Finding buried genetic test results in the electronic health record is inefficient and variable across institutions

Abstract

Background: The absence of standardized approaches for handling genetic test results in electronic health records (EHRs), combined with a lack of diagnostic codes for most rare disorders, hinders accurate and timely identification of patients with rare genetic variants. This impedes access to research opportunities and genomic-driven care. To reduce the diagnostic odyssey, identify research-eligible subjects, and ultimately enhance patient care, it is critical to optimize approaches to retrieve genetic results.

Objectives: To characterize resource requirements, yield, and biases among methods for identifying and retrieving genetic test results across 11 Intellectual and Developmental Disability Research Centers (IDDRC).

Design: A survey was used to collect details from the authors on approaches to identify EHRs from patients who had genetic testing and variants of interest were reported; surveys were completed in 2022.

Methods: Strengths and limitations in approaches to identify and retrieve genetic test results conducted from the implementation of EHR systems were evaluated. A standard template was used to collect genetic testing storage formats, methods to identify patients with rare disease variants, estimates of time/cost, nature of accessed data, method-specific bias in types of American College of Medical Genetics and Genomics classified variants identified. When possible, precision when performing gene name searches in the EHR was calculated.

Results: Four approaches were used: (1) manual searches, reviews, and extractions, (2) natural language processing software-aided manual reviews and extractions, (3) custom databases via testing lab collaborations, and (4) testing EHR vendor-designed genomics modules. The fully manual approach required minimal infrastructure and allowed access to clinical notes but missed variants of unknown clinical significance. Precision for gene name matches based on searches of 59 genes was 0.16. Natural language processing software minimized effort but required considerable informatics support. Custom databases and EHR vendor modules necessitated substantial computational support; however, genetic testing results retrieval was efficient.

Conclusion: Leveraging the IDDRC network, we found that methods to store, search and extract genetic testing results vary widely, especially regarding older test results, and have distinct benefits and limitations. Limitations are best addressed through practice guidelines that standardize storage and retrieval of genetic test results to facilitate efficient identification of research eligible subjects and genomic-informed patient care.

Keywords: biomedical informatics; electronic health records; genetics.

Figure 1.

Proportion of sites reporting information regarding approaches used to retrieve genetic testing results. Survey results at each participating site indicated that there were four main methods used to extract genetic testing results previously collected for clinical purposes following searches by gene name. For each method, the number of sites using the approach over the total number of sites is provided (some sites utilize more than one approach). The bubble plot then shows the proportion of sites who are using each respective method that reported the response on the y-axis to each question from the initial survey for each approach (red = fully manual, brown = NLP + manual, yellow = custom database, green = EHR Vendor Module). Structured data refers to information captured in a predefined format while unstructured refers to free text and attachments. Deidentified storage indicates no PHI is accessible compared to identified storage. Favors non-VUS indicates the method is biased toward identifying variants classified as pathogenic or likely pathogenic. EHR, electronic health record; NLP, natural language processing; VUS, variant of uncertain significance.

Figure 2.

Example approach to initially query a de-identified version of data extracted from an EHR system using structured data elements, followed by manual reviews and extraction of genetic testing results from the identified EHR to identify potentially eligible participants for a research study. EHR, electronic health record.

Figure 3.

Example approach to extract genetic testing results as aided by natural language processing from the identified EHR at the University of Iowa. EHR, electronic health record.

Figure 4.

Example approach to store genetic testing results that have been stored in a custom database that is not directly linked to the EHR at BCH. BCH, Boston Children’s Hospital; EHR, electronic health record; QC, quality control.