1. ImprovingAmerican Healthcare Productivity by Building a Cost Effective Health Care System Through a National Learning Health System THE LARGE DATA DEMONSTRATION PROJECT Stephen A. Weitzman, J.D., LL.M DataPharm (MedDATA) Foundation

2. "For an idea that does not at first seem insane, there is no hope." Albert Einstein 4/21/2012 LD DEMONSTRATION PROJECT 2

3. THE CHALLENGE Cooperation and Leadership 4/21/2012 LD DEMONSTRATION PROJECT 3

4. THE PURPOSE OF THE LD DEMO IS TO CREATE A TIMELY, WORKABLE LHS NETWORK Test an architecture for an LHS national data network design Concurrently address governance and other issues Demonstrate improvements in care to substantiate to patients the importance of the ultimate system for them and mobilize them Validate temporal and cost efficiencies of the network system 4/21/2012 LD DEMONSTRATION PROJECT 4

5. Credits This presentation is based upon the work and inspiration of: Charles P. Friedman, PhD, Joseph H. Kanter Family Foundation Learning Health Summit, May 2012 and CDISC International Interchange Keynote: A Learning Health System November 7, 2011 BTRIS, James J. Cimino, Chief, Laboratory for Informatics Development, NIH IBM Watson: From Jeopardy to Healthcare, David Gondek, PhD, Technical Lead, Watson Healthcare Adaptation, AMIA, October 2011 FasterCures, Health IT: Think Research, 2005; Still Thinking Research, 2011 (Adam Clark) Valuing Health Care, Ewing Marion Kauffman Foundation, Task Force Report, April 2012 The Strategic Health IT Advanced Research Projects (SHARP) Program, Office of the National Coordinator on Healthcare Dr. Eliot Siegel, University of Maryland School of Medicine; Dr. Watson A Promising Student in Pursuit of Smarter Medicine, NLM Briefing, 2011: Educating Dr. Watson To Usher in a New Era of Intelligent, Vigilant, and Personalized Medicine, IBM Toronto 2011, IBM 100th Anniversary Keynote. A Consensus Action Agenda for Achieving the National Health Information Infrastructure, J Am Med Inform Assoc. 2004 JulAug; 11(4): 332-338 The Reports of the Institute of Medicine on Learning Health. The 40 years of work of Dr. Donald Lindberg and Betsy Humphreys, and their fellows and staff at the (NLM) that created and maintain the UMLS, without which we could not move forward. AND SO MANY OTHERS 4/21/2012 LD DEMONSTRATION PROJECT 5

6. A LEARNING HEALTH SYSTEM A Learning Health System (LHS) is an "ultra large scale" system that can serve the entire nation to promote individual and population health that can mine and analyze electronic medical records to: track patient treatment over time across institutions; compare (CER) treatments; facilitate decision support systems; choose best outcomes for individual patients (Personalized Medicine); help identify potential research subjects (by characteristic); spot and track public health emergencies; and better monitor drug safety (Surveillance Post Market of New Drugs). one in which progress in science, informatics, and care culture align to generate new knowledge as an ongoing, natural by-product of the care experience, and seamlessly refine and deliver best practices for continuous improvement in health and health care. (Institute of Medicine)[Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches - Workshop 4/21/2012 LD DEMONSTRATION PROJECT 6

7. A Nationwide Data Resource 4/21/2012 LD DEMONSTRATION PROJECT 7

8. THE NEED FOR A LEARNING HEALTH SYSTEM The U.S. Spends 1.5-1.7x other industrialized countries U.S. is 37th in Outcomes $2.5 Trillion Spent annually o Of which CMS spends $900 bil. o $100 bil. To VA, DOD, Indian Health Services, and Federal Employee Health Benefit Plans o $1.0 Trillion of Federal Dollars spent on health goods and services $700 bil. Was not necessary A very big HIT: Americas health industry is preparing for a $30 billion splurge on information technology, The Economist, Nov 22nd 2010 Government Launches to providers and patients, they would have strong incentives to join the data revolution. Kauffman Task Force Report, Page 21. Research Initiative To Harness 'Big Data : March 29, 2012http://www.ihealthbeat.org/articles/2012/3/29/government-launches-research-initiative-to-harness- big-data.aspx#ixzz1smgkQjA6 Potential payoffs o The McKinsey Global Institute estimates that mobilizing health care information could yield more than $300 billion a year in additional value, or almost $1,000 a year for every person in the United States. o Of these sums, at least two-thirds would take the form of reduced national spending on health care. o If even a fraction of that unlocked value could be returned 4/21/2012 LD DEMONSTRATION PROJECT 8

