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Researchers across the translational research continuum have emphasized the importance of integrating genomics into their research program. To date capacity and resources for genomics research have been limited; however, a recent population-wide genomic screening initiative launched at the Medical University of South Carolina in partnership with Helix has rapidly advanced the need to develop appropriate infrastructure for genomics research at our institution. We conducted a survey with researchers from across our institution (n= 36) to assess current knowledge about genomics health, barriers, and facilitators to uptake, and next steps to support translational research using genomics. We also completed 30-minute qualitative interviews with providers and researchers from diverse specialties (n= 8). Quantitative data were analyzed using descriptive analyses. A rapid assessment process was used to develop a preliminary understanding of each interviewee’s perspective. These interviews were transcribed and coded to extract themes. The codes included types of research, alignment with precision health, opportunities to incorporate precision health, examples of researchers in the field, barriers, and facilitators to uptake, educational activity suggestions, questions to be answered, and other observations. Themes from the surveys and interviews inform implementation strategies that are applicable not only to our institution, but also to other organizations interested in making genomic data available to researchers to support genomics-informed translational research.We highlight the importance of integrating genomics into research, driven by a population-wide genomic screening initiative that has accelerated the need for infrastructure development in genomics research. Surveys and interviews with researchers assessed their knowledge, barriers, facilitators, and next steps for genomics research, informing implementation strategies applicable to the institution and other researchers supporting genomics-informed translational research.Researchers have recognized the significance of integrating genomics into their studies across the translational research continuum. However, limited capacity and resources have hindered progress in genomics research. We conducted a survey and qualitative interviews with researchers and healthcare providers from our institution to assess their understanding of genomics in health, identify barriers, and facilitators to its adoption, and determine next steps for supporting translational research using genomics. Themes identified included different types of research, alignment with precision health, opportunities to incorporate precision health, examples of researchers in the field, barriers, and facilitators to adoption, educational recommendations, unanswered questions, and other valuable observations. The insights gathered from the surveys and interviews informed the development of implementation strategies. These strategies can benefit not only our institution but also other researchers who are interested in providing access to genomic data to support genomics-informed translational research.

Electronic health records (EHRs) represent powerful tools to study rare diseases. Our objective was to develop and validate EHR algorithms to identify systemic lupus erythematosus (SLE) births across centers. We developed algorithms in a training set using an EHR with over 3 million subjects and validated the algorithms at 2 other centers. Subjects at all 3 centers were selected using ≥1 code for SLE International Classification of Diseases, Ninth Revision (ICD‐9) or SLE International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification (ICD‐10‐CM) and ≥1 ICD‐9 or ICD‐10‐CM delivery code. A subject was a case if diagnosed with SLE by a rheumatologist and had a birth documented. We tested algorithms using SLE ICD‐9 or ICD‐10‐CM codes, antimalarial use, a positive antinuclear antibody ≥1:160, and ever checked double‐stranded DNA or complement, using both rule‐based and machine learning methods. Positive predictive values (PPVs) and sensitivities were calculated. We assessed the impact of case definition, coding provider, and subject race on algorithm performance. Algorithms performed similarly across all 3 centers. Increasing the number of SLE codes, adding clinical data, and having a rheumatologist use the SLE code all increased the likelihood of identifying true SLE patients. All the algorithms had higher PPVs in African American versus White SLE births. Using machine learning methods, the total number of SLE codes and an SLE code from a rheumatologist were the most important variables in the model for SLE case status. We developed and validated algorithms that use multiple types of data to identify SLE births in the EHR. Algorithms performed better in African American mothers than in White mothers.

Objective We evaluate data agreement between an electronic health record (EHR) sample abstracted by automated characterization with a standard abstracted by manual review.Study Design and Setting We obtain data for an epidemiology cohort study using standard manual abstraction of the EHR and automated identification of the same patients using a structured algorithm to query the EHR. Summary measures of agreement (e.g., Cohen’s kappa) are reported for 12 variables commonly used in epidemiological studies.Results Best agreement between abstraction methods is observed among demographic characteristics such as age, sex, and race, and for positive history of disease. Poor agreement is found in missing data and negative history, suggesting potential impact for researchers using automated EHR characterization. EHR data quality depends upon providers, who may be influenced by both institutional and federal government documentation guidelines.Conclusion Automated EHR abstraction discrepancies may decrease power and increase bias; therefore, caution is warranted when selecting variables from EHRs for epidemiological study using an automated characterization approach. Validation of automated methods must also continue to advance in sophistication with other technologies, such as machine learning and natural language processing, to extract non-structured data from the EHR, for application to EHR characterization for clinical epidemiology.