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Quality Assurance Program Presenter: Erin Mustain. 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.

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Quality Assurance ProgramPresenter: Erin Mustain


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Recommendation 5Benchmarks

  • 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.


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Progress

  • Institutionalized Quality Assurance

  • Business Rules

  • Error chart

  • Steering committee and User groups collaboration

  • Eliminated system-generated violations for paper tracking SMRs

3


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Total Study Error

Data Population

Data Generation

Training

Migration

Manual Data Entry

System Limitations

SMRs

Manual

Non-numeric data (ND, QND, etc.) not handled by eSMR

Training Manuals don’t follow Business Rules

Errors in data entry form

Field not populated

Business Rules not followed

Lab errors

System can’t handle unique orders (several facilities under one permit)

Field auto-populated incorrectly

Typos

Sampling Errors

Calculation errors

Instruction not consistent

Difficulty Data Mining

No place to store data (enrollee history)

Field not appearing

Intentional manual errors

Lack of Training

Selecting the wrong link

Doesn’t enforce all of the Business Rules

Data doesn’t follow business rules

Duplicate entry

Can’t easily delete records

Data entered into SWIM incorrectly

System generated duplicates (SMARTS)



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What decisions will be

made with this

data?

SOPs

QAP

Audits

Business rules

Data

Validation

CIWQS

QA

Program

Data

Cleanup

DQOs

Data

Verification

Training

Communication

Corrective

Action

QA

Reports

Integration with

State Water Board

QMP

6


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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


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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


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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


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External Audit

Policies and procedures for data entry

Onsite audit of Regional Boards and State Board programs

Security, performance, and policies and procedures

Onsite audit of Division of Information Technology

Data audit using stratified random design approach

Accuracy of records

10


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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

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