Three steps to data quality

Krish Thiru*, Simon de Lusignan, Frank Sullivan, Sarah Brew, Alun Cooper

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

Background. The quality of data in general practice clinical information systems varies enormously. This variability jeopardises the proposed national strategy for an integrated care records service and the capacity of primary care organisations to respond coherently to the demands of clinical governance and the proposed quality-based general practice contract. This is apparent in the difficulty in automating the audit process and in comparing aggregated data from different practices. In an attempt to provide data of adequate quality to support such operational needs, increasing emphasis is being placed on the standardisation of data recording. Objective. To develop a conceptual framework to facilitate the recording of standardised data within primary care. Method. A multiprofessional group of primary care members from the South Thames Research Network examined leading guidelines for best practice. Using the nominal group technique the group prioritised the information needs of primary care organisations for managing coronary heart disease according to current evidence. Results. Information needs identified were prioritised and stratified into a functional framework. Conclusion. It has been possible within the context of a primary care research network to produce a framework for standardising data collection. Motivation of front-line clinicians was achieved through the incorporation of their views into the synthesis of the dataset.

Original languageEnglish
Pages (from-to)95-102
Number of pages8
JournalInformatics in Primary Care
Volume11
Issue number2
Publication statusPublished - 1 Dec 2003

Keywords

  • Computerised medical records
  • Core dataset
  • Coronary heart disease
  • Data quality
  • General practice
  • Primary care informatics
  • Quality improvement

Fingerprint

Dive into the research topics of 'Three steps to data quality'. Together they form a unique fingerprint.

Cite this