics – Hadoop*

Large DB – Hive*

Large Backup – Lustre*

Rich Visualization – Secure Data Analysis and Caching

Analytical Synchronization

End-to-End Machine-to-Machine Source-to-Source

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All Eyes on Data for Value

Data Source Text-Voice-Video-Sensor

Requesting Or M2M Communication

Batch – Business Applications

Traditional Solution Environments

ERP, CRM, Batch, OLTP-DB

Edge Servers

Data Center Provisioning Discrete Virtual

Cloud – As A Service HPC Big Data Storage Considerations

Traditional Storage Approaches Large Analytics – Hadoop*

Large DB – Hive*

Large Backup – Lustre*

Rich Visualization – Secure Data Analysis and Caching

Analytical Synchronization

End-to-End Machine-to-Machine Source-to-Source

Operational Solution Stack Example

Applications & Services

Visualization – File Structure & Analytical Tools

Data Delivery, Operational & Graphical Analytics

Data Management & Computational Analytics

Compute – Storage & Infrastructure Platforms

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Enterprise Big Data Architecture

Enterprise Data Warehouse

Spreadsheets

Visualize

Data Mining Dev IDE

ODS & Data Marts

ENTERPRISE TOOLS

Legacy Document

Types

Logs

Social & Web

Legacy

STRUCTURED

UNSTRUCTURED

Transcriptions & Notes

DATA PLATFORMS

RDBMS

No-SQL

In Memory DB

SQL

APPS

Node Node Node

Hadoop*

DATA PLATFORMS

Web Apps

MashUps

IMPORT

INSIGHTS

CONSUME

Create Map

REDUCE

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Big Data Architectural Framework

Data Sources GIS Diagnostic

Images

Human Genome & Drug Discovery

Medical Devices

Surveillance and Medical Device Streaming Data

Medical Records

Data Velocity

Security Services Privacy

Compliance

Social Media Log

Files

Provisioning Models-Storage & Connectivity Considerations

MPP Databases DW Appliances

Databases DBMS / NoSQL

10GBe Fast Fabric

Text, Video and Audio

Data Vulnerability

NAS - SAS and Distributed

Storage

Provisioning Models Can Vary by Data Characteristics

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Big Data Architectural Framework

Data Sources

Data as a Services

GIS Diagnostic

Images

Human Genome & Drug Discovery

Medical Devices

Surveillance and Medical Device Streaming Data

Medical Records

Data Velocity

Data Volume and

Quality

Security Services Privacy

Compliance

Social Media

Integration Tools

Distributed High Performance Data Processing

Hadoop* MapReduce

Data ingestion, Integration and Processing Services

Log Files

Provisioning Models-Storage & Connectivity Considerations

MPP Databases DW Appliances

Databases DBMS / NoSQL

10GBe Fast Fabric

Vertically Integrated Software

Intel AIM Suite

Text, Video and Audio

Data Vulnerability

HPC / TCP MIC

NAS - SAS and Distributed

Storage

Provisioning Models Can Vary by Data Characteristics

Data Characteristics

Persistence EDW, Marts

Distributed Event, Message

Virtual Real-Time, Cached, Federated

Cloud

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Big Data Architectural Framework

Data Sources

BI & Predictive Analytics MapReduce

Data as a Services

GIS

Existing BI/Analytics with in-database

data processing support

Textual Analytics

Streaming Analytics

Diagnostic Images

Human Genome & Drug Discovery

Medical Devices

Surveillance and Medical Device Streaming Data

Medical Records

Data Velocity

Data Volume

and Quality

Security Services Privacy

Compliance

Social Media

Integrated Analytics with

Hadoop Support

Integration Tools

Distributed High Performance Data Processing

Hadoop* MapReduce

Data ingestion, Integration and Processing Services

Log Files

Provisioning Models-Storage & Connectivity Considerations

MPP Databases DW Appliances

Databases DBMS / NoSQL

Custom Analytic Solutions

10GBe Fast Fabric

Vertically Integrated Software

Intel AIM Suite

Text, Video and Audio

NLP/Semantic Search/ Machine Learning

Knowledge Management

Data Vulnerability

HPC / TCP MIC

NAS - SAS and Distributed

Storage

Data Access User

Authentication

Provisioning Models Can Vary by Data Characteristics

Data Characteristics

Persistence EDW, Marts

Distributed Event, Message

Virtual Real-Time, Cached, Federated

Data Visibility

Cloud

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Accessing Big Data (Clients) “Know Me” “Free Me” “Express Me”

Smart Phone

Mobile Clinical

Assistant Tablet PCs

Laptops, Ultrabook™

Devices Fixed PCs

Digital Signage Kiosk

Mobility

Vital sign, I & O entry

Medication administration

Template data entry

Free-format text data entry

Large diagnostic images

Data inquiry

Manageability

“Link Me”

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Building on the Ecosystem Database and Analytics Environments Optimized on Intel

No Matter the Choice: All optimized on Intel® Xeon® processor based hardware

Database and compute infrastructure Analytics engines

Relational

Nonrelational

VOLTDB

EXALYTICS

Life Sciences Workloads & Solutions

Open Source: BLAST, FASTA, ClustalW, HMMER, Darwin, etc.