9. BENEFITS OF A LEARNING HEALTH SYSTEM The U.S. Healthcare System generates immeasurable amounts of Information Information Systems Lead to Transparency Businesses use information to identify waste and identify best practice We have used Claims information to do some analysis of treatments When clinical information is digitized in the form of an electronic health record (EHR) it becomes more valuable because it can be compared, searched, and queried in ways that can benefit the patient, other patients with the same disease or disorder, and the research enterprise, which is aiming to develop better diagnostics and therapies. (Adam Clark..) 4/21/2012 LD DEMONSTRATION PROJECT 9

10. OBSTACLES TO LEARNING HEALTH SYSTEMS Data is not owned by the patient o Patient has access rights and not ownership o Creator of the medical record is the owner o Owners have assigned those rights to HMOs and to their EHR system providers o Data is disbursed o Data is not in standardized format Economic Disincentives to Productivity and Cost Reduction o Insurers Spend Less Make Less o Patients Employer System Until Now No Burden on Patients if they consume more But now that Employee share of cost is rising o IF Patients help reduce Cost they get nothing for it Clinical data and Medical Records data are still fragmented Lack of Cooperation or Incentives to Cooperate Senator DASCHLE: I think that one of the things that we have faced all through our health sector is too much siloing and stove piping and not enough coordination. And it seems to me that one of the things we need to do at all levels, state level, the federal level and certainly in the private sector is to encourage more coordination and information sharing. Bipartisan Policy Center - Forum on Health IT Jan. 27, 2012. 4/21/2012 LD DEMONSTRATION PROJECT 10

11. DR. CHARLES FRIEDMAN I was invited to a meeting about building a learning healthcare system for cancer, and was asked to speak about how the ONC's activities are going to create a learning healthcare system. So as more and more data, maybe even information, is available in EHR systems, what are we doing to make that data useful for research? After a few hours of working on my speech it hit me we aren't; ONC is going to fall short of that goal. So I changed my way of thinking. Everybody is focused right now on getting eligible professionals and hospitals to the state of meaningful use and many can't fathom dealing with other uses of the data on top of that, at least not yet. But my answer is we can't afford not to do this. We are so sub-optimizing and failing to take full advantage of our investment. DR. CHARLES FRIEDMAN, OFFICE OF THE NATIONAL COORDINATOR FOR HEALTH INFORMATION TECHNOLOGY, HHS (JUNE 2010) 4/21/2012 LD DEMONSTRATION PROJECT 11

12. HEALTH SECTOR DEMANDS FOR INFOMATICS RELATIMG TO A LEARNING HEALTH SYSTEM 4/21/2012 LD DEMONSTRATION PROJECT 12 Source: PwC Analysis PwC Health Resource Institute 8

13. 2005 OBSERVATIONS OF FASTERCURES In 2005, FasterCures urged health systems to think research when developing or implementing EHR systems so as not to foreclose a golden opportunity to connect clinical data with research needs. At the time, FasterCures saw the opportunity for EHRs to not only provide a link between genes and disease, but also to: Monitor the health of the populations and detect emerging health problems. Identify populations at risk of disease, or those who might benefit most from therapies. Assess the usefulness of diagnostic tests and screening programs. Form hypotheses about disease initiation and progression. Conduct post-marketing surveillance studies of new drugs to identify adverse events, improve prescribing practices, or make labeling more accurate and complete. Identify potential study participants for clinical research. 4/21/2012 LD DEMONSTRATION PROJECT 13

14. 2005 FASTERCURES RECOMMENDATIONS Aggregate and Integrating practice databases for data mining. Developing more sophisticated abstraction and encryption systems to protect privacy. Developing database connection tools. Creating translational systems. Formulating online informed consent procedures. Evolving data mining and pattern recognition systems. Developing interactive patient query programs. Creating patient databases/warehouses/registries. Creating directories of clinical databases. 4/21/2012 LD DEMONSTRATION PROJECT 14

