RESUMEN
OBJECTIVE: Our objective was to determine the accuracy of a point-of-care instrument, the Hospitalizations-Office Visits-Medical Conditions-Extra Care-Social Concerns (HOMES) instrument, in identifying patients with complex chronic conditions (CCCs) compared to an algorithm used to identify patients with CCCs within large administrative data sets. METHODS: We compared the HOMES to Feudtner's CCCs classification system. Using administrative algorithms, we categorized primary care patients at a children's hospital into 3 categories: no chronic conditions, non-complex chronic conditions, and CCCs. We randomly selected 100 patients from each category. HOMES scoring was completed for each patient. We performed an optimal cut-point analysis on 80% of the sample to determine which total HOMES score best identified children with ≥1 CCC and ≥2 CCCs. Using the optimal cut points and the remaining 20% of the study population, we determined the odds and area under the curve (AUC) of having ≥1 CCC and ≥2 CCCs. RESULTS: The median (interquartile range [IQR]) age was 4 (IQR: 0, 8). Using optimal cut points of ≥7 for ≥1 CCC and ≥11 for ≥2 CCCs, the odds of having ≥1 CCC was 19 times higher than lower scores (odds ratio [OR] 19.1 [95% confidence interval [CI]: 9.75, 37.5]) and of having ≥2 CCCs was 32 times higher (OR 32.3 [95% CI: 12.9, 50.6]). The AUCs were 0.76 for ≥1 CCC (sensitivity 0.82, specificity 0.80) and 0.74 for ≥2 CCCs (sensitivity 0.92, specificity 0.74). CONCLUSIONS: The HOMES accurately identified patients with CCCs.