Volume 30, Issue 3 pp. 733-741

ORIGINAL ARTICLE

A cost analysis with the discrete-event simulation application in nurse and doctor employment management

Abdulkadir Atalan PhD,

Corresponding Author

Abdulkadir Atalan PhD

[email protected]

orcid.org/0000-0003-0924-3685

Department of Industrial Engineering, Gaziantep Islam Science and Technology University, Gaziantep, Turkey

Correspondence

Abdulkadir Atalan, PhD, Department of Industrial Engineering, Gaziantep Islam Science and Technology University, 192180 Street, 27010, Gaziantep, Turkey.

Email: [email protected]

Search for more papers by this author

Abdulkadir Atalan PhD,

Corresponding Author

Abdulkadir Atalan PhD

[email protected]

orcid.org/0000-0003-0924-3685

Department of Industrial Engineering, Gaziantep Islam Science and Technology University, Gaziantep, Turkey

Correspondence

Abdulkadir Atalan, PhD, Department of Industrial Engineering, Gaziantep Islam Science and Technology University, 192180 Street, 27010, Gaziantep, Turkey.

Email: [email protected]

Search for more papers by this author

First published: 13 January 2022

https://doi.org/10.1111/jonm.13547

Citations: 14

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Abstract

Aim

This study aimed to analyse the treatment cost of a patient, depending on the number of patients treated, patient waiting times, and the number of nurses and doctors employed in an emergency department of a private hospital.

Background

Within health systems, changes in health care resources can be very costly, especially if these changes are long-term. The discrete-event simulation method described in this paper allows for the monitoring and analysis of complicated changes in real systems by using computer-based modelling.

Method

The discrete event simulation model was derived from nine scenarios according to the number of nurses and doctors, and a comparison was made between the results of the scenarios and the actual results.

Results

Among the scenarios, scenario 6 provided the lowest treatment cost for a patient by employing three doctors and two nurses with the best performance. The cost of treatment for a patient varies between ŧ9.00 and ŧ11.00 depending on the value of δ, and the daily cost of these resources to the hospital is ŧ1300.77.

Conclusions

This study provides a clear picture of a cost analysis comparison based on changes made about the actual health system in the computer-based simulated environment.

Implications for Nursing Management

The workforce data of nurses and doctors offers enough detail for cost analysis in health care settings to calculate the cost of treatment for a patient.

CONFLICT OF INTEREST

The author declares that there is no conflict of interest.

Open Research

DATA AVAILABILITY STATEMENT

Data available on request from the authors.

