Literature DB >> 31214340

Forecasting the demand for radiology services.

Murray J Côté1, Marlene A Smith2.   

Abstract

Since the demand for health services is the key driver for virtually all of a health care organisation's financial and operational activities, it is imperative that health care managers invest the time and effort to develop appropriate and accessible forecasting models for their facility's services. In this article, we analyse and forecast the demand for radiology services at a large, tertiary hospital in Florida. We demonstrate that a comprehensive and accurate forecasting model can be constructed using well-known statistical techniques. We then use our model to illustrate how to provide decision support for radiology managers with respect to department staffing. The methodology we present is not limited to radiology services and we advocate for more routine and widespread use of demand forecasting throughout the health care delivery system.

Keywords:  Radiology services; demand forecasting; forecast error; multiple regression analysis; personnel staffing

Year:  2017        PMID: 31214340      PMCID: PMC6452837          DOI: 10.1080/20476965.2017.1390056

Source DB:  PubMed          Journal:  Health Syst (Basingstoke)        ISSN: 2047-6965


  5 in total

1.  Google Trends Data of Radiologists Who Accept Medicare: A Potential Tool for Predicting State Demand.

Authors:  Christine P Doepker; Haig Pakhchanian; Rahul Raiker; Dhairya A Lakhani; Jeffery P Hogg
Journal:  Curr Probl Diagn Radiol       Date:  2021-03-08

2.  Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic.

Authors:  Roozbeh Houshyar; Karen Tran-Harding; Justin Glavis-Bloom; Michael Nguyentat; John Mongan; Chantal Chahine; Thomas W Loehfelm; Marc D Kohli; Edward J Zaragoza; Paul M Murphy; Rony Kampalath
Journal:  Emerg Radiol       Date:  2020-06-05

3.  Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France.

Authors:  Amandine Crombé; Jean-Christophe Lecomte; Nathan Banaste; Karim Tazarourte; Mylène Seux; Hubert Nivet; Vivien Thomson; Guillaume Gorincour
Journal:  Insights Imaging       Date:  2021-07-22

4.  Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models.

Authors:  Lawrence Fulton; Clemens Scott Kruse
Journal:  J Med Internet Res       Date:  2019-10-29       Impact factor: 5.428

5.  A hybrid analytical model for an entire hospital resource optimisation.

Authors:  Muhammed Ordu; Eren Demir; Soheil Davari
Journal:  Soft comput       Date:  2021-07-30       Impact factor: 3.643

  5 in total

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