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Exploratory data analysis for pre and post 24/7/365 attending radiologist coverage support in an emergency department: fundamentals of data science

  • Sabeena JalalEmail author
  • Marshall E Lloyd
  • Faisal Khosa
  • Grace I-Hsuan Hsu
  • Savvas Nicolaou
Original Article

Abstract

Objective

To present a detailed exploratory data analysis for critically investigating the patterns in medical doctor (MD) to disposition time, pre and post 24/7/365 attending radiologist coverage, for patients presenting to an emergency department (ED).

Materials and methods

The process involved presenting several modeling techniques. To share an understanding of concepts and techniques, we used proportions, medians, and means, Mann-Whitney U test, Kaplan-Meier’s (KM) survival analysis, linear and log-linear regression, log-ranked test, Cox proportional hazards model, Weibull parametric survival models and tertile analysis. Retrospective chart review was conducted to obtain a data set which was used to determine the trends in MD to disposition time. Data comprised of patients who had visited the emergency department (ED) during two distinct time periods and whose imaging studies were read by an attending emergency and trauma radiologist.

Results

Median provided more insight into the data as compared with the mean. The Mann-Whitney U test was appropriate to evaluate MD to disposition time, but provided limited information. The Kaplan-Meier (KM) was able to offer more insight into the data since it did not assume an underlying model and that is the reason why it was appropriate. However, KM had limited ability to handle measured confounders and was unable to describe the magnitude of difference between curves. The Cox proportional hazards semi-parametric model or some other parametric model such as the Weibull could handle multiple measured confounders and described the magnitude of difference between two (survival) groups in the data set. However, both methods assumed underlying models that may not apply to the data set such as the one used in this study. Linear regression was unlikely to be appropriate due to the shape of survival time distributions, but log transforming the outcome could address the distribution issue. Nearly all the results of the KM subgroup analyses were consistent with the results of the log-transformed linear regression subgroup analyses and the interpretation of the results was the same for both.

Conclusion

Different statistical procedures may be applied to conduct exploratory subgroup analysis for a data set from a pre and post 24/7/365 attending coverage model. This could guide potential areas of further research to compare trends in MD to disposition time in ED. Pattern analysis provides evidence for various stakeholders to rethink the discourse about trends in MD to disposition time, pre and post 24/7/365 attending coverage.

Graphical Illustration: The role of Emergency and Trauma Radiology in an Emergency Department

Keywords

24/7/365 radiology 24/7/365 attending coverage Emergency and trauma radiology Data analysis 

Notes

Compliance with ethical standards

The study was approved by the institutional review board and was compliant with HIPAA. The requirement for written informed consent was waived due to the retrospective nature of the study.

Conflict of interest

Dr. Khosa is the recipient of the Young Investigator Award of Canadian Association of Radiologists (2019). The authors have no relevant disclosures.

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Copyright information

© American Society of Emergency Radiology 2019
corrected publication 2019

Authors and Affiliations

  • Sabeena Jalal
    • 1
    • 2
    Email author
  • Marshall E Lloyd
    • 2
  • Faisal Khosa
    • 1
  • Grace I-Hsuan Hsu
    • 3
  • Savvas Nicolaou
    • 1
  1. 1.Emergency & Trauma Radiology, Department of Radiology, Vancouver General HospitalVancouverCanada
  2. 2.McGill UniversityMontréalCanada
  3. 3.University of UtahSalt LakeUSA

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