Institute of Medicine Workshop: DIGITAL DATA PRIORITIES FOR CONTINUOUS LEARNING IN HEALTH AND HEALTH CARE
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1 Institute of Medicine Workshop: DIGITAL DATA PRIORITIES FOR CONTINUOUS LEARNING IN HEALTH AND HEALTH CARE Data Quality in Clinical Research 23 March 2012 Rebecca D. Kush, PhD President and CEO, CDISC CDISC
2 CDISC
3 JPMA Presentation Data Quality for Clinical Research CDISC
4 SPECULATION by R.D. Kush Quality Requirements Sushi-grade Clinical Decisions Regulated Research Signal Validation; Active Safety Surveillance Basic Research Signal or Trend Detection Non-medical Sales and Marketing All you can eat buffet grade Type of Project CDISC 2012
5 Key Messages Regulated Clinical Research requires sushigrade data quality Building quality in from the beginning is ideal Data standards improve data quality CDISC
6 Healthcare and Clinical Research: Parallel Universes CDISC 2012 Source: Landen Bain, CIO, DUMC ~ 2004
7 The Plight of the Site For clinicians doing research today ~ % of trials - data collected on paper (3- or 4- part NCR paper) ~ % of data are collected by eclinical point solutions (electronic data capture tools) data are entered 4-7 times total, 2-3 times by the clinicians or study coordinators An average active study site has 3 disparate solutions To report an unexpected or serious adverse event does not fit into normal clinical care workflow and takes excessive time. Most clinicians do one regulated clinical research study and no more. CDISC
8 Current Typical Research Data Flow 8
9 Cost of Quality $ (1995) Data Quality in Clinical Research: Cost of Error Correction Database Lock QA of Final Report Stats- >$8,000 Site ~$30 CRO ~$75 Tables Listings >$5,000 Point in Process for Error Detection/Correction (per error) CDISC
10 CFR - Code of Federal Regulations Title 21 - FDA TITLE 21--FOOD AND DRUGS CHAPTER I--FOOD AND DRUG ADMINISTRATION DEPARTMENT OF HEALTH AND HUMAN SERVICES SUBCHAPTER A--GENERAL PART 11 ELECTRONIC RECORDS; ELECTRONIC SIGNATURES The regulations in this part set forth the criteria under which the agency considers electronic records, electronic signatures, and handwritten signatures executed to electronic records to be trustworthy, reliable, and generally equivalent to paper records and handwritten signatures executed on paper. CDISC
11 Mapping (Source to Submission) CDISC
12 but also Submission to Source Trustworthiness CDISC
13 ODM & Audit Trail Who Why What When CDISC
14 The Road to Quality Clinical Data Build quality into the system Train and educate site personnel, project team and reviewers/auditors Decrease the amount of data collected Define the data set needed and specify requirements Standardize formats and procedures Also plan for data quality during post-marketing Decrease the number of times data are handled (Note: Anticipated by-products of these steps to improve quality are increased efficiency and lower costs.) Source: Assuring Data Quality and Validity in Clinical Trials for Regulatory Decision Making: Workshop Report, 2000 CDISC
15 Optimizing the Process data conception Healthcare Delivery (e)source Documents EHR esource (e)crfs Clinical Research auto reconciliation ~ 1997 B. Kisler, R. Kush CDISC
16 De-identified Data Site Research Archive EHR Continuity of Care Doc Data Sources EDC RFD* EHR Std. Common Research Dataset (+) Interoperability Specification Care and/or Research Site (Healthcare Location, Investigator, Site Personnel) Patient Value: Quality of Healthcare, Safety Research informs healthcare more effectively Build quality into process at beginning Research Results, esubmission Standard Formats Study Sponsor (e.g. ARO, CRO, Vendor, Principal Investigator, potentially AHRQ ) Scientific Publication Public Registries, IRB, DSMBs Reviewers Regulatory Authority CRO (e.g. or Research Partner Partner, Sponsor, Registry, Regulator, IRB, DSMB, Quality Measures) CDISC 2012
17 Press Release: June 10, 2010 Initial database required mapping to a standard (CDISC); can now be leveraged to collect data using the standard Database now has >6,000 research study patients Now being used by > 200 qualified research teams in 35 countries 17 CDISC 2012 Copyright C-Path 2011
