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Fam Pract. 2006 Apr;23(2):253-63. Epub 2005 Dec 20.

The use of routinely collected computer data for research in primary care: opportunities and challenges.

Author information

1
Primary Care Informatics, Division of Community Health Sciences, St George's Hospital Medical School, London, UK. slusigna@sghms.ac.uk

Abstract

INTRODUCTION:

Routinely collected primary care data has underpinned research that has helped define primary care as a specialty. In the early years of the discipline, data were collected manually, but digital data collection now makes large volumes of data readily available. Primary care informatics is emerging as an academic discipline for the scientific study of how to harness these data. This paper reviews how data are stored in primary care computer systems; current use of large primary care research databases; and, the opportunities and challenges for using routinely collected primary care data in research.

OPPORTUNITIES:

(1) Growing volumes of routinely recorded data. (2) Improving data quality. (3) Technological progress enabling large datasets to be processed. (4) The potential to link clinical data in family practice with other data including genetic databases. (5) An established body of know-how within the international health informatics community.

CHALLENGES:

(1) Research methods for working with large primary care datasets are limited. (2) How to infer meaning from data. (3) Pace of change in medicine and technology. (4) Integrating systems where there is often no reliable unique identifier and between health (person-based records) and social care (care-based records-e.g. child protection). (5) Achieving appropriate levels of information security, confidentiality, and privacy.

CONCLUSION:

Routinely collected primary care computer data, aggregated into large databases, is used for audit, quality improvement, health service planning, epidemiological study and research. However, gaps exist in the literature about how to find relevant data, select appropriate research methods and ensure that the correct inferences are drawn.

PMID:
16368704
DOI:
10.1093/fampra/cmi106
[Indexed for MEDLINE]

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