Large population-based databases based on electronic medical records (EMRs) of patients in primary care are a useful data source to investigate morbidity and health care utilization. Diagnoses recorded in EMRs are doctor-defined, but their validity can be disputed. In this study we investigated the validity of the diagnosis inflammatory arthritis (IA), a group of chronic rheumatic diseases, including rheumatoid arthritis, psoriatic arthritis and ankylosing spondylitis, in primary care based EMRs.
In five general practices, participating in the Netherlands Information Network of General Practice (LINH), EMRs of 219 patients with a diagnostic code of IA were systematically reviewed on characteristics which are not routinely extracted for the LINH database. The diagnosis IA was confirmed when we found, based on a correspondence with a medical specialist, the following diagnoses in the free text fields of the EMR: oligoarthritis, polyarthritis, rheumatoid arthritis and/or spondyloarthropathy. These results were used to determine the validity of the diagnosis IA in EMRs and to develop an algorithm to improve diagnostic validity.
From the 219 patients diagnosed as IA in the database, the diagnosis IA was confirmed in 155 patients (70.8%). The algorithm, which resulted in a group of patients with as many as possible confirmed IA-diagnosed patients without excluding too many patients from our dataset, was when patients fulfilled at least one of the following three criteria: 1) a repeat prescription for a disease-modifying antirheumatic drug (DMARD) and/or biological agent, 2) ≥ four contacts or one episode with a diagnostic code for IA, combined with at least two IA-related prescriptions (excluding DMARDs/biological agents), and 3) age at diagnosis ≥ 61 years. After applying this algorithm, the percentage of correctly diagnosed IA patients increased from 71% to 78% reducing the size of our study population by 36%.
Based on additional diagnostic information, the diagnosis IA from EMRs of patients in primary care is sufficiently valid when using the proposed algorithm. After applying the algorithm, the percentage of correctly diagnosed IA patients increased from 71% to 78%.