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



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.

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

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Change history

  • 14 January 2020

    The above article has an error in Figure 6 online (already correct in the PDF version) including the Graphical abstract figure. The original article has been corrected.


  1. 1.

    Faddy M, Graves N, Pettitt A (2009) Modeling length of stay in hospital and other right skewed data: comparison of phase-type, gamma and log-normal distributions. Value in Health. 12(2):309–314

  2. 2.

    Dodd S, Bassi A, Bodger K, Williamson P (2006) A comparison of multivariable regression models to analyse cost data. Journal of evaluation in clinical practice. 12(1):76–86

  3. 3.

    Shuaib W, Vijayasarathi A, Tiwana MH, Johnson J-O, Maddu KK, Khosa F (2014) The diagnostic utility of rib series in assessing rib fractures. Emergency radiology. 21(2):159–164

  4. 4.

    McCaig LF, Burt CW (2005) National hospital ambulatory medical care survey: 2003 emergency department summary. Adv data 358(1)

  5. 5.

    Lamb L, Kashani P, Ryan J, Hebert G, Sheikh A, Thornhill R, Fasih N (2015) Impact of an in-house emergency radiologist on report turnaround time. Canadian Journal of Emergency Medicine. 17(1):21–26

  6. 6.

    Southall AC, Harris VV (1999) Patient ED turnaround times: a comparative review. The American journal of emergency medicine. 17(2):151–153

  7. 7.

    Robinson JD, Hippe DS, Deconde R, Zecevic M, Mehta N (2019) Emergency radiology: an underappreciated source of liability risk. Journal of the American College of Radiology.

  8. 8.

    Chong ST, Robinson JD, Davis MA, Bruno MA, Roberge EA, Reddy S et al (2019) Emergency radiology: current challenges and preparing for continued growth. Journal of the American College of Radiology.

  9. 9.

    Headings. MS. length stay. 2016 [Available from: .

  10. 10.

    Zhan C, Miller MR (2003) Excess length of stay, charges, and mortality attributable to medical injuries during hospitalization. Jama. 290(14):1868–1874

  11. 11.

    Singh CH, Ladusingh L (2010) Inpatient length of stay: a finite mixture modeling analysis. The European Journal of Health Economics. 11(2):119–126

  12. 12.

    Austin PC, Rothwell DM, Tu JV (2002) A comparison of statistical modeling strategies for analyzing length of stay after CABG surgery. Health Services and Outcomes Research Methodology. 3(2):107–133

  13. 13.

    Marazzi A, Paccaud F, Ruffieux C, Beguin C (1998) Fitting the distributions of length of stay by parametric models. Medical care. 36(6):915–927

  14. 14.

    Dudley RA, Harrell FE, Smith LR, Mark DB, Califf RM, Pryor DB et al (1993) Comparison of analytic models for estimating the effect of clinical factors on the cost of coronary artery bypass graft surgery. Journal of clinical epidemiology. 46(3):261–271

  15. 15.

    Manning WG, Basu A, Mullahy J (2005) Generalized modeling approaches to risk adjustment of skewed outcomes data. Journal of health economics. 24(3):465–488

  16. 16.

    Lee AH, Fung WK, Fu B (2003) Analyzing hospital length of stay: mean or median regression? Medical care.:681–686

  17. 17.

    Lee AH, Gracey M, Wang K, Yau KK (2005) A robustified modeling approach to analyze pediatric length of stay. Annals of Epidemiology. 15(9):673–677

  18. 18.

    Basu A, Manning WG, Mullahy J (2004) Comparing alternative models: log vs Cox proportional hazard? Health economics. 13(8):749–765

  19. 19.

    Manning WG, Mullahy J (2001) Estimating log models: to transform or not to transform? Journal of health economics. 20(4):461–494

  20. 20.

    Samore MH, Shen S, Greene T, Stoddard G, Sauer B, Shinogle J et al (2007) A simulation-based evaluation of methods to estimate the impact of an adverse event on hospital length of stay. Medical care. 45(10):S108–SS15

  21. 21.

    Moran JL, Solomon PJ (2012) A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand intensive care adult patient data-base, 2008–2009. BMC medical research methodology. 12(1):68

  22. 22.

    Dabbo S, Varner C, Bleakney R, Ovens H (2014) Clinical impact of extending after-hours radiology coverage for emergency department computed tomography imaging. Open access emergency medicine: OAEM. 6:33

  23. 23.

    Bodanapally UK, Shanmuganathan K, Nutakki K, Mirvis SE, Sliker CW, Shet N (2013) Implementation of 24/7 radiology services in an academic medical centre level 1 trauma centre: impact on trauma resuscitation unit length of stay and economic benefit analysis. Injury. 44(1):75–79

  24. 24.

    J M. 24/7 Emergency radiology improves care in a single payer system. RSNA. [Abstract]. In press 2017.

  25. 25.

    Joseph L, Reinhold C (2003) Introduction to probability theory and sampling distributions. American journal of Roentgenology. 180(4):917–923

  26. 26.

    Joseph L, Reinhold C (2005) Statistical inference for continuous variables. American Journal of Roentgenology. 184(4):1047–1056

  27. 27.

