Appl Clin Inform 2017; 08(03): 731-741
DOI: 10.4338/ACI-2017-02-RA-0029
Research Article
Schattauer GmbH

Extracting autism spectrum disorder data from the electronic health record

Ruth A. Bush
1  Hahn School of Nursing and Health Science, Beyster Institute for Nursing Research, University of San Diego, San Diego, USA
2  Clinical Research Informatics, Rady Children’s Hospital-San Diego, San Diego, USA
,
Cynthia D. Connelly
1  Hahn School of Nursing and Health Science, Beyster Institute for Nursing Research, University of San Diego, San Diego, USA
,
Alexa Pérez
1  Hahn School of Nursing and Health Science, Beyster Institute for Nursing Research, University of San Diego, San Diego, USA
,
Halsey Barlow
1  Hahn School of Nursing and Health Science, Beyster Institute for Nursing Research, University of San Diego, San Diego, USA
,
George J. Chiang
3  Rady Children‘s Institute for Genomic Medicine, Rady Children‘s Hospital San Diego, San Diego, CA, USA
4  Department of Surgery, University of California-San Diego, San Diego, USA
› Author Affiliations
Funding This project was also supported in part by grant number K99/R00 HS022404 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.
Further Information

Publication History

received: 07 February 2016

accepted: 07 May 2017

Publication Date:
20 December 2017 (online)

Summary

Background: Little is known about the health care utilization patterns of individuals with pediatric autism spectrum disorder (ASD).

Objectives: Electronic health record (EHR) data provide an opportunity to study medical utilization and track outcomes among children with ASD.

Methods: Using a pediatric, tertiary, academic hospital’s Epic EHR, search queries were built to identify individuals aged 2–18 with International Classification of Diseases, Ninth Revision (ICD-9) codes, 299.00, 299.10, and 299.80 in their records. Codes were entered in the EHR using four different workflows: (1) during an ambulatory visit, (2) abstracted by Health Information Management (HIM) for an encounter, (3) recorded on the patient problem list, or (4) added as a chief complaint during an Emergency Department visit. Once individuals were identified, demographics, scheduling, procedures, and prescribed medications were extracted for all patient-related encounters for the period October 2010 through September 2012.

Results: There were 100,000 encounters for more than 4,800 unique individuals. Individuals were most frequently identified with an HIM abstracted code (82.6%) and least likely to be identified by a chief complaint (45.8%). Categorical frequency for reported race (2 = 816.5, p < 0.001); payor type (2 = 354.1, p < 0.001); encounter type (2 = 1497.0, p < 0.001); and department (2 = 3722.8, p < 0.001) differed by search query. Challenges encountered included, locating available discrete data elements and missing data.

Conclusions: This study identifies challenges inherent in designing inclusive algorithms for identifying individuals with ASD and demonstrates the utility of employing multiple extractions to improve the completeness and quality of EHR data when conducting research.

Citation: Bush RA, Connelly CD, Pérez A, Barlow H, Chiang GJ. Extracting autism spectrum disorder data from the electronic health record. Appl Clin Inform 2017; 8: 731–741 https://doi.org/10.4338/ACI-2017-02-RA-0029

Protection of Human Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research involving Human Subjects, and was reviewed by University of California, San Diego Institutional Review Board.