15. 2011 FINDINGS STILL THINKING Vendors of new EHR systems are not building research capacity into the architecture. The clinical research community is not actively involved in or does not have incentives to push for research-friendly EHR systems. Standards and universal exchange systems still challenge the actual transfer and translation of research-relevant data. Existing EHR systems are not being leveraged to screen, match, and enroll patients in clinical trials. The patient community is not fully engaged in or aware of the need to share their clinical data to advance research. Clinical trial screening and matching should be included as a measure for Meaningful Use of electronic health record systems. The National Institutes of Health (NIH) should articulate a strategy that will align its programs with the recommendations of the Office of the National Coordinator (ONC) Federal Health IT Strategic Plan. 4/21/2012 LD DEMONSTRATION PROJECT 15

16. 2011 RECOMMENDATIONS OF FASTERCURES 4/21/2012 LD DEMONSTRATION PROJECT 16

17. RECOMMENDATIONS OF THE KAUFFMAN FOUNDATION TASK FORCE (2012) Harnessing information: how systematically gathering and sharing data can unlock knowledge that produces systematically better choices. The key here is to incentivize a new corps of data entrepreneurs to collect and analyze existing medical data ... Valuing Healthcare: Improving productivity and Quality, Kauffman Foundation Task Force on Cost Effective Health Care Information, April 19, 2012 4/21/2012 LD DEMONSTRATION PROJECT 17

18. RECOMMENDATIONS (Continued) Allow patients and research subjects in studies to give their consent for their health data to be included in large research databases. The government should permit patients the right to let whomever they choose access their medical records efficiently and easily. The Department of Health and Human Services could provide regulatory assurance that there will be no punitive action against experimental pilot projects to pool health data. If HHS does not believe it has this authority, it should request it from Congress. The thousands of nonprofit organizations actively involved in studying diseases should partner to build a national health database. Employers should include as part of health benefits packages information on how employees can contribute their health data. The National Institutes of Health could more strictly enforce existing rules and otherwise require that federally funded data be shared, and that all grants require data-sharing plans. Follow-on NIH funding could be conditioned on data making it to the public domain and being re- used. 4/21/2012 LD DEMONSTRATION PROJECT 18

19. U.S. TECHNOLOGY RESOURCES In a century of staggeringly rapid improvements in medical knowledge and technology throughout the West and Asia, the United States towers over others. The United States funds (publicly and privately) more than $60 billion per year in medical research. We have, due to the work foster by ONC and the SHARP project demonstrated the ability you move data within a number of Health Information Exchange Networks.) [Infrastructure]. (Maryland CRISP, CCC, and Others) While we build a greater Electronic Medical Records capability by incentivizing Practioners and Hospitals that do not have electronic patient medical records systems we have shining examples of such systems in place. See Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care, IOM, 2011 4/21/2012 LD DEMONSTRATION PROJECT 19

20. GREAT EXAMPLES OF SYSTEMS IN 2011 MOSTLY IN SILOS The Strategic Health IT Advanced Research Program (SHARP) SHARP supports research projects on breakthrough advances in health IT that foster adoption, including security, patient support, healthcare applications, and network design, and secondary use of EHR data. The Mayo SHARP project. THE HEALTH MAINTENANCE ORGANIZATION RESEARCH NETWORK (HMORN) Federation Model THE eMERGE (ELECTRONIC MEDICAL RECORDS AND GENOMICS) NETWORK i2b2, based out of the Partners HealthCare System in Boston Researchers at the University of Utah are testing capabilities of i2b2 as an open source tool for bench-to-bed-side research conducted outside the Partners HealthCare network. Geisinger Health System, Kaiser Permanente, Columbia Presbyterian, and the Mayo Clinic are the grandparents of health IT and its use in clinical and research practice. THE PARTNERSHIP TO ADVANCE CLINICAL ELECTRONIC RESEARCH (PACeR) (New York) DR. SUSAN LOVE RESEARCH FOUNDATION - Army of Women RPDR AT PARTNERS HEALTHCARE is a centralized clinical data registry that gathers data from various hospital legacy systems and stores it in one place. NIH'S BIOMEDICAL TRANSLATIONAL RESEARCH INFORMATION SYSTEM (BTRIS) BTRIS is a repository developed from a complex network of information systems supporting clinical care and research data collection from NIH-sponsored clinical trials conducted by the NIH Clinical Center and the agency's intramural research program. Moffit, Sloan Kettering, Anderson . Kaiser Permanente, Mayo Clinic, Geisinger Health System, Intermountain Health, and Group Health Collaborative form new consortium to share patient e-health records on-demand and serve as a national model for data interoperability. AND THERE ARE MORE EXAMPLES. 4/21/2012 LD DEMONSTRATION PROJECT 20

