Big Data Planning a Course Toward Predictive Analytics

Transcription

1 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!

2 Housekeeping Moderator Anthony Guerra, editor-in-chief, healthsystemcio.com Ask A Question We will be holding a Q&A session after the formal presentations. You may submit your questions at any time by clicking on the QA 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. Download the Deck Go to: Shortened link below appears on all slides. View the Archive You will receive an when our archive recording is ready. Separate registration is required.

3 Agenda 45 Minutes (20 min) David Miller, Vice Chancellor/CIO, University of Arkansas for Medical Science (25 min) Q&A w/david Miller

4 Big Data Planning a Course Toward Predictive Analytics

5 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

6 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)

7 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)

8 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

9 New Paradigm for Clinical Information Processing Family History Whole Genome Clinical Data Patient Reported Monitoring Algorithms Clinical Decision Support

10 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: , 2/ 9/

11 Genetic Testing Today 11

15 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 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

16 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

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

18 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 of diverse sources. Health consumerism generating large amounts of unstructured data through consumers participation in social media.

19 Innovations Making Big Data Possible Increased use of electronic medical records (EMRs) and other 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.

20 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.

21 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

22 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

23 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 outcomes

24 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

25 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

26 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 organization Understand the impact of patient and provider misidentification for shared data

27 Big Data Roadmap External Information Sources Manage: Data Content Streaming Information Transactional & Collaborative Applications Integrate: Master Data Data Warehouse Analyze: Content Analytics Big Data Cubes Streams Business Analytics Applications Govern Quality Lifecycle Security/Privacy - Standards

28 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 28

29 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

30 Available Data in EDW (cont.) 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

31 UAMS EDW Data 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.

32 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 32

33 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. -national-library-of-medicine-explores-a-i/ 33

34 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 34

35 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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