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.
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.
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.
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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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The original version of this article was revised: The above article has an error in Figure 6 online (already correct in the PDF version) including the Graphical abstract figure.
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Jalal, S., Lloyd, M.E., Khosa, F. et al. Exploratory data analysis for pre and post 24/7/365 attending radiologist coverage support in an emergency department: fundamentals of data science. Emerg Radiol (2019) doi:10.1007/s10140-019-01737-5
- 24/7/365 radiology
- 24/7/365 attending coverage
- Emergency and trauma radiology
- Data analysis