21. PROGRESS IN EUROPE Will U.S. be left behind? The GPRD Soon to be the CPRD oFrom 5 million to 55 million patients in a database with a new $100 mil. Investment by the NHS (UK) The EHR4cr project to unify 7 European Countries funded by the EU Commission and the International Pharmaceutical Association with an initial $20.0 mil. 4/21/2012 LD DEMONSTRATION PROJECT 21

22. SO WHY NOT NOW? We have to take the opportunity that comes in front of us. There are now 20-30 million people whose care is delivered by an HMO that already has EMR so could we build an infrastructure to help look at the health delivery process to see what works, and have all these people available to answer these questions with quick turnaround? Francis S. Collins, MD, PhD, NIH, Forum Research America National Forum, March 14, 2011 So why hasnt all this been done? There is no shortage of raw information in the health care system. But it is locked in medical offices and hospitals across the country, and in the files of pharmaceutical companies who guard the results of their failed clinical trials. Kauffman Page 19 4/21/2012 LD DEMONSTRATION PROJECT 22

23. MOVING FORWARD FIRST STEPS Despite centuries of clinical research, data are still fragmented Sophisticated terminology is the key to reuse of disparate data Policy issues are bigger than technical issues Take on technical issues as a first step to demonstrate capability of solving individual patient problems 4/21/2012 LD DEMONSTRATION PROJECT 23

24. OBJECTIVES OF THE LARGE DATA DEMONSTRATION PROJECT The demonstration project (pilot) can show the benefits of data sharing can begin to flow before the whole health care system is networked without sweeping reforms or full implementation of eHR systems ten years away. It will show benefits to patients and thereby incentivize consumers to push for adoption of eHR systems by all practitioners from the bottom up and not from the top down. To use Existing Resources of Medical Information o Existing Electronic Medical Records Reduce and circumvent institutional obstacles. Share Data and Cooperate in a Model to Demonstrate Feasibility: I think that one of the things that we have faced all through our health sector is too much siloing and stove piping and not enough coordination. And it seems to me that one of the things we need to do at all levels, state level, the federal level and certainly in the private sector is to encourage more coordination and information sharing. Senator Tom Daschle - Bipartisan Policy Center - Forum on Health IT Jan. 27, 2012. Use the best available technology. Piece together the work of the last ten years in Health IT to build a solution. 4/21/2012 LD DEMONSTRATION PROJECT 24

25. A SOLUTION TO THE DATA HOARDING PROBLEM Centralized DATA Warehouse Or Distributed in Silos Answer: Both The Hybrid: DATA Remains in the Silos The Indexes (Inverted Files) Are Centralized 4/21/2012 LD DEMONSTRATION PROJECT 25

26. BASIC ATTRIBUTES OF THE LARGE DATA DEMONSTRATION PROJECT Common Front End Established Gatekeeper Requirements (Proprietary) Security Privacy Common Indexing in the Silos Use Unstructured Information Management Architecture and Semantic Search.. Compile Indexes (Inverted Files Into a Central Index) Can extract individual data sets from Silos for analysis into a Cloud Can do cross-patient queries for additional analysis Can view and visualize longitudinal medical records 4/21/2012 LD DEMONSTRATION PROJECT 26

27. BASIC ATTRIBUTES OF THE LARGE DATA DEMONSTRATION PROJECT BTRIS MODEL OF NIH o Biomedical data- Clinical Study Medical Records o Research data collected using clinical information systems o Clinical data collected using clinical data integration information systems o Research data from research information systems o Reuses of data to support translational research Attributes of BTRIS for Our Model o Common Front End User access and user interface o Terminology based queries o User requirements o Established Gatekeeper Requirements (Proprietary) Access Policies Access Policies o Policy Working Group o Security o Privacy 4/21/2012 LD DEMONSTRATION PROJECT 27

