Data quality principles underpin Department of Health data collection processes. These principles are based on the dimensions of quality from the Australian Bureau of Statistics Data Quality Framework. They consist of:
- institutional environment
- interpretability and accessibility.
A combination of input editing and output editing is undertaken to maintain a high level of data quality measured against these dimensions.
Performance Indicators for Coding Quality
Performance Indicators for Coding Quality (PICQTM) is a set of pre-determined indicators that identify records in datasets that are incorrectly coded based on Australian Coding Standards (ACS) and coding conventions. PICQ™ measures data accuracy against particular indicators, identifies problem areas in data and particular records for correction.
PICQ™ examines admitted patient morbidity data that has been coded using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) and the Australian Classification of Health Interventions (ACHI).
PICQ™ is owned by Beamtree. The department has an enterprise licence to provide PICQ™ statewide benchmarking information and monthly numerator reports to public hospitals. Health services wishing to run PICQ™ in-house must purchase their own licence from .
Contact the HDSS Helpdesk for a copy of the PICQ™ guide
The department provides quarterly statewide PICQ™ benchmarking results for public and private health services.
The department also provides monthly numerator reports to public hospitals. Numerator reports provide record-level information on PICQ™ indicators to allow health services to review and correct coded data in order to improve overall coding accuracy and VAED data quality.
Degrees of coding issue
PICQ™ indicators are categorised according to the degree of the potential coding issue. There are four indicator degrees as outlined in the table below.
|Degree||Degree name||Degree description||Health services action|
|F||Fatal indicator||Any record found by such an indicator has been coded incorrectly by definition (because any exceptions have been written into the indicators definition).||The department expects all fatal indicators to be reviewed, corrected and resubmitted to the VAED.|
|W1||Warning indicator -1%||Any record found by a 1% warning indicator (W1) indicates that individual codes or combinations of codes or data items are likely to be incorrect. In W1 indicators, the probability that the record is correct is low (less than 1%) and only likely in some rare circumstances.||The department expects all W1 indicators to be reviewed. If the W1 indicators detect coding errors, these should be corrected and resubmitted to the VAED.|
|W2||Warning indicator - other||Any record found by a warning indicator - other (W2) indicates that individual codes or combinations of codes or data items are likely to be incorrect (although it is possible)||The department expects all W2 indicators to be reviewed. If the W2 indicators detect coding errors, these should be corrected and resubmitted to the VAED.|
|R||Relative indicator||Any records found by such an indicator are counted and expressed as a ratio of a larger (usually) group of episodes. These indicators would generally be used to assess the overall quality of coded data rather than identify individual problem records.||The department does not expect health services to review individual records in this category.|
For Victorian statewide PICQ results prior to 2014-15, visit health data quality archive.
Reviewed 03 February 2023