The Importance of Metrology in Medicine

Karaböce, Baki; Durmus, Huseyin Okan; Cetin, Emel

doi:10.1007/978-3-030-17971-7_67

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Each observation in this database has been assigned a millisecond precision timestamp by at least two separate clocks, providing an ideal substrate for exploring the behavior of timekeeping systems in the critical care environment ( 48 ). As part of our efforts to develop robust physiological models and clinical decision support systems we have concluded it was prudent to explore potential biases and uncertainties around all sources of information used in these models, with an initial focus on the measurement of time ( 49 , 50 ).…”

Section: Introductionmentioning

confidence: 99%

Timing errors and temporal uncertainty in clinical databases—A narrative review

Goodwin

Eytan

Dixon

et al. 2022

Front. Digit. Health

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A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.

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Section: Introductionmentioning

confidence: 99%