They finish the claim by saying, âThe process worked as planned and no Trinidadian has been denied an emergency ... ability to respond to the next caseâ ("CEO Of GMRTT Ambulance Service Says Issues Being ...... Asson, Cecily. 2012.
THE UNIVERSITY OF THE WEST INDIES Faculty of Engineering Department of Mechanical &
Manufacturing Engineering
Final Report
Optimising the Emergency Medical Response Service in Trinidad & Tobago
Name: Dane McGibbon ID#: 814117753
Course Title: MENG 3019
Supervisor: Dr. Ruel Ellis Second Examiner: Dr. Terrence Lalla Moderator: Professor Kit Fai Pun
Acknowledgements This project would not have been possible without the help of my family, friends and lecturers. I would like to extend special thanks to:
- Dr. Ruel Ellis, my supervisor, who offered invaluable advise and feedback over the course of this project.
- Mr. Jainarine Bansee, who offered guidance in the creation, validation and verification of the system’s model.
- Ms. Ingrid Sheppard, who assisted in the arrangement of the interviews that were essential to the completion of this project.
- The many EMTs, Dispatchers and EMS users that contributed to the data that was collected for this project.
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Abstract This project examines the possible ways in which the emergency medical service in Trinidad & Tobago can be improved and to what extent. It was prompted by the public’s complaints about the length of time it takes for an ambulance to arrive at the scene of an emergency when requested. In approaching this problem, research was first conducted on the Emergency Medical Service currently provided by Global Medical Response of Trinidad & Tobago (GMRTT). The information gathered was used to create a model that imitates the system closely, allowing the effects of any changes proposed to be tested on the system. Using the insight gained through research of this system, three optimisation strategies were proposed: public education, on-site treatment, and the use of health centres. These strategies were then represented in the form of modified system models and simulated to observe their effects. During the testing and simulation of these models, a fourth optimisation strategy was proposed in the form of artificial delays. Using the product design and development technique of concept-screening, two strategies were eliminated from main consideration and combined with the remaining strategies to create hybrids which were further tested. These hybrids were then compared to their original strategy, with three of the four strategies being eliminated using the technique of concept-scoring. The remaining two strategies were then compared until one was eliminated, with the other becoming the chosen model for the optimised system. This optimised system was analysed and compared to the existing system, with each metric being represented on bar charts to visualise improvements. The project found that a combination of artificial delays and the use of health centres was the most ideal and realistic strategy for optimising this system. Analysis of the optimised model revealed a strong correlation between the average length of time an emergency spends in the ambulance queue and the overall efficiency of the system. As a result, a strategy that minimises queue time, such as the one chosen, will likely decrease the length of time it takes for an ambulance to arrive at the scene of an emergency.
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Table of Contents 1.0 Introduction
1
1.1 Problem Statement
1
1.2 Rationale 1.3 Objectives 1.4 Scope of Study 1.6 Limitations 1.5 Outline of Study
2 2 3 3 3
2.0 Literature Review
4
2.1 Introduction to Emergency Medical Services
4
2.1.1 What is EMS? 2.1.2 EMS Providers 2.1.3 Principles of EMS 2.1.4 Levels of Care in EMS 2.1.5 Models of Care 2.1.6 Strategies in EMS 2.1.7 EMS Standards 2.1.8 EMS System Model 2.1.8 Healthcare in Trinidad & Tobago
4 4 6 7 9 10 11 11 12
2.2 Simulation
14
2.2.1 What is Simulation? 2.2.2 Simulation in Engineering
14 14
2.3 System Modelling
14
2.3.1 What is a System? 2.3.2 System Efficiency 2.3.3 System Effectiveness 2.3.4 Scientific Modelling
14 15 15 15
2.4 Process Optimisation
15
2.4.1 What is Process Optimisation? 2.4.2 Areas of Process Optimisation
15 16
2.5 Conclusion
16
3.0 Methodology
17
3.1 Methodology Steps 3.2 Justification of Methodology
