Modern-day microbial diagnostics and genomics have the potential to revolutionize individual and population-level infectious disease prevention, patient care, and treatment. To realize the potential, we need new approaches to standardizing testing and genomic data so that complex data and knowledge can be integrated at the point of care reliably and without ambiguity. We provide a series of approaches to pathogen genomic information standardization and guides to improve data interoperability which is key to harnessing the power of modern testing and genomics data.

We develop generalizable knowledge and guidance to integrate the systems of terminology management, data modeling and inference that can provide flexibility for collaborative development across multiple domains (i.e. public health, clinical, academic research and open source communities) in order to significantly speed up the applications of pathogen genomic knowledge.

We observed numerous advantages to using healthcare-specific standards such as Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL). Advantages include convenient information models, mechanisms for verification, and the availability of tools, documentation and expertise to provide assistance during development. We also found the critical role of community-driven domain-specific ontologies which provide a source of terminologies thereby addressing content coverage gaps in the common clinical terminologies.

Strengths and limitations of this study To our knowledge, this is the first work of its kind to provide structured guidance on pathogen genomic data interoperability using HL7 FHIR resources for a clinical scenario involving whole genome sequencing. We believe this provides a clear path for broader stakeholders including implementors and knowledge curators on how to collaborate and facilitate automation in support of speedy exchange of complex knowledge for genomic epidemiology.

We believe the tools and documentation provided can be a resource for clinical informatics, researchers, and public health organizations who want to collaborate, grow and exchange pathogen genomic knowledge for critical public health applications.

We acknowledge the limitations of this work.

First, the tools developed here are limited in scope and not yet validated among the broader FHIR community. Therefore the ability to generalize for a broad set of pathogens is limited. Standardization of external ontology will require approval from the HL7 terminology authority. This approval process will require the demonstration of quality processes and measures and licensing and legal processes as well as community buy-ins.

The information model here is developed based on scenario modelling. Additional validation using real clinical scenarios and patient data will be required for future developments. As the whole genome sequencing process is only beginning to emerge in clinical practices, more patient-derived whole genome sequence result data from multiple facilities will be needed to create generalized clinically valid pathogen genomic tools.

The privacy issues surrounding the utilization of social determinants of health data (SDOH), while taking into account the relational and structural aspects of infectious disease outbreaks that impact vulnerable communities, will further require careful consideration prior to standardizing the discovery and access of SDOH data.

Preamble Modern-day medical diagnostics using microbial genomics have the potential to revolutionize individual and population-level disease prevention, patient care, and treatment. Clinical laboratories are increasingly pursuing pathogen genomics for infectious disease diagnosis and characterizing whole genome sequences of cultured isolates to help with infection prevention and control practices (IPAC) regarding outbreaks and surveillance

However, to achieve that goal, we need to consider the speed, complexity, and ability to integrate the point-of-care data with genomic data. We provide a series of approaches to pathogen genomic information standardization and guides to improve data interoperability, which is key to harnessing the power of modern testing and genomics data.

The authors have declared no competing interest.

This work has been supported by grant to Dr. William Hsiao from the Canadian Institutes of Health Research (Project Grant Number: PJT-159456) and the Genome BC and Genome Canada (Grant Number: 286GET)

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