Literature DB >> 30666882

The elephant in the record: On the multiplicity of data recording work.

Federico Cabitza1, Angela Locoro2, Camilla Alderighi, Raffaele Rasoini3, Domenico Compagnone, Pedro Berjano4.   

Abstract

This article focuses on the production side of clinical data work, or data recording work, and in particular, on its multiplicity in terms of data variability. We report the findings from two case studies aimed at assessing the multiplicity that can be observed when the same medical phenomenon is recorded by multiple competent experts, yet the recorded data enable the knowledgeable management of illness trajectories. Often framed in terms of the latent unreliability of medical data, and then treated as a problem to solve, we argue that practitioners in the health informatics field must gain a greater awareness of the natural variability of data inscribing work, assess it, and design solutions that allow actors on both sides of clinical data work, that is, the production and care, as well as the primary and secondary uses of data to aptly inform each other's practices.

Entities:  

Keywords:  data recording work; data work; inter-rater agreement; inter-rater reliability; observer variability

Year:  2019        PMID: 30666882     DOI: 10.1177/1460458218824705

Source DB:  PubMed          Journal:  Health Informatics J        ISSN: 1460-4582            Impact factor:   2.681


  4 in total

1.  The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records.

Authors:  Michela Assale; Linda Greta Dui; Andrea Cina; Andrea Seveso; Federico Cabitza
Journal:  Front Med (Lausanne)       Date:  2019-04-17

Review 2.  Bridging the "last mile" gap between AI implementation and operation: "data awareness" that matters.

Authors:  Federico Cabitza; Andrea Campagner; Clara Balsano
Journal:  Ann Transl Med       Date:  2020-04

3.  Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings.

Authors:  Andrea Seveso; Andrea Campagner; Davide Ciucci; Federico Cabitza
Journal:  BMC Med Inform Decis Mak       Date:  2020-08-20       Impact factor: 2.796

4.  As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI.

Authors:  Federico Cabitza; Andrea Campagner; Luca Maria Sconfienza
Journal:  BMC Med Inform Decis Mak       Date:  2020-09-11       Impact factor: 2.796

  4 in total

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