Gathering and Leveraging Quality-Centric Metadata in Clinical Trials

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Improving the quality of metadata in the clinical trials process can have profound effects on costs and efficiency. Gathering and leveraging the quality data can optimize your clinical trials.

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Gathering and Leveraging Quality-Centric Metadata in Clinical Trials

  1. 1. Aaron Gadberry Director, Software Architecture Gathering and Leveraging Quality- Centric Metadata in Clinical Trials
  2. 2. Increasing Initial Quality ► Quality data on the first pass ► Problem personnel ► Historic performance ► Dynamic Risk Based Monitoring ► Cost model adjustments 2
  3. 3. Lower Quality = Higher Cost ► Direct Cost • More cleaning ► Indirect Cost • Treatments could be less effective • Treatments could be less safe 3 Data Entry Data Cleaning$$$ Clean Data $$$
  4. 4. Responsibility? ► Who is managing site quality today? • Who is reporting on Entry Personnel • Who is holding them accountable –CRA’s –Site Audits ► What are the key metrics? 4
  5. 5. What Metrics Do We See? Completeness ► Page report ► Time to Entry ► Time to SDV ► Time to Sign ► Time to Resolve Queries Quality ► Query counts ► ► ► ► 5
  6. 6. What Metrics Don’t We See? ► Was the initial data accurate? • If not, was intervention required? • If so, how many interventions? ► What trends are visible from this? • How often is initial data accurate? • How often is a change self-initiated? 6
  7. 7. Difficult to Collect and Report On ► Applicable data takes time to gather • Hindered by turnover at sites ► Not preserved for the next trial ► Visible as points, but not aggregated ► We need to predict quality to realize the full benefits of Risk Based Monitoring 7
  8. 8. We Have a Wealth of History ► Leverage historic quality data during site selection • CTMS / Cloud ► Show quality over time • Cross-trial, cross-sponsor ► Rapid visibility • Hotspot reports over users 8
  9. 9. Number of Users by Data Quality 9
  10. 10. Volume of Data by Data Quality 10
  11. 11. With Quality Increased by 3% 11
  12. 12. With Quality Increased by 5% 12
  13. 13. With Quality Increased by 10% 13
  14. 14. Major Results from Minor Changes ► Increasing user initial quality • By 3% reduces data changes by 31% • By 5% reduces data changes by 48% • By 10% reduces data changes by 79% 14
  15. 15. Application of the Tools ► Measuring makes us aware ► But we can’t really solve the problem ► Only the site can truly manage • The personnel are theirs ► Why isn’t this happening today? • Lack of measurement, lack of incentive 15
  16. 16. We Can Change These ► Measurement • New Metrics ► Incentive • Contract for quality ► For Sites and ClinOps • Similar metrics for CRAs 16
  17. 17. The Value of Quality ► Performance and consistency gain value beyond repeat business • Additional Income • Exposure • Recruitment ► A Market Emerges for Quality-Centric Sites and Personnel 17
  18. 18. The Market will Adapt ► We will pay more for quality data But ► The overall cost is lower • Data is more accurate • Confirm and adjust • Dynamic Risk Based Monitoring ► Risk is user-based 18
  19. 19. A Quality-Centric Model ► Expectation to manage quality is set ► Quality directly influences payment ► Performance impacts future business ► Sites are motivated and compensated 19
  20. 20. from Concept to Cure with DATATRAK ONE DATATRAK International Cleveland, Ohio Bryan, Texas Cary, North Carolina 888.677.DATA (3282) Toll Free www.datatrak.com ® ®

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