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Developing Health Informatics Capabilities for Kansas University Medical Center Russ Waitman, PhD Associate Professor, Director Medical Informatics Department.

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Presentation on theme: "Developing Health Informatics Capabilities for Kansas University Medical Center Russ Waitman, PhD Associate Professor, Director Medical Informatics Department."— Presentation transcript:

1 Developing Health Informatics Capabilities for Kansas University Medical Center Russ Waitman, PhD Associate Professor, Director Medical Informatics Department of Biostatistics December 14, 2010

2 Outline  What is “Biomedical” Informatics?  What are the Clinical Translational Science Awards?  Biomedical Informatics Section Specific Aims to serve Health Services Research  Data Management Observations  Team Composition and Initial Guesses at Options for Wichita

3 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:

4 Background: William Stead The Individual Expert William Stead: Evidence Patient Record Synthesis & Decision Clinician

5 Facts per Decision 1000 10 100 5 Human Cognitive Capacity The demise of expert-based practice is inevitable 2000201019902020 Structural Genetics: e.g. SNPs, haplotypes Functional Genetics: Gene expression profiles Proteomics and other effector molecules Decisions by Clinical Phenotype William Stead:

6 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:

7 Background: Edward Shortliffe Biomedical Informatics Research Areas Edward Shortliffe: 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

8 “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

9 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

10 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.

11 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 CTSAHOME NIH Other Institutions Industry Dan Masys: Gap!

12 Existing KUMC 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

13 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. Implementing at KUMC for registries. Think Hyundai

14 CRIS Intro Screen

15 CRIS: sample e Case Report Form

16 CRIS: Document Adverse Events

17 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).

18 Aim #1: Create a Portal and Consult  Bring together existing resources  Translational Technologies Resource Center  HERON/i2b2, CRIS, Redcap, biorepository, etc.  Link to national resources  – Facebook for researchers  – 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


20 Portal: Access + Measurement

21 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.


23 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

24 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 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.

25 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.

26 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 Repackages and Routes Print SubSystem document Knowledge Base, Files SQL Orderables, Orderset DB Drug DB SQL Most Clinical Systems focus on transaction processing for workflow automation

27 Constructing a Repository: Understanding Differing Data Models used by Systems 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.

28 HERON: Repository Architecture

29 Workflow: System Access

30 Workflow & Oversight: Request Data

31 Implement NIH funded (i.e. i2b2) initiatives for accessing data.

32 i2b2: Count Cohorts

33 i2b2: Patient Count in Lower Left

34 i2b2: Ask for Patient Sets

35 i2b2: Analyze Demographics Plugin

36 i2b2: Demographics Plugin Result

37 i2b2: View Timeline

38 i2b2 : Timeline Results

39 Transform data into information using standard vocabularies and ontologies Source terminologyCompleted plannedNotes Demographics: i2b2April 2010 Using i2b2 hierarchy. Restricted search criteria to geographic regions (> 20,000 persons) instead of individual zipcodes Diagnoses: ICD9April 2010Using i2b2 hierarchy Procedures: CPTJune 2010UMLS extract scripts developed with UTHSC at Houston Lab terms: LOINCNovember 2010Plan to use i2b2 hierarchy Medication ontologies: NDF-RTDecember 2010 Physiologic effect, mechanism of action, pharmacokinetics, and related diseases. Nursing ObservationsJuly 2010- NDNQI pressure ulcers mapped to SNOMED CT to evaluate automated extraction of self reported activity. (Drs. Dunton and Warren.) Pathology: SNOMED CTFebruary 2011 Providing coded pathology results and patient diagnosis is a critical objective for defining cancer study cohorts in Aim 3. Clinical narrative2012 As hospital restructures clinical narrative documentation to use EPIC’s SmartData (CUI) concepts, will determine appropriate standard. National Center for Biological Ontology 2013In support of Aim 3 focus on bridging clinical and bioinformatics to advance novel methods.

40 Link clinical data sources to enhance their research utility. Data source Source System “Go-Live” Date Extraction completed planned Inpatient/Emergency demographics, “ADT” locations & services EPIC2006September Inpatient diagnoses (DRG, ICD9)EPIC1990September Outpatient visits services, diagnoses, procedures (ICD, CPT) IDX2002November Laboratory Results (inpatient/outpatient) EPIC2007November Electronic Medication AdministrationEPIC2007December Inpatient inputs, outputs and discrete nursing observations EPIC20072011 Clinical Research Information SystemCRIS20072011 Provider Order EntryEPIC20102011 Problem List and Provider NotesEPIC20092011 Microbiology, Cardiology, Radiology EPIC/Misys Theradoc 20062011 Medication reconciliationEPIC20072011 Perioperative schedule and indicatorsORSOS20052012 Social Security Death IndicatorSSDINa2012 Medicaid databasesKHPA20052012  Reality: Got data in October. Ahead and behind schedule  Four milestones:  Calculate Statistics  Alpha: unvalidated but “the promise”  Beta: System Access  HERON 1.0  Then subsequent milestones

41 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

42 Unique Combination of Telemedicine for Community Research and CRIS TitlePIGrant/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

43 “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

44 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


46 Timeline: Existing Projects, Aims 1 and 2

47 Timeline: Aims 3 and 4, Team Growth

48 Team Roles & Size Estimates  “The Data Warehouse Lifecycle Toolkit” by Ralph Kimball. Identifies 15 Roles.  Informatician (leader, architect, project mgt)  Database Administrator/Technical coordinator  Software Engineers  Extract, Load, Transform (ELT/ETL) - SE or DBA  Modify/augment systems (DB and i2b2)  Estimate 3 FTE for HERON slower technical build and maintain.  CRIS/Redcap team 2-4 people fractionally

49 Team continued and other “costs”  Hardware ~$20-30k initial. ~$5k annual  IT infrastructure from KUMC Information Resources: Sys Admin, Network, Security, virtual servers, DBA and OS consults.  Clinical/operational partner’s technical time  Legal time: Shelley Gebar estimate (also partners)  Governance time commitment for committees  Executives: quarterly 9 $$$$ people  Data Request Oversight: monthly? 10 $$$ people  Analyst, terminologist, customer liaison: to be hired, estimate 0.7 FTE

50 Guesses for Wichita to go Fishing  Roll your own. Necessary for me to build an academic informatics research effort. May not be necessary for Wichita.  Hire a consultant like Recombinant to do i2b2 or use a consultant for a commercial package (variety of vendors)  Partner institutions create infrastructure and fish in their ponds  Hire KUMC team to support Wichita. Similar to Vanderbilt building the RHIO for Memphis (see Mark Frisse and Midsouth eHealth Alliance  Wait for HIE to happen. Piggyback on RHIO capabilities.  Fish in Medicaid or AAMC, DARTNet ponds.  Fish in KUMC/KUH/UKP pond.

51 Questions?

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