Implementation Science Workshop<br />Ensuring High Quality Data<br />28 October 2010<br />Day Munatsi - Chief Data Manager...
Agenda<br />Data Quality – Definition<br />Quality Assurance Aspects<br />Quality Management Considerations<br />Quality P...
Data Quality - Definition<br />The degree of excellence exhibited by the data in relation to the portrayal of the actual s...
Quality Assurance<br />"A task well done is its own reward.” Ralph Waldo Emerson<br />
Quality Assurance<br />Quality assurance (QA) is the prevention, detection, and correction of errors or problems<br />QA i...
Quality Management Considerations<br />quality planning, <br />quality control,<br />quality improvement<br />
Quality Planning<br />Make use of :<br />Data Management Plan<br />Standard Operating Procedures (SOPs) <br />Best Practic...
Quality Planning <br />What data collection instrument will be used ?<br />Where will we store the data ?<br />Who will pe...
Where do we start?<br />Paper Data Collection Options<br />Case Report Forms (CRFs) <br />e.g. questionnaire, data collect...
CRF -Example<br />VL600<br />VL<br />600<br />02/03/2009<br />15:00<br />
Database Design Consideration…1<br />Hidden text fields, where text appears along with numeric values. <br />    E.g. 10-1...
Database Design Consideration…2<br />Header information<br />	E.g. PID, Visit Date, Visit Code, Study ID<br />Single check...
Dates – Special Note :<br />Dates on a CRF typically fall into three categories:<br />Known dates related to the study <br...
Other Considerations<br />Avoid repetition<br />Identify a unique identifier or primary key<br />Ensure that individual co...
Case of Bad Data <br />
Keep it Simple<br />
MS Access – Database Design<br />
MS Access – Database Design (2)<br />
Even for MS Excel<br />
Data Entry Form – Microsoft Access<br />
Training<br />SOP training<br />Data management system training<br />Specific workflows<br />Study specific procedures<br />
	Quality Control<br />
Data Entry Form – Epi Data<br />
Data entry considerations….1<br />Define “must enter” fields – <br />No record is complete unless: such and such is entere...
Data entry considerations….1<br />Have at least 2 levels of data validation if possible.<br />	Double Data Entry, <br />De...
Must Enter Fields<br />
Skip Patterns<br />
Check Constraints on Date Fields - 1<br />
Check Constraints on Date Fields - 2<br />
Pull Down Menu’s<br />
Quality Control Tools<br />Make use of:<br />Discrepancy management<br />Weekly/monthly reports<br />Quality Control repor...
	Quality Improvement<br />
Quality Improvement<br />Relies on:<br />Continuous training<br />Interim Quality Assurance audits<br />Change management ...
Using Existing Data….1<br />It should be:<br />Consistent<br />coding , variable naming, annotated<br />Complete (almost)<...
Using Existing Data….2<br />Also consider the impact of:<br />Free text<br />Outliers<br />Double / Single data entry<br /...
Source Data Verification<br />
Further Analysis<br />Data can be exported to other programs for analysis.<br />Examples include <br />SAS<br />SPSS<br />...
THE END<br />	Questions and Discussion<br />
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Ensuring high quality data

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Transcript of "Ensuring high quality data"

