Evolution of a Clinical Research Informatics Group within a Service-oriented Clinical Trials Data Management Organization
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1 Evolution of a Clinical Research Informatics Group within a Service-oriented Clinical Trials Data Management Organization B. McCourt, D. Fasteson-Harris, S. Chakraborty,, C. Bova Hill AMIA CRI Summit San Francisco, March 2010 Topics Context Challenges Organizational Design Solution 2 Year Experience Now and next steps 1
2 DCRI Context What is DCRI? DCRI is the largest academic clinical research organization (ARO) in the world A global coordinating center for multi-center clinical trials that integrates the medical expertise of Duke University Medical Center with the operational capabilities of a full-service CRO 2
3 DCRI Facts Founded in 1969 with the development of the Duke Databank for Cardiovascular Diseases 21 years of experience in coordinating multi-center trials in over 20 therapeutic areas 900+ staff and 120 clinical/statistical faculty Full-Service Capabilities 4,600 manuscripts in peer-reviewed reviewed journals More than 420 projects completed in 64 countries enrolling more than 579,900 patients DCRI Trials Experience by Phase and Size 3
4 CDM Challenges History ( ) History & Organizational Design Causal Issues Capacity for change Industry trends Environmental Issues Organizational changes Qualitative studies 4
5 Existing Org Structure ( ) Clinical Data Integration Highly nested team units Clinical Data Managers Data Management Teams Quality Control Case Report Form Design Clinical Programming Duke Follow-up Medical Coding 10 Year Headcount Trend 5
6 10 Year Headcount Trend Large paper trials locked Adopted new network Industry Trends (2007) Increasing number of projects with increasing complexity and decreasing data processing Increased demand for EDC (vs. paper) and increased demand for CDI consulting with sponsor EDC tools & implementations Increasing infrastructure initiatives supporting DCRI, DTMI, NIH & Industry to develop and adopt innovative operational methods. 6
7 Industry Trends (cont) Increased reliance on standards and technology to meet increasing variety of CDM requirements Emerging industry recognition of increasing CDM scope and trends Environmental Studies DCRI wide telephone survey Departmental web survey Horizontal focus groups 7
8 What did the studies tell us? Opportunities for improvement: Inconsistent management decisions Career progression More effective communications Trust Collaboration Mastery of administrative, technical, training and project management was difficult to achieve Organizational Design Solution 8
9 Resulting Org Structure (Dec 2007) Clinical Data Integration Clinical Data Management Debra Fasteson-Harris Associate Director Clinical Research Informatics Brian McCourt Associate Director Clinical Data Managers Data Management Teams Quality Control Case Report Form Design Medical Coding Research Informatics Clinical Programming Duke Follow-up Impact of matrixed CDM teams Administrative Reporting Relationships 90 of 138 Employees switching managers Project Teams Intact 88 projects total 69 have no changes to CDM Lead 12 the new lead has been involved already and lead role being formalized 4 leads will change after resources available 2 new projects not yet assigned 1 project transition is being scheduled 9
