Process Control Based Approach to Ensuring Quality Control in Data Requests

1. Process Control Based Approach to Ensuring Quality Control in Data Requests G. White, S. Vest, J. Jackson-Thompson Missouri Cancer Registry University of Missouri-Columbia Supported by CDC/NPCR Cooperative Agreement #U55/CCU721904-05 and a contract between the University of Missouri and the Missouri Department of Health and Senior Services

2. Overview • Process Control and Process\Quality Engineering • Application to Registry Operations • Case Study • Missouri Cancer Registry Data Requests • Methods • Results

3. What is Process Control? • Process/Quality Engineering • Design error opportunities out of the system • Evolutionary Process • Continually monitoring measures of quality • Identifying error opportunities • Updating system

4. Continuous Quality Improvement • Modern manufacturing relies on CQI • Began at Western Electric in the 1930’s • J.M. Juran and Edward Demming • Quality Management • Statistical Process Controls • WWII and Post-war • ISO certification

5. What is Quality • Quality is the inverse of variability • High Quality = Little Variation • Low Quality = Lots of Variation • “On-Target with Minimum Variation” • Minimum Variation typically more important than on target in process/quality control • Customer wants predictability • “You Get What You Pay For”

6. How this Applies to Registry Operations • Registries don’t manufacture tangible products – produce information • Production relies on a process • Optimization through Process Engineering • Work-flow design that minimizes error opportunities is the most efficient

7. Case Study:Missouri Cancer Registry Data Requests • How Data Requests are received • Oral • Written • Electronic • How Data Requests are filled • SEER*Stat • Customer Relations • Filled Requests returned

8. What Went Wrong • Data requests could take a day to weeks to fill • Mistakes made in filling requests, required multiple attempts to meet customer’s needs • No clear procedure for the process • No measures of quality available

9. Poor Process Design • Thought we had an “efficient” process • Example of “efficient” design not being optimal • No accountability • No process monitoring • No feedback to improve process

10. Observations • No one person was responsible for receiving and approving data requests • No tracking system for these requests • Prose descriptions of data requested • Confusion resulted in multiple attempts to fill requests

11. Defining The Problems • Two biggest problems • No tracking system – couldn’t tell where in the process a request was. • Unclear requests – customer requests were unclear and difficult to translate in to a SEER*Stat query.

12. The Solutions • A tracking system with clearly delineated responsibilities • A “caretaker” • Retain Multiple Approvers • A clearly designed data request form based on SEER*Stat

15. Results • New form substantially reduced confusion in filling data requests • New procedures and log file helped improve tracking and fill-time consistency • Higher quality in terms of reduced errors in the requests and in fill-time.

16. Other Results • May seem more complex and slower • Reducing error opportunities increased efficiency • Continual Improvement is an important part of new system

17. Conclusion and Lessons Learned • This process control-based approach is easily implemented • Resulted in noticeable improvement • Requires user feedback for continuous improvement

18. Future Prospects • This approach can be applied to all areas of operations • By focusing on designing work-flow to minimize error opportunities you automatically increase efficiency

19. Thank You For Your Attention