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Intel® Products and Software For Big Data

Network Intelligent scale-out

networking from 10GBe – 40GBe

Performance Client Rich modeling support

Client – server application management

Fast Fabric & Caching Investing in new fabric

approaches non-volatile memory that

provide capacity caching for data velocity

Intel Software EcoSystem Hadoop*

Lustre*

In-memory In stream data analysis

End to end security

Compute Intel® Xeon® processor E5-and E7 based servers up to

80% performance boost with hardware-enhanced security

Storage

Intelligent scale-out storage built with Intel Xeon

processor E5-based storage

Technical Compute Intel Xeon processor E5-

based servers for TCP Intel® Xeon Phi™ co-

processor Integrated Systems

Embedded Analysis Solutions From Intel ISG

Scaling Flexible Workloads &

Analysis Optimized

Data Delivery & Management

Software Ecosystem Interconnect

Efficiency Robust & Secure

Interconnect Visibly Mobile

Performance Client Rich Visualization Seamless Access

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Examples of Intel-powered Servers in Big Data and Analytics

The Dell | Cloudera* solution for Apache* Hadoop combines

Cisco* UCS Server1

Intel® Xeon® processor 5600

1 http://gigaom.com/cloud/ciscos-servers-now-tuned-for-hadoop/ 2 http://www.businesswire.com/news/home/20110804005376/en/Dell-Cloudera-Collaborate-Enable-Large-Scale-Data 3 http://www.itp.net/mobile/588145-oracle-unveils-exalytics-in-memory-machine

Dell* PowerEdge* C Series2

Intel Xeon processor 5500/5600

Cisco UCS server with EMC Greenplum MR software - “enterprise-class” Hadoop* distribution that features technology from MapR

Oracle* Sun Fire* server3

Intel Xeon processor E7-4800

Oracle Exalytics* In-Memory Machine, features the Oracle BI Foundation Suite and Oracle TimesTen In-Memory Database for Exalytics

Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. Baseline score of 271 published by Itautec on the Servidor Itautec MX203* and Servidor Itautec MX223* platforms based on the prior generation Intel® Xeon® processor X5690. New score of 492 submitted for publication by Dell on the PowerEdge T620 platform and Fujitsu on the PRIMERGY RX300 S7* platform based on the Intel® Xeon® processor E5-2690. For additional details, please visit www.spec.org. Intel does not control or audit the design or implementation of third party benchmark data or Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmark data are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase.

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Big Data Applications in Healthcare (PRC)

1. Pharma/Life Sciences

3. Claims, Utilization &

Fraud

4. Patient Behavior/

Social Networking

2. Clinical Decision

Support & Trends

(includes Diagnostic Imaging)

•药品研发 对药品实际 作用进行分析；实施药品市场预测 •基因测序 •分布式计算加快基因测序计算效率

•临床数据比对 匹配同类型的病人，用药 •临床决策支持 利用规则和数据实时分析给出智能提示

•公共卫生实时统计分析 发现公共卫生疫情及公民健康状况 •新农合基金数据分析 及时了解基金状况，预测风险 辅助制定农合基金的起付线，赔付病种等 •基本药物临床应用分析 分析基本药物在处方中的比例

•远程监控 采集并分析病人随身携带仪器数据，给出智能建议 •人口统计学分析 对不同群体人群的就医，健康数据实施人口统计分析 •了解病人就诊行为 发现病人的特定就诊行为，分配医疗资源

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Agenda

• Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps

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Use Case: Regional Health Info Network – China Real-time Clinical Decision Support • Real-time and recursive information

processing of health data (EHR, medical images) to support care coordination, clinical decision support, and public health management

• Enabling health data analytic with Hadoop* (HBase*/Hive*)

• Potential to scale cross geos and across sectors/segments

• Involving local ISVs, local OEMs • Technical Challenges

– Transforming the relational DB to Hadoop (HBase/Hive)

