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Background Empirical antibiotic therapy is facilitated by antibiograms as they provide local bacterial resistance data and patterns. Antibiograms are critical tools that offer comprehensive, institution-specific information on antimicrobial susceptibilities, enabling clinicians to make informed decisions about empirical treatment and guiding antimicrobial stewardship efforts. The rising incidence of multidrug-resistant (MDR) organisms is a significant challenge in countries like Pakistan.
View Article and Find Full Text PDFOHDSI-compliance: a set of document templates facilitating the implementation and operation of a software stack for real-world evidence generation.
Front Med (Lausanne)
May 2024
Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Berlin, Germany.
Introduction: The open-source software offered by the Observational Health Data Science and Informatics (OHDSI) collective, including the OMOP-CDM, serves as a major backbone for many real-world evidence networks and distributed health data analytics platforms. While container technology has significantly simplified deployments from a technical perspective, regulatory compliance can remain a major hurdle for the setup and operation of such platforms. In this paper, we present OHDSI-Compliance, a comprehensive set of document templates designed to streamline the data protection and information security-related documentation and coordination efforts required to establish OHDSI installations.
View Article and Find Full Text PDFMENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.
JAMIA Open
July 2024
Department of Medicine, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States.
Objectives: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7 FHIR) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.
Materials And Methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.
Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention.
Curr Med Sci
April 2024
The Nursing Department, Chinese PLA General Hospital, Beijing, 100853, China.
The global incidence of infectious diseases has increased in recent years, posing a significant threat to human health. Hospitals typically serve as frontline institutions for detecting infectious diseases. However, accurately identifying warning signals of infectious diseases in a timely manner, especially emerging infectious diseases, can be challenging.
View Article and Find Full Text PDFMENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.
medRxiv
November 2023
Department of Medicine, University of Colorado Anschutz Medical Campus, Denver CO.
Objective: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7 FHIR) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.
Materials And Methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.
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