Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text?

BMC Medical Research Methodology

Jackie A Cassell⁵  Tim Williams⁶  Irene Petersen²  Kevin A Davies¹  Greta Rait²  Helen E Smith⁵  Lesley Axelrod⁴  John Carroll⁴  A Rosemary Tate⁴  Rob Koeling⁴  Amanda Nicholson³  Elizabeth Ford⁵ 

[1]Division of Clinical Medicine, Brighton and Sussex Medical School, Brighton, UK

[2]Research Department of Primary Care and Population Health, University College London, London, UK

[3]Now at: Faculty of Health and Medicine, Lancaster University, Lancaster, UK

[4]Department of Informatics, University of Sussex, Brighton, UK

[5]Division of Primary Care and Public Health, Brighton and Sussex Medical School, Mayfield House, University of Brighton, Falmer, Brighton BN1 9PH, UK

[6]Clinical Practice Research Datalink, the Medicines and Healthcare products Regulatory Agency, London, UK

关键词: Coding;    Free text;    Rheumatoid arthritis;    Electronic medical records;    Electronic health records;   

Others  :  1091954
DOI  :  10.1186/1471-2288-13-105

 received in 2012-11-29, accepted in 2013-08-07,  发布年份 2013

【 摘 要 】

Background

Primary care databases are a major source of data for epidemiological and health services research. However, most studies are based on coded information, ignoring information stored in free text. Using the early presentation of rheumatoid arthritis (RA) as an exemplar, our objective was to estimate the extent of data hidden within free text, using a keyword search.

Methods

We examined the electronic health records (EHRs) of 6,387 patients from the UK, aged 30 years and older, with a first coded diagnosis of RA between 2005 and 2008. We listed indicators for RA which were present in coded format and ran keyword searches for similar information held in free text. The frequency of indicator code groups and keywords from one year before to 14 days after RA diagnosis were compared, and temporal relationships examined.

Results

One or more keyword for RA was found in the free text in 29% of patients prior to the RA diagnostic code. Keywords for inflammatory arthritis diagnoses were present for 14% of patients whereas only 11% had a diagnostic code. Codes for synovitis were found in 3% of patients, but keywords were identified in an additional 17%. In 13% of patients there was evidence of a positive rheumatoid factor test in text only, uncoded. No gender differences were found. Keywords generally occurred close in time to the coded diagnosis of rheumatoid arthritis. They were often found under codes indicating letters and communications.

Conclusions

Potential cases may be missed or wrongly dated when coded data alone are used to identify patients with RA, as diagnostic suspicions are frequently confined to text. The use of EHRs to create disease registers or assess quality of care will be misleading if free text information is not taken into account. Methods to facilitate the automated processing of text need to be developed and implemented.

【 授权许可】

   
2013 Ford et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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