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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
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
William Stead: http://courses.mbl.edu/mi/2009/presentations_fall/SteadV1.ppt
Evidence
Patient Record
Synthesis & Decision
Clinician
Fact
s pe
r Dec
isio
n
1000
10
100
5 Human Cognitive
Capacity
The demise of expert-based practice is inevitable
2000 2010 1990 2020
Structural Genetics: e.g. SNPs, haplotypes
Functional Genetics: Gene expression
profiles
Proteomics and other effector molecules
Decisions by Clinical Phenotype
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
Imaging Informatics
Clinical Informatics Bioinformatics Public Health
Informatics
Molecular and Cellular Processes
Tissues and Organs
Individuals (Patients)
Populations And Society
Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt
Background: Edward Shortliffe Biomedical Informatics Research Areas
Edward Shortliffe: http://www.dentalinformatics.com/conference/conference presentations/shortliffe.ppt
Biomedical Knowledge
Biomedical Data
Knowledge Base
Inferencing System
Data Base
Data Acquisition
Biomedical Research Planning & Data Analysis
Knowledge Acquisition
Teaching Human Interface
Treatment Planning
Diagnosis Information Retrieval
Model Development
Image Generation
Real-time acquisition Imaging Speech/language/text Specialized input devices
Machine learning Text interpretation Knowledge engineering
“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.”
Clinical and Translational Science Awards A NIH Roadmap Initiative
• 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
Background: Dan Masys NIH Goal to Reduce Barriers to Research
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 multi-
and 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
Trial Design
Advanced Degree-Granting
Programs
Participant & Community Involvement
Regulatory Support
Biostatistics
Clinical Resources
Biomedical Informatics
Clinical Research
Ethics
CTSA HOME
NIH
Other Institutions
Industry
Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt
Gap!
Bench Bedside Practice
Building Blocks and Pathways Molecular Libraries Bioinformatics Computational Biology Nanomedicine
Translational Research Initiatives
Integrated Research Networks Clinical Research Informatics NIH Clinical Research Associates Clinical outcomes Harmonization Training
Interdisciplinary Research Innovator Award Public-Private Partnerships (IAMI)
Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt
Reengineering Clinical Research
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 re-identification 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
Generic Interface
Engine (GIE)
Laboratory System
Pharmacy System
WizOrder Server
WizOrder Client
Mainframe DB2
Rx DB
HL7 Lab DB
Temporary Data queue (TDQ)
Internal Format
HL7
SQL
SQL
SQL
Repackages and Routes
Print SubSystem
document
Knowledge Base, Files
SQL Orderables, Orderset DB
Drug DB
SQL
SQL
Most Clinical Systems focus on transaction processing for workflow automation
Constructing a Repository: Understanding Differing Data Models used by Systems
http://www.cs.pitt.edu/~chang/156/14hier.html
http://www.ibm.com/developerworks/library/x-matters8/index.html Star Schemas: Data Warehouses
Hierarchical databases (MUMPS), still very common in Clinical systems (VA VISTA, Epic, Meditech)
Relational databases (Oracle, Access), dominant in business and clinical systems (Cerner, McKesson)
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.
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 April 2010 Using i2b2 hierarchy Procedures: CPT June 2010 UMLS extract scripts developed with UTHSC at Houston
Lab terms: LOINC November 2010 Plan to use i2b2 hierarchy Medication ontologies: NDF-RT December 2010 Physiologic effect, mechanism of action, pharmacokinetics, and
related diseases. Nursing Observations July 2010- NDNQI pressure ulcers mapped to SNOMED CT to evaluate
automated extraction of self reported activity. (Drs. Dunton and Warren.)
Pathology: SNOMED CT February 2011 Providing coded pathology results and patient diagnosis is a critical objective for defining cancer study cohorts in Aim 3.
Clinical narrative 2012 As hospital restructures clinical narrative documentation to use EPIC’s SmartData (CUI) concepts, will determine appropriate standard.
National Center for Biological Ontology
2013 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
System “Go-Live” Date
Data extraction completed planned
Inpatient/Emergency demographics, “ADT” locations & services
EPIC 2006 September 2010
Inpatient diagnoses (DRG, ICD9) EPIC 1990 September 2010
Outpatient visits services, diagnoses, procedures (ICD, CPT)
IDX 2002 November 2010
Laboratory Results (inpatient/outpatient) EPIC 2007 November 2010
Electronic Medication Administration EPIC 2007 December 2010 Inpatient inputs, outputs and discrete nursing observations
EPIC 2007 2011
Clinical Research Information System CRIS 2007 2011
Provider Order Entry EPIC 2010 2011 Problem List and Provider Notes EPIC 2009 2011 Microbiology, Cardiology, Radiology EPIC/Misys
Theradoc 2006 2011
Medication reconciliation EPIC 2007 2011 Perioperative schedule and indicators ORSOS 2005 2012
Social Security Death Indicator SSDI Na 2012 Medicaid databases KHPA 2005 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 CRIS
used? Describing and Measuring Tobacco Treatment in Drug Treatment
K. Richter R21DA020489, National Institute on Drug Abuse
Yes
Telemedicine for Smoking Cessation in Rural Primary Care
K. Richter R01HL087643, NIH National Heart, Lung and Blood Institute
No
Using CBPR to Implement Smoking Cessation in an Urban American Indian Community
C. Daley R24MD002773, National Center on Minority Health and Health Disparities
Yes
Centralized Disease Management for Rural Hospitalized Smokers
E. Ellerbeck R01CA101963, National Cancer Institute
Yes
Pediatric epilepsy prevalence study
D. Lindeman RTOI # 2008-01-01 AUCD, National Center for Birth Defects and Developmental Disabilities, CDC
No
Kansas Comprehensive Telehealth Services for Older Adults
E-L Nelsen, L. Redford
Health Resources and Services Administration Office for the Advancement of Telehealth
No
Promote capability for subject engagement and facilitate collaboration via tele-research meetings
“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
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