46 Forecasting the demand profile for a physician-led pre-hospital care service using a mathematical model. (21st May 2017)
- Record Type:
- Journal Article
- Title:
- 46 Forecasting the demand profile for a physician-led pre-hospital care service using a mathematical model. (21st May 2017)
- Main Title:
- 46 Forecasting the demand profile for a physician-led pre-hospital care service using a mathematical model
- Authors:
- Moultrie, CEJ
Corfield, AR
Pell, J
Mackay, D - Abstract:
- Abstract : Aim: We aimed to investigate if a queueing-theory derived, stochastic, computerised mathematical model could accurately predict the number and seasonal pattern of primary pre-hospital missions undertaken by a physician-led pre-hospital and retrieval service in 2016. Methods: We used queueing theory to derive parameters for a computer model built using the MATLAB software suite Simulink program. The model was primed with retrospective data, validated with contemporaneous data and then used to forecast 1 year ahead. A total of 100 iterations of the model were studied. The model output was compared to the real-world data with regard to total number of missions and seasonal pattern using standard statistical tests. Results: Our model forecast 547 missions (95% CI 516–586) during the prospective study period, compared to 565 real-world missions. (t-test p=0.21). The seasonal patterns were adequately matched to generate a non-significant result under the Kolmogorov-Smirnov test (p=0.14). Conclusion: Our model was able to correctly predict the number of pre-hospital primary retrieval missions undertaken by the ScotSTAR Emergency Medical Retrieval Service (EMRS) by demonstrating no statistically significant differences to the real-world mission numbers or distribution. This suggests that a queueing theory derived model is able to accurately replicate, and forecast, the real-world performance of ScotSTAR EMRS operations. This finding presents useful implications forAbstract : Aim: We aimed to investigate if a queueing-theory derived, stochastic, computerised mathematical model could accurately predict the number and seasonal pattern of primary pre-hospital missions undertaken by a physician-led pre-hospital and retrieval service in 2016. Methods: We used queueing theory to derive parameters for a computer model built using the MATLAB software suite Simulink program. The model was primed with retrospective data, validated with contemporaneous data and then used to forecast 1 year ahead. A total of 100 iterations of the model were studied. The model output was compared to the real-world data with regard to total number of missions and seasonal pattern using standard statistical tests. Results: Our model forecast 547 missions (95% CI 516–586) during the prospective study period, compared to 565 real-world missions. (t-test p=0.21). The seasonal patterns were adequately matched to generate a non-significant result under the Kolmogorov-Smirnov test (p=0.14). Conclusion: Our model was able to correctly predict the number of pre-hospital primary retrieval missions undertaken by the ScotSTAR Emergency Medical Retrieval Service (EMRS) by demonstrating no statistically significant differences to the real-world mission numbers or distribution. This suggests that a queueing theory derived model is able to accurately replicate, and forecast, the real-world performance of ScotSTAR EMRS operations. This finding presents useful implications for resource utilisation, asset allocation and investigating system capability. Reference: Kendall DG. Stochastic Processes Occurring in the Theory of Queues and their Analysis by the Method of the Imbedded Markov Chain. The Annals of Mathematical Statistics 24 (3):1953. Conflict of interest: None declared. Funding: Scottish Ambulance Service ScotSTAR … (more)
- Is Part Of:
- BMJ open. Volume 7(2017)Supplement 3
- Journal:
- BMJ open
- Issue:
- Volume 7(2017)Supplement 3
- Issue Display:
- Volume 7, Issue 3 (2017)
- Year:
- 2017
- Volume:
- 7
- Issue:
- 3
- Issue Sort Value:
- 2017-0007-0003-0000
- Page Start:
- A18
- Page End:
- A18
- Publication Date:
- 2017-05-21
- Subjects:
- Medicine -- Research -- Periodicals
610.72 - Journal URLs:
- http://www.bmj.com/archive ↗
http://bmjopen.bmj.com/ ↗ - DOI:
- 10.1136/bmjopen-2017-EMSabstracts.46 ↗
- Languages:
- English
- ISSNs:
- 2044-6055
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 18675.xml