A qualitative study of prevalent laboratory information systems and data communication patterns for genetic test reporting

• Published in: Genetics in medicine Vol. 23; no. 11; pp. 2171 - 2177
• Main Authors: Khalifa, Aly, Mason, Clinton C., Garvin, Jennifer Hornung, Williams, Marc S., Del Fiol, Guilherme, Jackson, Brian R., Bleyl, Steven B., Huff, Stanley M.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.11.2021; Elsevier Limited
• Subjects: Biomedical and Life Sciences; Biomedicine; Clinical Laboratory Information Systems; Communication; Electronic Health Records; Genetic Testing; Human Genetics; Humans; Information systems; Laboratory Medicine; Qualitative Research; United States > US
• ISSN: 1098-3600; 1530-0366
• DOI: 10.1038/s41436-021-01251-5

Purpose The availability of genetic test data within the electronic health record (EHR) is a pillar of the US vision for an interoperable health IT infrastructure and a learning health system. Although EHRs have been highly investigated, evaluation of the information systems used by the genetic labs has received less attention—but is necessary for achieving optimal interoperability. This study aimed to characterize how US genetic testing labs handle their information processing tasks. Methods We followed a qualitative research method that included interviewing lab representatives and a panel discussion to characterize the information flow models. Results Ten labs participated in the study. We identified three generic lab system models and their relevant characteristics: a backbone system with additional specialized systems for interpreting genetic results, a brokering system that handles housekeeping and communication, and a single primary system for results interpretation and report generation. Conclusion Labs have heterogeneous workflows and generally have a low adoption of standards when sending genetic test reports back to EHRs. Core interpretations are often delivered as free text, limiting their computational availability for clinical decision support tools. Increased provision of genetic test data in discrete and standard-based formats by labs will benefit individual and public health.