Implementation of Simulation for the Improvement of Emergency Service Quality by High-Educated Specialist Nurses Employment: Turkish Health System

Abstract

Emergency departments are the cornerstone of health care systems. In this study, the work of the emergency services system in Turkey were examined. The intensities of the emergency services are becoming immeasurable in the present situation. This is mainly due to the fact that the majority of patients who come to emergency services are not urgent in emergency departments. This study suggests that patients who are not urgent or outpatient in emergency services should be treated by highly-educated specialist nurses (YSN). In this case, it is aimed to treat more patients, to reduce the waiting time of the patients and therefore the duration of the patients' stay in the emergency services. It is also aimed to increase the efficiency of the resources employed in emergency services. According to the simulation example applied on 1/24 and 7/24 basis, it was observed that the number of patients treated by providing employment of YUH was increased by 26,71% on the basis of 1/24 and 15,13% on the basis of 7/24. The waiting time for treatment was reduced by 38.67% on 1/24 basis and 53.66% on 7/24 basis, respectively, from the time the patients were enrolled in emergency services. Likewise, the time required for a patient to be treated in emergency services for treatment was reduced from an average of 82.46 minutes to 53.97 minutes. Among the findings, it has been seen that the efficiency of the employment of YUH has provided a balance in the efficiency rates of the resources by not getting the efficiency as high as the resources employed in the emergency services. In addition, it has been found that the employment intensity of physicians decreases with the employment of YUH.    

Keywords

• Turkey Emergency Service System,High Educated Specialist Nurses,Simulation,Resources of Emergency Service

Yüksek-Eğitimli Uzman Hemşire İstihdamı ile Acil Servis Kalitesinin Yükseltilmesi için Simülasyon Uygulaması: Türkiye Sağlık Sistemi

Abstract

Acil servisler sağlık sistemlerin temel taşını oluşturmaktadırlar. Bu çalışmada, Türkiye’deki acil servislerin çalışma sistemleri incelenmiştir. Mevcut durumdaki acil servislere ait yoğunluklar ölçülemez hale gelmektedir. Bunun başlıca nedeni acil durumda olmayan hastaların acil servisleri meşgul etmesidir.  Bu çalışma ile acil servislerde acil olmayan ya da ayakta tedavi edilebilecek olan hastaların yüksek-eğitimli uzman hemşireler (YUH) tarafından tedavi edilmesi önerilmiştir. Bu durumda daha fazla hasta tedavi edilmesi, hastaların bekleme sürelerinin ve dolayısıyla hastaların acil servislerde kalma sürelerinin azaltılması amaçlanmıştır. Ayrıca acil servislerde istihdam edilen kaynakların verimliliğinin arttırılması hedeflenmiştir. 1/24 ve 7/24 esasına göre uygulanan simülasyon örneği ile YUH istihdamı sağlanarak tedavi edilen hasta sayısında 1/24 esasına göre %26,71 ve 7/24 esasına göre %15,13 oranında artış sağlandığı görülmüştür. Hastaların acil servise kayıt yaptıkları andan itibaren tedavi olmak için bekledikleri süre 1/24 esasına göre %38,67 ve 7/24 esasına göre %53,66 oranlarında iyileşme sağlanarak bekleme zamanı düşürülmüştür. Aynı şekilde bir hastanın tedavi olmak için acil servislerde geçirmesi gereken süre ortalama 82,46 dakikadan 53,97 dakikaya düşürülmüştür. Bulgular arasında, acil servislerde istihdam edilen kaynaklardan yeteri kadar verim alınamamasıyla YUH istihdamı sayesinde kaynaklara ait verimlilik oranlarında bir denge sağlandığı görülmüştür. Ek olarak, YUH istihdamı ile doktorların çalışma yoğunluklarının azaldığı tespit edilmiştir.

Keywords

• Türkiye Acil Servis Sistemi,Yüksek Eğitimli Uzman Hemşire,Simülasyon,Acil Servis Kaynakları

