Cross‐Network Directory Service: Infrastructure to enable collaborations across distributed research networks

Abstract

Introduction Existing large‐scale distributed health data networks are disconnected even as they address related questions of healthcare research and public policy. This paper describes the design and implementation of a fully functional prototype open‐source tool, the Cross‐Network Directory Service (CNDS), which addresses much of what keeps distributed networks disconnected from each other. Methods The set of services needed to implement a Cross‐Directory Service was identified through engagement with stakeholders and workgroup members. CNDS was implemented using PCORnet and Sentinel network instances and tested by participating data partners. Results Web services that enable the four major functional features of the service (registration, discovery, communication, and governance) were developed and placed into an open‐source repository. The services include a robust metadata model that is extensible to accommodate a virtually unlimited inventory of metadata fields, without requiring any further software development. The user interfaces are programmatically generated based on the contents of the metadata model. Conclusion The CNDS pilot project gathered functional requirements from stakeholders and collaborating partners to build a software application to enable cross‐network data and resource sharing. The two partners—one from Sentinel and one from PCORnet—tested the software. They successfully entered metadata about their organizations and data sources and then used the Discovery and Communication functionality to find data sources of interest and send a cross‐network query. The CNDS software can help integrate disparate health data networks by providing a mechanism for data partners to participate in multiple networks, share resources, and seamlessly send queries across those networks.

Identifying new drugs associated with pulmonary arterial hypertension: A WHO pharmacovigilance database disproportionality analysis

“…However, the recent advances in common data models to create platforms allowing international collaborations across different databases at a regional scale, such as the Exploring and Understanding Adverse Drug Reactions (EU‐ADR) project, the Observational Medical Outcomes Partnership (OMOP) experiment, or the Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium (PROTECT) will allow sufficient power to be reached to study rare diseases. 38 , 39 , 40 , 41 In addition, the methodological improvements in pharmacoepidemiological methodology to generate signals from healthcare databases or for risk quantification, notably through case‐based methods, may in the future make it possible to study rare ADR. 42 , 43 , 44 …”

“…Adverse Drug Reactions (EU-ADR) project, the Observational Medical Outcomes Partnership (OMOP) experiment, or the Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium (PROTECT) will allow sufficient power to be reached to study rare diseases. [38][39][40][41] In addition, the methodological improvements in pharmacoepidemiological methodology to generate signals from healthcare databases or for risk quantification, notably through case-based methods, may in the future make it possible study rare ADR. [42][43][44] The ICSRs aggregated in the WHO pharmacovigilance database are spontaneously reported by healthcare professionals and patients in 134 countries around the world.…”

A Review of Cross-Functional Collaborations in Enhancing Healthcare Delivery in Nigeria

“…Firstly, crossfunctional collaborations can bridge gaps in healthcare delivery by fostering a culture of knowledge sharing and mutual learning. For instance, the Cross-Network Directory Service (CNDS) was developed as an open-source tool to enable collaborations across distributed research networks, aiming to address the disconnection between large-scale distributed health data networks (Malenfant et al, 2019). Such tools and platforms facilitate the seamless sharing of data and insights, leading to more informed decision-making in patient care and research.…”

Perceived Risk of Re-Identification in OMOP-CDM Database: A Cross-Sectional Survey

“…Nonetheless, there are five limitations that induce disconnection and impede collaboration across networks or countries: 1) variance in governance policies and participation requirements between networks; 2) the lack of mechanism for broadcasting research capabilities or encouraging accessibility among participants; 3) a lack of security and reliability between networks for data requests and tracking response activity; 4) the absence of operational standards for describing data, which could allow for judgement of fitness-for-use of others’ data sources; and 5) the unavailability of reliable mechanisms for executing queries sent across networks. 5 …”