Big Data Planning a Course Toward Predictive Analytics A - - PowerPoint PPT Presentation

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Big Data Planning a Course Toward Predictive Analytics A - - PowerPoint PPT Presentation

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Big Data Planning a Course Toward Predictive Analytics A Complimentary Webinar From healthsystemCIO.com Your Line Will Be Silent Until Our Event Begins Thank You! Slide Deck: http://goo.gl/xNC85e Webex Support 1-866-223-3239 Event # 661

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Big Data – Planning a Course Toward Predictive Analytics

A Complimentary Webinar From healthsystemCIO.com Your Line Will Be Silent Until Our Event Begins Thank You!

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Housekeeping

  • Moderator – Anthony Guerra, editor-in-chief, healthsystemCIO.com
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Agenda — 45 Minutes

  • (20 min) David Miller, Vice Chancellor/CIO, University of Arkansas for

Medical Science

  • (25 min) Q&A w/David Miller
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“Big Data – Planning a Course Toward Predictive Analytics”

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Presenter’s Background

Clinician (3 years) – Registered Medical Technologist Hospital Operations (3 years) – Lead operations for hospital-based diabetes treatment centers IT Vendor (4 years) – Financial decision support, cost accounting, budgeting, EIS, BI IT and Management Consulting (15 years) – IT, process redesign/improvement, clinical transformation, strategic planning Healthcare IT Leadership (9 years) – Lead 300-bed hospital, then #2 at one of the top academic medical centers in the nation

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Presenter’s Current Professional Roles

CHIME CHIME national liaison to AHIMA – (September 2013 – Present) CHCIO Panel Reviewer (2011 – Present) HIMSS Committee Member, National HIE Committee - HIMSS (July 2013 – Present) President-elect, Arkansas HIMSS (July 2012 – Present) Board Member and HIE Chair, Arkansas HIMSS (May 2011 – Present) AAMC Chair, Group on Information Resources - AAMC (June 2013 – Present)

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Presenter’s Current Professional Roles

Other Chair, HIE Council - Arkansas Office of Health Information Technology (June 2011 – Present) Advisory Board Member - Pivot Point Consulting, LLC (October 2013 – Present) Academic Medical Centers Advisory Council Expert - Next Wave Connect (October 2013 – Present) Member, Information Technology Task Force - Novation (January 2013 – Present) Member, CIO Council – University Healthcare Consortium (August 2012 – Present)

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What are the challenges facing healthcare today?

We are being challenged by policy makers and society to:

  • Bend the cost curve
  • Increase quality
  • Enhance patient safety
  • Improve outcomes
  • Shift to proactive care
  • Effectively use IT
  • Comply with government regulations
  • Better educate future providers
  • Perform comparative effectiveness research

Source: AAMC, 2011

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New Paradigm for Clinical Information Processing

Family History | Whole Genome | Clinical Data | Patient Reported | Monitoring Algorithms

Clinical Decision Support

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Precision Medicine

“State-of-the-art molecular profiling to create diagnostic, prognostic, and therapeutic strategies precisely tailored to each patient's requirements.” “The success of precision medicine will depend on establishing frameworks for …interpreting the influx of information that can keep pace with rapid scientific developments.”

N Engl J Med 2012; 366:489-491, 2/ 9/2012

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Genetic Testing Today

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Genetic Testing Today

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Genetic Testing Today

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Genetic Testing Today

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Other New Streams of Data

Over the next 3 years

  • +1 billion smart phones will enter service
  • 3 billion IP-enabled devices by 2015

By 2016

  • 4.9 million patients will use remote health monitoring devices
  • 3 million patients will use a remote monitoring device via a smartphone hub
  • 142 million healthcare and medical app downloads

The Healthcare Data Explosion

  • Average person’s EHR ranges from 1 mb to 5 gb (based on age, etc.)
  • 2012 US digitized patient data – 600 pedabytes to 10 exabytes (est.)
  • US healthcare data is growing by 15 petabytes a day - currently
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Big Data Definition

“Big Data” is data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it…

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  • Ad-hoc querying and reporting
  • Data mining techniques
  • Structured data, typical sources
  • Small to mid-size datasets
  • Optimizations and predictive analytics
  • Complex statistical analysis
  • All types of data, and many sources
  • Very large datasets
  • More of a real-time

Analytics trends driving health to Big Data

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Market trends driving health to Big Data

  • Medical and health capabilities expanding.
  • Changing demographics, expanding the need for more services.
  • New care and reimbursement models emphasizing focus on

managing health across community and care settings.

  • Exponential growth in health and medical information from a variety
  • f diverse sources.
  • Health consumerism generating large amounts of unstructured data

through consumers’ participation in social media.

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Innovations Making Big Data Possible

  • Increased use of electronic medical records (EMRs) and
  • ther digital data.
  • New capabilities to combine and use of diverse data types from internal and

external sources.

  • Low-cost storage and process power.
  • New software to handle speed and volume, structured

and unstructured.

  • Revolution of clinical user experience—right information

at the right time, which improves decision support and care quality.

