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Metadata from Data: Identifying Holidays from Anesthesia Data.

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 workd...

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Published in:Journal of Medical Systems Vol. 39; no. 5; pp. 1 - 6
Main Authors: Starnes, Joseph, Wanderer, Jonathan, Ehrenfeld, Jesse
Format: research tables/charts Journal Article
Published: Springer Nature May2015
Online Access:View this record in EBSCOhost
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      dt: May2015
      vid: 39
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-015-0232-4
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        atl: Metadata from Data: Identifying Holidays from Anesthesia Data.
      aug:
        au:
          Starnes, Joseph
          Wanderer, Jonathan
          Ehrenfeld, Jesse
        affil: Department of Anesthesiology, Vanderbilt University Medical Center, 1301 Medical Center Dr. Nashville 37232 USA
      sug:
        subj:
          Metadata
          Holidays
          Anesthesia
          Information Science
          Medical Informatics
          Human
          Analysis of Covariance
          Time Factors
          Data Analysis
          Cluster Analysis
          Database Management Software
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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