Article

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

Primary Care Informatics, Division of Community Health Sciences, St George's Hospital Medical School, London, UK.
Informatics in primary care 02/2006; 14(1):59-66. DOI: 10.14236/jhi.v14i1.615
Source: PubMed

ABSTRACT 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.
To report the complexity of general practice computer data and factors that need to be taken into account in its processing and interpretation.
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, reference code set and create unique patient ID. The design stage is followed by appraisal of: data entry issues, how concepts might be represented in clinical systems, coding ambiguities, using surrogates where needed, validation and pilot-ing. The subsequent processing of data includes extraction, migration and integration of data from different sources, cleaning, processing and analysis.
Results are presented to illustrate issues with the population denominator, data entry problems, identification of people with unmet needs, and how routine data can be used for real-world testing of pharmaceuticals.
Routinely collected primary care data could contribute more to the process of health improvement; however, those working with these data need to understand fully the complexity of the context within which data entry takes place.

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    • "However, data retrieval from the EMR is still complex [10] and in some cases registration of data is inadequate [14]. Because clinical consultation is a complex interaction between caregiver and patient, in EMRs the information is often recorded as a mixture of free-text and coded data [15]. Registration, here understood to be all or part of patient information, is influenced by personal, cultural, technical, health system and financial factors [9]. "
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    BMC Health Services Research 12/2009; 9:241. DOI:10.1186/1472-6963-9-241 · 1.66 Impact Factor
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    • "Routinely collected general practice computer data are complex and require significant processing and interpretation in order to obtain meaningful information [36]. The research team has considerable experience and has developed a published method [37]. "
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    Implementation Science 02/2009; 4:39. DOI:10.1186/1748-5908-4-39 · 3.47 Impact Factor
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    • "Several models for obtaining routine data from primary health care services are reported from other countries. Examples are "Sentinel practice networks", networks of practices or municipalities that monitor one or more specific illness problems on a regular or continuous basis, "surveillance projects" which is observation of the incidence in short term (early warning) or long term to observe trends over time and to make statistics on annual levels [2-4,8,9]. One methodological paper describes a minimal standard for primary care based surveillance networks and lists seven criteria recommendations for their structure and operation [2]. "
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