ArticleJournal of medical systems2015
Metadata from data: identifying holidays from anesthesia data.
Article in Journal of medical systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Most Weekday Discharge Times at Acute Care Hospitals in the State of Florida Occurred After 3 PM in 2022, Unchanged from Before the COVID-19 Pandemic.Journal of medical systems · 2025Article
- Impact of COVID-19 response on global surgical volumes: an ongoing observational study.Bulletin of the World Health Organization · 2020Observational
- Assessing healthcare service quality using routinely collected data: Linking information systems in emergency care.Journal of medical systems · 2020Article
- The Distributions of Weekday Discharge Times at Acute Care Hospitals in the State of Florida were Static from 2010 to 2018.Journal of medical systems · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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