17 17
3.2.1 System Research and Problem Definition 3.2.2 Simulation of EMS System 3.2.3 Optimisation Analysis
17 18 18
3.3 Methods of Research 3.4 Model Verification Techniques
18 19
3.4.1 Structured Walk-Through 3.4.2 Deterministic Verification
19 19
3.5 Model Validation Techniques
19
3.5.1 Expert Intuition 3.5.2 Real System Measurements
19 19
3.6 Model Refinement Process
20
3.6.1 Identify System Needs 3.6.2 Establish System Metrics 3.6.3 Generate Optimised Models 3.6.4 Model Testing & Refinement 3.6.5 Select Final Model
20 20 20 20 20
4.0 The Existing System
21
4.1 Dispatch Procedure
21
4
4.2 On-Site Procedure
22
4.3 Hospital Handover & Re-Entry Procedure 4.4 Major Delays 4.5 Demand 4.6 Standards 4.7 Budget 4.7 Conclusion
22 23 23 23 24 24
5.0 The System Model
25
5.1 Model of the Existing System 5.2 Assumptions 5.3 Model Verification
25 27 27
5.3.1 Structured Walk-Through 5.3.2 Deterministic Verification
27 30
5.3 Model Validation
33
5.3.1 Expert Intuition 5.3.2 Real System Measurements
33 33
6.0 System Model Optimisation
35
6.1 Identify System Needs 6.2 Establish System Metrics 6.3 Optimisation Strategies
35 36 38
6.3.1 Strategy 1: Public Education 6.3.2 Strategy 2: On-Scene Treatment 6.3.3 Strategy 3: Health Centres
38 39 39
6.4 Optimised Models
40
6.4.1 Strategy 1: Public Education 6.4.2 Strategy 2: On-Scene Treatment 6.4.3 Strategy 3: Health Centres 6.4.4 Strategy 4: Artificial Delay
40 40 42 43
6.5 Performance of Optimised Models 6.6 Model Testing & Refinement
43 46
6.6.1 Strategy 2 Model Development 6.6.2 Strategy 3 Model Development
46 48
6.7 Final Model Selection
50
7.0 The Optimised System
52
7.1 Description of the Optimised System 7.2 Optimised System Performance Analysis
52 52
7.2.1 Percentage of ‘patients’ Out 7.2.2 Maximum, Average & Minimum ‘Wait Time’ 7.2.3 Maximum, Average & Minimum ‘Transfer Time’ 7.2.4 Maximum & Average Wait Until Ambulance Available 7.2.5 Maximum & Average Number of People Waiting Until Ambulance Available
53 54 54 55 55
7.3 Discussion of Performance Improvements 7.3 Potential Impact of the Optimised System 7.4 Conclusion 7.5 Recommendations for Future Studies
56 57 57 58
References Appendix Glossary
61 64 70
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List of Figures Figure 1: The Star of Life (Source: Waretown First Aid Squad 2017) 7 Figure 2: Comparison Between the Franco-German Model and Anglo-American Model (Source: Al-Shaqsi 2010) 10 Figure 3: Model of an EMS System (Source: Pinto, Silva and Young 2015) 12 Figure 4: Modified Product Design & Development Process 20 Figure 5: Model of the Existing EMS System Created in Arena 26 Figure 6: The Ambulances Enter the System 27 Figure 7: Calls Are Generated 28 Figure 8: The Information is Processed and the Emergency Classified 28 Figure 9: Dispatch Signal is Sent 29 Figure 10: Ambulance Enters Dispatched State 29 Figure 10: Ambulance Travels to Scene, Treats Patient, Travels to Hospital 29 Figure 11: Ambulance Hands Over Patient 30 Figure 12: Ambulance Rejoins Dispatch Queue 30 Figure 13: Model of Strategy 1 Created in Arena 40 Figure 14: Model of Strategy 2 Created in Arena 40 Figure 15: Ambulance Offers On-Scene Treatment 41 Figure 16: Model of Strategy 3 Created in Arena 42 Figure 17: Ambulance Takes Patient to Health Centre 42 Figure 18: Model of the Artificial Delay Strategy Created in Arena 43 Figure 18: Strategy 2 Hybrid Model Created in Arena 46 Figure 19: Strategy 3 Hybrid Model Created in Arena 48 Figure 20: Percent of ‘patients’ Out for Existing System Vs Optimised System 53 Figure 21: Maximum, Average & Minimum ‘Wait Time’ for Existing System Vs Optimised System 54 Figure 22: Maximum, Average & Minimum ‘Transfer Time’ for Existing System Vs Optimised System 54 Figure 23: Maximum & Average Wait Until Ambulance Available 55 Figure 24: Maximum & Average Number of People Waiting Until Ambulance Available 55 Figure 25: A Map of Trinidad & Tobago Showing the Locations of Health Centres & Hospitals ("The Ministry Of Health - Trinidad And Tobago" 2017) 69
