Research proposal

A recent trend has been observed among various researchers

Introduction

A recent trend has been observed among various researchers to reduce the patient waiting time as it is considered to be an essential criterion for the evaluation of the quality of the care provided by the health care system. The reduction in the patient waiting time effectively contributes to the reduction of the workload of the concerned hospital. Many researchers have utilized the discrete event simulation approach –FlexSim to provide optimal solutions to the problems. A discrete-event simulation (DES) models operates as a system or as sequence or series of events within a given time. Every event happens at a definite instant within a time frame and it marks the change of the state within the system. In between the two events that has occurred no change is expected and therefore the time of simulation can be considered to jump to the next occurrence of the event directly and the phenomena is known as next-event time progression. The need to use the model and the benefits after using this particular model with evidence has been discussed in detail in the assignment below. The use of this model in surgical settings also highlighted on certain factors such as reduction of the length of stay of the patient, lowering the expense of the hospital, reduction of the patient waiting time prior to surgeries, efficacy of the service provider gets enhanced. The model has been critically analysed with the other models that can be used to solve the problems. Moreover, how this particular approach is linked with Plan-Do-Study-Act (PDSA) cycle has also been discussed in detail.

Background

Change management strategy takes care of any organisation by designing new work process or implementing newer technologies. It will ensure the financial success of the organization which will depend on how the employees of the organisation adopt the change and how beautifully the organization chart and process diagrams are drawn. It is an essential tool which manages the people or employees to achieve the required business output. It brings changes in the management of employees by making it more systematic and in turn how it affects the organization by getting the work done within stipulated time by changing the day to day activities of employees. Change management is both a process and a competency (Rafiq, et al, 2004).

In the past few years several studies related to use of simulation approach in health care sector have prospered. Several approaches and models have been developed to evaluate the problems and to provide most favourable solutions to those healthcare related problems. Based on the study report conducted by researcher (Moon, et al, 2015, Prodel, et al, 2014, Raunak, et al, 2009,Proulx, V.K., et al, 1999)have utilized the simulation based approach to solve the problems of emergency health care system. There are several other models that have been developed and utilized to offer enhanced research management solutions for the health care departments [Roh, et al, 2014; Marmor, et al, 2013; Tyler, et al, 2003]. Several researchers have utilized the different simulation models to avoid the severity of pandemic diseases (Kabaso, et al, 2015; Adams, et al, 1998; Montañola-Sales, et al, 2015). There is a recent trend observed among the different works of literatures on how to reduce the patient waiting time, and enhancing the techniques related to appointment schedule of the patient and thereby in turn improving the overall performance of the health care system [Turkcan, et al, 2014; Kaandorp, et al, 2007]. It is evident from a survey that the factor of patient waiting time is considered to be a predicament in the current health care setting also because of the complicated, vibrant and developing nature of the health care systems. Therefore several researchers have given an effort to find solution to this problem via the use of a discrete event simulation approach –FlexSim.

The rationale of choosing this model:

The use of the discrete-event simulation approach enables the end users such as clinic manager and hospital administrator to evaluate the efficiency of the current health care delivery system. They can evaluate the efficiency of the system by asking questions such as "what if" and also helps them to design a new system. The approach of Discrete – event simulation has several applications such as it can be used to predict the effect of changes in the parameters such as patient flow, to investigate about the needs of any resources, and can also examine about the complicated relationship among the several variables of models. Based on this information the mangers can select different alternative techniques that might have application in reconfiguring the existing system, the performance of the system can be improved and can effectively formulate a plan or design a new system without changing the present system (Eldabi, et al, 2002). In recent years the application of simulation in healthcare has become increasingly more wide spread. There are several benefits achieved by the application of the simulation approach regarding hospital planning and planning a macro view point in terms of cost reductions (Brailsford, et al, 2011; Braly, et al, 1995; Cirillo, et al, 1996). The different simulation approaches have highlighted on the aspect of the functioning flow of the system with respect to healthcare delivery and capacity modelling. The different models reported in the previous literature had been applied in isolated parts of hospitals such as in the clinics, operating theatres and in the emergency rooms.

