Problems with primary care data quality: osteoporosis as an
exemplar

Lusignan, Simon de; Valentin, Thomas; Chan, Tom; Hague, N; Wood, Olivia Bennett; Vlymen, Jeremy van; Dhoul, N

doi:10.14236/jhi.v12i3.120

Cited by 38 publications

(37 citation statements)

References 27 publications

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“…Data in primary care may not be complete or accurate, or current, 27 and there may be measurable gaps in data quality. 28,29 Therefore, we need to extract and process data in a way that takes account of its limitations, 30 and this should include taking account of the presence or absence of ontological relationships.…”

Section: Discussionmentioning

confidence: 99%

Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach

Lusignan¹,

Liaw²,

Michalakidis³

et al. 2011

Journal of Innovation in Health Informatics

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Background The burden of chronic disease is increasing, and research and quality improvement will be less effective if case finding strategies are suboptimal. Objective To describe an ontology-driven approach to case finding in chronic disease and how this approach can be used to create a data dictionary and make the codes used in case finding transparent. Method A five-step process: (1) identifying a reference coding system or terminology; (2) using an ontology-driven approach to identify cases; (3) developing metadata that can be used to identify the extracted data; (4) mapping the extracted data to the reference terminology; and (5) creating the data dictionary. Results Hypertension is presented as an exemplar. A patient with hypertension can be represented by a range of codes including diagnostic, history and administrative. Metadata can link the coding system

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Section: Discussionmentioning

confidence: 99%

Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach

Lusignan¹,

Liaw²,

Michalakidis³

et al. 2011

Journal of Innovation in Health Informatics

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“…14, 15 The codes required to collect the necessary data are listed; queries are created to extract the data from the various GP computer systems; and the final variable names and labels are derived from them. Consequently, the metadata enables traceability back to the source Figure 1 Elements of the PCDQ metadata schema query and research question.…”

Section: Methodsmentioning

confidence: 99%

A system of metadata to control the process of query, aggregating, cleaning and analysing large datasets of primary care data

Vlymen¹,

Lusignan²

2005

Journal of Innovation in Health Informatics

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“…Many more patients are on anti-osteoporosis therapy than have the diagnosis. 22 However, feedback of the inter-practice variation in data recording does seem to improve data quality. 23 …”

Section: Designmentioning

confidence: 99%

Routinely-collected general practice data are complex, but with systematic processing can be used for quality improvement and research

Lusignan¹,

Hague²,

Vlymen³

et al. 2006

Journal of Innovation in Health Informatics

Self Cite

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Background UK general practice is computerised, and quality targets based on computer data provide a further incentive to improve data quality. A National Programme for Information Technology is standardising the technical infrastructure and removing some of the barriers to data aggregation. Routinely collected data is an underused resource, yet little has been written about the wide range of factors that need to be taken into account if we are to infer meaning from general practice data. Objective To report the complexity of general practice computer data and factors that need to be taken into account in its processing and interpretation. Method We run clinically focused programmes that provide clinically relevant feedback to clinicians, and overview statistics to localities and researchers. However, to take account of the complexity of these data we have carefully devised a system of process stages and process controls to maintain referential integrity, and improve data quality and error reduction. These are integrated into our design and processing stages. Our systems document the query,

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Problems with primary care data quality: osteoporosis as an exemplar

Abstract: Objective To report problems implementing a data quality programme in osteoporosis.

Cited by 38 publications

References 27 publications

Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach

Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach

A system of metadata to control the process of query, aggregating, cleaning and analysing large datasets of primary care data

Routinely-collected general practice data are complex, but with systematic processing can be used for quality improvement and research

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