Top

Journal of Imaging Informatics in Medicine

Published in:

01-06-2019

Probabilistic Modeling of Exam Durations in Radiology Procedures

Authors: Usha Nandini Raghavan, Christopher S. Hall, Ranjith Tellis, Thusitha Mabotuwana, Christoph Wald

Published in: Journal of Imaging Informatics in Medicine | Issue 3/2019

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.

Lloyd HD, Holsenback JE: The use of six sigma in health care operations: application and opportunity. Acad Health Care Manag J 2:41–49, 2006

Nitrosi A, Borasi G, Nicoli F, Modigliani G, Botti A, Bertolini M, Notari P: A filmless radiology department in a full digital regional hospital: quantitative evaluation of the increased quality and efficiency. J Digit Imaging 20:140–148, 2007CrossRefPubMedPubMedCentral

Towbin AJ, Iyer SB, Brown J, Varadarajan K, Perry LA, Larson DB: Practice policy and quality initiatives: decreasing variability in turnaround time for radiographic studies from the emergency department. Radiographics 33(2):361–371, 2013CrossRefPubMed

Boland GW, Halpern EF, Gazelle GS: Radiologist report turnaround time: impact of pay-for-performance measures. AJR Am J Roentgenol 195(3):707–711, 2010CrossRefPubMed

Gunn ML, Lehnert BE, Maki JH, Hall C, Amthor T, Senegas J, Beauchamp NJ: Using modality log files to guide MR protocol optimization and improve departmental efficiency. Radiological Society of North America (RSNA) Annual Meeting 2015. Health Services, Policy and Research (Medical/Practice Management) SSM12-02. 2015.

Gardiner JC, Luo Z, Xiaoqin T, Ramamoorthi RV: Fitting heavy-tailed distributions to health care data by parametric and Bayesian methods. J Stat Theory Pract 8(4):619–652, 2013. https://doi.org/10.1080/15598608.2013.824823 CrossRef

Marazzi A, Paccaud F, Ruffieux C, Beguin C: Fitting the distributions of length of stay by parametric models. Med Care 36:915–927, 1998. https://doi.org/10.1097/00005650-199806000-00014 CrossRefPubMed

Silva E, Lisboa P: Analysis of the characteristic features of the density functions for Gamma, Weibull and log-normal distributions through RBF network pruning with QLP. Proceedings of the 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, 2007;6: pp 223–228

Clarke DE, Muhammad E: Some useful properties of log-logistic random variables for healthcare simulation. Int J Stat Med Res 4:79–86, 2015CrossRef

10.

Levy R: Probabilistic models in the study of language, chapter 4–parameter estimation, under preparation, http://www.mit.edu/~rplevy/pmsl_textbook/chapters/pmsl_4.PDF. Last access - Nov 15, 2016

11.

Evans D, Drew J, Leemis L: The distribution of the Kolmogorov–Smirnov, Cramer–von Mises, and Anderson–Darling test statistics for exponential populations with estimated parameters. Commun Stat Simul Commun 37:1396–1421, 2008CrossRef

12.

Ramakrishnan S, Nagarkar K, Degennaro M, Srihari K, Courtney AK: A study of CT scan area of a healthcare provider. In: Ingalls RG, Rossetti MD, Smith JS, Peters BA Eds. Proceedings of the 2004 Winter Simulation Conference, 2005:2: pp 2025–2031. https://doi.org/10.1109/WSC.2004.1371565

13.

Granja C, Almada-Lobo B, Janela F, Seabra J, Mendes A: An optimization based on simulation approach to the patient admission scheduling problem: diagnostic imaging department case study. J Digit Imaging 27(1):33–40, 2014. https://doi.org/10.1007/s10278-013-9626-3 CrossRefPubMed

14.

Wang L: An agent-based simulation for workflow in emergency department. Systems and Information Engineering Design Symposium. Charlottesville, VA, 2009, pp 19–23.

Title: Probabilistic Modeling of Exam Durations in Radiology Procedures
Authors: Usha Nandini Raghavan
Christopher S. Hall
Ranjith Tellis
Thusitha Mabotuwana
Christoph Wald
Publication date: 01-06-2019
Publisher: Springer International Publishing
Published in: Journal of Imaging Informatics in Medicine / Issue 3/2019
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-018-00175-y

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Probabilistic Modeling of Exam Durations in Radiology Procedures

Abstract

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 3/2019

Automatic Lumbar MRI Detection and Identification Based on Deep Learning

Improving Sensitivity on Identification and Delineation of Intracranial Hemorrhage Lesion Using Cascaded Deep Learning Models

Utilization of Structured Reporting to Monitor Outcomes of Doppler Ultrasound Performed for Deep Vein Thrombosis

Restoration of Full Data from Sparse Data in Low-Dose Chest Digital Tomosynthesis Using Deep Convolutional Neural Networks

Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval

Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows