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Identifying Problem Sources at Data Entry and Collection. Nishan Ahmed. Regional Training Workshop on Influenza Data Management Phnom Penh, Cambodia July 27 – August 2, 2013. National Center for Immunization & Respiratory Diseases. Influenza Division. Methods to Identify Data Problem s.

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Identifying Problem Sources at Data Entry and Collection


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    1. Identifying Problem Sources at Data Entry and Collection Nishan Ahmed Regional Training Workshop on Influenza Data Management Phnom Penh, Cambodia July 27 – August 2, 2013 National Center for Immunization & Respiratory Diseases Influenza Division

    2. Methods to Identify Data Problems • Data collection • Review of paper form for completeness • Review of key fields for validation • Sign off by data collector and reviewer • Data entry • Double Data Entry • Built in checks at the data entry level • Field Validation Rules • Keeping data consistent across the record

    3. Data Collection • Review of paper form - onsite • Data collected on form • Form is reviewed by second person for completeness • Form sent to central location for entry into data management system • Review of key indicators– onsite • Data collected on form • Form reviewed by second person for accuracy and completeness of key indicators • Date of birth vs. date of admission • Gender vs. pregnancy • Temperature falls in pre-determined acceptable range • Sign off by data collector and reviewer

    4. Data Entry • Double Data Entry • Pros • All data is entered twice for ease of comparison • ACCESS - Programmed computer-run check for inconsistencies between the two entries • Useful in picking out keystroke mistakes • Cons • EXCEL – requires several steps to review and validate • Not useful when dealing with systematic errors or incorrect measurements • Time consuming procedure - costly

    5. Data Entry

    6. Data Entry

    7. Data Entry

    8. Data Entry

    9. Data Entry

    10. Data Entry

    11. Data Entry • Built-in checks may include: • Field Validation Rules • Date Validations • Date of onset should be on or before the date of specimen collection, date of consultation or admission • Date of sample collection should be before date received at laboratory • Other validity checks • Temperature should be a valid measured temperature (i.e. between 35ºC and 41ºC) • Pregnancy status should only be “yes” if patient is female and of child bearing age • Test results should be consistent with the type of test performed (i.e. a rapid test will not yield Influenza A subtyping results)

    12. Data Entry • Built-in checks may include: • Forced consistency across fields • Forced – • Data entry screen will not let you proceed with incorrect data, • Voluntary - • Gives a warning that the value entered may be wrong but will let you continue

    13. Data Entry Setting Up Field Validation Rules On Tables - ACCESS

    14. Data Entry Macros to Ensure Data Consistency Across Fields on Forms - ACCESS

    15. Data Entry Macros to Ensure Data Consistency Across Fields on Forms - ACCESS

    16. Data Entry Macros to Ensure Data Consistency Across Fields on Forms - ACCESS

    17. Data Entry • Built-in checks may include: • System queries at site to aid in fixing data errors immediately • Contain additional data validation criteria • Additional validations that might be important • For example: • Might check for dates that seem reasonable for the time period, or pull a query for dates that are too far apart (i.e. date of onset is more than a week from the date of consultation)

    18. Data Entry Setting up System Queries - ACCESS

    19. Data Entry Setting up System Queries - ACCESS

    20. Final Thoughts • There are many different ways to ensuredata quality at the data entry and data collection level • Double Data Entry • Good for finding mis-keyed values • Built-in checks at data entry • Can include both single field validations and controls to keep data consistent across fields • System queries • For systematic data checks and potential error identification • Use what works for you and your data process

    21. Exercise • Objective: • Create data validation controls/data checks for your system • Create built-in data checks in your database (i.e. date validations) • Create system queries so that sites can assist in the data cleaning process

    22. THANK YOU!!! National Center for Immunization & Respiratory Diseases Influenza Division