Learning from Our Mistakes: The Future of Validating Complex Diagnostics

Master, Stephen R.; Mayer-Schönberger, Viktor

doi:10.1373/clinchem.2014.231407

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…For instance, in the context of evaluating the efficacy of new forms of diagnosis or treatment, regulatory agencies may want to reconsider the role of data, and what exactly they require data to deliver as evidence of drug efficacy. We are not calling for a radical modification of evidence‐based research, but believe it is time to consider limited alternatives to the highly formalized approval process in health care that is currently in place, and – arguably for Big Data approaches – is slowing down unnecessarily the pace of discovery .…”

Section: Discussionmentioning

confidence: 99%

Big Data and medicine: a big deal?

Mayer-Schönberger

Ingelsson

2018

J Intern Med

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Section: Discussionmentioning

confidence: 99%

Big Data and medicine: a big deal?

Mayer-Schönberger

Ingelsson

2018

J Intern Med

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“…Second, additional information is needed for an adequate clinical interpretation of laboratory results. This could be preanalytical information about the patient or the sample [4][5][6], reference ranges for the specific analysis [7], additional information on the test kit or analytical instrument [8][9][10] or any other relevant "hidden" variable [11]. For the documentation of preanalytical variables, the Standard PREanalytical Code (SPREC) [12] was introduced and is currently in use at the BiobankBern.…”

Section: Introductionmentioning

confidence: 99%

Interoperability of laboratory data in Switzerland – a spotlight on Bern

Dahlweid

Kämpf

Leichtle

2018

LaboratoriumsMedizin

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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.

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