ArticleBMC medical research methodology2013
Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text?
Article in BMC medical research methodology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 4 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Facility-Based Maternal Quality of Care Frameworks: A Systematic Review and Best Fit Framework Analysis.Maternal and child health journal · 2023Pooled it
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature.BMC medical informatics and decision making · 2017Pooled it
- Extracting information from the text of electronic medical records to improve case detection: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2016Pooled it
- Identification of patients with epilepsy using automated electronic health records phenotyping.Epilepsia · 2023Trial
- Generalizable multilingual medical text anonymization using generative instruction tuning.Communications medicine · 2026Article
- A machine learning tool for identifying metastatic colorectal cancer in primary care.Scandinavian journal of primary health care · 2025Article
- Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care.BMC medicine · 2025Article
- Predicting new cases of hypertension in Swedish primary care with a machine learning tool.Preventive medicine reports · 2024Article
- Management of Rheumatoid Arthritis in Primary Care: A Scoping Review.International journal of environmental research and public health · 2024Article
- Validation of a diagnostic prediction tool for colorectal cancer: a case-control replication study.Family practice · 2023Article
- Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Transforming early cancer detection in primary care: harnessing the power of machine learning.Oncoscience · 2023Article
- Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality.BMC medical informatics and decision making · 2021Article
- A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study.JMIR medical informatics · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Different approaches to improve cohort identification using electronic health records: X-linked hypophosphatemia as an example.Intractable & rare diseases research · 2021Review
- Text Mining of Electronic Health Records Can Accurately Identify and Characterize Patients With Systemic Lupus Erythematosus.ACR open rheumatology · 2021Article
- Machine learning for identification of frailty in Canadian primary care practices.International journal of population data science · 2021Article
- Validation of canadian health administrative data algorithms for estimating trends in the incidence and prevalence of osteoarthritis.Osteoarthritis and cartilage open · 2020Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundPrimary 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.
methodsWe 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.
resultsOne 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.
conclusionsPotential 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.
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