Meme Merkezinde Hasta Akış Diyagramının Oluşturulması Ve İyileştirilmesi

Abstract

Sağlık kişilerin hayattaki en önemli değerlerinden biridir. Önemli olması da korunması ve iyileştirilmesini gerektirir. Ancak günümüzde gerek sağlık hizmetlerine olan talebin fazla olmasından dolayı gerekse kaynakların yetersiz olmasından ötürü, istenilen kalitede ve hızda sağlık hizmetleri sunulamamakta ve sağlık merkezlerinde uzun kuyruklar oluşmaktadır. Sağlık sektörünün en önemli temsilcisi olan hastanelerde ise hastalar tedavi olmak için beklemektedir. Hastanedeki kuyruk direkt olarak insan hayatıyla ilgili olduğu için diğer alanlardaki kuyruklardan daha önce dikkate alınması ve düzeltilmesi gerekmektedir. Her hastalığın tedavi süreci birbiriyle aynı olmayıp farklılık göstermekte, bazılarının süreci çok uzun ve karmaşık olarak sürmektedir. Kanser hastalığı hem hastalığın seyrinin hızlı olması hem tedavi sürecinin karmaşık olması hem de çağımızın hastalığı olması nedeniyle diğer hastalıklardan daha hızlı bir şekilde dikkate alınması gerekmektedir. Gerek Türkiye’de gerekse Dünya’da kanser görülme oranı her geçen sene artmaktadır. Meme kanseri ise ülkemizde en sık görülen kanser türüdür. Bu nedenle bu çalışmada bir vakıf üniversitesindeki meme merkezi bölümü incelenmiştir. Bölüme meme kanseri şüphesiyle ve tedavi sürecinde gelen hastaların geliş saatleri, hastane içinde izledikleri yol ve her bir bölümde ne kadar süre harcadıkları izlenmiş 30 gün boyunca kayıt edilmiştir. Bazı veriler için ise doktorların bilgisine başvurulmuştur. Kayıt edilen veriler ile meme merkezinin modeli oluşturulmuş ve kesikli olay simülasyonu kullanılarak model ARENA simülasyon programında çalıştırılmış ve darboğazlar belirlenmiştir. Performans kriteri olarak hasta kuyrukları seçilmiştir. Amaç hastaların kuyruktaki bekleme sürelerinin anlamlı bir şekilde azaltılmasıdır ve bunun için birçok senaryo üretilmiştir. Bu senaryolar; kaynak sayılarının değiştirilmesi, mesai saatlerinin değiştirilmesi ve hasta kabul saatlerinin değiştirilmesidir. Her bir senaryonun sonunda elde edilen değerlerin birbirinden farklı olması, bu farklılığın anlamlı olduğunun göstergesi olmayacağı için, her bir senaryo ve mevcut durumu karşılaştırmak için ANOVA istatistiksel metodu kullanılmıştır. ANOVA ile % 95 güven aralığında mevcut durum ve senaryolar arasındaki değişikliğin anlamlı olup olmadığı sınanmıştır. Birçok performans kriteri bu analize göre anlamlı farklılık içermekte iken bir kısmı ise mevcut durumdan farksızdır. Sistemin darboğaz radyoloji kısmında oluşmakta ve bu nedenle de radyoloji bölümünde yapılan iyileştirmeler anlamlı çıkmıştır.

