ABDULKADİR ATALAN, CEM ÇAĞRI DÖNMEZ, Yasemin AYAZ ATALAN

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

Acil servisler sağlık sistemin temel yapı taşını oluşturmaktadır. Bu çalışmada Türkiye’deki acil servislerin çalışma sistemleri incelenerek, acil servislerdeki problemler dikkate alınmıştır. Özellikle metropol şehirlerde acil servislerdeki yoğunluk ölçülemez haldedir. Bunun başlıca nedeni acil servislere gelen çoğu hastanın acil diye nitelendirilen bir sağlık sorununun bulunmamasıdır. Bu durum, hastaların bekleme ve hastanede kalış sürelerinin çok uzun olmasına ve tedavi edilen hasta sayısının azalmasına neden olmaktadır. Bu çalışma ile acil servislerde acil olmayan ya da ayakta tedavi edilebilecek olan hastaların yüksek eğitimli uzman hemşireler istihdamı ile tedavi edilerek hastanedeki bekleme süresinin azaltılması amaçlanmıştır. 1/24 (günlük) ve 7/24 (haftalık) çalışma esasına göre uygulanan kesikli-olay 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 süresi kısaltılmıştır. Aynı şekilde bir hastanın tedavi olmak için acil servislerde geçirmesi gereken süre ortalama 82,46 dakikadan 53,97dakikaya 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.

Implementation of Simulation to Increase Emergency Department Service Quality with High – Educated Specialist Nurses Employment: Turkish Health System

Emergency departments are the cornerstones of the healthcare systems. However, emergency services are not able to do their primary duty. This is mainly due to the fact that most of the patients who come to the emergency department do not have any serious health problem defined as emergency. This study examined the working system of emergency services in Turkey. Especially, in metropolitan cities the intensity in emergency services is becoming immeasurable. This situation causes the waiting time of the patients to be excessive. This research aimed to decrease the waiting time of the patients by being treated by high-educated specialist nurses (HSN) in the emergency departments that are not urgent or outpatient. HSN, as a different healthcare employee class, has been proposed to treat patients who are engaged in emergency services but in fact whose health status is not urgent. A discrete event simulation approach was applied to obtain tangible results with real numerical data. Strategies or scenarios that are expected to occur in normal life not only require high costs but also need a lot of time. In such studies, simulation applications enable to get results in a faster and shorter time. Otherwise, both higher budget and time are needed in the studies that require application to put it into practice. Therefore, it is indispensable for researchers to make simulation applications which need short time and low cost but give low margin of error. In this study, a three-dimensional simulation model has been developed with two scenarios for the emergency department. In these scenarios, the number of treated patients, the waiting time of the patients, the duration of the patients in the emergency department, and the utilization rates of resources of emergency department were compared. With the help of developed patient flow chart, the patient is examined in the triage area by HSN after the normal triage procedure and then is exited or referred to polyclinic if needed. The patient, who was examined by HSN, is no longer examined by the doctor again unless necessary. The patient is referred to the doctor according to the urgency and type of disease, and after the doctor’s examination, referral or outpatient referrals are carried out. In this case, not all the patients will have to wait for their doctor. According to the discrete-event simulation example applied on 1/24 and 7/24 basis, it was observed that the number of patients treated by providing employment of HSN 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. In the literature data, the rate of check in to emergency services of patients who do not have an emergency situation is usually above 50%. The lowest rate of patient waiting time was estimated as 50% (Physician) / 50% (HSN) on a 1/24 basis and 40% (Physician) / 60% (HSN) on a 7/24 basis. 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 HSN 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 HSN. In accordance with these results, it is observed that the HSN employment and the productivity ratios of the resources are balanced. In brief, the aim of this study is to improve the structure and the quality of the healthcare system and to increase the patient satisfaction. For this purpose, it is proposed to increase the authority and activity of the nurses in the healthcare system. The results obtained emphasize the accuracy and validity of this objective. As a result, emergency services in the healthcare system of Turkey should be reinforced with HSN employment by the government. With this recommendation, it will help to reduce the intensity of emergency services and to make the resources used more efficient. This study consists of five parts. In the first part, information about the function of the emergency services and the types of nurses in the healthcare system are provided. The second part contains a literature review of the methods developed and applied for the solution of problems in the healthcare sector. In the third part, there is a description of the method of this study. The identification of the patient flow chart and data required for the creation of the simulation model is made in this section. The construction and operation of the simulation model is included in this part. In the fourth section, simulation results are compared and discussed. In the last part, the conclusion and a brief review of the study are involved.

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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.
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.
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
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
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.
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
Atalan, A. (2014). Central Composite Design Optimization Using Computer Simulation Approach. Flexsim Quarterly Publication, 5-19.
Atalan, A., DeLuca, M., Gao, H., Liu, Q., Lu, Z., Silvoy, L., … Zhao, P. (2013). Quantifying HCP Burden of REMS Programs. FlexSim Quarterly, 14–22. Retrieved from https://www.flexsim.com/wp-content/uploads/2013/10/October2013.pdf
Austin, A., & Wetle, V. (2012). The United States Health Care System: Combining Business, Health, and Delivery (M. Cohen Ed. 2 ed.): Pearsonç
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.
Batun, S., & Begen, M. A. (2013). Optimization in healthcare delivery modeling: Methods and applications. In Handbook of Healthcare Operations Management (pp. 75-119): Springer.
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
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.
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).
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
Briggs, A., & Gray, A. (1999). Handling uncertainty when performing economic evaluation of healthcare interventions. Health Technol Assess, 3(2), 1-134.
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.
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. [19] event simulation. Pharmacoeconomics, 23(4), 323-332.
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.
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.
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
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.
Denton, B. T. (2013). Handbook of healthcare operations management: Springer.
Donabedian, A. (1985). The Methods and Findings of Quality Assessment and Monitoring: an Illustrated Analysis. Journal for Healthcare Quality, 7(3), 15.
Duguay, C., & Chetouane, F. (2007). Modeling and improving emergency department systems using discrete event simulation. Simulation, 83(4), 311-320.
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
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).
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.
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.
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
Graban, M. (2011). Statistics on healthcare quality and patient safety problems–errors & harm. Retrieved May, 26, 2011.
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.
Gupta, D., & Denton, B. (2008). Appointment scheduling in health care: Challenges and opportunities. IIE Transactions, 40(9), 800-819. doi:10.1080/07408170802165880
Hart, A. (2001). Making sense of statistics in healthcare: Radcliffe Publishing.
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
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.
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.
Keck, M. (2003). Hospital emergency department resource utilization and optimization system. In: Google Patents.
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).
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.
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.
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.
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
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.
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.
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
Munro, B. H. (2005). Statistical methods for health care research (Vol. 1): Lippincott Williams & Wilkins.
Oh, C., Novotny, A. M., Carter, P. L., Ready, R. K., Campbell, D. D., & Leckie, M. C. (2016). Use of a simulation-based decision support tool to improve emergency department throughput. Operations Research for Health Care. https://doi.org/10.1016/j.orhc.2016.03.002
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.
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
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
Scott, I., & Mazhindu, D. (2014). Statistics for healthcare professionals: An introduction: Sage.
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
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
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.
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
Türk-Tabibleri-Birliği. (2007). Hemşirelik Kanunu – 6283. Retrieved from http://www.ttb.org.tr/mevzuat/index.php?option=com_content&task=view&id=502&Itemid=28
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
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.
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
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.
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.
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.
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
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