Developing Clinical and
Translational Informatics
Capabilities for Kansas University
Medical Center
Russ Waitman, PhD
Associate Professor, Director Medical Informatics
Department of Biostatistics
September 29, 2010
Outline
 What is Biomedical Informatics?
 What are the Clinical Translational Science

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Awards?
Biomedical Informatics Section Specific
Aims
 Data Management Observations
Timeline
i2b2 demo
Questions
Background: Charles Friedman
 The Fundamental Theorem of Biomedical
Informatics:
 A person working with an information resource is
better than that same person unassisted.
 NOT!!
Charles P. Friedman: http://www.jamia.org/cgi/reprint/16/2/169.pdf
Background: William Stead
The Individual Expert
Clinician
Synthesis &
Decision
Evidence
Patient
Record
William Stead: http://courses.mbl.edu/mi/2009/presentations_fall/SteadV1.ppt
The demise of expert-based
practice is inevitable
Facts per Decision
1000
Proteomics and other
effector molecules
100
Functional Genetics:
Gene expression
profiles
10
Structural Genetics:
e.g. SNPs, haplotypes
Human Cognitive
Capacity 5
Decisions by Clinical
Phenotype
1990
2000
2010
2020
William Stead: http://courses.mbl.edu/mi/2009/presentations_fall/SteadV1.ppt
Background: Edward Shortliffe
Biomedical Informatics Applications
Basic Research
Applied Research
Biomedical Informatics Methods,
Techniques, and Theories
Bioinformatics
Imaging
Clinical
Informatics Informatics
Molecular and
Tissues and
Cellular
Organs
Processes
Individuals
(Patients)
Public Health
Informatics
Populations
And Society
Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt
Background: Edward Shortliffe
Biomedical Informatics Research Areas
Biomedical
Knowledge
Machine learning
Text interpretation
Knowledge engineering
Knowledge
Acquisition
Knowledge
Base
Model
Development
Information
Retrieval
Diagnosis
Biomedical
Data
Biomedical
Research
Planning &
Data Analysis
Inferencing
System
Treatment
Planning
Data
Acquisition
Real-time acquisition
Imaging
Speech/language/text
Specialized input devices
Data
Base
Human
Interface
Teaching
Image
Generation
Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt
Clinical and Translational Science Awards
A NIH Roadmap Initiative
“It is the responsibility of those of us
involved in today’s biomedical research
enterprise to translate the remarkable
scientific innovations we are witnessing
into health gains for the nation.”
Background: Dan Masys
NIH Goal to Reduce Barriers to Research
• Administrative bottlenecks
• Poor integration of translational resources
• Delay in the completion of clinical studies
• Difficulties in human subject recruitment
• Little investment in methodologic research
• Insufficient bi-directional information flow
• Increasingly complex resources needed
• Inadequate models of human disease
• Reduced financial margins
• Difficulty recruiting, training, mentoring scientists
CTSA Objectives:
The purpose of this initiative is to assist institutions to forge a
uniquely transformative, novel, and integrative academic home
for Clinical and Translational Science that has the consolidated
resources to:
1) captivate, advance, and nurture a cadre of well-trained multiand inter-disciplinary investigators and research teams;
2) create an incubator for innovative research tools and
information technologies; and
3) synergize multi-disciplinary and inter-disciplinary clinical and
translational research and researchers to catalyze the
application of new knowledge and techniques to clinical
practice at the front lines of patient care.
NIH CTSAs: Home for Clinical and
Translational Science
Clinical
Research
Ethics
Trial Design
NIH
Gap!
Biomedical
Informatics
Clinical
Resources
Biostatistics
Advanced
Degree-Granting
Programs
CTSA
HOME
Participant
& Community
Involvement
Regulatory
Support
Industry
Other
Institutions
Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt
Reengineering Clinical Research
Interdisciplinary
Research
Innovator Award
Bench
Building Blocks
and Pathways
Molecular Libraries
Bioinformatics
Computational
Biology
Nanomedicine
Public-Private Partnerships (IAMI)
Bedside
Translational
Research
Initiatives
Practice
Integrated Research Networks
Clinical Research Informatics
NIH Clinical Research Associates
Clinical outcomes
Harmonization
Training
Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt
Building a Plan: Environmental
Comparison Vanderbilt versus Kansas
 VUMC: unified leadership across hospitals, clinics,
academics
 Unified informatics: from network jack and server, to
library and bioinformatics cores
 Build/buy mix legacy -> complexity
 Large consolidated academic home for informatics
 Data sharing for research a non issue
 KU/KUMC, Rest of the world: not so homogenous
 What can one do with EPIC or Cerner + added
informatics?