There are several studies on the outpatient service highlighting on the reduction or management of patient waiting time as because this particular factor is considered to be an important aspect in relation to satisfaction of patients and determining the service quality. By reducing the patient waiting time we can help the patients and can also effectively decrease the workload of the concerned hospital. The huge amount of the expense is allocated by the surgical suits whereas they also contribute a large part in the hospital revenue. Optimization of the patient flow within a surgical suite occurs by excluding or reducing the blockage which basically causes the loss of time and therefore it is one of the most potent solutions for reducing the length of stay (LOS) of the patient. By reducing the LOS in the healthcare system the expense can be lowered, efficiency can be increased, and the patient service also gets enhanced. For the achievement of the goal initially the workflow model of the patients was created using the discrete-event simulation. Thereafter various upgrading scenarios have been applied within the simulated model of surgical suites. Among several scenarios observed the combined scenario comprising of the exclusion of waiting time of the patient and entrance to the surgical suit followed by beginning of the admission related procedure, being accurately on time for the first operating procedures and inclusion of the resource to the resources available of the transportation system and the recovery room is considered to be the best scenario. The outcome of the above mentioned simulation as observed was that it reduced the length of stay of the patient within the system to 22.15% (Günal, et al, 2010; Chen, et al, 2010; Najmuddin, et al, 2010). To create a proper working environment for the nurses that will be most favourable to patient safety may demand certain elementary changes which are applied across many health care organisations (HCO)—in such a way where the work is planned and the people are arranged, depending on the understanding of the culture of organisation and maintaining the science to ensure safety. To bring these changes, leadership capabilities are required for transforming not only the physical environment but also the conviction and customs of nurses along with the other health care workers who will offer care in that environment who are in charge to set up the policies and practices that eventually shapes the organisation and also those persons who belongs to the management of the organization.

Evidence based study:

An oncology department dedicated to surgery. To understand and optimize the performances of the organisation the strategies employed by the hospital management were simulated through experiments and the outcomes were compared. The experiments based on the simulation approach were done based on the mentioned official stages which include: observation of the system, collection of the data, implementation of the model or approach, evaluation of the run and validation and analysis of the outcome. There are many practical issues that had cropped up during the experimental analysis such as the errors that had appeared during the collection of the data, huge variability observed in the parameters of the system and the adjustment observed in the behaviour of the persons involved.

Therefore it is very important to evaluate the case study depending on the type of patient involved, i.e., the inpatient. In case of an inpatient who gets admitted to the hospital and stays there overnight for an indefinite period of time. Therefore, the possible early selection of the inpatient from the outpatients considerably reduces the waiting time of the patient. As because the diagnostic process is not a mathematical science, so sometimes it become unavoidable to rule out some of the outpatients from the experiment during the trial process. It can be considered to be an index to measure the performance level as it is directly linked up with the satisfaction level of every patient and the exact length of the waiting time before hospitalization. Therefore, to improve this condition, different strategies can be adopted such as: Lining up of the discipline based on the precedence rules; Progress achieved on the scheduling of the arrival of the patient; to enhance the employment of the surgery rooms; Improvising the procedures involved for the accommodation in the departmental beds.

It can be understood that the process of scheduling the patient had been adopted by several hospitals though not by everyone (Gupta, et al, 2008). The process of scheduling becomes effective if the system is determined and is subjected to low inconsistency. Unfortunately, this is not the scenario as the time of recovery exhibits a variability corresponding to the mean time. So the department concerned uses a very flexible schedule in which only the date when the patient will be ready for hospitalization will be mentioned along with the results of the diagnostic procedures that has been carried out prior hospitalization. The actual hospitalization will occur starting from that date and also depending on the availability of the bed.