References

1. 1. Agarana, M., & Olokunde, T. (2015). Optimization of Healthcare Pathways in Covenant University Health Centre Using Linear Programming Model. Far East Journal of Applied Mathematics, 91(3), 215.
2. 2. 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.
3. 3. Al-Abri, R., & Al-Balushi, A. (2014). Patient Satisfaction Survey as a Tool Towards Quality Improvement. Oman Medical Journal, 29(1), 3-7. doi:10.5001/omj.2014.02.
4. 4. Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60(9), 662-669. doi:10.4103/0019-5049.190623.
5. 5. Andel, C., Davidow, S. L., Hollander, M., & Moreno, D. A. (2012). The economics of health care quality and medical errors. Journal of health care finance, 39(1), 39.
6. 6. Anhang Price, R., Elliott, M. N., Zaslavsky, A. M., Hays, R. D., Lehrman, W. G., Rybowski, L., .. . Cleary, P. D. (2014). Examining the Role of Patient Experience Surveys in Measuring Health Care Quality. Medical care research and review : MCRR, 71(5), 522-554. doi:10.1177/1077558714541480.
7. 7. Atalan, A. (2014). Central Composite Design Optimization Using Computer Simulation Approach. Flexsim Quarterly Publication, 5-19.
8. 8. Austin, A., & Wetle, V. (2012). The United States Health Care System: Combining Business, Health, and Delivery (M. Cohen Ed. 2 ed.): Pearson.

