1 / 27

Data Quality Management Strategies in Health Systems

Learn the importance of data quality, techniques for ensuring accuracy, and error detection and prevention in health information systems. Understand the impact of inaccurate data and common data problems.

browned
Download Presentation

Data Quality Management Strategies in Health Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Health Management Information Systems Data Processing Topic 4 João Carlos de Timóteo Mavimbe Oslo, April 2007

  2. Presenting Interpreting Data Collation & Accuracy ANALYSIS Processing USE Collection Input Raw data

  3. Learning objectives (1) • Explain the use of the data handling process as a strategy to provide good data quality • Explain the techniques for providing and ensuring good quality data • Describe mechanisms for assessing data quality

  4. Learning objectives (2) • Examine the importance of good data quality • Appreciate the importance of accuracy in health data • Understand why errors occur • Acquire the skills required to detect, correct and prevent future errors • Establish and apply the logistics of error checking

  5. Presenting Interpreting Data Collation & Accuracy ANALYSIS Processing USE Collection Input Raw data Processing data in the information cycle

  6. Ensuring data accuracy • Once data has been collected, it should be checked for any inaccuracies and obvious errors. • Ideally this should be done as close to the point of data collection as possible. • But also at all steps of the information cycle.

  7. Why checking data is vital? • Use of inaccurate data is DANGEROUS • Producing data is EXPENSIVE • Inaccurate data are USELESS data • Producing inaccurate data is a WASTE of money and time

  8. Why checking data is vital? Better to have NO data, than to have inaccurate data!!!

  9. Common problems with data • large gaps • unusual month to month variations • duplication • Promoted by different vertical programs • inconsistencies • unlikely values • data is present where it should not be • typing errors • maths problems – poor calculation • data entered in wrong boxes

  10. Good quality data • WHAT? data that are complete, correct and consistent (and timely) • WHY? facilitates: good decision-making appropriate planning ongoing Monitoring & Evaluation improvement of coverage and quality of care • HOW? provides an accurate picture of health programmes and services

  11. Visual scanning (eyeballing) checking for 3 C’s • Completeness • Correctness • Consistency

  12. Are data complete? • submission by all (most) reporting facilities • physical events observed = events registered (how?) • registered data = collated data (how?) • all data elements registered

  13. Are data correct? • data within normal ranges • logical data • existing standardised definitions used adequately • legible handwriting • are there any preferential end digits used?

  14. Are data consistent? • data in the similar range as this time last year (last reporting period) • no large gaps • is the correct target population being used?

  15. Accuracy enhancing principles • Training • User-friendly collection/collation tools • Feedback on data errors • Feedback of analysed Information • Use of information (and prove it!)

  16. How do you detect Errors? • general accuracy checking measures • specific accuracy checking measures

  17. General accuracy checks • Completeness • Proper place • Friendly tools • Arithmetic

  18. Specific Accuracy Checks • Time-trend consistency • Time-trend variation • Minimum/maximum • Realism • Comparison • Parts vs whole • Preferential end-digits

  19. PREFERENTIAL END-DIGITS Other examples ?

  20. Practical error checking procedures • Check completeness of the data forms • Set minimum and maximum values • Examine a printout of data for errors using general and specific error checks • Hold an error feedback session

  21. What to do if you find errors? • Find the cause • Correct the error • Prevent future errors

  22. Good data quality10 steps to achieve it

  23. small, essential dataset - EDS • clear definitions - standardized • careful collection and collation of data – good tools • local analysis of data using relevant indicators • presentation of information to all collectors • regular feedback on both data and information • supportive supervision - at all levels • ongoing training and support • discussion of information at facility team meetings • monitoring use of information

  24. Please remember…!

  25. Data, in order to be locally useful, should be: • AVAILABLE ON TIME fix dates for reporting • AVAILABLE AT ALL LEVELS who reports to whom? - feedback mechanisms • RELIABLE & ACCURATE check that all data is correct, complete, consistent • COMPREHENSIVE collected from all possible data sources • USABLE if no action, throw data away • COMPARABLE same numerator and denominator definitions used by all

  26. Controlling quality with DHIS • Maximum / minimum values • 13-month retrospective • Regression line • Validation rules: absolute statistical • Validation reminders

More Related