– Addressing the usages of big data analytics in RHIN

Public Health Hospital Primary care

(Grassroots)

Ancillary Data &

Services

Health Information

DW

EHR Data &

Services

Registries Data &

Services

Longitudinal Record Services

Health Information Access Layer

Care Coordination Clinical decision support …

Data Analytic R&D …

RHIN

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Distributed Data Service System

Presentation (Report, Viewer)

Integrated User Interface（Citizen, Physician, Health Authority）

Data Mining （Mahout*）

Distributed Batch Processing Framework

(MapReduce)

Coordination Service (ZooKeeper*)

Structured Data Collector (Sqoop*)

Log Date Collector (Flume*)

Distributed File System (HDFS)

Health Information Access Layer (HIAL)

Cloud -based Regional Healthcare Service System

Hospital Hospital

Real-time Database (HBase*)

Language & Compiler (Hive*)

Grassroots Care

Institution

Pubic Health

Medical Service

Drug Mgt. Service

New Rural Medical

Insurance

Server Virtualization

Storage Virtualization

Network Virtualization

Infrastructure Virtualization

Multi-Tenancy Application

EHR data Repository

Operation Mgt.

RHIN/Grassroots Solution with Big Data (Hadoop*)

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Sequencing

3 Billion Base Pairs

Data Processing

Cloud Storage Visualization

Millions of Variants

Interpretation & Analytics

Millions of Variants Millions of Patients

Commercializing Targeted

Therapeutics Companion Diagnostics

Actionable Biomarkers

Use Case: NEXTBIO Analytics for Genomics Data

• Cost to sequence a genome has fallen by 800x in the last 4 years

• Each genome has ~4 million variants • Growth in the genomics data in the public

and private domain • Data available in variety of sources

– Structured, semi-structured, unstructured

• New aggregated data growing exponentially

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Use Case: NEXTBIO Patient Correlation Data

Novel Discoveries

Biomarkers Disease Mechanism

Drug Indications Clinical Trial Parameters

Patient Care Options

Large content repository of public and private genomic data combined with proprietary and patented correlation engine

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Use Case: NEXTBIO Nextbio & Intel Collaboration

Technical Challenge: • Immutable Data – write once,

never change, read many times • Traditional Bloom Filters works • Hadoop* & HBase* well suited

1 genome 10 million rows

100 genomes 1billion rows

1M genomes 10 trillion rows

100M genomes 1 quadrillion 1,000,000,000,000,000 rows

• App can dynamically partitions HBase as data size grows

Intel Optimizations for Hadoop: • Optimized Hadoop stack in open

source • Stabilize HBase to provide reliable

scalable deployment • Optimize and support scale-out as

data size dramatically grows • Exploring cluster auto tuning,

Security & Compliance, etc.

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Use Case: Big Data at Kaiser Permanente

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Data Trends

STRUCTURED DATA

80% UNSTRUCTURED

DATA

• 80% of world’s data is unstructured (Rise of Mobility devices, and machine generated data)

• 44x as much data over the coming decade (35 zettabytes by 2020)

• Majority of data growth is driven by unstructured data (Active archives, Medical images, Online movies and storage, Pictures)

• Information is centric to new wave of opportunities (Retail, Financing, Insurance, Manufacturing, Healthcare,…)

• Industry is employing Big Data Technologies for Information extraction

World’s Data

UNSTRUCTURED DATA

• 90% of Kaiser’s data is unstructured (80% of EHR and Image data)

• 25x as much data over the coming decade (One exabyte by 2020)

• Majority of data growth is driven by unstructured data (Medical Images, Videos, Text, Voice)

• Information is centric to providing Real-time Personalized Healthcare (Requires Contextual – device, environment, spatial, Demographics, Social and Behavioral profiles in addition to medical information)

• Kaiser is evaluating Big Data Technologies…

Kaiser’s Data

STRUCTURED DATA

90% UNSTRUCTURED

DATA

Source: Kaiser

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Data Platform Compute Trends – Distributed Compute

Discontinuous Change

SAN/NAS

Master

Slave(s)

• Fault-tolerant MasterSlave Architecture capable of withstanding partial system failures

• Data is distributed across processing slave

nodes

• Resources containing data are not shared

• Master manages the data distribution, job scheduling across slave nodes and aggregating result sets

• Integrate built/bought Real-time Predictive Analytical Solutions or Processing logic

SMP (5$)

MPP (10$)

In-Memory (50$)

SAN/NAS

SAN/NAS

Share-Nothing Distributed Storage and

Compute ($)

DAS

SAN/NAS

SMP (Disk Caching, High Speed Network)

