The Scene Of The Process Nursing Essay

The patient starts to enter through the models with arrival at the (A&E departments) from a emergency services, As soon as they arrive in the system children are moved to the Pediatrics facility. Additionally, all patients are allocated a triage category, performed by initial assessment from medical staff in the A&E department. Furthermore, patients are directed into the suitable area within an Assessment Unit, depending on the triage category .However, If patients are in the lower spectrum of triage and there is a nearer Community Casualty Facility (CCF) patients are directed to be present there instead of the A&E.After emergency treatment, the patients are either one is quitted or come to be inpatients under the relevant field. The second batch of patients arriving the system is the scheduled arrivals to the inpatient sites for elective care procedures. The third batch will enter to the system; can be from direct GP admissions where patients arrive the system straight away to an inpatient bed.

The performance measures the study seeks to improve

The simulation model study has been used to describe and prove the necessary number of beds at Ayr and Crosshouse Hospitals under each of the possibilities for the future delivery of acute care in Ayrshire and Arran. , the objective of using the simulation models is to determine the required bed capacity.

The changes which the study thought might improve performance

The models were therefore built, focusing on determining occupancy, by assessing issues and processes within the flow of patients.

1-The length of time patients’ stay was designed according to distributions generated using historical data and

Input into the model in the form of probability profiles. Literature and practice would recommend that using a distribution is a more accurate technique to evaluate bed capacity as it better reflects the randomness of the real world.

2-Six possible scenarios were established with the major variation represented in the configuration of the front door at the Ayr Hospital site. The different scenarios follow the explanation of the options. Furthermore, the models vary in the configuration of sub-specialty care behind the front door.

3-it was reflected that is unsuitable to use the maximum occupancy figure in planning bed capacity, since it will

Miscalculate bed necessities. Therefore, neither has been used in separation to define a recommended number of beds for each for a model.

An methodology was used to determine bed numbers that taking to consideration for both average and maximum occupancy. So by Using the average as baseline for the suggested number of beds and taking into consideration the maximum necessity offered by the models, the formula below and can be used to determine suitable number of beds for each department.

Formula = Average + (Maximum – Average)/2 =

(Average + Maximum) /2

4- Discovering how variations in the percentage of patients discharged from the Assessment Units impact the inpatient activity and the resulting effects on capacity requirements. Precisely, the percentage of patients discharged from the Assessment Units was increased to 60% and decreased to 20% and 0%.


5-The sensitivity of the number of attendances at A&E was tested with an growth of 10% of all attendances across the two hospitals for the scenario characterized in model 7.

‘How to Build Valid and Credible Simulation Models’

1- Formulate the Problem

The simulation model study has been used to describe and prove the necessary number of beds at Ayr and Crosshouse Hospitals under each of the possibilities for the future delivery of acute care in Ayrshire and Arran. , the objective of using the simulation models is to determine the required bed capacity.

2-Collect Information/Data and Construct Assumptions Document

In model of service redesign there are several number of assumptions that was been taking based on the best Available evidence. These assumptions are :-

Triage Patterns

It was assumed that past presentations at Ayr and Crosshouse A&E are characteristic of future outlines of request in terms of volumes of patients and sources of referrals. It was assumed that current triage quantities by source of referral into A&E will continue and present quantities of admissions to the inpatient sites depending on the triage category will keep on constant also.

Community Casualty Facility Catchment

In the simulation modeling, the patients are redirected to Community Casualty Facilities (CCFs) depending on their home postcode area and their triage category.

Assessment Unit

It was assumed that 65% of Yellow patients will go through this facility and stay in a bed for 24 hours and the following 40% will be discharged and 60% become inpatients.

Inpatient Activity

It has been assumed that past inpatient activity is demonstrative of future outlines of demand in terms of volumes of emergency and elective inpatients, specialties and lengths of stay.

Community Hospitals Activity

it was assumed that sub-acute beds for model 1 will have a maximum length of stay of 2 weeks and 70% of patients will be discharged within 3 days.

GP Referrals

it is assumed that there will be around 2000 GP direct referrals per annum to the inpatient services.

Specialty Activity Data

The Patients were categorized based on specialties for the simulation modeling depending on the data records and a established of assumptions to detect patients by procedures codes in the specialties under the proposals.

Bypasses or Transfers

it was assumed that several numbers of patients will move from Ayr Hospital to Crosshouse Hospital. Therefore Individual assumptions have been prepared for each of the options modeled, depending on how the front-door services will operate the necessities of patients, as identified by triage category.

3-Is the Assumptions Document Valid?

It was assumed that patients are redirected to Community Casualty Facilities (CCFs) based on their home postcode area and their gravity of illness. This analysis has cautions related to the assumption made that people are at home when they have an accident and they go to the nearer CCF according to the grouping by postcode area considered.

It is assumed that past inpatient activity is demonstrative of future outlines of demand in terms of volumes of emergency and elective inpatients. However, presently elective care is influenced by unscheduled emergency care and this is a problem that is anticipated to be fixed with the Review of Services project. In the proposals, the elective care will be better planned and work more individually and will not be influenced by emergency activity. Consequently, enhanced use of the beds is anticipated with smoother occupancy levels when separating emergency and elective care. A number of assumptions were conducted when structuring the model and the sensitivity of these assumptions was tested to evaluate the degree of uncertainty around predictions by modifying several inputs to the model and evaluating the effect this has on the outcomes.

4-Program the Model

The computer model in Simul8 was programmed using the conceptual model improved to understand the system configuration and the knowledge of the processes occurring in that system. Statistical analysis of historical data was conducted to report to or update the model. The model development and configuration focused on delivering the outputs that would Influence the decision making process.

5- Is the Programmed Model Valid?

The results of the simulation models have been compared against present performance, for example the numbers of unscheduled admissions at both sites per year that will occur with each of the models against the number of admissions in one year worth of data. These proved the variances between the front door scenarios and the future influence on the inpatient sites. Likewise the results produced were assessed against the outcomes from the modeling carried out in previous stages of the project and proved the differences as a result of variation in data and assumptions. At several phases in the model building process, the time was used to consult on the models themselves and the results that they were producing. The (RoS ) team was requested to contribute on the assumptions used in the model and to observe on the results from the model. Therefore, by using this feedback approach, it has improved a degree of confidence in the results of the models.

A number of assumptions were conducted when structuring the model and the sensitivity of these assumptions was tested to evaluate the degree of uncertainty around predictions by modifying several inputs to the model and evaluating the effect this has on the outcomes. For Example, discovering how variations in the percentage of patients discharged from the Assessment Units can impact the inpatient activity and the causing effects on capacity requirements. Precisely, the Percentage of patients discharged from the Assessment Units was raised to 60% and decreased to 20% and 0%. The effect on the total bed number was restricted. This amounted to be between 55 and 60 beds per 20% variation in the discharge rate. The sensitivity of the number of being present at A&E was tested with an growth of 10% of all attendances across the two hospitals for the scenario given in model 7..

6- Design, Conduct, and Analyze Experiments

1-The simulation was conducted for a period of two years and the time unit is in hours. Then a warm-up period is used in order that the model will not start when the hospital is empty. A graphical method was desired for predicting the warm-up period as a result of its easiness; it includes visual inspection of the time-series outcomes and human judgment. The warm-up period was predicted to be six weeks by inspecting time-series of key output statistics such as occupancy levels of specialties.

2-The historical data gathered was not in the correct arrangement to input in the model and it was challenging and time consuming to detect patients and their classification under specialties.

3-After offering the time graphs for the occupancy at the specialties, it turn out to be obvious that it will be beneficial to be able to see time graphs of the trials results. These will show confidence intervals represented in a graph with the variation over time.

4- The provision of services in the public is possible to have an influence on the number of patients going to the acute sites. Additional analysis will be carried out to account for those patients that would be present at the community facilities rather than the District General Hospitals under the new proposals.

