Despite major advances in genome sequencing and identification of the genetic basis of disease, clinical use of genomic analysis is limited in part by the costs and coordination associated with correlating genome results with phenotypic findings (Segal 2015). Clinicians are overwhelmed by the huge amounts of information needed to assess a genome, and laboratory scientists are hampered by getting sparse information about patient findings (phenotypic elements on physical examination, history, and testing). We have developed components of a process to address these issues. These components include the SimulConsult Genome-Phenome Analyzer (Segal et al. 2015), with its ad-hoc Electronic Health Record (EHR) integration (Segal et al. 2017). As part of a Geisinger-led team, we have demonstrated that Geisinger?s GenomeCOMPASS tool for Return of Genomic Results, integrated with the Genome-Phenome Analyzer, enhances understanding of those results by families and referring clinicians (Williams et al. 2016, 2017, Stuckey et al. 2015). Separately, Geisinger has developed methods for mining the findings in the EHR. However, lack of access to clinical genomic data and lack of standardization in genome analysis and frequent changes in capabilities have made it challenging to use such components. Accordingly, we propose to develop a prototype platform for a Genotype-Phenotype Archiving and Communication System (G-PACS), analogous to the radiology PACS, with archiving and communication of 2 types of information ? patient findings and annotated genomic variants, enabling genome-phenome analysis using these components. AIM 1: Develop phenotype processing, archiving and communication. A Patient Finding List will be built in the G-PACS, distinct from the EHR?s Problem List. The Patient Finding List will have a Candidate findings section populated by the Phenotype Builder EHR text mining program being implemented at Geisinger, and an Accepted findings section to which Candidate Findings can be accepted or be added using applications such as SimulConsult. Applications will save Session snapshots to the G-PACS, which can be used to re-launch using that data, or for a new analysis, using parts of the Patient Finding List. In all cases, the application will have access to the Candidate and Accepted Findings, and highlight information on SimulConsult?s Useful Findings screen to signal what information may already be known about the patient. AIM 2: Empower clinicians to use full data in genomic diagnosis. A pipeline will be built for annotating and combining variant files, with archival capability, optimized for speed. This and findings chosen using the Patient Finding List or Session snapshots, plus those entered with the Useful Findings feature of SimulConsult will enable the clinician to do a full Genome-Phenome analysis, and report using GenomeCOMPASS. Aim 3: Evaluate the end-to-end G-PACS solution. A mixed-methods approach will be used for 10 clinicians to test 3 de-identified cases with known diagnoses, using G-PACS and Epic?s development environment.
Project Terms: Address; Age; Age of Onset; Archives; Clinical; clinical care; clinical Diagnosis; Code; Communication; Copy Number Polymorphism; cost; Data; data format; Databases; Development; Diagnosis; Diagnostic; Disease; Electronic Health Record; Elements; Environment; Family; file format; General Practitioners; Genes; Genetic; Genetic Diseases; Genetic Materials; Genetic study; genetic variant; Genome; genome analysis; genome sequencing; genomic data; Genomics; Genotype; Health system; improved; interoperability; Laboratory Scientists; Link; Methods; Mining; non-genomic; Patients; Phase; phenome; Phenotype; Physical Examination; Process; programs; prototype; Radiology Specialty; Recording of previous events; Reporting; Research; Resources; Retrieval; Signal Transduction; Specialist; Speed; Standardization; Surface; System; Terminology; Testing; text searching; Time; tool; usability; Variant; whole genome;
