Metadata from data: identifying holidays from anesthesia data

Journal of Medical Systems
Joseph R StarnesJesse M Ehrenfeld

Abstract

The increasingly large databases available to researchers necessitate high-quality metadata that is not always available. We describe a method for generating this metadata independently. Cluster analysis and expectation-maximization were used to separate days into holidays/weekends and regular workdays using anesthesia data from Vanderbilt University Medical Center from 2004 to 2014. This classification was then used to describe differences between the two sets of days over time. We evaluated 3802 days and correctly categorized 3797 based on anesthesia case time (representing an error rate of 0.13%). Use of other metrics for categorization, such as billed anesthesia hours and number of anesthesia cases per day, led to similar results. Analysis of the two categories showed that surgical volume increased more quickly with time for non-holidays than holidays (p < 0.001). We were able to successfully generate metadata from data by distinguishing holidays based on anesthesia data. This data can then be used for economic analysis and scheduling purposes. It is possible that the method can be expanded to similar bimodal and multimodal variables.

References

Oct 8, 1998·Journal of Clinical Epidemiology·P BlomqvistA Ekbom
Nov 24, 2006·Anesthesia and Analgesia·Franklin Dexter, Richard H Epstein
Aug 30, 2011·Journal of Biomedical Informatics·Hai HuRichard J Mural
Jan 17, 2013·Journal of Medical Systems·Li-Hui LeeDer-Ming Liou
May 17, 2014·International Journal of Metadata, Semantics and Ontologies·Fusheng WangPeiya Liu
Jul 21, 2014·Journal of Medical Systems·Christian D PeccoraRichard D Urman
Oct 16, 2014·Journal of Medical Systems·Rodney A GabrielRichard D Urman

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Citations

Mar 30, 2018·Anesthesia and Analgesia·Richard H Epstein, Franklin Dexter
Nov 13, 2020·Bulletin of the World Health Organization·Vikas N O'Reilly-ShahCraig S Jabaley

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