Three steps to data quality
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 govern...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
BCS, The Chartered Institute for IT
2003-06-01
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Series: | Journal of Innovation in Health Informatics |
Subjects: | |
Online Access: | https://hijournal.bcs.org/index.php/jhi/article/view/559 |
Summary: | 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 prioritized 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. |
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ISSN: | 2058-4555 2058-4563 |