The recent COVID-19 pandemic has underscored the critical need for efficient data exchange among institutions to effectively respond to health emergencies and combat global health threats. While the digitization of data represents a significant advancement over traditional paper-based methods, it alone falls short when information is siloed in disparate systems worldwide, each utilizing incompatible formats. The collection of citizens' health data is becoming increasingly pervasive through active and passive means, including telemedicine, wearable devices, and health apps. Yet, to fully leverage the wealth of data amassed, enhancing interoperability across systems is imperative.
Presently, the amalgamation of data from diverse systems necessitates laborious and error-prone transformation processes, hampering data quality and integration.
By fostering data exchange between research and healthcare domains, not only can healthcare delivery be enhanced, but collaborative opportunities across institutions can also be nurtured. Research stands to gain significantly from access to vast repositories of data, facilitating more reliable and expeditious outcomes that can inform healthcare practices. The advancement of precision medicine hinges on the availability of extensive, high-quality data. Additionally, a higher level of interoperability of health data would enable the possibility to apply central analysis algorithms to networks of hospitals removing the need for local analysis expertise. In this context, the establishment and adoption of common data models and interoperability standards for the terminology and structure of data is of paramount importance. Citizens should be adhering to the FAIR principles—findability, accessibility, interoperability, and reusability—when collecting or disseminating data serves as a beacon, promoting traceability, exchange, and reuse of information. Metadata associated with FAIR data bolster data quality by minimizing ambiguities and streamlining reuse.
Amidst this pursuit, due consideration must be accorded to data protection and security, ensuring the safeguarding of sensitive information within this expansive data ecosystem.
Citizens are at the center of this ecosystem and should be empowered to control their data.
The potential of digital medicine, particularly with the integration of artificial intelligence and large language models (LLMs), is undeniably exciting. However, it also brings with it significant risks if data is not handled with meticulous care and strategies to enhance data quality and mitigate data bias and security risks need to be implemented.
The areas of interest for this Research Topic concern the management of health data and include, but are not limited to:
• Interoperability
• Data harmonization and standardisation
• Common data models
• Bias in health data
• Health data quality
• Federated systems for health data analysis
• Imaging data structure
• Health data security
• Artificial intelligence
• Large language models
• Precision medicine
• Health apps
The recent COVID-19 pandemic has underscored the critical need for efficient data exchange among institutions to effectively respond to health emergencies and combat global health threats. While the digitization of data represents a significant advancement over traditional paper-based methods, it alone falls short when information is siloed in disparate systems worldwide, each utilizing incompatible formats. The collection of citizens' health data is becoming increasingly pervasive through active and passive means, including telemedicine, wearable devices, and health apps. Yet, to fully leverage the wealth of data amassed, enhancing interoperability across systems is imperative.
Presently, the amalgamation of data from diverse systems necessitates laborious and error-prone transformation processes, hampering data quality and integration.
By fostering data exchange between research and healthcare domains, not only can healthcare delivery be enhanced, but collaborative opportunities across institutions can also be nurtured. Research stands to gain significantly from access to vast repositories of data, facilitating more reliable and expeditious outcomes that can inform healthcare practices. The advancement of precision medicine hinges on the availability of extensive, high-quality data. Additionally, a higher level of interoperability of health data would enable the possibility to apply central analysis algorithms to networks of hospitals removing the need for local analysis expertise. In this context, the establishment and adoption of common data models and interoperability standards for the terminology and structure of data is of paramount importance. Citizens should be adhering to the FAIR principles—findability, accessibility, interoperability, and reusability—when collecting or disseminating data serves as a beacon, promoting traceability, exchange, and reuse of information. Metadata associated with FAIR data bolster data quality by minimizing ambiguities and streamlining reuse.
Amidst this pursuit, due consideration must be accorded to data protection and security, ensuring the safeguarding of sensitive information within this expansive data ecosystem.
Citizens are at the center of this ecosystem and should be empowered to control their data.
The potential of digital medicine, particularly with the integration of artificial intelligence and large language models (LLMs), is undeniably exciting. However, it also brings with it significant risks if data is not handled with meticulous care and strategies to enhance data quality and mitigate data bias and security risks need to be implemented.
The areas of interest for this Research Topic concern the management of health data and include, but are not limited to:
• Interoperability
• Data harmonization and standardisation
• Common data models
• Bias in health data
• Health data quality
• Federated systems for health data analysis
• Imaging data structure
• Health data security
• Artificial intelligence
• Large language models
• Precision medicine
• Health apps