 Validated solutions more likely to scale.
 Data sharing involves multiple organizations
KUMC Opportunities
 Quality Focused Hospital
 Without every solution involving informatics
 Prior CTSA goal: Data “Warehouse”
 Advance research and clinical quality
 “Green Field” for newer technologies
 State and Region
 KUMC strong in community outreach research
 Link our data to external information? (Ex: KHPA
Medicaid data)
 Health Information Exchange “window”
 KU-Lawrence Researchers, Cerner, Stowers Institute
Existing Teams
 Clinical Research & Medical Informatics
 CRIS: Comprehensive Research Information System Team
 New Medical Informatics plus KUMC Information Resources
critical contributors for infrastructure
 Bioinformatics
 K-INBRE Bioinformatics Core
 Center for Bioinformatics and Engineering School
 Dr. Gerry Lushington
 Center for Health Informatics
 World Class Telemedicine
 Leading Health Information Exchange for the State
 Terminology, Training, Simulation Expertise
 Dr. Judith Warren
Clinical Research Information Systems
 KUMC has purchased Velos eResearch and calls it “CRIS”
 Define Studies, Assign Patients to Studies
 Design and Capture data on electronic Case Report Forms
(CRFs) – ideally in real time.
 Capture Adverse Events, Reports, Export Data for analysis.
 Options: Samples, Financials, Regulatory IRB
 Other Approaches
 OnCore by Forte Research Systems – more expensive,
highly customized for Cancer Centers…. Ferrari to Velos’
Audi.
 RedCap by Paul Harris at Vanderbilt University – “free but
not open source”, capabilities growing. Think Hyundai
CRIS Intro Screen
CRIS: sample e Case Report Form
CRIS: Document Adverse Events
KUMC CTSA Specific Aims
1. Provide a HICTR portal for investigators to access clinical and
translational research resources, track usage and outcomes, and
provide informatics consultative services.
2. Create a platform, HERON (Healthcare Enterprise Repository for
Ontological Narration), to integrate clinical and biomedical data for
translational research.
3. Advance medical innovation by linking biological tissues to clinical
phenotype and the pharmacokinetic and pharmacodynamic data
generated by research cores in phase I and II clinical trials (addressing
T1 translational research).
4. Leverage an active, engaged statewide telemedicine and Health
Information Exchange (HIE) effort to enable community based
translational research (addressing T2 translational research).
Aim #1: Create a Portal and Consult
 Bring together existing resources
 Translational Technologies Resource Center
 Link to national resources
 www.vivo.org – Facebook for researchers
 www.eagle-i.org – National resources (rodents, RNAi, to
patient registries)
 Develop tools to measure and track our investment
 Pilot funding requests, electronic Institutional Review Board
process
 Provide a hands on informatics consult service
 Also organize existing resources
Portal: Access + Measurement
Aim #2: Create a data “fishing” platform
 Develop business agreements,
policies, data use agreements and
oversight.
 Implement open source NIH funded
(i.e. i2b2) initiatives for accessing
data.
 Transform data into information
using the NLM UMLS Metathesaurus
as our vocabulary source.
 Link clinical data sources to enhance
their research utility.
Develop business agreements, policies,
data use agreements and oversight.
 September 6, 2010 the hospital, clinics and
university signed a master data sharing agreement
to create the repository. Four Uses:
 After signing a system access agreement, cohort identification
queries and view-only access is allowed but logged and audited
 Requests for de-identified patient data, while not deemed human
subjects research, are reviewed.
 Identified data requests require approval by the Institutional
Review Board prior to data request review. Medical informatics
will generate the data set for the investigator.
 Contact information from the HICTR Participant Registry have their
study request and contact letters reviewed by the Participant and
Clinical Interactions Resources Program
Constructing a Research Repository:
Ethical and Regulatory Concerns
 Who “owns” the data? Doctor, Clinic/Hospital, Insurer,
State, Researcher… perhaps the Patient?
 Perception/reality is often the organization that paid for the system
owns the data.
 My opinion: we are custodians of the data, each role has rights and
responsibilities
 Regulatory Sources:
 Health Insurance Portability and Accountability Act (HIPAA)
 Human Subjects Research
 Research depends on Trust which depends on Ethical
Behavior and Competence
 Goals: Protect Patient Privacy (preserve Anonymity),
 Growing Topic: Quanitifying Re-identification risk.