(10$)

Kaiser is looking to exploit this capability…

• Structured, Relational Tabular Data

• Interactive Query Support • Real-time Analytics • SQL Transaction Data

• Unstructured, Non-tabular Data

• Rich Ad Hoc Integration • Real-time Analytics • UQL ALL Data

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Big Data Platform – Requirements

(Sensors, EMR, Claims, Pharmacy, Images)

(SLAs, Real-time Decision Support & Contextual Intelligence)

Variety

Ingestion

Integration

Interrogation

Information

(Data Model, Metadata Reference Data, Store)

(Alignment, Semantics, Completeness, Quality)

(Clustering, Statistical, Quality, Semantics)

Intuition (Simulation, Optimization, Stochastic Optimization)

(Standard & Ad Hoc reporting, Query, Alerts, Forecasting, Access)

Volume

(Structured, Text, Unstructured, Documents, Images)

Process Characteristics

A unified information storage methodology enabling users to manage data from ALL sources.

A portfolio of tools to manage (profile, cleanse, classify, synchronize, aggregate,

integrate, share) ALL types of data.

Support current BI tools focused on structured information. Build/buy packaged unstructured

data processing and analytics tools.

Ability to model information and transition from multiple access methods to generating, sharing, collaborating and acting on insights anytime,

anywhere on any device.

Velocity

Information drives process optimizations with strategic impact. Modeling business intuition

from data deluge.

Data Characteristics

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Big Data – Selection Criteria DATA SIZE

DATA TYPE

DATA CLASS

DATA CATALOG

DATA VELOCITY

DATA ACCESS

DATABASE TYPE

SERVER ARCHITECTURE

STORAGE ARCHITECTURE

Gigabytes, Terabytes, Petabytes Structured, Semi-Structured, Unstructured Human Generated, Machine Generated Text, Image, Audio, Video

Batch, Streaming

Analytics, Search, Transaction (ACID, BASE)

Relational , File Based, Columnar, NoSQL, Document, Graph, RDF

SMP, MMP, Distributed Processing

NAS, SAN, Direct Access Storage, Spinning Disks, Flash, SSD

FRAMEWORKS Financial, Computer Vision Engine, Geospatial, Machine Learning, Mathematical, Natural Language Processing, Neural Networks, Statistical Modeling, Time-Series Analysis, Voice Engine

ANALYTICS Standard Reporting, Ad hoc Reporting, Query/Drill downs, Alerts Forecasting, Simulations, Optimization, Stochastic Optimizations

DISTRIBUTED PROCESSING Appliance, Commodity Cluster (CC) < 1K nodes, CC >1K nodes

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Agenda

• Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps

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Summary

• We are at an inflection point in Big Data and analytics in healthcare

• We need to make Big Data efficient and accessible

• Focus on innovation, rely on the ecosystem for services outside your core competency

• Adopt standards and best practices leveraging worldwide models

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Next Steps

Help build the Big Data Health Continuum: • Create technology-differentiated offerings,

advocating open standards and best practices

• Identify potential customers and ecosystem partners in core healthcare usage models

• Deliver industry proof points to accelerate adoption

• Develop joint marketing programs to raise awareness, amplify our thought leadership and generate customer value

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Together, We Create the Network Effect

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Additional Sources of Information • Big Data and Analytics at Intel - Intel® Big Data and Analytics • Healthcare Blogs – Intel Healthcare IT Professionals • Whitepapers

– The Growing Importance of Big Data, Real Time Analytics – SAP In-Memory Appliance Software: Real-Time Business

Intelligence – Oracle: Big Data for Enterprise – Big Data: The next frontier for innovation, competition, and

productivity

• Videos – SAP-HANA – A Collaboration Between SAP & Intel

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• Intel® Virtualization Technology (Intel® VT) – Provides flexibility and maximum system utilization by consolidating multiple environments into a single server, workstation, or PC

• Intel® vPro™ Technology – Designed specifically for the needs of business, notebooks and desktops with Intel® vPro™ technology have security and manageability built right into the chip

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• Intel® Anti-Theft Technology (Intel® AT) – Providing the option to activate hardware-based client-side intelligence to secure the PC and its data in the event the notebook is lost or stolen

• Intel® AES New Instructions (Intel® AES-NI) – The Advanced Encryption Standard (AES) algorithm is now widely used across the software ecosystem to protect network traffic, personal data, and corporate IT infrastructures

• Intel® Identity Protection Technology (Intel® IPT) – Two-factor authentication directly into the processors of select 2nd generation Intel® Core™ processor-based PCs