28. BASIC ATTRIBUTES OF THE LARGE DATA DEMONSTRATION PROJECT (Continued) Aggregate disparate data sets Silos Prioritize data sources based upon compliance with relational database standards Common Re-Indexing of Silos Use NLM UMLS and standard source terminologies (SNOMED-CT,LOINC, RxNorm) as implemented by IBM Watson Systems Apply the Watson NLP to a mirror of the data in the Silos Data Remains in Silos along with the New Index (Inverted File) Inverted Files are Compiled and located at a Central Index Hub All Queries of the complete DATA-Set are directed to the Central Index Hub

29. PROJECT DEFINITIONS Data Original health or medical records either flat file (unstructured text) or a structured data record including fields of unstructured text Index - A relational database inverted file of all terms and phrases with pointers to the data source for that terms or phrase Silo Individual Institution or Practioners data warehouse User Person that queries the system or their bot User Profile Includes names of systems to which the user has access to the raw data under its data use agreement with each Silo 4/21/2012 LD DEMONSTRATION PROJECT 29

30. ASSETS FOR THE DEMO PROJECT We have good systems out there to harness for a demonstration project using available technical capabilities A sister of the Worlds tenth fastest Computer (IBM) Low cost data storage capabilities Data Warehouse Models Standardized Indexing Capabilities thanks to the NLM UMLS and standard source terminologies (SNOMED-CT,LOINC, RxNorm) A high speed National Internet 2 ??? Lots of data that meets HL7 etc standards that can be shared 4/21/2012 LD DEMONSTRATION PROJECT 30

31. THINGS WE MAY NEED For Purposes of the Demonstration Project and to Share Data We Need Some Governance Requirements We can live within HIPAA through a Federation Model and a de-identification process To achieve some level of uniformity we propose to re-index all data in the data silos through a single common indexing platform based upon UMLS and standard source terminologies (SNOMED-CT,LOINC, RxNorm) which will uniformly code data (Relational databases and text imbedded therein (semantic standardization). In the project we will develop a future business model to fund and/or compensate cooperating institutions for their data investment and overhead and a means to financial model to sustain a national LHS. 4/21/2012 LD DEMONSTRATION PROJECT 31

32. ONE POSSIBLE MODEL: BTRIS BTRIS Collects Data From All Over NIH 4/21/2012 LD DEMONSTRATION PROJECT 32

33. Clinical Data at NIH is Collected Into BTRIS For Use by NIH Authorized Researchers 4/21/2012 LD DEMONSTRATION PROJECT 33

34. WHAT IS IN BTRIS? 4/21/2012 LD DEMONSTRATION PROJECT 34

35. Biomedical Translational Research Information System (BTRIS) Database Data Standards (RED) Data Access SecurityPreferences

36. O n t o l o g y Data Acquisition Processes Coding Indexing De-Identifying Permission Setting BTRIS BTRIS HAS Data Repository Data Retrieval Functions Authorization Subject-Oriented Cross-Subject Re-Identification NLP Data Analysis Tools Subject Recruitment Hypothesis Generation Hypothesis Testing

37. BTRIS*: The NIH Biomedical Translational Research Information System as Model Clinical data repository to collect data from ancillary systems Has BTRIS Standards Has a Front End with A Gatekeeper System Has all Governance Requirements Some Aspects of Columbia Presbyterian Systems o Reorganized for use as Clinical Data Warehouse o Back end for clinical information systems (CIS, WebCIS, PatCIS, PalmCIS, QingCIS, MendonIS) Built by an NLM Ontology Fellow who participated in the development of the UMLS Coded with the Research Entities Dictionary Is available for replication *The clinical research data repository of the US National Institutes of Health. Cimino JJ, Ayres EJ. Stud Health Technol Inform. 2010;160(Pt 2):1299-303. http://people.dbmi.columbia.edu/cimino/Publications/2010%20-%20Medinfo%20- %20The%20Clinical%20Research%20Data%20Repository%20of%20the%20US%20National%20Institutes %20of%20Health.pdf 4/21/2012 LD DEMONSTRATION PROJECT 37

38. Advantage of Using BTRIS Model 1. A model compilation and integration of clinical and research data from multiple disparate sources 2. Understands the authorization issues related to reuse of patient clinical data 3. Understands the terminology issues related to the reuse of coded clinical and research data 4. Familiar with the approach being taken at the National Institutes of Health to collect, integrate, and code clinical and research data into a single repository, for authorized reuse in biomedical research. 4/21/2012 LD DEMONSTRATION PROJECT 38

