Interoperability of laboratory data in Switzerland – a spotlight on Bern

Abstract

Laboratory data is a treasure chest for personalized medicine: it is – in general – electronically available, highly structured, quality controlled and indicative for many diseases. However, it is also a box with (probably more than) seven locks: laboratories use their own internal coding systems, results are reported in different languages (four official languages plus English with very distinct features in Switzerland), report formats are not uniform, standard nomenclature (e.g. Logical Observation Identifiers Names and Codes [LOINC]) is not routinely used and even these coding systems lack important information, including data, for example, about the specific “kit” used for testing or preanalytical procedures affecting the sample quality and result interpretability. Visualization of complex laboratory and reporting “-omics” data are additional challenges. Currently, there is no “passepartout” key for all these locks available, and also newer concepts such as Fast Health Interoperability Resources (FHIR) might not provide enough plasticity to unconditionally render laboratory data interoperable. In this short overview, we present current approaches in Switzerland with a specific focus on the exemplary Bernese implementations.

Big Data in Laboratory Medicine—FAIR Quality for AI?

“…Yet, this openness represents the basis for modern technologies, in particular deep learning or artificial intelligence, which can bring diverse advantages not only for diagnostics but also for laboratory medicine as an academic and research-based medical discipline. Many steps that are required in the transformation of laboratory medicine data into “Big Data” [ 22 ] can be used for research make sense anyway for lean, efficient, sustainable, and complete data management and can lead to a cleansing and “aggiornamento” (modernization) of laboratory data. If laboratory medicine shies away from these developments, it will be degraded to a pure number generator in the foreseeable future or disappear completely as an academic subject in integrated diagnostic devices.…”

“…Therefore, LISs that are not connected to central clinical data warehouses must be accessed through the laboratory IT personnel. This often leads to an enormous amount of additional work, since laboratory data are highly attractive for a variety of research projects [ 22 ]. For use in clinical data warehouses, the LISs must be electronically connected, and the data prepared via ETL processes (Extract, Transform, Load).…”

Big Data in Laboratory Medicine – FAIR Quality for AI?

“…Yet this openness represents the basis for modern technologies in particular deep learning or artificial intelligence which can bring diverse advantages for diagnostics, but also for laboratory medicine as an academic and research-based medical discipline. Many steps that are required in the transformation of laboratory medicine data into "Big Data" [22] that can be used for research make sense anyway for lean, efficient, sustainable, and complete data management and can lead to a cleansing and "aggiornamento" of laboratory data. If laboratory medicine shies away from these developments, it will be degraded to a pure number generator in the foreseeable future or disappear completely as an academic subject in integrated diagnostic devices.…”

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“…24 Pseudonymized demographical, clinical, and laboratory data were extracted from the patient documentation and transferred by the Insel Data Science Center (IDSC). 25 Additional clinical characteristics were retrieved to explore factors affecting immune response among inpatients. A REDCap database survey was constructed to collect data of medical personnel (demographic, symptoms, comorbidities, and risk factors).…”