1 / 55

Russ Waitman, PhD Associate Professor, Director Medical Informatics Department of Biostatistics

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?

yaholo
Télécharger la présentation

Russ Waitman, PhD Associate Professor, Director Medical Informatics Department of Biostatistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Outline • What is Biomedical Informatics? • What are the Clinical Translational Science Awards? • Biomedical Informatics Section Specific Aims • Data Management Observations • Timeline • i2b2 demo • Questions

  3. Background: Charles Friedman Charles P. Friedman: http://www.jamia.org/cgi/reprint/16/2/169.pdf • The Fundamental Theorem of Biomedical Informatics: • A person working with an information resource is better than that same person unassisted. • NOT!!

  4. Evidence Synthesis & Decision Background: William SteadThe Individual Expert Clinician Patient Record William Stead: http://courses.mbl.edu/mi/2009/presentations_fall/SteadV1.ppt

  5. The demise of expert-based practice is inevitable 1000 Proteomics and other effector molecules Facts per Decision 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

  6. Applied Research Background: Edward ShortliffeBiomedical Informatics Applications Biomedical Informatics Methods, Techniques, and Theories Basic Research Imaging Informatics Clinical Informatics Public Health Informatics Bioinformatics Molecular and Cellular Processes Tissues and Organs Individuals (Patients) Populations And Society Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt

  7. Background: Edward ShortliffeBiomedical Informatics Research Areas Biomedical Knowledge Biomedical Data Biomedical Research Planning & Data Analysis Real-time acquisition Imaging Speech/language/text Specialized input devices Machine learning Text interpretation Knowledge engineering Knowledge Acquisition Data Acquisition Data Base Knowledge Base Inferencing System Image Generation Model Development Information Retrieval Treatment Planning Human Interface Diagnosis Teaching Edward Shortliffe: http://www.dentalinformatics.com/conference/conference_presentations/shortliffe.ppt

  8. Clinical and Translational Science AwardsA 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.”

  9. Background: Dan MasysNIH 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

  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 NIH Other Institutions Clinical Research Ethics Trial Design Gap! Advanced Degree-Granting Programs Biomedical Informatics CTSA HOME Industry Participant & Community Involvement Clinical Resources Biostatistics Regulatory Support Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt

  12. Reengineering Clinical Research Interdisciplinary Research Innovator Award Public-Private Partnerships (IAMI) Bench Bedside Practice Building Blocks and Pathways Molecular Libraries Bioinformatics Computational Biology Nanomedicine Integrated Research Networks Clinical Research Informatics NIH Clinical Research Associates Clinical outcomes Harmonization Training Translational Research Initiatives Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt

  13. 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

  14. 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

  15. 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

  16. Clinical Research Information Systems • KUMC has purchased VeloseResearch 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

  17. CRIS Intro Screen

  18. CRIS: sample e Case Report Form

  19. CRIS: Document Adverse Events

  20. KUMC CTSA Specific Aims • Provide a HICTR portal for investigators to access clinical and translational research resources, track usage and outcomes, and provide informatics consultative services. • Create a platform, HERON (Healthcare Enterprise Repository for Ontological Narration), to integrate clinical and biomedical data for translational research. • Advance medical innovation by linking biological tissues to clinical phenotype and the pharmacokinetic and pharmacodynamicdata generated by research cores in phase I and II clinical trials (addressing T1 translational research). • Leverage an active, engaged statewide telemedicine and Health Information Exchange (HIE) effort to enable community based translational research (addressing T2 translational research).

  21. 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

  22. Portal: Access + Measurement

  23. 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.

  24. 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

  25. 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.

  26. 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.

  27. WizOrder Client Lab DB Generic Interface Engine (GIE) WizOrder Server Pharmacy System Rx DB Print SubSystem Mainframe DB2 Constructing a Repository: Understanding Source Systems, Example CPOE Most Clinical Systems focus on transaction processing for workflow automation Repackages and Routes SQL Laboratory System Internal Format HL7 HL7 SQL Temporary Data queue (TDQ) SQL document Knowledge Base, Files SQL SQL Orderables, Orderset DB Drug DB SQL

  28. 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. Star Schemas: Data Warehouses http://www.ibm.com/developerworks/library/x-matters8/index.html Relational databases (Oracle, Access), dominant in business and clinical systems (Cerner, McKesson)

  29. HERON: Repository Architecture

  30. Workflow: System Access

  31. Workflow & Oversight: Request Data

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

  33. i2b2: Count Cohorts

  34. i2b2: Patient Count in Lower Left

  35. i2b2: Ask for Patient Sets

  36. i2b2: Analyze Demographics Plugin

  37. i2b2: Demographics Plugin Result

  38. i2b2: View Timeline

  39. i2b2: Timeline Results

  40. Transform data into information using standard vocabularies and ontologies

  41. Link clinical data sources to enhance their research utility.

  42. 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

  43. 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

  44. 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

  45. Unique Combination of Telemedicine for Community Research and CRIS Promote capability for subject engagement and facilitate collaboration via tele-research meetings

  46. “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

  47. 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

More Related