10 DCRI Research Informatics Support research data integration projects with complex research requirements. Evaluate and operationalize new ideas for data management tools and methods ( data management pipeline ) Develop and implement clinical data standards 10 Year Headcount Trend 10
11 Emergence of CRI Papers on issues Clinical Research Informatics context Duke Translational Medicine Institute (DTMI) DTRI DCRU DCRI DNRI DCCR GHI 11
12 class Ortel ELISA_AVG_DATA ELISA_RAW_ DATA *PK AVGDATID: NUMBER(10) *PK TESTID: NUMBER(6) *PK RAWDATID: NUMBER(10) SPECMCD: VARCHAR2(40) TSTCD: VARCHAR2(20) *FK PLATEID: NUMBER(6) *FK PLATEID: NUMBER(6) +PK_ELISA_TEST TSTNAM: VARCHAR2(100) +PK_ELISA_TEST SPECMCD: VARCHAR2(40) *FK TESTID: NUMBER(6) RPTU: VARCHAR2(20) (TESTID = TESTID) +FK_ELISA_AVG_DATA_TEST *FK TESTID: NUMBER(6) RPTRESC: VARCHAR2(2048) = NULL 1 LBTEST: VARCHAR2(100) 1 (TESTID = TESTID) WELLNUM: VARCHAR2(10) RPTRESN: FLOAT LBTESTCD: VARCHAR2(20) +FK_ELISA_RAW_DATA_TEST RPTRESC: VARCHAR2(2048) = NULL CALSD: FLOAT RAWVALU: FLOA T CALCV: FLOAT «PK» +PK_ELISA_TEST DILUTION: NUMBER(6) = NULL RPTRFLG: VARCHAR2(10) + PK_ELISA_TEST(NUMBER) CALCVALU: FLOAT 1 RPTRFLG: VARCHAR2(10) +PK_ELISA_TEST 1 * T RANSMID: NUMBER(6) + FK_ELISA_AVG_DATA_ELISA_PLATE(NUMBER) + FK_ELISA_AVG_DATA_TEST(NUMBER) +FK_ELISA_RAW_DATA_ELISA_PLATE COMMENT_4488 «PK» +FK_ELISA_AVG_DATA_ELISA_PLATE (TESTID = TESTID) + FK_ELISA_RAW_DATA_ELISA_PLATE(NUMBER) + PK_ELISA_AVG_DATA(NUMBER) ELISA_PLATE + FK_ELISA_RAW_DATA_TEST(NUMBER) «PK» SPECMCD: VARCHAR2(40) +elisadatacollection (PLATEID = PLATEID) + PK_ELISA_RAW_DATA(NUMBER) FK TRANSMID: NUMBER(6) +PK_ELISA_PLATE *PK PLATEID: NUMBER(6) SPECCOM: VARCHAR2(2048) * TRANSMID: NUMBER(6) +FK_ELISA_STD_DATA_TEST SPECCND: VARCHAR2(2048) 1 PLATENAM: VARCHAR2(40) +PK_ELISA_PLATE PLATENAM: VARCHAR2(40) ELISA_STD_DATA +sample 1 TSTDTM: DATE TSTCOM: VARCHAR2(2048) TSTTYP: VARCHAR2(22) = S 1 +commentcollection TECHNAME: VARCHAR2(40) SAMPLES_4488 TSTDESC: VARCHAR2(40) COMMDATE: DATE *PK STDDATID: NUMBER(6) TSTSTAT: VARCHAR2(13) = D DILUTION: NUMBER(6) *FK TESTID: NUMBER(6) +elisaplate RPTRTYP: VARCHAR2(12) = N PLBNUM: VARCHAR2(20) = ORTEL STDNAM: VARCHAR2(40) SPECMCD: VARCHAR2(40) BATTRNAM: VARCHAR2(40) = Elisa Quantific... STUDYID: VARCHAR2(20) = commentcollection FK PLATEID: NUMBER(6) FK TRANSMID: NUMBER(6) 1 BATTRID: VARCHAR2(20) = AIM2 BATTRID: VARCHAR2(20) WELLNUM: VARCHAR2(10) SPECMNUM: VARCHAR2(20) SOFTWARE: VARCHAR2(40) +PK_ELISA_PLATE +sample CONC: FLOAT SPECSEQ: VARCHAR2(10) INSTRMT: VARCHAR2(40) (PLATEID = PLATEID) SUBJID: VARCHAR2(20) INSTMSER: VARCHAR2(40) 1 CALCVALU: FLOAT 1 +FK_ELISA_STD_DATA_ELISA_PLATE + FK_COMMENT_4488_TRANSMISSION(NUMBER) +FK_COMMENT_4488_TRANSMISSION RPTRESN: FLOAT = NULL VISIT: VARCHAR2(40) (TESTID = TESTID) RPTRSTAT: VARCHAR2(11) = F CALCV: FLOAT VISITNUM: VARCHAR2(20) = NULL FILENAME: VARCHAR2(40) 1..