The identification of primary care consultation visits for otitis media: development of a software algorithm screening tool

BACKGROUND: Identifying diagnoses from noncoded healthcare visit records presents logistical challenges when large number of records are screened. This study aimed to develop a screening process to identify otitis media (OM) diagnoses in free-text primary care visit records. METHODS: The free-text primary care records of 200 children aged 0 to 4 years were reviewed independently by three clinicians to determine whether OM was a diagnosis considered during each visit. Terms (abbreviations, words, and phrases) identifying visits where OM was considered or excluded were documented. These terms were used to design a software algorithm subsequently used to detect OM diagnosis within these primary care records. The diagnostic performance of the software algorithm was determined against the gold standard clinicians' review and described using sensitivity, specificity, predictive values (PVs), and likelihood ratios (LRs) with 95% confidence intervals (CIs). RESULTS: The 200 children had 10,034 primary care visits. Clinician review identified 917 (9%) visits where OM was considered, and 9117 (91%) visits where OM was excluded. The software algorithm identified 801/917 visits where OM was considered and 8705/9117 visits where OM was excluded. The algorithm sensitivity was 87% (95% CI 85-89), specificity 96% (95% CI 95-96), positive PV 66% (95% CI 63-69), negative PV 99% (95% CI 98-99), positive LR 19.33 (95% CI 17.54-21.31), and negative LR 0.13 (95% CI 0.11-0.16). CONCLUSION: Software algorithms can assist in screening healthcare visit records. When combined with clinician review, they enable accurate identification of OM visits from non-coded records.