List of Tables Table 1: Theoretical Calculations vs Arena Simulated Output of Measured Times Table 2: Arena Simulated Output of Number of Entities In & Out Table 3: The Time Taken Between Placing Call & the Arrival of an Ambulance Table 4: Measured Time Taken for Time Intervals Table 5: Arena Simulated Wait Time Table 6: Arena Simulated Time Intervals Table 7: The System Needs Table 8: The System Metrics Table 9: The Existing Model Vs Optimised Models Table 10: Concept-Screening Matrix for Optimised Models Table 11: The Strategy 2 Model Vs Hybrid Models Table 12: Concept-Scoring Matrix for Strategy 2 Hybrid Models Table 13: The Strategy 3 Model Vs Hybrid Models Table 14: Concept-Scoring Matrix for Strategy 3 Hybrid Models Table 15: The Strategy 3 Model Vs Hybrid Models Table 16: Concept-Scoring Matrix for the Final Models Table 17: The Existing System Model Vs The Optimised System Model Table 18: Existing System Model Call Arrival Schedule Table 19: Existing System Model Delay Times Table 20: Existing System Model Decisions & Probabilities Table 21: Optimisation Strategy 1 System Model Decisions & Probabilities Table 22: Optimisation Strategy 2 System Model Delay Times Table 23: Optimisation Strategy 3 System Model Delay Times Table 24: Optimisation Strategy 4 System Model Delay Times Table 25: Optimised System Model Delay Times
32 32 33 33 34 34 35 36 44 45 47 47 49 49 50 51 53 66 66 67 67 67 68 68 68
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1.0 Introduction 1.1 Problem Statement In Trinidad & Tobago, the Emergency Medical Service (EMS) aims to provide timely care to victims of sudden and unforeseen accidents, injuries and illnesses. It is an integral part of the health care system, and in many cases is the difference between life and death. The basic function of the system is to assist citizens in an efficient and organised manner, using minimal resources, for all emergency events that require medical services ("The Ministry Of Health - Trinidad And Tobago" 2017). However, there have been many complaints over several years about the length of time it takes for an ambulance to arrive at the scene of an accident. This calls into question the fulfilment of the Service’s basic function, its overall efficiency, and by extension its effectiveness.
As of October 2005, Global Medical Response of Trinidad & Tobago (GMRTT) has been contracted by the national government with the responsibility of dispatching ambulances through its EMS system. GMRTT claims that before their services began, “The prior operation was unable to respond to nearly 25% of the requests for emergency ambulance service that came into their dispatch centre due to a lack of staffed and available ambulances. GMR immediately reduced, and then totally eliminated these "denied calls" based on a zero-tolerance performance standard put in practice by the Communication Centre in conjunction with field staff” ("GMR - Global Medical Response" 2017). They finish the claim by saying, “The process worked as planned and no Trinidadian has been denied an emergency ambulance response since GMR assumed operations” (“GMR - Global Medical Response”). This statement is however in direct contradiction to many reports of the company’s insufficiencies expressed in the parliament and the press, with accusations ranging from unbearably long wait times (Hassanali 2014) to the more serious denial of service (Asson 2012).
Assuming all citizens are equally served, there is still the looming question of if an ambulance, when requested, will arrive within the time necessary to potentially save a life. The issues identified to be affecting GMRTT’s ability to deliver more efficient service include the public’s common mistake of calling the wrong emergency number (Ali 2011), and the service provider’s apparent inability to provide the contractually obligated number of ambulances (Boodram 2015). Most of all, though, the greatest limiting factor appears to be the handover times at public hospitals. According to the CEO of GMRTT, “That is a procedure that should take at least 15 to 20 minutes. It takes over an hour and in some cases, it takes sometimes six hours. That delay is caused by the incapacity of the health facility...the staffing, the unavailability of beds and so on. When that starts to backlog, it impairs our ability to respond to the next case” ("CEO Of GMRTT Ambulance Service Says Issues Being Addressed To Improve Reliability of Their Service." 2015). This issue was confirmed by the Minister of 1
Health, Terrance Deyalsingh, stating “The problem with response times is this, it was carried in the media that I made a surprise visit to Mount Hope Accident and Emergency (A&E) and that is true” (Sing 2015).