Re-identification Risk Example
Will the released columns in
combination with publicly
available data re-identify
individuals?
What if the released columns
were combined with other items
which “may be known”?
Sensitive columns, diagnoses or
very unique individuals?
New measures to quantify reidentification risk.
Reference: Benitez K, Malin B. Evaluating re-identification risks with respect to the
HIPAA privacy rule. J Am Med Inform Assoc. 2010 Mar-Apr;17(2):169-77.
Constructing a Repository: Understanding
Source Systems, Example CPOE
Most Clinical Systems focus on transaction processing for workflow automation
WizOrder
Client
Repackages and
Routes
Internal
Format
HL7
Generic
Interface
Engine (GIE)
HL7
WizOrder
Server
Temporary
Data queue
(TDQ)
Orderables,
Orderset DB
Pharmacy
System
SQL
document
Mainframe
DB2
Knowledge
Base, Files
SQL
Laboratory
System
SQL
Drug
DB
Print
SubSystem
SQL
SQL
SQL
Lab
DB
Rx
DB
Constructing a Repository: Understanding
Differing Data Models used by Systems
Hierarchical databases (MUMPS), still very common in
Clinical systems (VA VISTA, Epic, Meditech)
http://www.cs.pitt.edu/~chang/156/14hier.html
Murphy SN, Weber G, Mendis M, Gainer V, Chueh HC, Churchill S, Kohane I.
Serving the enterprise and beyond with informatics for integrating biology and
the bedside (i2b2). J Am Med Inform Assoc. 2010 Mar-Apr;17(2):124-30.
http://www.ibm.com/developerworks/library/x-matters8/index.html
Star Schemas: Data Warehouses
Relational databases (Oracle, Access), dominant in
business and clinical systems (Cerner, McKesson)
HERON: Repository Architecture
Workflow: System Access
Workflow & Oversight: Request Data
Implement NIH funded (i.e. i2b2)
initiatives for accessing data.
i2b2: Count Cohorts
i2b2: Patient Count in Lower Left
i2b2: Ask for Patient Sets
i2b2: Analyze Demographics Plugin
i2b2: Demographics Plugin Result
i2b2: View Timeline
i2b2: Timeline Results
Transform data into information using
standard vocabularies and ontologies
Source terminology
Completed planned
Notes
Demographics: i2b2
April 2010
Using i2b2 hierarchy. Restricted search criteria to geographic
regions (> 20,000 persons) instead of individual zipcodes
Diagnoses: ICD9
Procedures: CPT
April 2010
June 2010
Using i2b2 hierarchy
UMLS extract scripts developed with UTHSC at Houston
Lab terms: LOINC
Medication ontologies: NDF-RT
November 2010
December 2010
Nursing Observations
July 2010-
Pathology: SNOMED CT
February 2011
Plan to use i2b2 hierarchy
Physiologic effect, mechanism of action, pharmacokinetics, and
related diseases.
NDNQI pressure ulcers mapped to SNOMED CT to evaluate
automated extraction of self reported activity. (Drs. Dunton and
Warren.)
Providing coded pathology results and patient diagnosis is a
critical objective for defining cancer study cohorts in Aim 3.
Clinical narrative
2012
National Center for Biological
Ontology
2013
As hospital restructures clinical narrative documentation to use
EPIC’s SmartData (CUI) concepts, will determine appropriate
standard.
In support of Aim 3 focus on bridging clinical and bioinformatics
to advance novel methods.
Link clinical data sources to enhance
their research utility.
Data source
Source
System
EPIC
System
“Go-Live”
Date
2006
Data extraction
completed
planned
September 2010
Inpatient/Emergency demographics, “ADT”
locations & services
Inpatient diagnoses (DRG, ICD9)
EPIC
1990
September 2010
Outpatient visits services, diagnoses,
procedures (ICD, CPT)
Laboratory Results (inpatient/outpatient)
IDX
2002
November 2010
EPIC
2007
November 2010
Electronic Medication Administration
Inpatient inputs, outputs and discrete nursing
observations
Clinical Research Information System
EPIC
EPIC
2007
2007
December 2010
2011
CRIS
2007
2011
Provider Order Entry
Problem List and Provider Notes
Microbiology, Cardiology, Radiology
2010
2009
2006
2011
2011
2011
Medication reconciliation
Perioperative schedule and indicators
EPIC
EPIC
EPIC/Misys
Theradoc
EPIC
ORSOS
2007
2005
2011
2012
Social Security Death Indicator
Medicaid databases
SSDI
KHPA
Na
2005
2012
2012
Aim #3: Link biological tissues to clinical
phenotype and our research cores’ results
 Support Cancer Center, IAMI, and bridge to Lawrence Research
 First focus: Incorporate clinical pathology and biological tissue
repositories with HERON and CRIS to improve cohort
identification, clinical trial accrual, and improved clinical trial
characterization
 Aligned with existing enterprise objectives to improve
biological tissue repository information systems
 Clinical trial accrual identified by many as a weak point
institutionally
 Target both biological research specimens and routine
clinical pathology
Aim #3: Link biological tissues to clinical
phenotype and our research cores’ results
 Second focus: Support IAMI clinical trials by integrating and
standardizing information between bioinformatics and
pharmacokinetic/pharmacodynamic (PK/PD) program and the
Comprehensive Research Information System (CRIS)
 Clinical research findings, clinical records, and research
laboratory results are not integrated.