Healthcare is one of the trend topics in the world. There is an increase in standardization. As health is one of the most valuable issue in human life, it must be maintained with rehabilitation. Nowadays, the increased demand versus insufficient sources caused in healthcare services due to poor quality with long queues during the diagnosis and treatment processes. Hospitals are the most important parts of healthcare service systems, as it is also the place where patients can wait for a long time just to be treated. Since queues in hospitals are directly affecting the quality of human life, it should have priority compared to other types of queues. Special efforts should be made to shorten patient queues. Unfortunately there is no single type of disease, neither one type of treatment. On the other hand each type of diseases may need different types of treatments. In addition some treatments can take more time than the other and the complexity of each treatment may vary. Queuing theory is expressed as the mathematical study of waiting lines or queues. The beginning of the queuing theory is based on 100 years ago. In a classical queuing system; customers arrive for the service system, they wait in the line, if not urgent, and finally they get the service and leave the system. In order to better explain and distinguish between systems, Kendall has symbolize the systems in 1953 which includes 6 symbols; the arrival pattern, the service pattern, the number of servers, the queuing discipline, the system capacity, the population size respectively. In order to eliminate the problems in the phone line Erlangen uses formulas and methods in 1900s; which are not only used for the telecommunication field, but also in many fields such as supermarkets, computer area, traffic control, production systems, airport traffic, and many more areas in the health service using has become a part of life. Simulation is the set of implemented strategies in order to establishment of a real system model, examining the behavior of the system and improvement of the system. Towards the end of the 1960s, simulation was only used by large companies that require large capital investments as a tool which was very special and expensive. Simulation was started to use by other companies in 1990s. Good animations, ease of use, fast computers and fitting easily with other packets made simulation a standard tool in many companies. There are many advantages to using simulation. Creating a mathematical model of the system provides a better understanding of the system and it allows to monitoring periods longer. It allows running and understanding the system at a lower cost. There is no need to stop the working system in order to monitor the system or changing solutions for alternative tests. Moreover, system bottlenecks can be determined. By animations it makes operations visual. In a complex system, it provides visibility of internal interactions. There are different simulation types such as deterministic-stochastic, discrete-continuous, dynamic-static. Arena, Extend, Micro Saint and AutoMod are some examples of simulation languages. Arena is a flexible and powerful simulation tool consisting of template modules, uses SIMAN simulation language that visually enhanced. This program is used in the analysis of the effects under varying conditions of important and complex new designs in the supply chain, manufacturing, processes, logistics, distribution and storage. Cancer is a type of an illness which is seen in the form of uncontrolled growth and division of cells or to change the location of the cells. Rather than a single disease, cancer is a disease that affects cells and tissues. Therefore, it is a quite complex illness and early diagnosis is important. Because of treatment process of that disease is complex, many specialties are required to work together. It can be taken as one of the example of a type of disease that progresses fast needs, and complex treatment processes. Cancer became a global issue which has to be taken in the most priority. Optimization studies in the health sector have recently been made on especially triage (priority relative to each other), emergency services and appointment systems. However, more emphasis should be given for cancer clinical studies because of quite complex system and the criticality of patients. In Turkey, studies for the improvement of oncology centers are quite inadequate. In our country there has not been a study for the improvement of patient flow diagram in oncology centers yet. Therefore, in this study, breast oncology department from a private university hospital in Istanbul, is taken into consideration in order to determining patient flow diagram for the current situation and also the situations after making improvements that is introduced. There is a very rapid increase of cancer patients in the world and as well as in Turkey makes this disease to be considered more seriously. Among cancer types, breast cancer is the most encountered type in Turkey. Therefore, in this thesis, breast cancer center in a private university in Turkey is investigated and tried to be improved. The arrival times of patients who are suspicious about being breast cancer patients or the patients already taking treatments, the way they take the treatment, and the time that they spend inside the hospital are recorded during 30 days. In addition to the observation, interviews were also done with each responsible doctor from each unit. The flowchart of the breast cancer center is modeled by means of commercial software ARENA. Discrete event simulations in ARENA are performed by using recorded data to find bottleneck of the system. Patients’ queues are chosen as performance criteria. Various scenarios are derived to obtain a significant decrease of waiting time. These scenarios include change of number of sources, working hours and patient admission times. There are 9 performance criteria chosen such as; time spent from the first physical examination of breast surgeon to the dispose, additional waiting time for the MR and USG reports, time spent from the first visit to the hospital without having biopsy dispose, patient waiting time from visiting the hospital till leaving without surgery, time for waiting biopsy, physical examination of surgeon, physical examination of radiologist, consultation of surgeon and radiologist, and evaluation of the results by surgeon. There are 4 scenarios which are applied for improvement of the current situation. First scenario is changing the patient admission time to 16:00, which is 16:50 for the current situation. In that scenario only 3 performance criteria is improved which are; time spent from the first physical examination of breast surgeon to the dispose, time spent from first visit to the hospital without having biopsy dispose and physical examination of surgeon. The second scenario is adding one more radiologist and one more radiology technician. In this scenario only the time for waiting biopsy is improved. Third scenario is about increasing the number of the breast surgeon to two. In that scenario time spent by patient from the first physical examination of breast surgeon to the dispose, time spent from first visit to the hospital without having biopsy dispose, physical examination of surgeon and consultation of surgeon and radiologist is improved. In the fourth scenario, USG machine number is increased to two, which the number of USG machine is one for the current situation. The time for waiting biopsy and physical examination of radiologist is the improved performance criteria. ANOVA statistical method is used to compare the results of each scenario with that of the current status since different results obtained from different scenarios do not necessarily guarantee that the change (improvement) is significant. The significances of the improvements obtained from different scenarios are investigated by applying 95% confidence interval in ANOVA. The results of this test show that most of the performance criteria give significant differences compared to the actual situation whereas some of them are insignificant. According to the results of scenarios and ANOVA test, with scenario 3, more improvement is seen in the patient flow time. Improvement caused by scenario 3 is lightening the workload both for surgeons and radiologists, as it is proved that the system bottleneck was exactly at the workload of the surgeon and radiologist. For this reason scenario 3 is chosen as the critical improvement scenario for the breast cancer center patient flow system.