Approaching this problem as an engineer will require the acceptance of existing limitations such as budget, traffic, and staff while proposing a solution or combination of solutions to either eliminate or lessen the impact of the issues within the system.
1.2 Rationale This project was created based on the fact that the time taken for an ambulance to respond to an emergency often influences the outcome of the incident (Minge 2013). The inefficiency of the national Emergency Medical Service not only affects the service provider and the Ministry of Health, but all stakeholders, which includes every person residing in Trinidad & Tobago. Whether sick or healthy, it is safe to assume that most individuals may require the services of the EMS at some point. It is therefore necessary to fully assess the Service’s efficiency through modelling the system, proposing and testing possible optimisation strategies, demonstrating evidence that improvements can be made, and communicating the significance of these improvements. This optimised model of the system will reduce ambulance response times while improving service reliability and effectiveness, potentially having real-world, life-saving implications.
To achieve the goal of optimising the EMS system, proposed strategies should address both the issues identified by the public and by the company. The strategies should be conscious of the system’s limitations and maximise the utilisation of existing resources. The optimised system should also not sacrifice the Service’s effectiveness for its efficiency or vice versa. The findings of this research study will hopefully be used to improve the EMS in Trinidad & Tobago, as well as in other countries.
1.3 Objectives 1. To investigate and describe the Emergency Medical Service provided by Global Medical Response of Trinidad & Tobago (GMRTT)
2. To design a model that can accurately represent the EMS system in Trinidad & Tobago
3. To propose changes necessary to improve the average wait time of the system by at least 20%
4. To demonstrate the improvements made on the system as a result of these changes
5. To evaluate the impact of these changes on the EMS system and the country at large
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1.4 Scope of Study • This study focuses on the Emergency Medical Service provided by Global Medical Response of Trinidad & Tobago.
• The entirety of GMRTT’s operations including dispatch, service on-scene and hospital handover will be investigated and described.
• The principles used in the modelling, simulation and optimisation of this system include ComputerAided Modelling, Computer-Aided Simulation, and Process Optimisation.
• The system is to be made specific to Trinidad & Tobago, and will be designed accordingly.
• The optimised model should be built within the limitations of the existing system, and have realistic, implementable strategies.
1.6 Limitations • Access to detailed information and operation data is limited due to GMRTT’s confidentiality policy.
• All data provided by GMRTT and its employees has to be considered skewed by nature — whether intentionally or unintentionally — which may have an effect on the accuracy of the system model.
• This study will be conducted over a period of six (6) months, which is enough time to make a relatively accurate analysis of the existing system, but not enough time to establish a comprehensive understanding of all the processes involved.
1.5 Outline of Study This project involves:
1. Revision of literature relevant to Emergency Medical Services and System Modelling
2. Investigation of the Emergency Medical Service currently provided in Trinidad & Tobago
3. Development of the existing EMS system model
4. Identification of possible optimisation strategies specific to the system
5. Analysis of the effects of optimisation strategies on the simulated system
6. Development of an optimised EMS system model
7. Comparison of the existing system with the optimised EMS model
8. Speculation on the implications of this optimised system
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2.0 Literature Review 2.1 Introduction to Emergency Medical Services 2.1.1 What is EMS? Emergency medical services refer to the out-of-hospital treatment and medical transport of people with illnesses or injuries that prevent them from transporting themselves ("What Is EMS?" 2017). Most emergency medical services aim to either provide on-site treatment to patients that require urgent medical care or facilitate the prompt transportation of patients from the site of the emergency to the closest point of definitive care. The term ‘Emergency Medical Service’ represents the shift from basic, transportation-focused ambulances systems to a system that offers some form of pre-hospital medical care, both given on scene and during transport.
In most countries, the EMS can be requested by any member of the public, as well as businesses, authorities or other emergency services, through a toll-free telephone number. This emergency number connects the caller to the control facility, which has the responsibility of dispatching the resources best suited to handle the situation (European Commission 2001). The emergency medical service may also have the responsibility of transporting patients between medical facilities in the case that a higher level or more specialised field of care is necessary. In addition, the service will transfer patients from the specialised facilities to local hospitals or nursing homes once the specialised services are no longer needed. In these instances, the EMS is requested by the relevant clinical professionals.
