Data Processing Topic 4 Health Management Information Systems João Carlos de Timóteo Mavimbe Oslo, April 2007.
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Data Processing
Topic 4
Health Management Information Systems
João Carlos de Timóteo Mavimbe
Oslo, April 2007
Collection
InputRaw data
PresentingInterpreting
USEANALYSIS Processing
Data Collation & Accuracy
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
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
Processing data in the information cycle
Collection
InputRaw data
PresentingInterpreting
USEANALYSIS Processing
Data Collation & Accuracy
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.
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
Why checking data is vital?
Better to have
NO data,
than to have
inaccurate data!!!
Common problems with data
large gaps unusual month to month variations
duplicationPromoted 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
Good quality data
• WHAT?WHAT?data that are complete, correct and consistent (and
timely) • WHY?WHY?
facilitates: good decision-making
appropriate planningongoing Monitoring & Evaluationimprovement of coverage and quality of
care• HOW?HOW?
provides an accurate picture of health programmes and services
Visual scanning (eyeballing) checking for 3 C’s
• Completeness
• Correctness
• Consistency
Are data complete?
submission by all (most) reporting facilities
physical events observed = events registered (how?)
registered data = collated data (how?)
all data elements registered
Are data correct?
data within normal ranges
logical data
existing standardised definitions used adequately
legible handwriting
are there any preferential end digits used?
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?
Accuracy enhancing principles
• Training
• User-friendly collection/collation tools
• Feedback on data errors
• Feedback of analysed Information
• Use of information (and prove it!)
How do you detect Errors?
• general accuracy checking measures
• specific accuracy checking measures
General accuracy checks
• Completeness
• Proper place
• Friendly tools
• Arithmetic
Specific Accuracy Checks
• Time-trend consistency
• Time-trend variation
• Minimum/maximum
• Realism
• Comparison
• Parts vs whole
• Preferential end-digits
Minimum and Maximum Values
0
500
1000
1500
2000
2500
3000
Jan Feb March April May June July
Num
ber
Minimum and Maximum Values
Maximum
Minimum
PREFERENTIAL END-DIGITS
JAN FEB MARCH APRIL MAY JUNE JULY
255 235 245 225 235 245 255
Other examples ?
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
What to do if you find errors?
• Find the cause
• Correct the error
• Prevent future errors
Good data qualityGood data quality 10 steps to achieve it10 steps to achieve it
1. small, essential dataset - EDS
2. clear definitions - standardized
3. careful collection and collation of data – good tools
4. local analysis of data using relevant indicators
5. presentation of information to all collectors
6. regular feedback on both data and information
7. supportive supervision - at all levels
8. ongoing training and support
9. discussion of information at facility team meetings
10. monitoring use of information
Please remember…!
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
Controlling quality with DHIS
• Maximum / minimum values
• 13-month retrospective
• Regression line
• Validation rules: absolute statistical
• Validation reminders
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