Sandhu N, Krusina A, Quan H, Walker R, Martin EA, Eastwood CA, Southern DA. Automated extraction of weight, height, and obesity in electronic medical records are highly valid.
Obes Sci Pract 2024;
10:e705. [PMID:
38263997 PMCID:
PMC10804327 DOI:
10.1002/osp4.705]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 01/25/2024] Open
Abstract
Objective
Coding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under-reported and thus unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule-based algorithm for automating the detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMRs).
Methods
In this cross-sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs. A rule-based algorithm was created which looks for patients' height and weight values recorded in EMRs. Clinical notes were split into sentences and searched for height and weight, and BMI was computed.
Results
The study cohort consisted of 1904 patients with 50.8% females and 43.3% > 64 years of age. The final model to identify obesity within EMRs resulted in a sensitivity of 92.9%, specificity of 98.4%, positive predictive value of 96.7%, negative predictive value of 96.6%, and F1 score of 94.8%.
Conclusions
This study developed a highly valid rule-based EMR algorithm that detects height and weight. This could allow large-scale analyses using obesity that were previously not possible.
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