  1. 1. Implementation Science Workshop<br />Ensuring High Quality Data<br />28 October 2010<br />Day Munatsi - Chief Data Manager<br />Centre for the AIDS Programme of Research in South Africa<br />
  2. 2. Agenda<br />Data Quality – Definition<br />Quality Assurance Aspects<br />Quality Management Considerations<br />Quality Planning<br />Quality Control<br />Quality Improvement<br />Further Analysis<br />Summary<br />
  3. 3. Data Quality - Definition<br />The degree of excellence exhibited by the data in relation to the portrayal of the actual scenario.<br />The state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use.<br />The processes and technologies involved in ensuring the conformance of data values to project requirements and acceptance criteria<br />
  4. 4. Quality Assurance<br />"A task well done is its own reward.” Ralph Waldo Emerson<br />
  5. 5. Quality Assurance<br />Quality assurance (QA) is the prevention, detection, and correction of errors or problems<br />QA is closely tied to regulatory compliance<br />Good practice must be closely tied to following regulations.<br />
  6. 6. Quality Management Considerations<br />quality planning, <br />quality control,<br />quality improvement<br />
  7. 7. Quality Planning<br />Make use of :<br />Data Management Plan<br />Standard Operating Procedures (SOPs) <br />Best Practices (Guidelines)<br />
  8. 8. Quality Planning <br />What data collection instrument will be used ?<br />Where will we store the data ?<br />Who will perform data entry?<br />Training? On-line help? <br />How data entry will be performed?<br />
  9. 9. Where do we start?<br />Paper Data Collection Options<br />Case Report Forms (CRFs) <br />e.g. questionnaire, data collection forms<br />Patient forms<br />Existing data sources<br />Databases<br /> e.g. MS Access, Epi Data, DataFax, OpenClinica<br />Spreadsheets <br />e.g. MS Excel<br />
  10. 10. CRF -Example<br />VL600<br />VL<br />600<br />02/03/2009<br />15:00<br />
  11. 11. Database Design Consideration…1<br />Hidden text fields, where text appears along with numeric values. <br /> E.g. 10-15 , <22<br />Dates of all kinds<br /> E.g. “6/-/95” for June 1995 , mm/dd/yyyy Vs dd/mm/yyy<br />Text fields and annotations<br /> E.g. Categorical (coded) values, Short comments, Reported terms, Long comments.<br />
  12. 12. Database Design Consideration…2<br />Header information<br /> E.g. PID, Visit Date, Visit Code, Study ID<br />Single check boxes<br />E.g. Check if any adverse events: [ ]<br />Calculated or derived values<br />E.g. age ,number of days on treatment, weight in kilograms<br />
  13. 13. Dates – Special Note :<br />Dates on a CRF typically fall into three categories:<br />Known dates related to the study <br />(visit date, lab sample date)<br />Historical dates <br />(previous surgery, prior treatment)<br />Dates closely related to the study but provided by the patient <br /> (concomitant medication, adverse events [AEs])<br />
  14. 14. Other Considerations<br />Avoid repetition<br />Identify a unique identifier or primary key<br />Ensure that individual column values are valid<br />Follow “Do and Review” policy<br />
  15. 15. Case of Bad Data <br />
  16. 16. Keep it Simple<br />
  17. 17. MS Access – Database Design<br />
  18. 18. MS Access – Database Design (2)<br />
  19. 19. Even for MS Excel<br />
  20. 20. Data Entry Form – Microsoft Access<br />
  21. 21. Training<br />SOP training<br />Data management system training<br />Specific workflows<br />Study specific procedures<br />
  22. 22. Quality Control<br />
  23. 23. Data Entry Form – Epi Data<br />
  24. 24. Data entry considerations….1<br />Define “must enter” fields – <br />No record is complete unless: such and such is entered;<br />Define “skip patterns” – <br />If answer on field 1 is ‘No’ then jump to field 5.<br />Define “edit checks”<br /> If Date Of Birth is inconsistent with today’s date , raise a flag <br />Make data entry fool proof. <br /> Grade level can be entered as a number (4 or 4th or four). By using a pull-down menu with the correct data format these mistakes can be avoided.<br />
  25. 25. Data entry considerations….1<br />Have at least 2 levels of data validation if possible.<br /> Double Data Entry, <br />Define missing value codes <br /> D for Not Done , U for Unknown, A for Not Applicable<br />
  26. 26. Must Enter Fields<br />
  27. 27. Skip Patterns<br />
  28. 28. Check Constraints on Date Fields - 1<br />
  29. 29. Check Constraints on Date Fields - 2<br />
  30. 30. Pull Down Menu’s<br />
  31. 31. Quality Control Tools<br />Make use of:<br />Discrepancy management<br />Weekly/monthly reports<br />Quality Control reports<br />Audit trails (if applicable)<br />Visit reminders<br />Data queries<br />External system checks<br />
  32. 32. Quality Improvement<br />
  33. 33. Quality Improvement<br />Relies on:<br />Continuous training<br />Interim Quality Assurance audits<br />Change management procedures<br />Documentation <br />Knowledge Base<br />
  34. 34. Using Existing Data….1<br />It should be:<br />Consistent<br />coding , variable naming, annotated<br />Complete (almost)<br />few missing data<br />Relevant to study question<br /> avoid selection bias<br />
  35. 35. Using Existing Data….2<br />Also consider the impact of:<br />Free text<br />Outliers<br />Double / Single data entry<br />The QC process<br />Source Data Verification<br />
  36. 36. Source Data Verification<br />
  37. 37. Further Analysis<br />Data can be exported to other programs for analysis.<br />Examples include <br />SAS<br />SPSS<br />MINITAB<br />Excel<br />
  38. 38. THE END<br /> Questions and Discussion<br />

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