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Probabilistic Modeling of Exam Durations in Radiology Procedures

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Abstract

In this paper, we model the statistical properties of imaging exam durations using parametric probability distributions such as the Gaussian, Gamma, Weibull, lognormal, and log-logistic. We establish that in a majority of radiology procedures, the underlying distribution of exam durations is best modeled by a log-logistic distribution, while the Gaussian has the poorest fit among the candidates. Further, through illustrative examples, we show how business insights and workflow analytics can be significantly impacted by making the correct (log-logistic) versus incorrect (Gaussian) model choices.

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Authors and Affiliations

  1. Philips Healthcare, Andover, MA, USA

    Usha Nandini Raghavan, Christopher S. Hall & Thusitha Mabotuwana

  2. Philips Research North America, Cambridge, MA, USA

    Ranjith Tellis

  3. Lahey Hospital and Medical Center, Burlington, MA, USA

    Christoph Wald

Authors
  1. Usha Nandini Raghavan
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  2. Christopher S. Hall
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  3. Ranjith Tellis
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  4. Thusitha Mabotuwana
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  5. Christoph Wald
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Correspondence to Usha Nandini Raghavan.

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Raghavan, U.N., Hall, C.S., Tellis, R. et al. Probabilistic Modeling of Exam Durations in Radiology Procedures. J Digit Imaging 32, 386–395 (2019). https://doi.org/10.1007/s10278-018-00175-y

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  • DOI: https://doi.org/10.1007/s10278-018-00175-y

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