The model will be re-designed and the additional reform capacity in the community setting. It is predicted that this Simulation will be used for the Execution stage of the project, the desired model can be developed and improved by making an addition of details on the flow of patients, identification of subspecialties and the use of updated data.

The simulation models offered, it delivers a sound basis for enhancement and improvement of a model that can be used to update the execution phase of the review of services project. In addition, by using the model, the necessary number of beds can be measured and verified using different triage data that will support the execution phase.

7- Document and Present the Simulation Results

The results are of trials of 15 runs, being the most suitable number of replications after testing results with different numbers of runs. As a result, 95% confidence intervals were produced for objects go into each of the facilities of interest and for the average and maximum queue size for each of the specialties for each of the scenarios

Improved. The Volumes of patients for each of the triage categories in each scenario were given as well as volumes of patients be present on each of the CCFs. We discovered the time graphs for each of the specialties, which exemplify the occupancy across two years, for example, we can see the occupancy of General Medicine beds in the time graph .

Consequently, the anticipated numbers of beds needed for each of the specialties were delivered for each of the models. These were re-organized based on the preferred hospital under the proposals at that certain moment in time and so total number of beds per hospital was given for each of the models that represent a dissimilar

Model of care. This can be seen in Table 2 (go to Appendix ).

Q5 :Critically assess the contribution of business process modelling and simulation in a business area of your choice

The competitive pressure that organizations is facing can force them to minimize the time and cost it takes to improve a product and satisfy the their customers whilst maximizing profits, this competitive pressure has made the organizations to focus on the topic of Business Process Re-engineering in order to create new methods of doing business ( Tumay,1995).It is an engineering strategy that critically studies current business procedures, rethinks them and then redesigns the mission-critical products, processes, and services (Prassad, 1999).There are several contributions of business process modeling in the Healthcare business area, the managers can use the simulation for evaluating current performance and forecasting the influence of operational changes (Wierzbicki, 2007). Generally , the Simulation concept is utilized to solve issues in hospital sectors such as emergency departments and operating rooms, where resources are limited and patients arrive at unbalanced times (Jun et al., 1999) and efficiently combine data mining (Ceglowski et al., 2006) for improved results. In addition , the Simulation is also can be effective for other sectors such as outpatient clinics, where several alternative configurations can be assessed and tested (Swisher and Jacobson, 2002).The mangers have used discrete-event simulation as an active tool for assigning limited resources to develop patient flow, while reducing health care delivery costs and maximizing patient satisfaction. For example, such simulation results have shown a sufficient of major reduction in patients average waiting time, whilst minimizing the depend on overloaded nursing resources, that can resulted in an maximization of staff consumption between 7% and 10%, The planned most favorable staff schedule decreases the average waiting time of patients by 57% and also contributes to minimize number of patients left without treatment to 8% instead of 17%. (Vasilakis et al. 2006).

Furthermore, a study of a surgical unit using simulation was also offered by Cardoen and Demeulemeester (2007) which shows the impact of the modifications of bed capacity on the utilizations of beds. The stated results have shown that applying the master surgery schedule can decreases the waiting list and the number of overruns by about 25% and 10% correspondingly (Vasilakis et al. 2006). Additionally, the simulation is used to study the influence of health interventions on performance of healthcare procedures, for example; designing a new house staff work schedule (Dittus et al., 1996) or ambulance schedules (Ingolfsson et al., 2003); developing capacity consumptions in intensive care units (Kim et al., 1999, Litvak et al., 2008); and formation healthcare services (Oddoye et al., 2009).However, the simulation modeling can efficiently contribute to the understanding and analysis of business operations; it does not provide the ability of determining the optimum values of decision variables and optimizing a business process (van der Aalst et al., 2003).