Moreover the process of pre hospitalization can be considered to be a way to hospitalize the concerned patient just on time for the operating procedures and this also ensures saving the number of beds (Qi, et al, 2006). Another major improvisation that can be achieved is to cluster the beds in two defined groups namely standard patients and long term patients. The category of the long term patients causes delay of the admission of the new patients to the surgery department. Therefore, the comparative size of the two groups can be re billed based on the needs of the current situation (Akkerman, et al, 2004). It is not advisable to carry on experiments on the actual patient, therefore it was decided to take an alternative route towards the simulated experiments.

Comparison to other existing models:

Various approaches could be applied to model and develop the patient flow namely Markov and semi-Markov models, theory related to queuing; analytically solving the problems and by discrete event simulation (Xiong, et al, 1994, Vissers, et al, 1998). The model of queuing theory is developed on certain simple assumptions which include exponential inter-arrival and service time. But if we consider the real world complex setting the DES models are considered to be more adjustable and adaptable (Davies, et al, 1994, Koo, et al, 2010). The model of the patient flow basically takes the shape of a queuing network along with G/G/m servers. The factors such as the inter-arrival times and the process times pursue a general distribution. As observed there are m workstations available in the server and the queue projected as waiting list is considered to be practically unlimited.

There is another way to develop the system with no input of additional costs such as to deal with the effort concerning the reduction of variables on the waiting time prior to surgery (WT2), by increasing the number of investigations carried on prior to hospitalizations and decreasing the number of investigations done during hospitalization. The health care process can be reorganised by implementing the pre hospitalization process. It is a proven fact that anticipation of any examination outside the organisation can reduce the time of waiting inside the hospital. Therefore, to enhance the particular scenario the discrepancy of waiting time prior to surgery (WT2) has been particularly reduced by considering an organisational approach to admit only the urgent patients and the patients having a pre-hospitalization period (B patients) and by taking into account of the waiting time prior to surgery is equivalent to the average of the real values.

Scheduling of the operating or surgical room centre had been studied in detail using the discrete-event simulation model. The review of literature on the use of this particular model for preparation of surgical rooms had been conducted by (Magerlein and Martin, 1976) Other researchers (Murphy and Sigal, 1985) investigated about the scheduling of the surgical block in which the block of the time of the operating room had been reserved for more than one surgeons. Another study conducted by Fitzpatrick et al, (1993) investigated about the significance of “first-come-first-serve”, fixed schedule of the same slab of time within the same slot of time everyday of the week, variable schedule which operates based on the seasonal fluctuations of the demands, and mixed block schedule which is considered to be amalgamation of fixed and variable) for the surgical rooms of the hospital. The result obtained revealed about the benefits observed due to variable block schedule in terms of scheduling of the policies, utilization of the facilities, the They observed that variable block scheduling is superior to all scheduling policies, in terms of facility utilization, patient data, the average waiting time of the patient, and the queue length of the patient.

Plan-Do-Study-Act (PDSA) cycle:

Among the several quality improvement tools (QI) and methods, the Plan-Do-Study-Act (PDSA) cycle is considered to be the one which mainly highlights on the parameters such as on the core of the change process, the conversion of the various ideas and the objectives into actions. Moreover the PDSA cycle along with the perception of iterative assessments of change are considered to be fundamental to many of the QI approaches which includes the lean, improvement model, total quality management and six sigma.

With the help of PDSA a structured experimental learning model can be used to examine the changes. In the past several concerns have been raised concerning the reliability of the application of this QI tool which had demoralized the efforts of learning, the complication involved to bring the tool in practice and the correctness of the PDSA method to face the significant impact of challenges for the development of the health care.

The main features of the PDSA tool is that it helps in quick learning and helps in deciding that whether an intervention within a particular setting is working properly and accordingly to make any adjustments in order to increase the various chances of delivery and in sustaining the targeted improvement. When compared with the controlled trials, PDSA helps in learning new concepts which can be introduced into the experimental processes. This QI tool is simple in its concept but it is not easy to apply. Due to the factors such as inadequate human resources and damage of the financial support, several projects fails to produce the desired results and also demoralize the cultures of the organisation which results in the development of tiredness and disappointment as no such improvements could be observed. Therefore, it is very crucial from both the project and programme level to understand the resources demands for the successful development of PDSA for the concerned project and the process should be managed well (Langley, et al, 2009; Toussaint, et al, 2013; Schroeder, et al, 2008).

