Importance of different electronic medical record components for chronic disease identification in a Swiss primary care database: a cross-sectional study

Abstract

BACKGROUND: Primary care databases collect electronic medical records with routine data from primary care patients. The identification of chronic diseases in primary care databases often integrates information from various electronic medical record components (EMR-Cs) used by primary care providers. This study aimed to estimate the prevalence of selected chronic conditions using a large Swiss primary care database and to examine the importance of different EMR-Cs for case identification. METHODS: Cross-sectional study with 120,608 patients of 128 general practitioners in the Swiss FIRE (“Family Medicine Research using Electronic Medical Records”) primary care database in 2019. Sufficient criteria on three individual EMR-Cs, namely medication, clinical or laboratory parameters and reasons for encounters, were combined by logical disjunction into definitions of 49 chronic conditions; then prevalence estimates and measures of importance of the individual EMR-Cs for case identification were calculated. RESULTS: A total of 185,535 cases (i.e. patients with a specific chronic condition) were identified. Prevalence estimates were 27.5% (95% CI: 27.3–27.8%) for hypertension, 13.5% (13.3–13.7%) for dyslipidaemia and 6.6% (6.4–6.7%) for diabetes mellitus. Of all cases, 87.1% (87.0–87.3%) were identified via medication, 22.1% (21.9–22.3%) via clinical or laboratory parameters and 19.3% (19.1–19.5%) via reasons for encounters. The majority (65.4%) of cases were identifiable solely through medication. Of the two other EMR-Cs, clinical or laboratory parameters was most important for identifying cases of chronic kidney disease, anorexia/bulimia nervosa and obesity whereas reasons for encounters was crucial for identifying many low-prevalence diseases as well as cancer, heart disease and osteoarthritis. CONCLUSIONS: The EMR-C medication was most important for chronic disease identification overall, but identification varied strongly by disease. The analysis of the importance of different EMR-Cs for estimating prevalence revealed strengths and weaknesses of the disease definitions used within the FIRE primary care database. Although prioritising specificity over sensitivity in the EMR-C criteria may have led to underestimation of most prevalences, their sex- and age-specific patterns were consistent with published figures for Swiss general practice.

Developing and testing a framework for coding general practitioners’ free-text diagnoses in electronic medical records - a reliability study for generating training data in natural language processing

“…Unsurprisingly, the most frequently assigned diagnostic codes were those for the most common chronic or recurrent conditions, particularly those of the musculoskeletal and cardiovascular systems [ 46 ]. Several of these diagnoses were already identifiable in the FIRE database based on algorithms applied to routine data such as prescribed medications (e.g., antidiabetic drugs to identify diabetes) or results of clinical or laboratory tests (e.g., body mass index for obesity) [ 47 ]. However, there are several important and prevalent diagnoses for which sufficiently specific identification criteria based on routine data are lacking, including musculoskeletal conditions, cancer, tobacco use, depression, sleep disorders and many others, which are important targets of research in general practice.…”