Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T21:57:51.071Z Has data issue: false hasContentIssue false
  • English
  • Français

Acomprehensive computer database for medical physics on-call program

Published online by Cambridge University Press:  14 May 2019

Mohamed Mohamed  and
James C. L. Chow Open the ORCID record for James C. L. Chow [Opens in a new window]
Mohamed Mohamed
Affiliation:
Department of Physics, Ryerson University, Toronto, Ontario, Canada
James C. L. Chow*
Affiliation:
Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
*
Author for correspondence: Dr James Chow, Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5G 2M9, Canada. Tel: 416 946 4501. Fax: 416 946 6566. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Purpose: A comprehensive and robust computer database was built to record and analyse the medical physics on-call data in emergency radiotherapy. The probability distributions of the on-call events varying with day and week were studied.

Materials and methods: Variables of medical physics on-call events such as date and time of the event, number of event per day/week/month, treatment site of the event and identity of the on-call physicist were input to a programmed Excel file. The Excel file was linked to the MATLAB platform for data transfer and analysis. The total number of on-call events per day in a week and per month in a year were calculated based on the physics on-call data in 2010–18. In addition, probability distributions of on-call events varying with days in a week (Monday–Sunday) and months (January–December) in a year were determined.

Results: For the total number of medical physics on-call events per week in 2010–18, it was found that the number was similar from Sundays to Thursdays but increased significantly on Fridays before the weekend. The total number of events in a year showed that the physics on-call events increased gradually from January up to March, then decreased in April and slowly increased until another peak in September. The number of events decreased in October from September, and increased again to reach another peak in December. It should be noted that March, September and December are months close to Easter, Labour Day and Christmas, when radiation staff usually take long holidays.

Conclusions: A database to record and analyse the medical physics on-call data was created. Different variables such as the number of events per week and per year could be plotted. This roster could consider the statistical results to prepare a schedule with better balance of workload compared with scheduling it randomly. Moreover, the emergency radiotherapy team could use the analysed results to enhance their budget/resource allocation and strategic planning.

Keywords

Databaseemergency radiotherapyhealth care managementmedical staff schedulingphysics on-call
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 19 , Issue 1 , March 2020 , pp. 10 - 14
Check for updates
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Mitera, G, Swaminath, A, Wong, S et al. Radiotherapy for oncologic emergencies on weekends: examining reasons for treatment and patterns of practice at a Canadian cancer centre. Curr Oncol 2009; 16: 55–60.CrossRefGoogle Scholar
Andersson, L, Sousa, P. An innovative way of caring: palliative radiation therapy rapid response clinic. Can Oncol Nurs J 1998; 8: 146–147.Google ScholarPubMed
Danjoux, C, Chow, E, Drossos, A et al. An innovative rapid response radiotherapy program to reduce waiting time for palliative radiotherapy. Support Care Cancer 2006; 14: 38–43.CrossRefGoogle ScholarPubMed
Christian, E, Adamietz, IA, Willich, N et al. Radiotherapy in oncological emergencies–final results of a patterns of care study in Germany, Austria and Switzerland. Acta Oncol 2008; 47: 81–89.CrossRefGoogle ScholarPubMed
Kwok, Y, DeYoung, C, Garofalo, M et al. Radiation oncology emergencies. Hematol Oncol Clin North Am 2006; 20: 505–522.CrossRefGoogle ScholarPubMed
Bauza, X, Chow, JCL. An automated scheduling system for radiotherapy physicist on-call using Monte Carlo simulation. Australas Phys Eng Sci Med 2019; 42(1): 27–32.CrossRefGoogle ScholarPubMed
Chow, JCL. Internet-based computer technology on radiotherapy. Rep Pract Oncol Radiother 2017; 22: 455–462.CrossRefGoogle ScholarPubMed
Haghani, A, Hu, H, Tian, Q. An optimization model for real-time emergency vehicle dispatching and routing. 82nd Annual Meeting of the Transportation Research Board, 12–16 January, Washington, DC: Transportation Research Board. 2003.Google Scholar
Santibáñez, P, Chow, VS, French, J et al. Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency’s ambulatory care unit through simulation. Health Care Manag Sci 2009; 12(4): 392–407.CrossRefGoogle ScholarPubMed
Gupta, D, Denton, B. Appointment scheduling in health care: challenges and opportunities. IIE Trans 2008; 40(9): 800–819.CrossRefGoogle Scholar
Harrell, CR, Lange, V. Healthcare simulation modeling and optimization using MedModel. Proceedings of the 2001 Winter Simulation Conference, 9–12 December, Arlington, VA. Washington, DC: IEEE Computer Society, 2001: 233–238.Google Scholar
Abdou, H, Pointon, J. Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intell Syst Account Finance Manag 2011; 18: 59–88.CrossRefGoogle Scholar