* +commentcollection 0..1 CALSD: FLOAT VISITTYP: VARCHAR2(11) = S FILCRDTM: DATE RPTRESC: VARCHAR2(2048) = NULL LBDTM: VARCHAR2(25) = NULL STUDYID: VARCHAR2(20) = 4488 RAWVALU: FLOA T STUDNAM: VARCHAR2(200) RAWAVG: FLOA T STUDYID: VARCHAR2(20) = 4488 «PK» RAWSD: FLOAT = NULL LBSPEC: VARCHAR2(40) = PLAS + PK_ELISA_PLATE(NUMBER) RAWCV: FLOAT = NULL PROCNAM: VARCHAR2(100) * TRANSMID: NUMBER(6) SHPPLBDT: DATE SHPTO: VARCHAR2(50) +FK_SAMPLES_4488_TRANSMISSION +FK_ELISA_PLATE_TRANSMISSION (TRANSMID = TRANSMID) + FK_ELISA_STD_DATA_ELISA_PLATE(NUMBER) +testdat e 1 + FK_SAMPLES_4488_TRANSMISSION(NUMBER) + FK_ELISA_STD_DATA_TEST(NUMBER) +sample CLOTTING_ DATA «unique» «PK» [SPECMCD = SPECMCD] + PK_ELISA_STD_DATA(NUMBER) +clottingdatacollection 1 + UQ_4488_SAMPLES_SPECMCD(VARCHAR2) (TRANSMID = TRANSMID) +PK_TRANSMISSION 1 CLDATID: NUMBER(6) TRANSMISSION«flow» *FK TESTID: NUMBER(6) +PK_TRANSMISSION «table» CDISC_UNKNOWNS *FK TRANSMID: NUMBER(6) CDISC_UNKNOWNS SPECMCD: VARCHAR2(40) 1 *PK TRANSMID: NUMBER(6) RPTRESC: VARCHAR2(2048) *FK LABID: NUMBER(5) RPTRESN: FLOAT VERSION: VARCHAR2(7) = V SITEID: VARCHAR2(20) +FK_CLOTTING_DATA_TEST +PK_TRANSMISSION RPTU: VARCHAR2(20) TRMSRNUM: VARCHAR2(20) = ORTEL INVID: VARCHAR(20) TSTDTM: DATE 1 TRMSRNAM: VARCHAR2(40) = Duke Hemostasis... INVNAM: VARCHAR2(80) TSTTYP: VARCHAR2(22) = S TRMTYP: VARCHAR2(11) = I SCRNNUM: VARCHAR2(20) TSTDESC: VARCHAR2(40) +FK_CLOTTING_DATA_TRANSMISSION (TRANSMID = TRANSMID) +PK_TRANSMISSION FILENAME: VARCHAR2(40) SUBJSID: VARCHAR2(20) TSTSTAT: VARCHAR2(13) = D LACTDTM: VARCHAR2(25) SUBJNIT: VARCHAR2(4) SOFTWARE: VARCHAR2(40) = ACL TOP RECEXTYP: VARCHAR2(25) = BASE SEX: VARCHAR2(1) INSTRMT: VARCHAR2(40) = ACL TOP LOADDTM: DATE SEXCD: VARCHAR2(40) INSTMSER: VARCHAR2(40) = STUDYID: VARCHAR2(20) = 4488 BRTHDTM: DATE BATTRNAM: VARCHAR2(40) = Functional Assay FILCRDTM: DATE RACE: VARCHAR2(20) BATTRID: VARCHAR2(20) = AIM1 +FK_TRANSMISSION_LABORATORY TRANCOMM: VARCHAR2(200) RACECD: VARCHAR2(4 0) RPTRTYP: VARCHAR2(12) = N VISITMOD: VARCHAR2(20) RPTRSTAT: VARCHAR2(11) = F (LABID = LABID) ACCSNNUM: VARCHAR2(20) + FK_TRANSMISSION_LABORATORY(NUMBER) SPECNUM: VARCHAR2(10) LABORATORY +PK_LABORATORY PTMEL: VARCHAR2(9) «PK» + FK_CLOTTING_DATA_TEST(NUMBER) «flow» 1 PTMELTX: VARCHAR2(40) + PK_TRANSMISSION(NUMBER) + FK_CLOTTING_DATA_TRANSMISSION(NUMBER) COLENDTM: DATE *PK LABID: NUMBER(5) RCVDTM: VARCHAR2(25) TRMSRNUM: VARCHAR2(20) = ORTEL SPECICOM: VARCHAR2(2048) TRMSRNAM: VARCHAR2(40) = Duke Hemostasis... AGEATCOL: NUMBER(3) PLBNAM: VARCHAR2(40) = Duke Hemostasis... «table» CDISC_UNKNOWNS AGEU: VARCHAR2(6) PLBNUM: VARCHAR2(20) = ORTEL FASTSTAT: VARCHAR2(7) LBNAM: VARCHAR2(40) = Duke Hemostasis... LBLOINC: VARCHAR2(10) LBNUM: VARCHAR2(20) = ORTEL LOINCCD: VARCHAR2(40) RPTRESCD: VARCHAR2(40) «PK» RPTRESNP: VARCHAR2(5) + PK_LABORATORY(NUMBER) RPTNRLO: VARCHAR2(40) RPTNRHI: VARCHAR2(40) RPTUCD: VARCHAR2(40) CNVRESC: VARCHAR2(204 8) CNVRESCD: VARCHAR2(4 0) CNVRESN: VARCHAR2(20) CNVRESNP: VARCHAR2(5) CNVU: VARCHAR2(20) CNVUCD: VARCHAR2(40) SIRESC: VARCHAR2(204 8) SIRESCD: VARCHAR2(40) SIRESN: FLOAT SIRESNP: VARCHAR2(5) SINRLO: VARCHAR2(4 0) SINRHI: VARCHAR2(40) SIU: VARCHAR2(20) SIUCD: VARCHAR2(40) ALRTFL: VARCHAR(14) DELTFL: VARCHAR2(2) TOXGR: VARCHAR2(1) TOXGRCD: VARCHAR2(40) EXCLFL: VARCHAR2(14) BLNDFL: VARCHAR2(24) RPTDTM: VARCHAR2(25) TEST 2 Year Experience BioSignatures \ Biomarker Studies 100% esource Metadata heavy Sample management New workflow Design 1..* (PLATEID = PLATEID) 12
