Simulation of the surgery staff in a hospital

• Project name: Simulation of surgery staff in a hospital
• Class: 4IT496 Simulation of Systems (WS 2014/2015)
• Author: Martina Nováková
• Model type: Discrete-event simulation
• Software used: SimProcess

Problem definition

In this simulation the medical office in the real concrete hospital is simulated. Because in this medical office there is a long waiting time, it is necessary to optimize the number of doctors. After this optimization a patient doesn’t have to wait for more than half an hour. The goal of the simulation is to model a real process of patients waiting for examination by doctor. The main experiment shall determine how long a patient has to wait in the waiting room until he is going for the examination by doctor. If we want to make this situation real , there must be simulated other aspects, for example an examination, patients which are coming from an examination and other real situations which extend the patient waiting time.

Method

Although the simulation can be solved by other methods, it looks like a good idea to solve it with SIMPROCESS. Process in this simulation tool is clearer and simpler than in other simulation tools like Netlogo. Simulation can be illustrated in simple processes. Because there was used a trial version of SIMRPOCESS, there are some limitations, e.g. in number of entities or resources.

Model

This model of simulation is divided in three main processes. These three processes are “Incoming patients”, the complex process “Examination” and the simple process “Going home“. The illustration below depicts a patient moving to the examination by doctor in his office. A patient could be sent for the examination, stay in hospital, or he could be sent home for waiting for results. .The model is based on real numbers from the hospital information system and on information from employees who work in this part of hospital.

Entities

SeriouslyInjuredPatient – this is a seriously injured patient with the priority number one. It means that he is preferred over other types of patients. This entity is generated in Poisson distribution (2.0) a day in time 9:00-16:00. Outside this time people are going on emergency staff.

HospitaledPacient - this is a patient which is coming from the same hospital. He is prevailed over the standard patient. he is known to the doctor, because he usually has got accompaniment from the hospital. These patients usually come in the morning between 7:00-11:00. Poisson distribution for this time is Poi (4.0) for an hour. In time 11:00-16:00 there are noticeably fewer patients. Poisson distribution for this time is Poi (2.0) for an hour.

StandardPatient – this patient is coming from the outside of hospital and his injury is not extensive or he has any other non-serious problem (at least he doesn’t know that it is serious, he has no large bleeding e.g.). These patients are coming usually between 7:00 and 11:00. Poisson distribution for this is Poi (10.0) for an hour. In the afternoon (11:00-16:00) around 5 patients come Poi(5.0) in an hour.

Explanation of other situations: The simulation doesn´t take into account patients attending the medical office on Saturdays and Sundays, because on weekends patients usually attend emergency departments because they know the medical office is closed.

Resources

There are defined downtimes of the doctors. One is for lunch. It is downtime during 12.15-13:00. The second downtime is during time when doctor is home. It is time during 16:30

PreDoctor – this is usually a nurse or a doctor which only decides whether a seriously injured patient shall be taken to Intensive care or shall be examined in office. This process usually takes around 2,5minutes on average – Exp(2.5)

Office doctor – his working time in office is from 8:30 to 16:30, he usually has a lunch break for 45minutes, and sometimes starts examining about around 20 minutes later (8:30-8:50 – later Norm(2.0,1.0)). This resource has set up downtime. There is downtime when doctors have a lunch, and when they don’t work and are at home. In the week of simulation, there aren´t simulated any doctors on vacation.

Processes

Generation of patients

There are generated three types of entities. These entities introduced three types of patients which are coming to the waiting room and will wait for examination.

Examination

Queue of patients

Queue is divided by entities. There is different between standard, seriously injured and hospitalized patients. Seriously injured patients (SIP) are preferred over others and they are pre-examined at first. This pre-examination is done by a nurse or a doctor from other staff and is determinative for other progress of this patient. The patient could be sent to the intensive care unit if his injury is really serious (this is around 45% of patients) or he can be sent to the office (this is about 55%). Hospitalized patients have no other branch off. Standard patients are coming from outside of hospital and there is one branch off which is divided by percent. It is because of there are 9%of patients who don’t want to wait and leave the waiting room before an examination by doctor. Other 91% are waiting for an examination by doctor.

Examination by doctor

This part of simulation simulates examination or treatment by the doctor. It is illustrated by normal distribution Nor(10.0, 3.0, 1) in minutes. In the next step after examination by the doctor it is decided if a patient needs another examination (for example CT, X-ray, MR or other blood tests. There are usually 30% of patients who need another examination. 55% of patients are sent home. And the rest (15%) stays in the hospital and are hospitalized.

Other examinations

This simulates other examinations which are needed for right decisions of a doctor in the office. There is used exponential distribution Nor (15.0, 3.0, 1) minutes. After this patients can be sent back to the waiting room (95%), or can be sent home (5%) because results will be known after 20-23hours. These patients go home, but there are 2% of them who don’t come back to get their results. The rest (98%) comes back for their results. The distribution of the time when they come back is given by triangular distribution Tri (22.0, 23.0, 23.5).

This simulation lasts for 4 day 23 hours 59 minutes and 59 sec.