Objectives: To derive a method of automated identification of delayed diagnosis of two serious pediatric conditions seen in the emergency department (ED): new-onset diabetic ketoacidosis (DKA) and sepsis.
Methods: Patients under 21 years old from five pediatric EDs were included if they had two encounters within 7 days, the second resulting in a diagnosis of DKA or sepsis. The main outcome was delayed diagnosis based on detailed health record review using a validated rubric. Using logistic regression, we derived a decision rule evaluating the likelihood of delayed diagnosis using only characteristics available in administrative data. Test characteristics at a maximal accuracy threshold were determined.
Results: Delayed diagnosis was present in 41/46 (89 %) of DKA patients seen twice within 7 days. Because of the high rate of delayed diagnosis, no characteristic we tested added predictive power beyond the presence of a revisit. For sepsis, 109/646 (17 %) of patients were deemed to have a delay in diagnosis. Fewer days between ED encounters was the most important characteristic associated with delayed diagnosis. In sepsis, our final model had a sensitivity for delayed diagnosis of 83.5 % (95 % confidence interval 75.2-89.9) and specificity of 61.3 % (95 % confidence interval 56.0-65.4).
Conclusions: Children with delayed diagnosis of DKA can be identified by having a revisit within 7 days. Many children with delayed diagnosis of sepsis may be identified using this approach with low specificity, indicating the need for manual case review.
Keywords: administrative data; diabetes; diagnostic error; emergency medicine; healthcare system; pediatrics.
© 2023 Walter de Gruyter GmbH, Berlin/Boston.