A practical approach to identifying autistic adults within the electronic health record. Issue 1 (15th November 2022) - Malow, Beth A.; Veatch, Olivia J.; Niu, Xinnan; Fitzpatrick, Kasey A.; Hucks, Donald; Maxwell‐Horn, Angie; Davis, Lea K.
Abstract: The electronic health record (EHR) provides valuable data for understanding physical and mental health conditions in autism. We developed an approach to identify charts of autistic young adults, retrieved from our institution's de‐identified EHR database. Clinical notes within two cohorts were identified. Cohort 1 charts had at least one International Classification of Diseases (ICD‐CM) autism code. Cohort 2 charts had only autism key terms without ICD‐CM codes, and at least four notes per chart. A natural language processing tool parsed medical charts to identify key terms associated with autism diagnoses and mapped them to Unified Medical Language System Concept Unique Identifiers (CUIs). Average scores were calculated for each set of charts based on captured CUIs. Chart review determined whether patients met criteria for autism using a classification rubric. In Cohort 1, of 418 patients, 361 were confirmed to have autism by chart review. Sensitivity was 0.99 and specificity was 0.68 with positive predictive value (PPV) of 0.97. Specificity improved to 0.81 (sensitivity was 0.95; PPV was 0.98) when the number of notes was limited to four or more per chart. In Cohort 2, 48 of 136 patients were confirmed to have autism by chart review. Sensitivity was 0.95, specificity was 0.73, and PPV was 0.70. Our approach, which included using key terms, identified autism charts with high sensitivity, even in the absence of ICD‐CM codes. Relying on ICD‐CM codes alone may result in inclusion of false positive cases and exclusion of true cases with autism. Lay Summary: Clinical notes in patient charts contain valuable data for helping to understand health problems in autistic adults. We describe a computer‐based method to identify charts belonging to autistic young adults. The computer program looked for key terms ("autism, " "Asperger's, " etc.) in clinical notes, and was able to identify notes belonging to people with autism with good accuracy.