Data quality:adapted from a presentation given by

Mr. B Sikhakhane, Gauteng

...data to knowledge Data is raw material in the form of numbers,

characters, images that gives information after being analyzed.

Information is analyzed data that adds context through relationships between data to allow for interpretation & use.

Knowledge adds understanding to information which is communicated and acted upon.

Data Information Knowledge

Data analysis Data analysis is the process of systematically

applying techniques to summarise, describe

and compare raw data

Interpretation involves looking at the

information and making sense of it

important to know the health care context,

demographics & disease profiles

Examine & answer questions such as whether priority

patient needs are met, are services available,

accessible, acceptable and used.

…data collection Tools used for collecting research data MUST be standardised, but when designing tools for collecting routine data, the following must be kept into consideration:

Purpose of data collection (patient care or monitoring)

Type of data (patient or aggregated)

Health facility environment (number of patients, small facility with integrated care, large facility with specialised care ...)

Available resources (staff, computers, networks...) Paper based (tick or tally, daily or longitudinal registers) Electronic for monitoring Electronic for patient management

What is data quality... really?

Refers to the value of the information

collected

Measures how well an information system

reflects the real situation

Refers to data that is fit for use and

meets reasonable standards when

checked against criteria for quality

Accurately reflects true performance

Criteria for quality data

Validity – measure what is supposed to be

measured

Reliability – same results when repeated

Integrity – complete and truthful

Precision - level needed for use

Timeliness – for reports and decisions

Data flow process

Clinic /Hospital

Sub-District Information Office

District Information Office

• Due date 7th of each month

• Facility/ institutional CEO signs it off

• Quality checks are done and recorded• Data to reach this office by the 7th of

every month. • Manager to sign data off• Quality checks are done and recorded • Data leaves this office to the next level

by the 15th of the month • Date reaches this block by the 15th

• Quality checks are done and recorded• Data leaves this office to the next by

the 20th

Provincial Information Office

• Data reach this office by the 20th

• Quality checks are done and record

• Data leaves this office by the 26th

National Information Office

• Data reach this office by the 26th

• Quality checks are done and record

• Data leaves this office WHO set date

DHISMonthl

y reports

Facilityregisters

Sourcedocument

s

Avg:18,83%

Avg:6,59%

Compounded Error Rate

Compounded Error Rate

Dataflow: Illustration of errors

Data quality affected by...

Doctor or nurse interacts with

patient

Patient

record

Data transcribed to

Sub-set of data recorded in register and/or tally

sheet

Data capture in DHIS

Step 1

Step 2 Manual

recording

Monthly summaries collated

Step 5

Monthly summary report compiledStep 3

Step 4

Data analysis and feedbackStep 6

Incomplete, illegible, undated data

Multiplicity of DCT’s, duplicated,

non-standardised

Inability to collate data accurately

Inability to collate data accurately

Data capture errorsIncorrect data

elements activatedValidation not done

No feedbackLittle data analysis by

program managers

Common problems with data large gaps

unusual month to month variations

inconsistencies – unlikely values

duplication

data present where there should not be

thumb-sucking

data entered in wrong boxes

typing errors

Calculation errors