    Joseph L, Reinhold C (2005) Statistical inference for proportions. American Journal of Roentgenology. 184(4):1057–1064

  28. 28.

    Romagnuolo J, Bardou M, Rahme E, Lawrence J (2003) Magnetic resonance cholangiopancreatography: a meta-analysis of test performance in suspected biliary disease. Annals of internal medicine. 139(7):547

  29. 29.

    Obuchowski NA (2005) Fundamentals of clinical research for radiologists. American Journal of Roentgenology. 184(2):364–372

  30. 30.

    Karlik SJ (2003) Exploring and summarizing radiologic data. American Journal of Roentgenology. 180(1):47–54

  31. 31.

    Jarvik JG (2001) Fundamentals of clinical research for radiologists. AJR-American Journal of Roentgenology. 176(4):873–877

  32. 32.

    Karlik SJ (2001) How to develop and critique a research protocol. American Journal of Roentgenology. 176(6):1375–1380

  33. 33.

    Blackmore CC, Cummings P (2004) Observational studies in radiology. American Journal of Roentgenology. 183(5):1203–1208

  34. 34.

    Budovec JJ, Kahn CE Jr (2010) Evidence-based radiology: a primer in reading scientific articles. American Journal of Roentgenology. 195(1):W1–W4

  35. 35.

    Hollingworth W (2005) Radiology cost and outcomes studies: standard practice and emerging methods. American Journal of Roentgenology. 185(4):833–839

  36. 36.

    Jarvik JG (2001) The research framework. American Journal of Roentgenology. 176(4):873–878

  37. 37.

    Stolberg HO, Norman G, Trop I (2004) Fundamentals of clinical research for radiologists. AJR. 183:1539–1544

  38. 38.

    Blackmore CC (2001) The challenge of clinical radiology research. American Journal of Roentgenology. 176(2):327–331

  39. 39.

    Brakenhoff TB, Van Smeden M, Visseren FL, Groenwold RH (2018) Random measurement error: Why worry? An example of cardiovascular risk factors. PloS one. 13(2):e0192298

  40. 40.

    van Smeden M, Lash TL, Groenwold RH van Smeden M. Five myths about measurement error in epidemiologic research.

  41. 41.

    Brakenhoff TB, Mitroiu M, Keogh RH, Moons KG, Groenwold RH, van Smeden M (2018) Measurement error is often neglected in medical literature: a systematic review. Journal of clinical epidemiology. 98:89–97

  42. 42.

    Raja FS, Amann J (2012) After-hours radiology consultation in an academic setting, 2005-2009. Canadian Association of Radiologists Journal. 63(3):165–169

  43. 43.

    Bluman AG. Elementary statistics: a step by step approach: McGraw-Hill Higher Education New York; 2009.

  44. 44.

    Spitler K, Vijayasarathi A, Salehi B, Dua S, Azizyan A, Cekic M et al (2018) 24/7/365 Neuroradiologist coverage improves resident perception of educational experience, referring physician satisfaction, and turnaround time. Current problems in diagnostic radiology.

  45. 45.

    Hirschorn DS, Hinrichs CR, Gor DM, Shah K, Visvikis G (2001) Impact of a diagnostic workstation on workflow in the emergency department at a level I trauma center. Journal of digital imaging. 14(1):199–201

  46. 46.

    Nishisaki A, Pines JM, Lin R, Helfaer MA, Berg RA, TenHave T et al (2012) The impact of 24-hr, in-hospital pediatric critical care attending physician presence on process of care and patient outcomes. Critical care medicine. 40(7):2190–2195

  47. 47.

    Krupinski EA, Berbaum KS, Caldwell RT, Schartz KM, Kim J (2010) Long radiology workdays reduce detection and accommodation accuracy. Journal of the American College of Radiology. 7(9):698–704

  48. 48.

    Moore DF. Applied survival analysis using R: Springer; 2016.

  49. 49.

    Chung JH, Strigel RM, Chew AR, Albrecht E, Gunn ML (2009) Overnight resident interpretation of torso CT at a level 1 trauma center: an analysis and review of the literature. Academic radiology. 16(9):1155–1160

  50. 50.

    Ruutiainen AT, Scanlon MH, Itri JN (2011) Identifying benchmarks for discrepancy rates in preliminary interpretations provided by radiology trainees at an academic institution. Journal of the American College of Radiology. 8(9):644–648

  51. 51.

    Walls J, Hunter N, Brasher PM, Ho SG (2009) The DePICTORS Study: discrepancies in preliminary interpretation of CT scans between on-call residents and staff. Emergency radiology. 16(4):303–308

  52. 52.

    Stevens KJ, Griffiths KL, Rosenberg J, Mahadevan S, Zatz LM, Leung AN (2008) Discordance rates between preliminary and final radiology reports on cross-sectional imaging studies at a level 1 trauma center. Academic radiology. 15(10):1217–1226

  53. 53.

    Carney E, Kempf J, DeCarvalho V, Yudd A, Nosher J (2003) Preliminary interpretations of after-hours CT and sonography by radiology residents versus final interpretations by body imaging radiologists at a level 1 trauma center. American Journal of Roentgenology. 181(2):367–373

Download references

Author information

Correspondence to Sabeena Jalal.

Ethics declarations

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.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation


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