39. BTRIS ATTRIBUTES Multiple Data sources Data model integration Research Entities Dictionary Access policies User requirements User access and user Interface Terminology-based queries 4/21/2012 LD DEMONSTRATION PROJECT 39 Data sources Data model integration Research Entities Dictionary Access policies User requirements User access and user Interface

40. Re-using Data in De-Identified Form Aggregate and standardize disparate and isolated data sets Automate and streamline processes that are traditionally manual and cumbersome Prioritize data sources and functionality based on needs of user community Pose hypothetical research questions Apply Analytical Tools And Create Reports Find unexpected correlations Determine potential subject profiles and sample sizes for Clinical Studies Find potential collaborators Need to extract individual data for analysis Need cross-patient queries for additional analysis Data may require transformation: o De-identification and Re-identification o Indexing o Aggregation by time o Abstraction by classification o Conversion to relevant concepts 4/21/2012 LD DEMONSTRATION PROJECT 40

41. THE PROPOSED LD DEMO This is a research & development project. It will have a structure similar to omop. It will have four components: 1. Technology Feasibility 2. Governance 3. Business Models 4. Public Policy Changes 4/21/2012 LD DEMONSTRATION PROJECT 41

42. 1. THE PROPOSED LD DEMO MODEL Technology Feasibility All Data Remains in Silos All Data in Silos are indexed by a single common set of coding systems based upon the UMLS and standard source terminologies (SNOMED-CT,LOINC, RxNorm) At the Silos the new indexes reside on dedicated hardware A duplicate of the indexes at the Silos is transmitted to the Central Index Repository The Data indexes at the Silos are updated 24/7 as new Data arrives The Central Index Repository is simultaneously updated 4/21/2012 LD DEMONSTRATION PROJECT 42

43. PATIENT DATA SOURCES INDEXED IN SILOS, UPDATED 24/7; DUPLICATE INDEX COMPILED AT NETWORK CENTER, UPDATED 24/7 Pharmaceutical Firms Clinical Research & Post Market Data Integrated Delivery System Community & Specialty Practice Health Maintenance Organization Health DATA Network Index Center State & Federal Medical Institutions that Provide Patient Care Pharmacy Lab Tests

44. QUERYING THE SYSTEM THROUGH THE DATA NETWORK CENTER 4/21/2012 LD DEMONSTRATION PROJECT 44

45. OPERATION OF THE SYSYTEM Authorized User Queries the System at the Health Data Network Center Point of Entry The Central Index is polled for all records related to the question A report is generated listing the Silos in which relevant data is located All relevant deidentified data is extracted to a cloud where records can be examined to determine relevancy In the cloud the data can analyzed and processed Reports can be generated The Query is logged with its results: both a list of Silos, relevant records, and reports generated 4/21/2012 LD DEMONSTRATION PROJECT 45

46. USING IBM SYSTEMS FOR INDEXING AND QUERYING Query: Authorized User Queries the System at the Health Data Network Center Point of Entry Through a Thesaurus Based Query Using Natural Language to ask the question the system will o Analyze the Question o Create the list of words and phrases based upon the UMLS UMLS and standard source terminologies (SNOMED-CT,LOINC, RxNorm) to pose the question to the system The Central Index is polled for all records related to the question o A report is generated listing the Silos in which relevant data is located o The system gathers the relevant records o All relevant deidentified data is extracted to a cloud where records can be examined to determine relevancy o Records of a single patient may be organized longitudinally In the cloud the data can analyzed and processed Reports can be generated The Query is logged with its results: both a list of Silos, relevant records, and reports generated 4/21/2012 LD DEMONSTRATION PROJECT 46

47. WHY WATSON TOOLS Semantic standardization using LOINC, SNOMED, rxnorm, etc. Watson tools to code questions and data will facilitate selection of data - patient medical records for comparison Watson tools generate alternative words, phrases, and codes for the query and for indexing data - (umia) enabling semantic analysis High speed parallel processor sysytems & software 4/21/2012 LD DEMONSTRATION PROJECT 47

48. 4/21/2012 LD DEMONSTRATION PROJECT 48

49. 4/21/2012 LD DEMONSTRATION PROJECT 49

50. HOW WATSON TOOLS WORK FOR ANALIZING DATA IN MEDICINE 4/21/2012 LD DEMONSTRATION PROJECT 50

51. YOU HAVE TO CONNECT THE DOTS but the dots are not cooperating (different expressions, meaning highly dependent on context) 4/21/2012 LD DEMONSTRATION PROJECT 51