The training level and qualifications possessed by the employees of an emergency medical service can vary widely. In some systems, there are employees with no medical training who are only qualified to drive the ambulances, though most systems require their employees to have at least basic first aid certifications ("Town Of Colonie Emergency Medical Services Department" 2017). The EMS systems in many countries are also staffed with paramedics, nurses, and physicians, otherwise known as Advanced Life Support (ALS) personnel.
2.1.2 EMS Providers In some countries, the EMS industry is closely regulated, requiring all persons working on an ambulance to have a certain level of medical qualification, while in other countries, there may be a significant difference in the level of training between each type of operator.
4
Government Ambulance Service These ambulance services receive their funding from local, provincial or national governments, operating as a separate entity from the fire and police services, but still working with them to serve the public. While some countries only have these governmental services in larger cities, there are services in which practically all emergency ambulances are a part of the national health system ("NHS Ambulance Services - Emergency And Urgent Care - NHS Choices" 2017).
Private Ambulance Service This service provider refers to commercial companies that are contracted by the local or national government. Private EMS providers are sometimes only required to transport patients, but they may also be contracted to provide emergency care or operate as a secondary service when full-time emergency ambulance crews are occupied ("First Aid Services" 2017). In some cases, a private service may only be given the responsibility of attending to minor injuries such as cuts and bruises or assisting the disabled and elderly in situations where they do not need treatment. This system allows emergency crews to be available in case there are emergencies of a higher priority.
Hospital-based Service Some hospitals provide their own ambulance services in communities where ambulance care is unreliable or expensive. They may use ambulances that are specially equipped to transfer patients from one hospital to another, and often do not have many ambulances in their fleet. Both public and private hospitals, however, are allowed to use their discretion when deciding whether or not to provide this service outside of transfers ("Ambulance Service India, Emergency Medical Services" 2017).
Fire or Police Linked Service Ambulances are sometimes operated by local fire or police services, with fire-based EMS being the most common model, and police-based EMS being somewhat rare (International Association of Fire Chiefs 2010).
Volunteer Ambulance Service Charities and non-profit organisations may provide ambulance services for a particular area in the event of an emergency or when there is a need for patient transport. They may operate as medical support to certain events or assist in providing ambulances services to underserved communities. Organisations like The Red Cross, St John Ambulance, and the Order of Malta Ambulance Corps operate on a volunteer basis and sometimes as a Private Ambulance Service ("Ambulance Support | 5
British Red Cross" 2017). They are sometimes connected to a volunteer fire service, with volunteers working with both. The volunteer ambulances provide support to full-time ambulance services, and in some cases, the charity may employ paid staff alongside the volunteers to operate a full-time ambulance service ("Ambulance Service India, Emergency Medical Services" 2017).
Combined Emergency Service Places such as airports and large universities may sometimes have their own dedicated emergency medical services, with all employees often having medical, firefighter and peace officer training. This multi-functionality allows the service to make the most of the limited resources in these areas where size or budget does not justify the need for separate services, giving them the advantage of having a single team that can respond to any emergency ("Ambulance Service India, Emergency Medical Services" 2017).
Company Ambulance Large factories and other industrial centres may have on-site ambulance services to limit employer liability and guarantee the welfare of their staff. These services are used as first response vehicles in the case of a fire or explosion.
2.1.3 Principles of EMS The goal of any emergency medical services should be to preserve life, prevent further injury, and promote recovery, these are the basic principles of first aid. The “Star of Life” as shown below demonstrates this common theme in medicine ("Star Of Life DOT HS 808 721" 2017). Each of the arms represent the six stages of pre-hospital care, those being:
1. Early detection — identifying the incident and understanding the problem as quickly as possible
2. Early reporting — contacting the emergency medical services while providing the details necessary to enact a response
3. Early response — dispatching and arriving on scene in as little time as possible
4. On scene care — providing the most appropriate treatment to the patient while limiting further injury
5. Care in transit — transporting the patient by suitable means, while continuing to provide the most appropriate treatment during the journey
6. Transfer to definitive care — handing the patient over into more comprehensive care
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Figure 1: The Star of Life (Source: Waretown First Aid Squad 2017)
2.1.4 Levels of Care in EMS The levels of EMS care available can be classified into three main categories: Basic Life Support (BLS), Advanced Life Support (ALS), and Critical Care Transport (CCT).
Basic Life Support (BLS) First Responder This emergency worker provides immediate, life-saving care in the event of a medical emergency. All first responders are expected to have the bare minimum training, which includes advanced first aid, CPR, oxygen administration, and automated external defibrillator (AED) usage. First responders are typically dispatched by the emergency medical service to provide immediate assistance until an ambulance arrives, after which their job is to assist the ambulance crew ("First Responder" 2009).
