Presentation and formatting of laboratory results: a narrative review on behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group “postanalytical phase” (WG-POST)

Cadamuro, Janne; Hillarp, Andreas; Unger, Axel; Meyer, Alexander; Bauçà, Josep Miquel; Plekhanova, O.A.; Linko-Parvinen, Anna; Watine, Joseph; Leichtle, Alexander Benedikt; Buchta, Christoph; Haschke-Becher, Elisabeth; Eisl, Christoph; Winzer, Johannes; Kristoffersen, Ann Helen

doi:10.1080/10408363.2020.1867051

Cited by 19 publications

(12 citation statements)

References 66 publications

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“…However, when considering that the input information may consist of anamnesis, clinical examination results, imaging diagnostics, laboratory reports, current medication, pathology reports, patient history including pre-existing conditions and even the individuals social environment, it must become more than obvious that this amount of data is not processable by humans in a reasonable fashion. This illustration does not even acknowledge the fact that most of these pieces of information are often distributed across several sub-systems of electronic health record (EHR) systems or the fact that the formatting thereof often does not fit the purpose and vital information may be overlooked [1].…”

Section: Introductionmentioning

confidence: 99%

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence - A Narrative Review

Cadamuro¹

2021

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Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future.

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Section: Introductionmentioning

confidence: 99%

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence - A Narrative Review

Cadamuro¹

2021

Preprint

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“…Laboratory medicine has always been a data-driven discipline—more so than ever with the advent of multi-parametric and “-omics” technologies. On the other hand, the discipline has been largely fossilized by a way of working that has remained almost unchanged for decades and by the specific requirements of clinicians and regulatory bodies for reporting findings [ 88 ]. This is especially true for routine clinical diagnostics, so opening up to “Big Data” represents a challenge that should not be underestimated.…”

Section: Discussionmentioning

confidence: 99%

Big Data in Laboratory Medicine—FAIR Quality for AI?

et al. 2022

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Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.

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“…Laboratory medicine has always been a data-driven discipline -more so than ever with the advent of multi-parametric and "-omics" technologies. On the other hand, the discipline has been largely fossilised by a way of working that has remained almost unchanged for decades and by specific requirements of clinicians and regulatory bodies for reporting findings [85]. This is especially true for routine clinical diagnostics, so opening up to "Big Data" represents a challenge that should not be underestimated.…”

Section: Discussionmentioning

confidence: 99%

Big Data in Laboratory Medicine – FAIR Quality for AI?

Blatter¹,

Witte²,

Nakas³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day - from simple numerical results from e.g. sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and meta-data. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved for example by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or they can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: The growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.

show abstract

Presentation and formatting of laboratory results: a narrative review on behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group “postanalytical phase” (WG-POST)

Cited by 19 publications

References 66 publications

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence - A Narrative Review

Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence - A Narrative Review

Big Data in Laboratory Medicine—FAIR Quality for AI?

Big Data in Laboratory Medicine – FAIR Quality for AI?

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