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Quality Assurance Program Presenter: Erin Mustain 1 Recommendation 5 Benchmarks Data quality issues have been categorized and quantified. A detailed plan exists for addressing sources of continuing errors and correcting historical errors. The plan has been validated with representative data samples Substantive progress has been made toward correcting major categories of errors. The Steering Committee agrees that progress is being made and that there is a high probability that existing data problems will be resolved. 2 Progress Institutionalized Business Rules Error chart Quality Assurance Steering committee and User groups collaboration Eliminated system-generated violations for paper tracking SMRs 3 Total Study Error Data Population Migration Manual Data Entry Business Rules not followed Field autopopulated incorrectly Typos Difficulty Data Mining Selecting the wrong link Data doesn’t follow business rules Training System Limitations Field not populated Field not appearing Data Generation Duplicate entry Non-numeric data (ND, QND, etc.) not handled by eSMR System can’t handle unique orders (several facilities under one permit) No place to store data (enrollee history) SMRs Training Manuals don’t follow Business Rules Instruction not consistent Lack of Training Manual Lab errors Sampling Errors Errors in data entry form Calculation errors Intentional manual errors Doesn’t enforce all of the Business Rules Can’t easily delete records Data entered into SWIM incorrectly System generated duplicates (SMARTS) 4 Current vs. Historical Data 5 What decisions will be made with this data? SOPs QAP Audits Data Validation DQOs Business rules CIWQS QA Program Data Verification Data Cleanup Training Communication Corrective Action Integration with State Water Board QMP QA Reports 6 Plan and Procedures Quality Assurance Plan Scope Roles and responsibilities Data quality indicators Quality objectives Assurance activities Problem reporting and corrective action Audits Migration and future projects 7 Plan and Procedures Standard Operating Procedures Data Cleanup Training Document Management Corrective Action Quality Assurance Reports Audit End-user-layer enhancements and testing Database enhancement prioritization Report prioritization 8 Plan and Procedures DIT Standard Procedures Document Maintenance & Documentation Requirements System Environment Data model, database, data integration, & maintenance Application source code integration & maintenance Application and database source code & scripts repository CVS Database tools & scripts standards Issue routing Maintenance implementation 9 External Audit Onsite audit of Regional Boards and State Board programs Policies and procedures for data entry Onsite audit of Division of Information Technology Security, performance, and policies and procedures Data audit using stratified random design approach Accuracy of records 10 Next Steps Quantifying the data quality issues – Audit Correcting historical data – recommendations to management after audit results Validate the QAP and SOPs with representative data samples Implement training program Continuing process improvement at all levels (QA Program is not static) Establish a mechanism for communication between QA Program and panel 11