Developing Health Informatics Capabilities for Kansas University Medical Center

Transcription

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 Clinician Synthesis & Decision Evidence Patient Record William Stead:

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

6 Background: Edward Shortliffe Biomedical Informatics Applications Basic Research Biomedical Informatics Methods, Techniques, and Theories Applied Research Bioinformatics Imaging Informatics Clinical Informatics Public Health Informatics Molecular and Cellular Processes Tissues and Organs Individuals (Patients) Populations And Society Edward Shortliffe:

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

8 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.

9 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

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 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.

11 NIH CTSAs: Home for Clinical and Translational Science Gap! Biomedical Informatics Clinical Research Ethics CTSA HOME Trial Design Advanced Degree-Granting Programs NIH Clinical Resources Participant & Community Involvement Industry Biostatistics Regulatory Support Dan Masys: Other Institutions

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

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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.

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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 reidentification risk. Reference: Benitez K, Malin B. Evaluating re-identification risks with respect to the HIPAA privacy rule. J Am Med Inform Assoc Mar-Apr;17(2):

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

27 Constructing a Repository: Understanding Differing Data Models used by Systems 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 Mar-Apr;17(2): Star Schemas: Data Warehouses

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 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 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.

40 Link clinical data sources to enhance their research utility. Data source Source System System Go-Live Date Extraction completed planned Inpatient/Emergency demographics, EPIC 2006 September ADT locations & services Inpatient diagnoses (DRG, ICD9) EPIC 1990 September Outpatient visits services, diagnoses, IDX 2002 November procedures (ICD, CPT) Laboratory Results EPIC 2007 November (inpatient/outpatient) Electronic Medication Administration EPIC 2007 December Inpatient inputs, outputs and discrete EPIC nursing observations Clinical Research Information System CRIS Provider Order Entry EPIC Problem List and Provider Notes EPIC Microbiology, Cardiology, Radiology EPIC/Misys Theradoc Medication reconciliation EPIC Perioperative schedule and indicators ORSOS Social Security Death Indicator SSDI Na 2012 Medicaid databases KHPA 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 Title PI Grant/Agency CRIS used? 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 Centralized Disease Management for Rural Hospitalized Smokers Pediatric epilepsy prevalence study Kansas Comprehensive Telehealth Services for Older Adults K. Richter R21DA020489, National Institute on Drug Abuse K. Richter R01HL087643, NIH National Heart, Lung and Blood Institute C. Daley R24MD002773, National Center on Minority Health and Health Disparities E. Ellerbeck R01CA101963, National Cancer Institute D. Lindeman RTOI # AUCD, National Center for Birth Defects and Developmental Disabilities, CDC E-L Nelsen, L. Redford Health Resources and Services Administration Office for the Advancement of Telehealth Promote capability for subject engagement and facilitate collaboration via tele-research meetings Yes No Yes Yes No No

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

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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?