• Intel® Cloud Access 360 – Protection Enterprise Access to Cloud and Protecting Enterprise Applications in the Cloud

• Intel® Expressway Service Gateway – High performance security, xml acceleration and routing. Cross-domain service mediation, threat prevention, policy enforcement. Interoperable ESB gateway

• McAfee Cloud Security Platform* – Consistent security policies, reporting, and threat intelligence across all cloud traffic—now available from a single platform

• Intel® Scale-out Storage – Tackle your data center’s challenges with enterprise storage solutions powered by the world’s most advanced multi-core architecture

• Intel® Solid State Drives – High performance, Self-Encrypting Solid State Drives for protecting sensitive data at rest

• Intel Unified Networking – Unified Networking enables cost-effective connectivity to the LAN and the SAN on the same Ethernet fabric

Intel Technologies

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• Intel® Identity Protection Technology (Intel® IPT): No system can provide absolute security under all conditions. Requires an Intel® Identity Protection Technology-enabled system, including a 2nd Generation Intel® Core™ processor enabled chipset, firmware and software, and participating website. Consult your system manufacturer. Intel assumes no liability for lost or stolen data and/or systems or any resulting damages. For more information, visit http://ipt.intel.com.

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Risk Factors The above statements and any others in this document that refer to plans and expectations for the first quarter, the year and the future are forward-looking statements that involve a number of risks and uncertainties. Words such as “anticipates,” “expects,” “intends,” “plans,” “believes,” “seeks,” “estimates,” “may,” “will,” “should” and their variations identify forward-looking statements. Statements that refer to or are based on projections, uncertain events or assumptions also identify forward-looking statements. Many factors could affect Intel’s actual results, and variances from Intel’s current expectations regarding such factors could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the following to be the important factors that could cause actual results to differ materially from the company’s expectations. Demand could be different from Intel's expectations due to factors including changes in business and economic conditions; customer acceptance of Intel’s and competitors’ products; supply constraints and other disruptions affecting customers; changes in customer order patterns including order cancellations; and changes in the level of inventory at customers. Uncertainty in global economic and financial conditions poses a risk that consumers and businesses may defer purchases in response to negative financial events, which could negatively affect product demand and other related matters. Intel operates in intensely competitive industries that are characterized by a high percentage of costs that are fixed or difficult to reduce in the short term and product demand that is highly variable and difficult to forecast. Revenue and the gross margin percentage are affected by the timing of Intel product introductions and the demand for and market acceptance of Intel's products; actions taken by Intel's competitors, including product offerings and introductions, marketing programs and pricing pressures and Intel’s response to such actions; and Intel’s ability to respond quickly to technological developments and to incorporate new features into its products. The gross margin percentage could vary significantly from expectations based on capacity utilization; variations in inventory valuation, including variations related to the timing of qualifying products for sale; changes in revenue levels; segment product mix; the timing and execution of the manufacturing ramp and associated costs; start-up costs; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials or resources; product manufacturing quality/yields; and impairments of long-lived assets, including manufacturing, assembly/test and intangible assets. Intel's results could be affected by adverse economic, social, political and physical/infrastructure conditions in countries where Intel, its customers or its suppliers operate, including military conflict and other security risks, natural disasters, infrastructure disruptions, health concerns and fluctuations in currency exchange rates. Expenses, particularly certain marketing and compensation expenses, as well as restructuring and asset impairment charges, vary depending on the level of demand for Intel's products and the level of revenue and profits. Intel’s results could be affected by the timing of closing of acquisitions and divestitures. Intel’s current chief executive officer plans to retire in May 2013 and the Board of Directors is working to choose a successor. The succession and transition process may have a direct and/or indirect effect on the business and operations of the company. In connection with the appointment of the new CEO, the company will seek to retain our executive management team (some of whom are being considered for the CEO position), and keep employees focused on achieving the company’s strategic goals and objectives. Intel's results could be affected by adverse effects associated with product defects and errata (deviations from published specifications), and by litigation or regulatory matters involving intellectual property, stockholder, consumer, antitrust, disclosure and other issues, such as the litigation and regulatory matters described in Intel's SEC reports. An unfavorable ruling could include monetary damages or an injunction prohibiting Intel from manufacturing or selling one or more products, precluding particular business practices, impacting Intel’s ability to design its products, or requiring other remedies such as compulsory licensing of intellectual property. A detailed discussion of these and other factors that could affect Intel’s results is included in Intel’s SEC filings, including the company’s most recent Form 10-Q, report on Form 10-K and earnings release. Rev. 1/17/13