13 T1 Challenges in CDM Services Context Evolving science causes evolving data requirements Scope, pricing, workflow, bio and information science skills Discovery based work now within scope of FDA regs and pharma traditions Mismatch of burden/benefit and common understanding of regulations Data Management systems are immature or don t exist. Bioinformatics tools lack data management workflow IT -> > Research IT\Computer Science Decision support methodology: implementation on a clinical trial Site contact information Enrollment and randomization information Lab results Treatment arm (as needed) Decision Support Tools Computer-assisted treatment recommendations Web-based reports Web-based Study Database (21CFR Part 11 compliant) MD reviewer evaluates and enters a Treatment Decision Study participants (patients) Site investigators Clinical Operations reviews and manages site communications Wilgus,, et al. Poster presented at AMIA Annual Meeting,
14 T2 Challenges in CDM Services Context New risk profile of research tasks within patient care process. EHR s -> > Disease cohorts -> > Trials -> > EHR Complex governance Consent; research vs quality improvement; future use; Identification. Data collection design Interoperability CDISC : HL7; WHO Drug : RxNorm; MedDRA : ; Metadata! 14
15 Duke CDR & Knowledge Repository Study Meta Data Consent Geospatial/ Environmt. Sample Data CRF/ Clinical Data Omics Data Operational Data Electronic Health Records Discovery CDR & Knowledge Repository Cohort selection Decision support Imaging Data External sources Key Issues Unanticipated acceleration of trends CTSA, ARRA, Duke Center for Health Informatics, DCRI BioSignatures Program What is informatics? Depends on who you ask Functional bounds of interdisciplinary domain Field is still immature Too few examples Talent gap Cost constraints & allocation Project vs infrastructure 15
16 Where Next? Practical need for CRI will meet vision (bottom up meets top down) Grow as research partner, beyond service provider CRI Faculty Data Infrastructure Adopt HL7 Development Framework as methodology Heavily use & contribute to standards Leadership, data governance Conclusions The challenges discussed at this meeting have corresponding business challenges The research trends driving the growth of CRI exist Not only as CTSA phenomena Can be expected to impact services market CRI is not yet well defined or established Need to demonstrate value 16
17 Acknowledgements Connor Blakeney Robert Harrington, MD Meredith Nahm James Tcheng,, MD Swati Chakraborty, M.Eng. Carol Hill, PhD Cindy Kluchar,, MS James Topping, MS Becky Wilgus,, RN, MSN Evolution of a Clinical Research Informatics Group within a Service-oriented Clinical Trials Data Management Organization B. McCourt, D. Fasteson-Harris, S. Chakraborty,, C. Bova Hill AMIA CRI Summit San Francisco, March
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