Ambulance Driver Most services require at least two EMS employees to operate a licensed ambulance, separating the roles of ‘driver’ and ‘attendant’. In some cases, ambulance drivers may not be required to have any medical qualification, or just a first aid certificate, to be employed. These ambulance drivers are usually trained in ambulance operations, emergency response driving and radio communication ("Volunteer Member Categories" 2017).
Ambulance Care Assistant Ambulance Care Assistants have varying abilities depending on the country, with some only being given the responsibility of transporting patients, while others may have first aid training or other extended skills. They may also serve as cover in case of an emergency when other EMS units 7
unavailable, or when assisting a qualified paramedic or technician ("Ambulance Care Assistant And Patient Transport Service (PTS) Driver" 2017).
Emergency Medical Technicians Emergency medical technicians, also known as Ambulance Technicians, have a wide range emergency care skills, which include oxygen therapy, automated defibrillation, and spinal injury care. They are however not required to perform most advanced procedures (Department of Health, New York State 2008).
Emergency Medical Dispatcher An emergency medical dispatcher provides pre-arrival instructions to callers through the use of carefully structured questioning techniques, and a set of pre-defined instructions for the immediate treatment of critical issues. Because many medical emergencies change in seconds, the use of this system and its “zero response time” approach can positively impact the outcome of the situation.
Advanced Life Support (ALS) Paramedic Typical paramedics have a high level of pre-hospital medical training, as they can identify and treat any life-threatening conditions while assessing the patient for indications that may require further emergency treatment ("Paramedics" 2008).
Critical Care Paramedic This specialised paramedic transports critically ill or injured patients from the referring facility to a hospital that offers a higher level of care (The Board for Critical Care Transport Certification 2011).
Paramedic Practitioner/Emergency Care Practitioner An emergency care practitioner bridges the link between ambulance care, and the more definitive care of a general practitioner, through extra training that makes them practitioners in their own right. They usually have degrees in Emergency Medical Care or are qualified paramedics authorised to perform specialised techniques ("Emergency Care Practitioners Pgds And Medicines Protocol For SWASFT Staff" 2018).
Traditional Healthcare Professionals Registered Nurses 8
Registered nurses are involved in the emergency medical services of many countries, even being the primary healthcare worker in certain regions. They typically work under the direct supervision of a physician, but in certain cases, they can work independently ("EMS In The Netherlands: A Dutch Treat?" 2005).
Physician Physicians, as the leaders of medical retrieval teams, may assist with the transport of patients between hospitals to guarantee that the highest level of care is provided. While it is a rarity in some countries, there are models of care in which paramedics do not exist, and advanced life support is performed by physicians ("Notfall: Notkompetenz I" 2017).
2.1.5 Models of Care The varying philosophical approaches to EMS can generally be placed into two categories: one being physician-led and the other being led by pre-hospital staff.
The physician-led model of care, also known as the Franco-German model due to its geographical origin, is staffed by doctors who respond to all emergencies that require more than basic treatment. The physicians and nurses may provide medical support to multiple ambulances through the use of rapid response vehicles instead of ambulances, or they may travel in an ambulance along with a driver. In France, where paramedics are not used, physicians provide all medical interventions for the patient, while in Germany, where paramedics do exist, they are often not permitted to give advanced treatment unless in the case of life-threatening conditions, or in the presence of a designated physician ("Notfall: Notkompetenz I" 2017). Ambulances in the Franco-German model attempt to bring the emergency department to the patient by being better equipped with more advanced medical devices. This option of providing on-site medical care to the patients until they attain stability is preferred, as transport to hospitals at high speeds may prove to be unnecessarily unsafe.
The Anglo-American model on the other hand uses emergency medical technicians and paramedics, to staff ambulances, which may be given different rankings depending on the relative abilities of the ambulance staff. In this model, physicians are not typically apart of the pre-hospital staff. Physicians serve mostly as medical oversight for the work of the ambulance crews through off-line medical control methods, such as protocols for certain types of medical procedures, or on-line medical control, in which the technician receive direct orders for certain types of medical interventions. In certain systems, a paramedic may not require the permission of a physician to provide care. In these models, the patients may receive treatment equivalent to the skill level of the ambulance staff, after 9
which they are transported to a site that offers definitive care. Due to the relative difference in skill of the ambulance staff in this model, less care is given and as a result, it often has a shorter time onscene than in the Franco-German model (Al-Shaqsi 2010).