Case study (1) -Memorial Health System (MHS)

Memorial Health System (MHS) is making an addition of the capacity to its operating rooms to manage with a 5% annual increase in demand for surgeries. The (MHS) found that the price tag for the expansion project is about a $31 million, so the Operations Improvement team desired to guarantee that the expansion will fulfill their requirements. Several new proposals were tested in SIMUL8, including the adding of a third elevator, and they have reached to a settlement on new designs for the development. The Operations Improvement team wanted to perform a simulation to test feasibility of the proposals and to guarantee that any development will continue to provide on patient experience.  The capacity In the health system has Faced with a 3% to 5% annual rise in the number of surgeries in latest years, one of the project aspects is to see two floors of surgical space added at the medical Centre. The expansion project will make an addition of five operating rooms. Furthermore , In order to test the feasibility of the proposals, System Director of Operations Improvement at MHS, have utilized the SIMUL8 software in order to simulate flow for all characteristics of the operating room expansion project including; pre-op admission, transport to the Operating Room, Operating Room time, and post-anesthesia care units for admitted and outpatient surgery. The Director of Operations Improvement used the simulation that enables him to examine the assumptions for capacity depending on expansion of the five operating rooms and raise the volumes of 15% over the next 5 years.

The rooms split over two levels, the proposals contained within two elevators to transport patients between floors. The Director of Operations Improvement have used the simulation that allowed him to visually determine all process bottlenecks and identify that, with a surge of patients transported to the Operating Rooms for first and second-case begins, that two elevators from the pre-op holding area to the Operating Rooms will not be sufficient for flow. Based on this, it was established that this bottleneck may result in employees and patient disappointment. According to this a several of scenarios were tested with to eliminate the bottleneck with the best solution being the introduction of a third elevator shaft.

The simulation process has also underlined that the problem enlarged overall variation by 30 minutes per case throughout the day. The detection of a downstream increase in variation have been achieved by the simulation, Unlike Excel ,because the simulation is able to capture the unpredictable variations and randomness of real life ,for example , the patients arriving late or employees being unavailable. The simulation is able to model this variation, so this can allows the company have a better understanding of the system behavior under a variation of scenarios and avoid misleading results. When the results were offered to management the decision was taken to add the third elevator to fulfill flow demand guaranteeing staff and patient satisfaction. The management was able to see the first hand the bottlenecks at the elevators, because the simulation has visual and interactive nature.    

Case study (2) - the Johns Hopkins Comprehensive Transplant Center

The number of chronic disease cases is increasing at remarkable rates as our wide-ranging population ages. End-stage diseases eventually depend on transplantation as the suitable solution to make a patient to go back to a reasonable health. Therefore, the demand has increased recently, so because of this high demand, the line of patients waiting to receive organ transplants is apparently limitless. Also there is a shortage of organs being donated to fulfill this demand .However; the transplant community’s major issue is having enough resources to fulfill the cumulative demand.

NovaSim is most appreciated SIMUL8 Certified Solution Providers, worked with Johns Hopkins in improving simulation as an important instrument in their determinations to develop process efficiency. These steps are essential in order that the Johns Hopkins Comprehensive Transplant Center will be in a position to satisfy of the growing demand for transplant care in the future.

In any healthcare situation, the needs of the staff resources that are highly specialized is an essential factor ,these resources are having a complex availability forms as a result of the need to fulfill responsibilities in other departments of the hospital. Johns Hopkins have Used the simulation, which allowed him to emulate the very complex process that is involved in every transplant case. This is after assigning the required staff resources, time commitments for all of the several activities they perform.

Furthermore, they were able to simulate the flow-through of each of the different phases leading to transplant and then chronic follow-up. Eventually they were able to model the flow of the whole process so that it reasonably reflects what is happing in the reality of the hospital.

The simulation has helped Johns Hopkins to effectively determine the areas that will allow them to automate steps to off-load staff responsibilities. Additionally, they can obviously see the influence that outsourcing several steps of the process will have to limit internal growth of the center and still maintain quality of care. Finally, the simulation has facilitated them to understand how all of the complex components involved in transplant care fit together, allowing Johns Hopkins to know how to use its limited resources better to the benefit of their patients.