ERIC Number: EJ1280630
Record Type: Journal
Publication Date: 2020-Sep
Pages: 17
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1092-4388
EISSN: N/A
Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems
Walters, Courtney E.; Nitin, Rachana; Margulis, Katherine; Boorom, Olivia; Gustavson, Daniel E.; Bush, Catherine T.; Davis, Lea K.; Below, Jennifer E.; Cox, Nancy J.; Camarata, Stephen M.; Gordon, Reyna L.
Journal of Speech, Language, and Hearing Research, v63 n9 p3019-3035 Sep 2020
Purpose: Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities (Casey et al., 2016). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method: We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample (N = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N = 13,652 EHRs. Results: In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions: APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders.
Descriptors: Language Impairments, Developmental Disabilities, Automation, Disability Identification, Data Analysis, Information Systems, Records (Forms), Health Services, Speech Language Pathology
American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Institute on Deafness and Other Communication Disorders (NIDCD); National Institutes of Health (DHHS), Office of the Director; National Center for Advancing Translational Sciences (NCATS) (NIH), Clinical and Translational Science Awards (CTSA) Program
Authoring Institution: N/A
Grant or Contract Numbers: R01DC016977; R03DC014802; K18DC017383; R01DC017175; R03DC015329; UL1TR000445