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
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
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