9. 9. Baesler, F. F., & Sepúlveda, J. A. (2001). Healthcare II: multi-objective simulation optimization for a cancer treatment center. Paper presented at the Proceedings of the 33nd conference on Winter simulation.
10. 10. Batun, S., & Begen, M. A. (2013). Optimization in healthcare delivery modeling: Methods and applications. In Handbook of Healthcare Operations Management (pp. 75-119): Springer.
11. 11. Binu, V. S., Mayya, S. S., & Dhar, M. (2014). Some basic aspects of statistical methods and sample size determination in health science research. Ayu, 35(2), 119-123. doi:10.4103/0974-8520.146202.
12. 12. Blake, J. T., Carter, M. W., & Richardson, S. (1996). An analysis of emergency room wait time issues via computer simulation. INFOR: Information Systems and Operational Research, 34(4), 263-273.
13. 13. Bleustein, C., B Rothschild, D., Valen, A., Valatis, E., Schweitzer, L., & Jones, R. (2014). Wait Times, Patient Satisfaction Scores, and the Perception of Care (Vol. 20).
14. 14. Brailsford, S., & Schmidt, B. (2003). Towards incorporating human behaviour in models of health care systems: An approach using discrete event simulation. European journal of operational research, 150(1), 19-31. doi:https://doi.org/10.1016/S0377-2217(02)00778-6.
15. 15. Briggs, A., & Gray, A. (1999). Handling uncertainty when performing economic evaluation of healthcare interventions. Health Technol Assess, 3(2), 1-134.
16. 16. Cabrera, E., Luque, E., Taboada, M., Epelde, F., & Iglesias, M. L. (2012). ABMS optimization for emergency departments. Paper presented at the Proceedings of the winter simulation conference.
17. 17. Cabrera, E., Taboada, M., Iglesias, M. L., Epelde, F., & Luque, E. (2011). Optimization of healthcare emergency departments by agent-based simulation. Procedia computer science, 4, 1880-1889.
18. 18. Caro, J. J. (2005). Pharmacoeconomic analyses using discrete event simulation. Pharmacoeconomics, 23(4), 323-332.
19. 19. Ceglowski, R., Churilov, L., & Wasserthiel, J. (2007). Combining data mining and discrete event simulation for a value-added view of a hospital emergency department. Journal of the operational research society, 58(2), 246-254.
20. 20. Chassin, M. R., & Galvin, R. W. (1998). The urgent need to improve health care quality: Institute of Medicine National Roundtable on Health Care Quality. Jama, 280(11), 1000-1005.
21. 21. CHSRF. (2010). Myth: Emergency room overcrowding is caused by non-urgent cases. Journal of Health Services Research & Policy, 15(3), 188-189. doi:10.1258/jhsrp.2010.010310.
22. 22. Connelly, L. G., & Bair, A. E. (2004). Discrete event simulation of emergency department activity: A platform for system‐level operations research. Academic Emergency Medicine, 11(11), 1177-1185.
23. 23. Denton, B. T. (2013). Handbook of healthcare operations management: Springer.
24. 24. Donabedian, A. (1985). The Methods and Findings of Quality Assessment and Monitoring: an Illustrated Analysis. Journal for Healthcare Quality, 7(3), 15.
25. 25. Duguay, C., & Chetouane, F. (2007). Modeling and improving emergency department systems using discrete event simulation. Simulation, 83(4), 311-320.
26. 26. Durand, A.-C., Palazzolo, S., Tanti-Hardouin, N., Gerbeaux, P., Sambuc, R., & Gentile, S. (2012). Nonurgent patients in emergency departments: rational or irresponsible consumers? Perceptions of professionals and patients. BMC Research Notes, 5, 525-525. doi:10.1186/1756-0500-5-525.
27. 27. Ely, E. W., Stephens, R. K., Jackson, J. C., Thomason, J. W. W., Truman, B., Gordon, S., . . . Bernard, G. R. (2004). Current opinions regarding the importance, diagnosis, and management of delirium in the intensive care unit: A survey of 912 healthcare professionals*. Critical Care Medicine, 32(1).
28. 28. England, W., & Roberts, S. D. (1978). Applications of computer simulation in health care. Paper presented at the Proceedings of the 10th conference on Winter simulation - Volume 2, Miami Beach, FL.
29. 29. Fenton, J. J., Jerant, A. F., Bertakis, K. D., & Franks, P. (2012). The cost of satisfaction: a national study of patient satisfaction, health care utilization, expenditures, and mortality. Archives of internal medicine, 172(5), 405-411.
30. 30. Goodman, S. N., Altman, D. G., & George, S. L. (1998). Statistical Reviewing Policies of Medical Journals: Caveat Lector? Journal of General Internal Medicine, 13(11), 753-756. doi:10.1046/j.1525-1497.1998.00227.x
31. 31. Graban, M. (2011). Statistics on healthcare quality and patient safety problems-errors & harm. Retrieved May, 26, 2011.
32. 32. Günal, M. M., & Pidd, M. (2010). Discrete event simulation for performance modelling in health care: a review of the literature. Journal of Simulation, 4(1), 42-51.
33. 33. Gupta, D., & Denton, B. (2008). Appointment scheduling in health care: Challenges and opportunities. IIE Transactions, 40(9), 800-819. doi:10.1080/07408170802165880.
34. 34. Hart, A. (2001). Making sense of statistics in healthcare: Radcliffe Publishing.
35. 35. 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.
36. 36. Jun, J., Jacobson, S. H., & Swisher, J. R. (1999). Application of discrete-event simulation in health care clinics: A survey. Journal of the operational research society, 109-123.
37. 37. Keck, M. (2003). Hospital emergency department resource utilization and optimization system. In: Google Patents.
38. 38. Kim, S. E., Kim, C. W., Lee, S. J., Oh, J. H., Lee, D. H., Lim, T. H., . . . Jung, J. H. (2015). A questionnaire survey exploring healthcare professionals' attitudes towards teamwork and safety in acute care areas in South Korea. BMJ Open, 5(7).