52. NEJM Medical Concept Annotations 4/21/2012 Draft 52 Medications SymptomsDiseases Modifiers/nOPQRST Annotation using Metamap, DeepQA annotators

53. MAPPING FROM LANGUAGE TO MEDICAL CONCEPTS 4/21/2012 LD DEMONSTRATION PROJECT 53 [ C0020538 ] Hypertensive diseaseblood pressure was 140/100 mm Hg Systolic blood pressure 84 mm Hg [C0043352] Xerostomia fever dry mouth thirst [C0015967] Fever [C0039971] Thirst Mapping to Canonical Forms Fast heart rate [C0039231] Tachycardia (condition) [C2029900] Fast heart rate (symptom) diabetes [C0011849] Diabetes Mellitus [C0011860] Diabetes Mellitus, Non Insulin-Dependent [C0011847] Diabetes Disambiguation monomorphic wide- complex tachycardia ??? Representational Complexity decreased saliva Converting from measurements requires background knowledge of ranges, patient demographics etc. Heart rate of 240 bpm [C0039231] Tachycardia [C2029900] Fast heart rate(symptom) [ C0232105 ] Normal blood pressure

54. MAPPING ONTOLOGY 4/21/2012 LD DEMONSTRATION PROJECT 54

55. COLLECT (GATHER) RELEVANT RECORDS 4/21/2012 LD DEMONSTRATION PROJECT 55

56. EFFECTIVENESS RESEARCH VALUE PROPOSITION Challenges Medical Record Information is in Silos o Patient data in medical records is extensive and difficult and time consuming to extract information Standardization o Silos data in not uniformly structure (Some are and some not) Governance Issues o Privacy o Data access rules of Silos Business Model o Compensation for data use o Sustainability of system 4/21/2012 LD DEMONSTRATION PROJECT 56 Watsons Value In Retrieving Relevant Records For Analysis Common indexing of source data (medical record) Structuring and reasoning over natural language content to form the query Generating relevant records for analysis o Affording drill-down into each dimension to explore evidence

57. THREE OTHER AREAS TO BE EXPLORED IN THE STUDY 2. Governance 3. Business Models 4. Public Policy Changes 4/21/2012 LD DEMONSTRATION PROJECT 57

58. 2. GOVERNANCE Federation of some type Participation of Stakeholders Data Providers Data Use Agreements Access to System Publication of Reports (with data) De-Identification Patient Consent for Research Use of identifiable information for clinical study recruiting Standards Linking in other health related data (Birth, Death, Prescription and other databases to add more content) 4/21/2012 LD DEMONSTRATION PROJECT 58

59. 3. BUSINESS MODELS Compensation of Participants (Data Partners) o Payment for structuring data o Payment for use of data Sustainability of the Overall System o The National System Must Be Sustainable o PCORI Model for funding Incentives to overcome data hoarding 4/21/2012 LD DEMONSTRATION PROJECT 59

60. 4. PUBLIC POLICY CHANGES Laws and Regulations oPatient Rights In Medical Records oSharing of Publically Funded Data oPortable Consent Budget Priorities and Reallocation oInvest $1.0 bil. of the $30.0 bil. now in LHS 4/21/2012 LD DEMONSTRATION PROJECT 60

61. DELIVERABLES FIRST YEAR Budget Prioritization Recommendations for LHS Operational Model System In Place Business Case Options New Incentives Review and Recommendations for change in legislation and regulation State & Federal) 4/21/2012 LD DEMONSTRATION PROJECT 61

62. NEXT STEPS Adopt LHS Principles Use Non-Profit Framework for LD Project Convene Healthcare Funders & Stakeholders Prepare and Approve Major Grant Application Draft Set Up Organization Similar to OMOP with Stakeholder Groups Sign Up Supporters Sign Up Data Participants Sign Up Other Resource Contributors Apply for Major Grant Implement Grant Application Convene Stakeholder Groups to Develop Governance Procedures and Business Model for a Self Sustained System 4/21/2012 LD DEMONSTRATION PROJECT 62

63. WE ARE ALL LATE 4/21/2012 Walt Disney Productions 63LD DEMONSTRATION PROJECT