 PK/PD should be the most common research analysis for
phase 1 trials.
 Third focus: Apply molecular bioinformatics methods to
enhance T1 translational research in areas such as molecular
biomarker discovery efforts and pharmaceutical lead
optimization
 Also promote clinical domains for Lawrence researchers
Aim #4: Leverage telemedicine and Health
Information Exchange (HIE) for community
based translational research
 The HITECH Act and “meaningful use” are a landmark event for
Biomedical Informatics and Health Information Technology
 State Health Information Exchanges
 Regional Extension Centers
 Incentives (then penalties) for Providers
 Provide health informatics leadership to ensure state and
regional healthcare information exchange (HIE) and health
information technology initiatives foster translational research
 Dr. Connors chaired the formation Kansas Health
Information Exchange
 Drs. Greiner and Waitman also participated in KHPA and
Regional Extension Center Activities
 Engage so research has a place at the table
Unique Combination of Telemedicine for
Community Research and CRIS
Title
PI
Grant/Agency
Describing and Measuring
Tobacco Treatment in Drug
Treatment
Telemedicine for Smoking
Cessation in Rural Primary Care
Using CBPR to Implement
Smoking Cessation in an Urban
American Indian Community
K. Richter
R21DA020489, National Institute on
Drug Abuse
K. Richter
R01HL087643, NIH National Heart,
Lung and Blood Institute
R24MD002773, National Center on
Minority Health and Health Disparities
No
Centralized Disease Management
for Rural Hospitalized Smokers
Pediatric epilepsy prevalence
study
E. Ellerbeck
R01CA101963, National Cancer
Institute
RTOI # 2008-01-01 AUCD, National
Center for Birth Defects and
Developmental Disabilities, CDC
Yes
Kansas Comprehensive Telehealth
Services for Older Adults
E-L Nelsen, L.
Redford
Health Resources and Services
Administration Office for the
Advancement of Telehealth
No
C. Daley
D. Lindeman
Promote capability for subject engagement and
facilitate collaboration via tele-research meetings
CRIS
used?
Yes
Yes
No
“Wire” clinical information systems to provide a
laboratory for translational informatics research
EPIC Rollout
 Disseminate translational research findings and evidence in the
clinical workflow
 The “last” mile: measure the translation’s adoption
Engage with community providers adopting
EHRs as a platform for translational research.
 Alignment with State Medicaid Goals
 KHPA primary goal for the State Medicaid HIT Plan (SMHP):
implementing a medical home for all Medicaid recipients
 Unique compared with other states: current incentives in
HITECH may promote information disparities
 Partner with Regional Extension Center
 Their mission is to be the consultants on the ground
helping providers adopt and use systems
 The Kansas Physicians Engaged in Practice Research (KPEPR)
Network
 pilot connections between rural clinical systems and
HERON to evaluate clinical research in rural settings
Collaborate with the Personalized Medicine
and Outcomes Center (PMOC)
 Enhance data maintained by the state, national registries, and
regional collaborators
 State Medicaid, State Employees, Social Security Death
Indexes, Educational databases, Nursing Quality Indicators
 Will likely involved distributed methods of data integration
as opposed to explicit management of all datasets.
 Collaborate with the PMOC to provide complex risk models for
decision support to other clinical specialties and settings
 Current focus is customized informed consent forms for
cardiology procedures (bare metal versus drug eluting
stent)
 Target integration with EPIC in latter years of CTSA grant.
Timeline: Existing Projects, Aims 1 and 2
Timeline: Aims 3 and 4, Team Growth
Questions and i2b2 demo