Unbiased identification of patients with disorders of sex development.

Abstract

Disorders of sex development (DSD) represent a collection of rare diseases that generate substantial controversy regarding best practices for diagnosis and treatment. A significant barrier preventing a better understanding of how patients with these conditions should be evaluated and treated, especially from a psychological standpoint, is the lack of systematic and standardized approaches to identify cases for study inclusion. Common approaches include "hand-picked" subjects already known to the practice, which could introduce bias. We implemented an informatics-based approach to identify patients with DSD from electronic health records (EHRs) at three large, academic children's hospitals. The informatics approach involved comprehensively searching EHRs at each hospital using a combination of structured billing codes as an initial filtering strategy followed by keywords applied to the free text clinical documentation. The informatics approach was implemented to replicate the functionality of an EHR search engine (EMERSE) available at one of the hospitals. At the two hospitals that did not have EMERSE, we compared case ascertainment using the informatics method to traditional approaches employed for identifying subjects. Potential cases identified using all approaches were manually reviewed by experts in DSD to verify eligibility criteria. At the two institutions where both the informatics and traditional approaches were applied, the informatics approach identified substantially higher numbers of potential study subjects. The traditional approaches yielded 14 and 28 patients with DSD, respectively; the informatics approach yielded 226 and 77 patients, respectively. The informatics approach missed only a few cases that the traditional approaches identified, largely because those cases were known to the study team, but patient data were not in the particular children's hospital EHR. The use of informatics approaches to search electronic documentation can result in substantially larger numbers of subjects identified for studies of rare diseases such as DSD, and these approaches can be applied across hospitals.