Figure 2: Comparison Between the Franco-German Model and Anglo-American Model (Source: Al-Shaqsi 2010)
2.1.6 Strategies in EMS In on-scene care, the most significant decision that has to be made is if the patient should be taken to the hospital, or if definitive care should be taken to the patient’s location.
The strategy used in North America for on-scene care is based on the theory that a patient is more likely to survive a traumatic incident if they are promptly taken to the operating room, also known as the “Golden Hour” theory. The this theory aims to bring the patient to the point of definitive care within 60 minutes of the traumatic incident. This theory is most reliable in cases of internal bleeding caused by stab or gunshot wounds. As a result, as little time as possible is spent on scene, and the patient is taken to the nearest emergency room as quickly as possible (National Academy of Sciences 1966).
The "Scoop and Run" method is another strategy in which the aim is to transport the patient within ten minutes of the ambulance arriving on-scene, resulting in the phrase "the platinum ten minutes" being commonly used in EMT training programs. This strategy was created to deal with traumatic incidents, as opposed to typical medical emergencies, but this may be changing. The time to 10
treatment has been established in recent studies as a significant factor in the outcome of heart attacks, implying that the use of the "Scoop and Run" method may be appropriate outside of traumatic incidents (Bogaty 2004).
2.1.7 EMS Standards Emergency response time has been used for many years as a major performance indicator when evaluating an EMS system’s performance, though an explicit relationship has not been established between reducing time to definitive care and clinically significant improvements in patient outcomes. The generally accepted knowledge, however, is that shorter time to definitive care is associated with improved outcomes in critically injured, stroke, and cardiac patients. Therefore, getting those types of patients to definitive care within the golden hour is very important. Although the outcome of emergencies depend on many other factors such as the severity of an injury or preexisting conditions, the time required for an EMS unit to arrive at the scene (response time) and the time required for a patient to receive definitive care (overall response time) play a significant role in patient outcome (Samra, Qin and He 2014).
2.1.8 EMS System Model The basic model of an EMS system contains five main stages. In the first stage of the system, an emergency call is made to the dispatch call centre. The dispatcher processes the emergency’s information and decides whether it warrants dispatch or not. If dispatch is warranted, it is then added to a queue of calls to wait until an ambulance becomes available to attend to the emergency. Once dispatched, the ambulance travels to the scene where it decides if service on-scene is necessary or not. Assuming service is given on-scene, the ambulance can then decide if the emergency requires further treatment at the hospital, and if so, begins to transport the patient. Once at the hospital, the ambulance can then deliver the patient, after which the staff asses if it needs to be restocked with any materials. If yes, the materials are replaced and the ambulance can travel to its base location and end that service. There are also certain instances where this process does not end in the delivery of a patient. In the case that the dispatcher does not think the emergency warrants dispatch, the service can be immediately ended. After arrival on-scene, the ambulance staff may decide that the emergency does not require service, after which they can either start a new service or travel back to their base location and end the current service. In the event that the patient does not require transportation to the hospital after being treated on-scene, the ambulance can be restocked and either a new service is started or the service can be ended (Pinto, Silva and Young 2015).
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Figure 3: Model of an EMS System (Source: Pinto, Silva and Young 2015)
2.1.8 Healthcare in Trinidad & Tobago Trinidad & Tobago operates under a two-tier healthcare system, utilising both private and public facilities. The Ministry of Health does not directly control the health facilities in Trinidad & Tobago, however, it plays an important role in ensuring that they are properly run by setting policies, goals and targets for Regions based on assessment of health needs, though it is shifting its focus to concentrate on policy development, planning, monitoring and evaluation, regulation, financing and research. The Ministry allocates resources to the Regional Health Authorities (RHA), acting as the main financier of the public health system. Citizens can access free health care at public healthcare facilities where health insurance is not required ("The Ministry Of Health - Trinidad And Tobago" 2017).
Public Healthcare is paid for by the Government and taxpayers and is therefore free to everyone in Trinidad & Tobago. However, the Government has been developing a National Health Service in which a package of specific services will be offered, although a financing strategy has not been finalised. There are several major hospitals throughout the country as well as smaller health centres and clinics located regionally throughout (Focus 2017).