REFERENCES

Ahmed, M. A., & Alkhamis, T. M. (2009). Simulation optimization for an emergency department healthcare unit in Kuwait. European Journal of Operational Research, 198(3), 936–942. https://doi.org/10.1016/j.ejor.2008.10.025
10.1016/j.ejor.2008.10.025
Web of Science® Google Scholar
Alvarado, M. M., Cotton, T. G., Ntaimo, L., Pérez, E., & Carpentier, W. R. (2018). Modeling and simulation of oncology clinic operations in discrete event system specification. Simulation, 94(2), 105–121. https://doi.org/10.1177/0037549717708246
10.1177/0037549717708246
Google Scholar
Anderson, G. H., Jenkins, P. J., McDonald, D. A., Van Der Meer, R., Morton, A., Nugent, M., & Rymaszewski, L. A. (2017). Cost comparison of orthopaedic fracture pathways using discrete event simulation in a Glasgow hospital. BMJ Open, 7(9), e014509. https://doi.org/10.1136/bmjopen-2016-014509
10.1136/bmjopen-2016-014509
PubMed Web of Science® Google Scholar
Andersson, J., Skoogh, A., & Johansson, B. (2011). Environmental activity based cost using discrete event simulation. Proceedings of the 2011 Winter Simulation Conference (WSC), 891–902. https://doi.org/10.1109/WSC.2011.6147815
10.1109/WSC.2011.6147815
Google Scholar
Antonelli, D., Litwin, P., & Stadnicka, D. (2018). Multiple system dynamics and discrete event simulation for manufacturing system performance evaluation. Procedia CIRP, 78, 178–183. https://doi.org/10.1016/j.procir.2018.08.312
10.1016/j.procir.2018.08.312
Google Scholar
Atalan, A., & Dönmez, C. C. (2020). Optimizing experimental simulation design for the emergency departments. Brazilian Journal of Operations & Production Management, 17(4), 1–13. https://doi.org/10.14488/BJOPM.2020.026
10.14488/BJOPM.2020.026
Web of Science® Google Scholar
Bai, J., Fügener, A., Schoenfelder, J., & Brunner, J. O. (2018). Operations research in intensive care unit management: A literature review. Health Care Management Science, 21(1), 1–24. https://doi.org/10.1007/s10729-016-9375-1
10.1007/s10729-016-9375-1
PubMed Web of Science® Google Scholar
Bair, A. E., Song, W. T., Chen, Y.-C., & Morris, B. A. (2010). The impact of inpatient boarding on ED efficiency: A discrete-event simulation study. Journal of Medical Systems, 34(5), 919–929. https://doi.org/10.1007/s10916-009-9307-4
10.1007/s10916-009-9307-4
PubMed Web of Science® Google Scholar
Baril, C., Gascon, V., & Miller, J. (2020). Design of experiments and discrete-event simulation to study oncology nurse workload. IISE Transactions on Healthcare Systems Engineering, 10(1), 74–86. https://doi.org/10.1080/24725579.2019.1680581
10.1080/24725579.2019.1680581
Google Scholar
Cabrera, E., Luque, E., Taboada, M., Epelde, F., & Iglesias, M. L. (2012). ABMS optimization for emergency departments. Proceedings - Winter Simulation Conference. https://doi.org/10.1109/WSC.2012.6465116
Google Scholar
Calpin-davies, & Akehurst. (1999). Doctor–nurse substitution: The workforce equation. Journal of Nursing Management, 7(2), 71–79. https://doi.org/10.1046/j.1365-2834.1999.00113.x
10.1046/j.1365-2834.1999.00113.x
PubMed Google Scholar
Dayarathna, V. L., Mismesh, H., Nagahisarchoghaei, M., & Alhumoud, A. (2020). A discrete event simulation (DES) based approach to maximize the patient throughput in outpatient clinic. Engineering Science & Technology Journal, 1(1), 1–11. https://doi.org/10.51594/estj.v1i1.36
10.51594/estj.v1i1.36
Google Scholar
Devapriya, P., Strömblad, C. T. B., Bailey, M. D., Frazier, S., Bulger, J., Kemberling, S. T., & Wood, K. E. (2015). StratBAM: A discrete-event simulation model to support strategic hospital bed capacity decisions. Journal of Medical Systems, 39(10), 130. https://doi.org/10.1007/s10916-015-0325-0
10.1007/s10916-015-0325-0
PubMed Web of Science® Google Scholar
Dieleman, J. M., Myles, P. S., Bulfone, L., Younie, S., van Zaane, B., McGiffin, D., Moodie, M., & Gao, L. (2020). Cost-effectiveness of routine transoesophageal echocardiography during cardiac surgery: A discrete-event simulation study. British Journal of Anaesthesia, 124(2), 136–145. https://doi.org/10.1016/j.bja.2019.10.023
10.1016/j.bja.2019.10.023
PubMed Web of Science® Google Scholar
Hoot, N. R., LeBlanc, L. J., Jones, I., Levin, S. R., Zhou, C., Gadd, C. S., & Aronsky, D. (2008). Forecasting emergency department crowding: A discrete event simulation. Annals of Emergency Medicine. https://doi.org/10.1016/j.annemergmed.2007.12.011, 52, 116, 125
10.1016/j.annemergmed.2007.12.011
PubMed Web of Science® Google Scholar
Hung, G. R., Whitehouse, S. R., O'Neill, C., Gray, A. P., & Kissoon, N. (2007). Computer modeling of patient flow in a pediatric emergency department using discrete event simulation. Pediatric Emergency Care, 23(1), 5–10. https://doi.org/10.1097/PEC.0b013e31802c611e
10.1097/PEC.0b013e31802c611e
PubMed Web of Science® Google Scholar
Iannoni, A. P., Morabito, R., & Saydam, C. (2009). An optimization approach for ambulance location and the districting of the response segments on highways. European Journal of Operational Research, 195(2), 528–542. https://doi.org/10.1016/j.ejor.2008.02.003
10.1016/j.ejor.2008.02.003
Web of Science® Google Scholar
Johnston, M. J., Samaranayake, P., Dadich, A., & Fitzgerald, J. A. (2009). Modelling radiology department operation using discrete event simulation (pp. 678–684). MODSIM, International Congress on Modelling and Simulation.
Google Scholar
Kaasalainen, K., Kalmari, J., & Ruohonen, T. (2020). Developing and testing a discrete event simulation model to evaluate budget impacts of diabetes prevention programs. Journal of Biomedical Informatics, 111, 103577. https://doi.org/10.1016/j.jbi.2020.103577