Fig: 1 The PSDSA (Plan-Simulate-Do-Study-Act) model: the figure depicts the fusion of the PDSA cycle and the model for simulation.

Conclusion

At last it can be concluded that the factors such as scheduling of the patients and the admission procedure including the appointment time of the patient have a significant impact upon the utilization of the clinician and on the waiting time of the patient. Generally studies conducted on discrete event simulation highlighted that the policies and regulations can be brought in use and this will in turn help to balance the exchange between the utilization rate of the physician and the waiting time of the patient based on the exceptional features of each clinical set up. The exchange between the factors of convenience of the patient and the utilization of the care giver depends on certain external market factors and on the ways employed by the health care organisation. In case of highly economical markets, the clinics may prefer the convenience of the patient over the utilization of the staff with an aim to restore the share of the market. The optimal solution for the problems faced by the health care organisation depends mainly on the extinguishing features as decided by the makers of the organisation depending on the existing environmental factors. Different studies have shown the effect of the approach on the arrival time of the patient and the service time provided. The reduction in the variables of the facilities provided significantly improves the performance.

Based on the isolated units available within the hospitals a researcher conducted a study using the discrete-event simulation model to evaluate the surgical intensive care unit in terms of several bed levels and the future needs. When the outcome was analysed it can be seen that the volume of the unit comprises mostly of week day patients or the routine admissions and therefore to ensure a good average occupancy level overall would not be possible without giving any strain on the system.

References:

