Data Processing &

Data Quality

A virtuous cycleThe implicit assumptions underlying information systems are

twofold: first, that good data, once available, will be transformed into useful information which, in turn, will influence decisions; second, that such information-based decisions will lead to a more effective and appropriate use of scarce resources through better procedures, programmes, and policies, the execution of which will lead to a new set of data which will then stimulate further decisions, and so forth in a spiral fashion.

(Sauerborn 2000 in Lippeveld et .al. Design and implementations of health information systems)

How do we process it?

How do we present it?

How do we use it? Reliable Information

Information CycleWhat do we collect?

Stages Tools Outputs

data sources & tools

Timely Quality data

Data quality checks & analysisInformation

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. – Identify cause– Prevent future errors

Remember Johan’s little investigation

Why checking data is vital?

Use of inaccurate data leads to– Wrong priorities (focus on the wrong data)– Wrong decisions (not applying the right actions)– Garbage in = garbage out

Producing data is EXPENSIVE– Waste of resources and time to collect poor data

Data, in order to be useful, should be:

RELIABLE: correct, complete, consistent

TIMELY: fixed deadlines for reporting

AVAILABLE : who reports to whom? feedback mechanisms

ACTIONABLE: no action = throw data away

COMPARABLE: same numerator and denominator definitions used by all (e.g. geography vs org. unit function)

Complete data?

Geography: submission by all (most) reporting facilities

Time: can you do analysis over time? Consistency?

Does your services cover the full population? Many indicators depend on population figures as denominators

Correct data?

Are we even collecting the right data?

The data seems sensible/plausible?

The same definition applied uniformly

Legible handwriting

Are there any preferential end digits used?

PREFERENTIAL END-DIGITS

JAN FEB MARCH APRIL MAY JUNE JULY

250 230 240 220 230 240 250

Consistent data?Data in the similar range as this time last year or similar to

comparable reporting organization units

No large gaps or missing data

No multiplicity of data (same data from multiple sources –which one to trust?)

Timely data?

Some data needs to be acted upon immediately

Late reports weaken the potential for comparison, and action can be too late,

but still useful for documenting trends

Accuracy enhancing principles

Capacity building through training (90% of HISP activities)

User-friendly collection/collation tools Feedback on data errors (but not only!) Feedback of analysed Information Local Use of information

Controlling quality with DHIS2

Maximum / minimum valuesValidation rulesValidation Checks/RemindersCompleteness and timeliness reports (input for a league

table?)

- Will be covered in lab session -

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

Good data quality 10 steps to achieve it

1. Small, Essential Data Set – EDS2. Use of data locally by the collectors3. Clear definitions - standards4. Careful collection and collation of data – good tools5. Sharing of information6. Regular feedback7. Supportive supervision - at all levels8. Ongoing capacity building through training and

support9. Regular discussion of information at facility team

meetings10. Monitoring & Rewarding good information (League

Table)

or else…

limited capacity to manage or analyse data

Using evidence not perceived as a winning strategy

A vicious cycle A vicious cycle

Data not trusted

Weak demand

Weak HIS

Poor data quality

Limited investment in HIS

Decisions not evidence-basedDonors get their own

Fragmentation