  • Real-time and predictive analytics.
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Office of National Coordinator for Health Information Technology

  • Big data will revolutionize healthcare, says a new five-year strategic plan from the

Office of the National Coordinator for Health Information Technology

  • "Through a learning health system, the right information will be available to

support a given decision, whether it is about the efficacy of a treatment or medication for an individual patient, predicting a national pandemic, or deciding whether to proceed with the research and development for a potential new treatment," the plan states.

http://www.fiercegovernmentit.com/story/big-data-will-transform-healthcare-saysonc/2011-03-27#ixzz1Y2aW2zea

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The Five Vs of Big Data

  • Volume – quantity, from terabytes to zettabytes
  • Variety – structured, unstructured, semi-structured
  • Velocity – time-sensitive, real-time, predictive
  • Veracity – quality, relevance, predictive value, meaningfulness
  • Value – It must solve strategic problems
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Types of Big Data Value

  • Treatment planning
  • Health and social services continuity planning
  • Waste and fraud detection
  • Increased awareness of consumer trends
  • Population health management
  • Surveillance and health management
  • Improved research
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Examples of Big Data Value

  • Access medical images from across the organization to speed patient

diagnosis

  • Capture and analyze physiological data in ICUs in real time to detect

problems before they happen

  • Integrate patient health information, patient preferences and insights from

best practices and evidence generation.

  • Continuously aggregate and analyze public health data to detect and

manage potential outbreaks

  • Analyze clinical data & claims for improved and more predictable
  • utcomes
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Factors to Consider In Big Data Analytics

  • BI Architecture – Optimized infrastructure (e.g., data marts, ODS)
  • Data Sources – Web, patient, genomics, EMR – real time data extraction
  • Types of Analysis/Use of Analytics – Analytics combining multiple and

complex data sources

  • Data Models – TBD by each organization
  • Data Governance - TBD by each organization
  • Tools - TBD by each organization
  • Skills needed - TBD by each organization
  • Culture/enterprise data literacy - TBD by each organization
  • BI Governance/Organizational Structure – TBD by each organization
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Advanced Iterative Analytics

  • Analytics on non-relational, multi-structured, machine-generated data
  • Analytics that need to scale to big data sizes
  • Analytics that require reorganization of data into new data structures –

graph, time & path analysis

  • Analytics that require fast, adaptive iteration
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Keys to Success

  • Never underestimate the importance of data quality as a foundation
  • Make sure all the stakeholders are represented
  • Understand the downstream impacts of data use & re-use
  • Start with tools and models you are already familiar with
  • Allow adequate time & resources to address governance,

accountability and stewardship

  • Allow adequate time & resources to address data literacy across the
  • rganization
  • Understand the impact of patient and provider misidentification for

shared data

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Big Data Roadmap

Manage:

  • Data
  • Content
  • Streaming

Information

Integrate:

Master Data Data Warehouse

Analyze:

  • Content Analytics
  • Big Data
  • Cubes
  • Streams

External Information Sources Business Analytics Applications Transactional & Collaborative Applications

Govern Quality – Lifecycle – Security/Privacy - Standards

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The UAMS Journey - EDW

Phase 1 April, 2011 – October, 2011

  • Data Sources targeted:
  • Sunrise
  • Logician
  • Medipac
  • Softlab

Phase 1.5

  • Physician billing data - Live: March, 2012

Phase 2 June, 2012 – Dec, 2012

  • Data Sources targeted
  • 6 AHEC EMRs
  • Tissue Bank/Tumor Registry
  • Weekly refresh for all Data sources
  • Data available for over 800,000 patients, over 17 million Encounters, over 60 million lab results

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Available Data in EDW

Demographics

  • Name
  • Age
  • Gender
  • Zip Code (5-digit)
  • Language
  • Marital Status
  • Race and Ethnicity
  • Religion

Diagnoses (ICD-9 & CCS) { Sunrise, Centricity, Medipac } Laboratory tests MSDRG Discharge Disposition Visit Type Claims/Billing

  • Total Charges/Balance
  • Total Adjustments
  • Payment (Patient/Insurance)
  • Insurance Company
  • Charge Code Description
  • Cost Threshold

Provider

  • Provider id (NPI, EIN etc)
  • Provider Specialty
  • Provider Type
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Medications

  • Sunrise Ordered
  • Sunrise Administered
  • Sunrise/Logician Prescribed

Procedures { Sunrise, Centricity, Medipac}

  • CPT
  • ICD-9/ICD-9 - CCS
  • HCPCS

Vaccinations

  • Ordered { Sunrise, Centricity }
  • Administered { Sunrise, Centricity }

Vital Signs

  • Temperature
  • Pulse
  • BP Systolic
  • BP Diastolic
  • BMI

Hospitalization

  • LOS {by value}
  • Admit Type
  • Emergency
  • Elective
  • Newborn
  • Trauma
  • Urgent

Available Data in EDW (cont.)

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Key Points:

  • 1. We update the data twice monthly
  • 2. There is significant monthly growth of data in existing source

systems

  • 3. Different systems are the key sources of particular data types,

so as we bring new systems online, we better capture the types.

UAMS EDW Data

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The UAMS Journey – Next Steps

  • Data Governance Council – Spring, 2013
  • Enterprise Epic Implementation – Complete by March, 2014
  • Enterprise Implementation of SAP Business Objects – In process
  • Molecular biology (genomics) lab implementation – In process
  • Other external data sources to add to EDW – In process

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Artificial Intelligence in Medicine

  • Developing a search engine that will scan

thousands of medical records to turn up documents related to patient queries.

  • Learn based on how it is used
  • “We are not contemplating ― unless this

were an unbelievably fantastic success ― letting a machine practice medicine.” http://www.health2news.com/2012/02/10/the

  • national-library-of-medicine-explores-a-i/

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IBM Watson

  • Medical records, texts, journals and research documents are all

written in natural language – a language that computers traditionally struggle to understand. A system that instantly delivers a single, precise answer from these documents could transform the healthcare industry.

  • “This is no longer a game”

http://tinyurl.com/3b8y8os

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Q&A

Click on the Q&A panel located in the lower right corner of your screen, type in your questions in the text field and hit send. Please keep the send to default as “All Panelists.”

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