• Rafiq, M. and Ahmed, P.K., 2000. Advances in the internal marketing concept: definition, synthesis and extension. Journal of services marketing.
• Moon, I.C., Bae, J.W., Lee, J., Kim, D., Lee, H., Lee, T., Cha, W.C., Kim, J.H. and Kim, G.W., 2015, December. EMSSIM: emergency medical service simulator with geographic and medical details. In 2015 Winter Simulation Conference (WSC) (pp. 1272-1284). IEEE.
• Raunak, M., Osterweil, L., Wise, A., Clarke, L. and Henneman, P., 2009, May. Simulating patient flow through an emergency department using process-driven discrete event simulation. In 2009 ICSE Workshop on Software Engineering in Health Care (pp. 73-83). IEEE.
• Proulx, V.K., 1999. Hospital emergency room simulation: object oriented design issues for CS2. ACM SIGCSE Bulletin, 31(1), pp.92-94.
• Kabaso, E.M., Currie, C.S. and Brailsford, S.C., 2015, December. Simulating the provision of antiretroviral therapy in Zambia. In 2015 Winter Simulation Conference (WSC) (pp. 1285-1294). IEEE.
• Adams, A.L., Barth-Jones, D.C., Chick, S.E. and Koopman, J.S., 1998. Simulations to evaluate HIV vaccine trial designs. Simulation, 71(4), pp.228-241.
• Montañola-Sales, C., Gilabert-Navarro, J.F., Casanovas-Garcia, J., Prats, C., López, D., Valls, J., Cardona, P.J. and Vilaplana, C., 2015, December. Modeling tuberculosis in Barcelona. A solution to speed-up agent-based simulations. In 2015 Winter Simulation Conference (WSC) (pp. 1295-1306). IEEE.
• Roh, T.P., Huschka, T.R., Brown, M.J. and Marmor, Y.N., 2014, December. Data-driven simulation use to determine bed resource requirements for the redesign of pre-and post-operative care areas. In Proceedings of the Winter Simulation Conference 2014 (pp. 1168-1176). IEEE.
• Marmor, Y.N., Rohleder, T.R., Cook, D.J., Huschka, T.R. and Thompson, J.E., 2013. Recovery bed planning in cardiovascular surgery: a simulation case study. Health care management science, 16(4), pp.314-327.
• Tyler, D.C., Pasquariello, C.A. and Chen, C.H., 2003. Determining optimum operating room utilization. Anesthesia & Analgesia, 96(4), pp.1114-1121.
• Turkcan, A., Toscos, T. and Doebbeling, B.N., 2014. Patient-centered appointment scheduling using agent-based simulation. In AMIA Annual Symposium Proceedings (Vol. 2014, p. 1125). American Medical Informatics Association.
• Kaandorp, G.C. and Koole, G., 2007. Optimal outpatient appointment scheduling. Health care management science, 10(3), pp.217-229.
• Eldabi, T., Irani, Z. and Paul, R.J., 2002. A proposed approach for modelling health‐care systems for understanding. Journal of management in medicine.
• Brailsford, S. and Vissers, J., 2011. OR in healthcare: A European perspective. European journal of operational research, 212(2), pp.223-234.
• Braly, D., 1995. Seek alternatives via simulation software. Health Management Technology, 16(13), pp.13-15.
• Cirillo, J.A. and Wise, L.A., 1996. Testing the impact of change using simulation. In 1996 Annual HIMSS Conference and Exhibition.
• Günal, M.M. and Pidd, M., 2010. Discrete event simulation for performance modelling in health care: a review of the literature. Journal of Simulation, 4(1), pp.42-51.
• Chen, B.L., Li, E.D., Kazunobu, Y., Ken, K., Shinji, N. and Miao, W.J., 2010. Impact of adjustment measures on reducing outpatient waiting time in a community hospital: application of a computer simulation. Chinese medical journal, 123(5), pp.574-580.
• Najmuddin, A.F., Ibrahim, I.M. and Ismail, S.R., 2010. A simulation approach: improving patient waiting time for multiphase patient flow of obstetrics and gynecology department (O&G Department) in local specialist centre. WSEAS transactions on mathematics, 9(10), pp.778-790.
• Gupta, D. and Denton, B., 2008. Appointment scheduling in health care: Challenges and opportunities. IIE transactions, 40(9), pp.800-819.
• Akkerman, R. and Knip, M., 2004. Reallocation of beds to reduce waiting time for cardiac surgery. Health care management science, 7(2), pp.119-126.
• Qi, E., Xu, G., Huo, Y. and Xu, X., 2006, June. Study of hospital management based on hospitalization process improvement. In 2006 IEEE International Conference on Service Operations and Logistics, and Informatics (pp. 74-78). IEEE.
• Vissers, J.M., 1998. Patient flow-based allocation of inpatient resources: a case study. European journal of operational research, 105(2), pp.356-370.
• Xiong, H.H., Zhou, M. and Manikopoulos, C.N., 1994, October. Modeling and performance analysis of medical services systems using Petri nets. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics (Vol. 3, pp. 2339-2342). IEEE.
• Davies, R.H.T.O. and Davies, H.T.O., 1994. Modelling patient flows and resource provision in health systems. Omega, 22(2), pp.123-131.
• Koo, P.H., Nielsen, K.B., Jang, J. and Kolker, A., 2010, February. Simulation-based patient flow analysis in an endoscopy unit. In 2010 IEEE Workshop on Health Care Management (WHCM) (pp. 1-6). IEEE.
• Magerlein, J.M. and Martin, J.B., 1978. Surgical demand scheduling: A review. Health services research, 13(4), p.418.
• Murphy, D.R., Duket, S.D. and Sigal, E., 1985, December. Evaluating surgical block schedules using computer simulation. In Proceedings of the 17th conference on Winter simulation (pp. 551-557).
• Langley, G.J., Moen, R., Nolan, K.M., Nolan, T.W., Norman, C.L. and Provost, L.P., 2009. Changes that result in improvement. The improvement guide: a practical approach to enhancing organizational performance, pp.15-25.
• Toussaint, J.S. and Berry, L.L., 2013, January. The promise of Lean in health care. In Mayo clinic proceedings (Vol. 88, No. 1, pp. 74-82). Elsevier.
• Schroeder, R.G., Linderman, K., Liedtke, C. and Choo, A.S., 2008. Six Sigma: Definition and underlying theory. Journal of operations Management, 26(4), pp.536-554.