39. 39. Komashie, A., & Mousavi, A. (2005). Modeling emergency departments using discrete event simulation techniques. Paper presented at the Proceedings of the 37th conference on Winter simulation.
40. 40. Köse, A., Köse, B., Öncü, M. R., & Tuğrul, F. (2010). Bir devlet hastanesi acil servisine başvuran hastaların profili ve başvurunun uygunluğu. Gaziantep Medical Journal, 17(2), 57-62.
41. 41. Larimer, M. E., Malone, D. K., Garner, M. D., Atkins, D. C., Burlingham, B., Lonczak, H. S., . . . Hobson, W. G. (2009). Health care and public service use and costs before and after provision of housing for chronically homeless persons with severe alcohol problems. Jama, 301(13), 1349-1357.
42. 42. Lichtenstein, R. L. (1993). The United States' health care system: Problems and solutions. Survey of Ophthalmology, 38(3), 310-316. doi:10.1016/0039-6257(93)90080-Q
43. 43. Mancilla, C., & Storer, R. H. (2013). Stochastic Integer Programming in Healthcare Delivery. In P. M. Pardalos, P. G. Georgiev, P. Papajorgji, & B. Neugaard (Eds.), Systems Analysis Tools for Better Health Care Delivery (pp. 37-48). New York, NY: Springer New York.
44. 44. Mandahawi, N., Al-Shihabi, S., Abdallah, A. A., & Alfarah, Y. M. (2010). Reducing waiting time at an emergency department using design for Six Sigma and discrete event simulation. International Journal of Six Sigma and Competitive Advantage, 6(1-2), 91-104.
45. 45. Mohamed, A. W. (2017). Solving stochastic programming problems using new approach to Differential Evolution algorithm. Egyptian Informatics Journal, 18(2), 75-86. doi:https://doi.org/10.1016/j.eij.2016.09.002
46. 46. Munro, B. H. (2005). Statistical methods for health care research (Vol. 1): Lippincott Williams & Wilkins.
47. 47. Raunak, M., Osterweil, L., Wise, A., Clarke, L., & Henneman, P. (2009). Simulating patient flow through an emergency department using process-driven discrete event simulation. Paper presented at the Proceedings of the 2009 ICSE Workshop on Software Engineering in Health Care.
48. 48. Salway, R. J., Valenzuela, R., Shoenberger, J. M., Mallon, W. K., & Viccellio, A. (2017). Emergency Department (Ed) Overcrowding: Evidence-Based Answers to Frequently Asked Questions. Revista Médica Clínica Las Condes, 28(2), 213-219. doi:https://doi.org/10.1016/j.rmclc.2017.04.008.
49. 49. Schuur , J. D., & Venkatesh , A. K. (2012). The Growing Role of Emergency Departments in Hospital Admissions. New England Journal of Medicine, 367(5), 391-393. doi:10.1056/NEJMp1204431
50. 50. Scott, I., & Mazhindu, D. (2014). Statistics for healthcare professionals: An introduction: Sage.
51. 51. Sheingold, B. H., & Hahn, J. A. (2014). The history of healthcare quality: The first 100 years 1860-1960. International Journal of Africa Nursing Sciences, 1, 18-22. doi:https://doi.org/10.1016/j.ijans.2014.05.002.
52. 52. Spadaro, S., Karbing, D. S., Fogagnolo, A., Ragazzi, R., Mojoli, F., Astolfi, L., . . . Volta, C. A. (2017). Simulation Training for Residents Focused on Mechanical Ventilation: A Randomized Trial Using Mannequin-Based Versus Computer-Based Simulation. Simulation in Healthcare, 12(6), 349-355. doi:10.1097/sih.0000000000000249.
53. 53. Swisher, J. R., & Jacobson, S. H. (2002). Evaluating the design of a family practice healthcare clinic using discrete-event simulation. Health Care Management Science, 5(2), 75-88.
54. 54. Tsai, J. C.-H., Liang, Y.-W., & Pearson, W. S. (2010). Utilization of Emergency Department in Patients With Non-urgent Medical Problems: Patient Preference and Emergency Department Convenience. Journal of the Formosan Medical Association, 109(7), 533-542. doi:https://doi.org/10.1016/S0929-6646(10)60088-5.
55. 55. Türk-Tabibleri-Birliği. (2007). Hemşirelik Kanunu- 6283. Retrieved from http://www.ttb.org.tr/mevzuat/index.phpoption=com_content&task=view&id=502&Itemid=28
56. 56. Unwin, M., Kinsman, L., & Rigby, S. (2016). Why are we waiting? Patients' perspectives for accessing emergency department services with non-urgent complaints. International Emergency Nursing, 29, 3-8. doi:https://doi.org/10.1016/j.ienj.2016.09.003.
57. 57. Uscher-Pines, L., Pines, J., Kellermann, A., Gillen, E., & Mehrotra, A. (2013). Deciding to Visit the Emergency Department for Non-Urgent Conditions: A Systematic Review of the Literature. The American journal of managed care, 19(1), 47-59.
58. 58. 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. doi:https://doi.org/10.1111/j.1524-4733.2009.00678.x
59. 59. Wang, T., Guinet, A., Belaidi, A., & Besombes, B. (2009). Modelling and simulation of emergency services with ARIS and Arena. Case study: the emergency department of Saint Joseph and Saint Luc Hospital. Production Planning and Control, 20(6), 484-495.
60. 60. Wickramasinghe, N., Bali, R. K., Gibbons, M. C., Choi, J., & Schaffer, J. L. (2009). A systematic approach: optimization of healthcare operations with knowledge management. Journal of healthcare information management: JHIM, 23(3), 44-50.
61. 61. Wiler, J. L., Gentle, C., Halfpenny, J. M., Heins, A., Mehrotra, A., Mikhail, M. G., & Fite, D. (2010). Optimizing emergency department front-end operations. Annals of emergency medicine, 55(2), 142-160. e141.
62. 62. Yates, K., Kelly, J., Lindsay, D., & Usher, K. (2012). The experience of rural midwives in dual roles as nurse and midwife: "I'd prefer midwifery but I chose to live here". Women and Birth, 26(1), 60-64. doi:10.1016/j.wombi.2012.03.003
63. 63. Young Ik, C., Timothy, P. J., & Jonathan, B. V. (2013). Enhancing Surveys of Health Care Professionals: A Meta-Analysis of Techniques to Improve Response. Evaluation & the Health Professions, 36(3), 382-407. doi:10.1177/0163278713496425.