18 Why Standards? Efficient collaboration Clinical Trials What is the average age of patients enrolled in 11 industry trials for Alzheimer s Disease? Better science Regulatory efficiency Clinical Trials Date of Birth Jan. 15, ATM 2011 January 15, /15/11 1/15/ /1/11 15 January
19 What was learned? ADAS-Cog Variability ADNI J&J Wyeth sanofiaventis Pfizer AstraZeneca Abbott Item 1 Word Recall Word Recall Word Recall Word Recall Word Recall Word Recall Word Recall Item 2 Commands Name Obj/fing. Name Obj/fing. Commands Name Obj/fing. Name Obj/fing. Name Obj/fing. Item 3 Constr. Praxis Delayed recall Commands Constr. Praxis Commands Commands Commands Item 4 Delayed recall Commands Constr. Praxis Delayed recall Delayed recall Constr. Praxis Constr. Praxis Naming Item 5 Obj/fing. Constr. Praxis Idea Praxis Name Obj/fing. Constr. Praxis Idea. Praxis Idea. Praxis Item 6 Idea. Praxis Idea Praxis Orientation Idea. Praxis Idea. Praxis Orientation Orientation Item 7 Orientation Orientation Word Recog Orientation Orientation Word Recog Word Recog Item 8 Word Recog. Word Recog. Remem. Instr. Word Recog Word Recog Remem. Instr. Spoken Lang Abil. Item 9 Remem Instr. Remem Instr. Spoken Lang. Abil. Remem. Instr. Remem. Instr. Spoken Lang. Abil. Comprehension Spoken Lang. Word Finding Spoken Lang Spoken Lang Word Finding Word Finding Item 10 Comprehension Abil. Dif. Abil. Abil. Dif. Dif. Item 11 Word Finding Dif. Word Finding Dif. Diff. Spont. Comprehension Speech Word Finding Dif. Comprehension Remem. Instr. Spoken Lang. Item 12 Abil. Comprehension Concentration Comprehension Comprehension Concentration CDISC 2012 Item 13 Number cancel. Concentration Concentration Concentration
20 Now published. CDISC Alzheimer s Disease SDTM User Guide (Version 1.0) CDISC 2012 See also Questionnaires on the CDISC website under Standards & Innovations. 20
21 CAMD Cognition Test Data
22 10-Year Disease Progression by Severity at Entry ADAS-cog Mild Moderate Severe Mean (line) and 90% Credible Intervals* (gray shaded area) * the posterior probability of the average individual's mean ADAS-cog being in the interval is 90%
23 Conclusions from CAMD Data from individual clinical trials of patients with Alzheimer s Disease had limited power and frequently failed due to variability in outcome and small sub-groups. Mapping control arm data to a standard format for >6,000 patients in 20 trials created a dataset with higher quality (common methodology for ADAS-cog) and greater power to assess variables affecting progression Severity and age at entry ApoE4 genotype Data standards can 1. Increase learning from clinical research study analysis 2. Facilitate data sharing across research studies 3. Create databases with which to design more informative and efficient research studies NOTE: Data standards are most valuable (significantly reducing time and resources) when implemented from the beginning. CDISC
24 Value of Standards Using the data wisely Making sure it is accurate, that it is used as agreed, and that we can find it! We owe it to the patients who agree to participate in research studies and share their data. CDISC
25 Key Messages Regulated Clinical Research requires sushigrade quality data.and this translates to a robust quality process that is not easy to execute and must be trustworth Building quality in from the beginning is ideal.adding in quality at the back end (e.g. through mapping or normalization) can certainly be done but only at a high cost (time and resources) Data standards improve data quality..especially when implemented in the data collection steps at the beginning CDISC
26 26
27 Information from healthcare (private, aggregated) to enable research Healthcare Quality healthcare Informed decisions Personalized medicine Patient safety and privacy Public health Improved therapies Efficiencies/reduced costs Research findings to inform healthcare decisions Research Discovery of new therapies Understanding diseases Assessing efficacy Monitoring safety Public health/quality evaluations Understanding responses (genomics, biomarkers) Testing/comparing therapies (CER) Post-marketing surveillance CDISC 2012
28 CDISC
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