Applying Fast Healthcare Interoperability Resources (FHIR) for Pathogen Genomics at the Point of Care
Notice bibliographique
Résumé
Abstract 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,121 | 0,136 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,008 | 0,013 |
| Science ouverte | 0,004 | 0,013 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».