10.1016/j.jbi.2020.103577
PubMed Web of Science® Google Scholar
Kendall, K., Mangin, C., & Ortiz, E. (1998). Discrete event simulation and cost analysis for manufacturing optimisation of an automotive LCM component. Composites Part A: Applied Science and Manufacturing, 29(7), 711–720. https://doi.org/10.1016/S1359-835X(98)00003-7
10.1016/S1359-835X(98)00003-7
Web of Science® Google Scholar
Kohl, S., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2019). The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science, 22(2), 245–286. https://doi.org/10.1007/s10729-018-9436-8
10.1007/s10729-018-9436-8
PubMed Web of Science® Google Scholar
Lidberg, S., Aslam, T., Pehrsson, L., & Ng, A. H. C. (2020). Optimizing real-world factory flows using aggregated discrete event simulation modelling. Flexible Services and Manufacturing Journal, 32(4), 888–912. https://doi.org/10.1007/s10696-019-09362-7
10.1007/s10696-019-09362-7
Web of Science® Google Scholar
Lim, M. E., Worster, A., Goeree, R., & Tarride, J. É. (2013). Simulating an emergency department: The importance of modeling the interactions between physicians and delegates in a discrete event simulation. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/1472-6947-13-59, 13
10.1186/1472-6947-13-59
PubMed Web of Science® Google Scholar
Mamiya, H., Tahara, R. K., Tolaney, S. M., Choudhry, N. K., & Najafzadeh, M. (2017). Cost-effectiveness of palbociclib in hormone receptor-positive advanced breast cancer. Annals of Oncology, 28(8), 1825–1831. https://doi.org/10.1093/annonc/mdx201
10.1093/annonc/mdx201
CAS PubMed Web of Science® Google Scholar
Mostafa, S., & Chileshe, N. (2018). Application of discrete-event simulation to investigate effects of client order behaviour on off-site manufacturing performance in Australia. Architectural Engineering and Design Management, 14(1–2), 139–157. https://doi.org/10.1080/17452007.2017.1301367
10.1080/17452007.2017.1301367
Web of Science® Google Scholar
Reynolds, M., Vasilakis, C., McLeod, M., Barber, N., Mounsey, A., Newton, S., Jacklin, A., & Franklin, B. D. (2011). Using discrete event simulation to design a more efficient hospital pharmacy for outpatients. Health Care Management Science, 14(3), 223–236. https://doi.org/10.1007/s10729-011-9151-1
10.1007/s10729-011-9151-1
PubMed Web of Science® Google Scholar
Schmid, V. (2012). Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European Journal of Operational Research, 219(3), 611–621. https://doi.org/10.1016/j.ejor.2011.10.043
10.1016/j.ejor.2011.10.043
Web of Science® Google Scholar
Siciliani, L., Stanciole, A., & Jacobs, R. (2009). Do waiting times reduce hospital costs? Journal of Health Economics, 28, 771–780. https://doi.org/10.1016/j.jhealeco.2009.04.002
10.1016/j.jhealeco.2009.04.002
PubMed Web of Science® Google Scholar
Simwita, Y. W., & Helgheim, B. I. (2016). Simulation analysis of resource flexibility on healthcare processes. Journal of Multidisciplinary Healthcare, 9, 519–528. https://doi.org/10.2147/JMDH.S112713
10.2147/JMDH.S112713
PubMed Google Scholar
Spedding, T. A., & Sun, G. Q. (1999). Application of discrete event simulation to the activity based costing of manufacturing systems. International Journal of Production Economics, 58(3), 289–301. https://doi.org/10.1016/S0925-5273(98)00204-7
10.1016/S0925-5273(98)00204-7
Web of Science® Google Scholar
Starc, J. (2018). Stress factors among nurses at the primary and secondary level of public sector health care: The case of Slovenia. Open Access Macedonian Journal of Medical Sciences, 6(2), 416–422. https://doi.org/10.3889/oamjms.2018.100
10.3889/oamjms.2018.100
PubMed Google Scholar
van Gestel, A., Severens, J. L., Webers, C. A. B., Beckers, H. J. M., Jansonius, N. M., & Schouten, J. S. A. G. (2010). Modeling complex treatment strategies: Construction and validation of a discrete event simulation model for Glaucoma. Value in Health, 13(4), 358–367. https://doi.org/10.1111/J.1524-4733.2009.00678.X
10.1111/j.1524-4733.2009.00678.x
PubMed Web of Science® Google Scholar
Villamizar, J. R., Coelli, F. C., Pereira, W. C. A., & Almeida, R. M. V. R. (2011). Discrete-event computer simulation methods in the optimisation of a physiotherapy clinic. Physiotherapy, 97(1), 71–77. https://doi.org/10.1016/j.physio.2010.02.009
10.1016/j.physio.2010.02.009
CAS PubMed Web of Science® Google Scholar
Zhu, Z., Hoon Hen, B., & Liang Teow, K. (2012). Estimating ICU bed capacity using discrete event simulation. International Journal of Health Care Quality Assurance, 25(2), 134–144. https://doi.org/10.1108/09526861211198290
10.1108/09526861211198290
PubMed Google Scholar

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A cost analysis with the discrete-event simulation application in nurse and doctor employment management

Abstract

Aim

Background

Method

Results

Conclusions

Implications for Nursing Management

CONFLICT OF INTEREST

Open Research

DATA AVAILABILITY STATEMENT

REFERENCES

Citing Literature

References

Information

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A cost analysis with the discrete-event simulation application in nurse and doctor employment management

Abstract

Aim

Background

Method

Results

Conclusions

Implications for Nursing Management

CONFLICT OF INTEREST

Open Research

DATA AVAILABILITY STATEMENT

REFERENCES

Citing Literature

References

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