Deriving a sociotechnical model for discovery in genomics-enabled learning health systems

Thesis (Ph.D.)--University of Washington, 2022Recent advances in genetic sequencing technologies and analysis tools have made genomic data widely available for medical research. Despite the expectation that genomic data will revolutionize medicine, there exist major evidence gaps in demonstrating the utility of clinical genomics for improving patient outcomes and increasing healthcare efficiency. One promising avenue for reducing this evidence gap and accelerating the pace of clinically relevant discoveries is to foster environments in which genomic research and clinical care exist symbiotically. However, the technical and sociocultural requirements for conducting genomic research in clinical environments are not well-defined. The learning health system (LHS) framework is one lens through which the barriers and enablers of clinical genomic discovery can be identified and organized. Furthermore, drawing on experiences from clinical research consortia like the Clinical Sequence Evidence-Generating Research (CSER) Consortium and the Electronic Medical Records and Genomics (eMERGE) Network can help identify requirements that are unique to genomic research initiatives that straddle the research-clinical boundary. In this work, we sought to derive a sociotechnical model for clinical genomic discovery in genomics-enabled learning health systems (GLHSs). We first identified data coordination challenges, strategies, and recommendations from the clinical genomics research data integration process in the CSER Consortium and found that the social processes involved in data coordination are tantamount to the informatics tools used to facilitate data coordination (Aim 1). We then explored medical geneticist perspectives on clinical genomic discovery by interviewing 20 board-certified medical geneticists in CSER, eMERGE, and the University of Washington medical system (Aim 2). Using constructivist grounded theory methods, we developed a preliminary model of GLHS discovery that utilizes the concepts of representation, responsibility, risks and benefits, relationships, and resources (“5R”) to capture the negotiations and constraints involved in clinical-research integration in genomics. To demonstrate the utility of merging electronic health record (EHR) data with genomic data for discovery, we then conducted a logistic regression-based genome-wide association study for C. diff. infection (CDI) using merged genetic and EHR data from 12 clinical sites in the eMERGE Network and found a strong gene-disease association in the HLA-DRB locus (P=8.06 x 10-14) that predisposed carriers to CDI (Aim 3). Finally, we conducted a systematic literature review of proposed enablers of clinical genomic discovery and synthesized the qualitative results from the literature review and recommendations from Aim 1 with the a priori framework developed in Aim 2 using best-fit framework synthesis (BFFS) (Aim 4). We found that the vast majority of themes identified in the literature were accommodated by the a priori framework, suggesting that the 5R model of GLHS discovery is an adequate representation of processes involved in learning health research. Using additional qualitative evidence identified during BFFS, we developed an enhanced 5R sociotechnical model to demonstrate how iterative, multidirectional negotiation and tool development can facilitate virtuous cycles of learning in clinical genomics research