Developing Clinical and Translational Informatics Capabilities for Kansas University Medical Center

Russ Waitman, PhDAssociate Professor, Director Medical Informatics

Department of BiostatisticsSeptember 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 SteadThe 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

5Human Cognitive

Capacity

The demise of expert-based practice is inevitable

2000 20101990 2020

Structural Genetics: e.g. SNPs, haplotypes

Functional Genetics: Gene expression

profiles

Proteomics and othereffector molecules

Decisions by Clinical Phenotype

William Stead: http://courses.mbl.edu/mi/2009/presentations_fall/SteadV1.ppt

Background: Edward ShortliffeBiomedical Informatics Applications

Basic Research

Applied Research

Biomedical Informatics Methods, Techniques, and Theories

Imaging Informatics

Clinical InformaticsBioinformatics Public Health

Informatics

Molecular andCellularProcesses

Tissues andOrgans

Individuals(Patients)

PopulationsAnd Society

Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt

Background: Edward ShortliffeBiomedical Informatics Research Areas

Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt

BiomedicalKnowledge

BiomedicalData

KnowledgeBase

InferencingSystem

DataBase

DataAcquisition

BiomedicalResearchPlanning &Data Analysis

KnowledgeAcquisition

TeachingHumanInterface

TreatmentPlanning

DiagnosisInformationRetrieval

ModelDevelopment

ImageGeneration

Real-time acquisitionImagingSpeech/language/textSpecialized input devices

Machine learningText interpretationKnowledge 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 AwardsA 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 MasysNIH 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& CommunityInvolvement

RegulatorySupport

Biostatistics

ClinicalResources

BiomedicalInformatics

ClinicalResearch

Ethics

CTSAHOME

NIH

OtherInstitutions

Industry

Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt

Gap!

Bench Bedside Practice

Building Blocks and PathwaysMolecular LibrariesBioinformaticsComputational BiologyNanomedicine

TranslationalResearchInitiatives

Integrated Research NetworksClinical Research Informatics NIH Clinical Research AssociatesClinical outcomesHarmonizationTraining

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 Aims1. 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 ExampleWill 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)

LaboratorySystem

PharmacySystem

WizOrderServer

WizOrderClient

MainframeDB2

RxDB

HL7Lab DB

TemporaryData queue (TDQ)

InternalFormat

HL7

SQL

SQL

SQL

Repackages and Routes

Print SubSystem

document

KnowledgeBase, Files

SQLOrderables, 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 hierarchyProcedures: CPT June 2010 UMLS extract scripts developed with UTHSC at Houston

Lab terms: LOINC November 2010 Plan to use i2b2 hierarchyMedication 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 completedplanned

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 2011Problem List and Provider Notes EPIC 2009 2011Microbiology, Cardiology, Radiology EPIC/Misys

Theradoc2006 2011

Medication reconciliation EPIC 2007 2011Perioperative schedule and indicators ORSOS 2005 2012

Social Security Death Indicator SSDI Na 2012Medicaid 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 CRISTitle 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