gms | German Medical Science

Introduction: The COVID-19 pandemic demonstrated the vulnerability of the healthcare system when it comes to an uncontrolled spread of viruses. This is attended by a demand for tracing infections at an early stage with, simultaneously, creating a higher burden for infection control specialists and surveillance systems than ever. Particularly, the situation is different from nosocomial bacterial infections: as there is a continuous admission of potentially infected persons and entry through employees, the rapid identification of networks in case of unexpected infections is of high relevance for early initiating the right measures [1]. Here, the potentials of healthcare digitalization come to effect, as e.g. routine data can be reused for analyzing patient movements. By this, we developed an open application called smart infection control system (Co-Surv-SmICS) to support infection control specialists.

Methods: Co-Surv-SmICS is based on the visualizations of the HiGHmed-SmICS for cluster detection of bacterial infections but with new models, interfaces and algorithms [2], [3], [4]. Data sets are modelled as (inter)nationally consented openEHR-data models, enriched with terminologies such as SNOMED CT, and integrated into local data platforms directly accessible by Co-Surv-SmICS using Archetype Query Language (AQL) [5], [6], [7], [8]. Within the B-FAST and CODEX projects of the Netzwerk Universitätsmedizin (University Hospital Network, NUM), SmICS has been enhanced from different perspectives [9], [10], [11]. To enable use of Co-Surv-SmICS in the full network of German university medical centers, an implementation of the defined open source policies and an alignment to the standards of the Medical Informatics Initiative (MII) and the COVID-19 consensus data set (GECCO) [12] was required. Co-Surv-SmICS consists of microservices: “SmICS Core” is a .NET 5.0 web app that connects via HTTP client to an openEHR-server, uses its RESTful-API for sending AQL queries, transforms the results, and offers an own RESTful-API for predefined queries used by other microservices (“SmICS Visualization”, “SmICS Algorithms”).

Results: A Co-Surv-SmICS-prerelease, with test data and a demonstrator, has been published [13], [14], [15]. Co-Surv-SmICS interactively visualizes patient movements and virology findings in a spatiotemporal context, patient contact networks, epidemiology curves and infection statistics. Co-Surv-SmICS is built strictly compliant to openEHR and usable with any reference model implementation, e.g. open source EHRbase [16]. As the MII defined FHIR profiles as minimum requirement of interoperability, the EHRbase “FHIR bridge” SDK tool (developed in NUM CODEX to translate FHIR to openEHR) was successfully extended for Co-Surv-SmICS.

Discussion: Substantial efforts were required to tackle the new challenges and to ease nationwide deployment. The prerelease is just a starting point and will be enhanced, e.g. with a contract-tracing module for detecting potential transmissions. Furthermore, EHRbase and GECCO have been work in progress resulting in yet unstable interfaces. Besides, new aspects to be considered in the next versions, such as “vaccination” and “symptoms”, appear constantly during the pandemic.

Conclusion: Tracing of infected patients is time-consuming and complex. Co-Surv-SmICS uses data and visualization to help specialists to reach a timely patient look-back to prevent transmissions. The open application design facilitates its distribution to all hospitals. Its impact for keeping the pandemic under control is then to be evaluated.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.