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 7 th of each month Facility/ institutional CEO signs it off Quality checks are done and recorded Data to reach this office by the 7 th of every month. Manager to sign data off Quality checks are done and recorded Data leaves this office to the next level by the 15 th of the month Date reaches this block by the 15 th 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

DHIS Monthly reports Facility registers Source documents Avg:18,83% Avg:6,59% 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 compiled Step 3 Step 4 Data analysis and feedback Step 6 Incomplete, illegible, undated data Multiplicity of DCT’s, duplicated, non-standardised Inability to collate data accurately Data capture errors Incorrect data elements activated Validation not done No feedback Little 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