Key result
Timing errors and temporal uncertainty in clinical databases can be mitigated by adopting a holistic approach to modeling temporal uncertainty, providing a basis for enhanced model generalizability.
Why the study?
Clinical models implicitly assume timestamps are accurate and precise, but this custom needs re-evaluation for algorithms to make meaningful use of high-frequency physiological data when timestamps come from unsynchronized clocks.
Accurate timekeeping and quantification of temporal uncertainty are essential for robust physiological modeling and clinical decision support in critical care.
May enhance robustness of critical care models; leaves open prospective validation of holistic temporal approaches.
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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Goodwin et al. (2022) conducted a review in Timing errors and temporal uncertainty in clinical databases. Timing errors and temporal uncertainty in clinical databases can be mitigated by adopting a holistic approach to modeling temporal uncertainty, providing a basis for enhanced model generalizability.
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