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Is Quality Assurance a Commodity RIEDLINGER

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Is Quality Assurance a Commodity RIEDLINGER

  1. 1. Is Quality Assurance a Commodity? Commodity: something of use of advantage of value
  2. 2. Contributors:  Karen Riedlinger  Jeanette Bardsley  Alan Bauck  Gwyn Saylor  Don Bachman  Arthur Dixon  Sabrina Luke
  3. 3. Win a Prize!Count the FYing words in the presentation  electriFY  justiFY  stupeFY  commodiFY  codiFY  quantiFY  demystiFY  clariFY  indemniFY  rectiFY  uniFY  skeptiFY
  4. 4. Funding for health researchis getting tighter
  5. 5. The investigator pondersbudget line items
  6. 6. How do I justIFYspending proposal money for QA? Is there benefit I can quantiFY? Will it help me indemniFY my conclusions? (indemnify=guarantee)QA may expose issues,but does it electriFY?
  7. 7. The investigator votes tocommodiFY QA, focusing on what benefits his project
  8. 8. Early discovery of poor data givesyou time to extract better data.
  9. 9. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK DEMOG 6 PROCEDURE 1 2 9 During 1. ENROLL trend analysis, 7 discovered site was VITALS 5 8 missing many years of DEATH 10 3 data. LAB 4
  10. 10. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK DEMOG 6 PROCEDURE 1 2 9 ENROLL 7 VITALS 5 8 2. When comparing DEATH 10 3 distribution of procedures, LAB 4 this site was missing some major categories.
  11. 11. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK 3. When comparing death DEMOG 6 PROCEDURE dates to utilization dates, 1 2 9 ENROLL many discrepancies found. 7 VITALS 5 8 DEATH 10 3 LAB 4
  12. 12. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK DEMOG 4. Site, after looking at median and 6 PROCEDURE 1 2 9 units, realized they had included an ENROLL 7 incorrect set of labs into one testtype. VITALS 5 8 DEATH 10 3 LAB 4
  13. 13. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK DEMOG 6 PROCEDURE 1 2 9 ENROLL 7 VITALS 5 8 5. This site had never built DEATH 10 3 vitals and so could not LAB 4 participate in this project unless built.
  14. 14. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK DEMOG 6 PROCEDURE 1 2 9 6. A high percentage of ENROLL 7 cohort was missing VITALS 5 8 demographic information. DEATH 10 3 LAB 4
  15. 15. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK DEMOG 6 PROCEDURE 1 2 9 ENROLL 7 VITALS 7. After viewing 5 8 DEATH comparisons of rates of 10 3 LAB 4 insurance types site decided enrollment data needed to be reviewed.
  16. 16. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK DEMOG 6 PROCEDURE 1 2 9 ENROLL 7 VITALS 5 8 8. DEATH comparing rates of After 10 3 vital records across sites LAB 4 decided they were missing records.
  17. 17. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK DEMOG 6 PROCEDURE 1 2 9 ENROLL 9. Comparing their 7 VITALS distribution of 5 8 DEATH px_codetypes with other 10 3 LAB 4 sites, site investigated and found additional mapping resources.
  18. 18. Early discovery of issuesallows time to fix tables Table SiteB SiteC SiteE SiteG SiteH SiteK 10. rectifY: this site, DEMOG 6 PROCEDURE 1 2 after comparing 9 ENROLL with other sites, 7 VITALS reinvestigated, 5 8 DEATH 10 found new 3 LAB sources and 4 doubled the size of death file.
  19. 19. Early QA work makes youranalysis model more accurate DEMOGENCOUNTER ENROLL CENSUS CAUSE OF DEATH PHARMACY
  20. 20. Early QA work makes youranalysis model more accurate Produced local individual reports: % in project cohort vs overall site DEMOG population.ENCOUNTER ENROLL CENSUS CAUSE OF DEATH PHARMACY
  21. 21. Early QA work makes youranalysis model more accurate DEMOG Reviewed department and provider missingness and distribution to determine if combination could be used as ENCOUNTER proxy variable. ENROLL CENSUS CAUSE
  22. 22. Early QA work makes youranalysis model more accurate DEMOG ENCOUNTER demystiFY: Asked sites to review their distribution across different enrollment plans and comment on large variances when compared ENROLL with other sites. CENSUS CAUSE OF DEATH PHARMACY
  23. 23. Early QA work makes youranalysis model more accurate DEMOGENCOUNTER ENROLL Discovered percent in census variables (such as education) have CENSUS at least three different formats. CAUSE OF DEATH PHARMACY
  24. 24. Early QA work makes youranalysis model more accurate DEMOGENCOUNTER ENROLL CENSUS Produced local lists of people who CAUSE are in COD but do not have OF underlying causetype or have DEATH more than one. PHARMACY
  25. 25. Early QA work makes youranalysis model more accurate DEMOGENCOUNTER ENROLL CENSUS CAUSE OF DEATH Produced local individual reports:PHARMACY % with drug coverage
  26. 26. My QA budget doesNOT need to break the bank
  27. 27. Take advantage of past QA work Confirmed with VDW Issue Tracker
  28. 28. CodiFY: Adapt existing QA programs for your project *------------------------------------------------------------------------------------- Program Name: vdw_cod_qa_local_wp01v01.sas VDW Version: V3 Purpose: Create QA counts and statisitics for a sites VDW Cause of Death file. Generate reports to assist in verifying or improving data. Generate datasets to be returned and analyzed. -------------------------------------------------------------------------------------- Dependencies : VDW Content Areas &_VDW_CAUSE_OF_DEATH &_VDW_DEATH Other Files -------------------------------------------------------------------------------------- Folders appearing in the root directory are described below. document, input, local_only, sas, share -------------------------------------------------------------------------------------- document: Contains the workplan for this program. -------------------------------------------------------------------------------------- input: qa_macros.sas contain the QA Macros developed by CESR DCC called in this program --------------------------------------------------------------------------------------
  29. 29. clariFY: Early in process,investigator meets with project staff to define important/fixable data to QA Important Important Not fixable Fixable Not important Not important Fixable Not fixable
  30. 30. skeptiFY: We often do not know what we don’t know .
  31. 31. uniFYfocused,multisite QA isvaluable
  32. 32. Funding for healthresearch is getting tighter . . . . . . but how can I NOT justiFY spending proposal money for QA?
  33. 33. I vote to commodiFY QA.  I will include QA as a line item in my proposal.  Each project of mine can do limited and focused QA. My project, with minimal amount of QA funding, will help make the VDW better.
  34. 34. FOCUSED QUALITY ASSURANCE IS A COMMODITY
  35. 35. stupeFY:did you get the right count of FYing words? 14

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