The major hospitals in Trinidad & Tobago include:
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• Port of Spain General Hospital
• San Fernando General Hospital
• San Fernando Teaching Hospital
• Sangre Grande Hospital
• Point Fortin Hospital
• Eric Williams Medical Science Complex
• Scarborough General Hospital
These hospitals are assisted by many District Health Facilities located throughout the country.
The private healthcare sector in Trinidad & Tobago is relatively small compared to the public sector, though the quality of healthcare it offers is often much better. Private healthcare across the island is predictably very expensive, which is why it is mainly well paid citizens and expatriates who rely on this sector for their medical requirements.
It is possible for anyone to access ambulance services on the islands by dialling 811. The patient will be transported to the Accident and Emergency Department of the nearest national health facility for immediate medical attention.
Many citizens rely on District Health Facilities for a majority of their medical needs. These health facilities, located in Arima, Chaguanas, Couva, Mayaro and Princes Town, are open 24 hours a day and offer accident and emergency services, along with other types of general practices, such as:
• Ante-natal and post-natal services
• Child health services
• Dental services for children and adults (only at some facilities)
• Family planning services
• Health promotion fitness programs
• Pharmacy services
• Radiology (X-ray and ultrasound) services
• Specialist services for chronic and or lifestyle diseases
Most health centres are generally operational from 8:00 a.m. to 4:00 p.m., though some open until 6:00 p.m.. The main focus of these health centres is the prevention and cure of common diseases as well as the treatment of injuries (Focus 2017).
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2.2 Simulation 2.2.1 What is Simulation? Simulation can be defined as the imitation of the operation of a real-world process or system over time (Banks 2001). Simulating an entity first requires a model to be developed, with this model representing the key characteristics, behaviours and functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time.
Simulation is used in the scientific modelling of natural systems or human systems to gain insight into how they function ("Modelbenders - Encyclopedia" 2018). It can be used to show the eventual or immediate effects of alternative conditions and courses of action on the real system. simulation may be used when the real system cannot be engaged, because it may not be accessible, or it may be dangerous or unacceptable to engage, or it is being designed but not yet built, or it may simply not exist (Sokolowski and Banks 2009).
2.2.2 Simulation in Engineering Simulation is an important aspect of engineering systems as well as other any system that involves many processes. Most engineering simulations utilise mathematical modelling and computer-assisted investigation, though there are instances where mathematical modelling is not reliable. For example, the simulation of fluid dynamics problems often require both mathematical and physical simulations, as the physical models require dynamic similitude. Physical and chemical simulations are also used for direct, realistic purposes, as opposed to purely research purposes (Sokolowski and Banks 2009).
2.3 System Modelling 2.3.1 What is a System? A system is an organised, purposeful structure that consists of interrelated and interdependent elements such as components, entities, factors, members and parts. These elements continually influence each other, both directly and indirectly, to maintain their activity and the existence of the system in order to achieve the goal of the system ("What Is A System?" 2017).
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2.3.2 System Efficiency Efficiency is the quality of being able to do a task successfully, without wasting time or energy, hence a system’s efficiency can be measured in terms of time or resource usage ("Efficiency" 2017).
2.3.3 System Effectiveness Effectiveness is the degree to which something is successful in producing a desired result, therefore a system’s effectiveness can be judged by its ability to achieve its intended purpose ("Effectiveness" 2017).
2.3.4 Scientific Modelling Scientific modelling aims to make a particular part or feature of the world easier to understand, define, quantify, visualise, or simulate by referencing it to existing and usually commonly accepted knowledge. It requires the selection and identification of the relevant aspects of real world situations and use of different types of models for different aims. These include the use of conceptual models to better understand, operational models to operationalise, mathematical models to quantify, and graphical models to visualise the subject. Modelling is an essential and inseparable part of many scientific disciplines, each of which has its own preferences and ideas about specific types of modelling.
A scientific model seeks to represent empirical objects, phenomena, and physical processes in a logical and objective way. All models are simplified reflections of reality that, despite being only approximations, can be extremely useful (Box and Draper 1987). Complete and true representation may be impossible, but scientific debate often concerns which is the better model for a given task.
For a scientist, the model is also a way in which human thought processes can be amplified (Churchman 1983). Models that are created using software allow scientists to leverage computational power to simulate, visualise, manipulate and gain intuition about the entity, phenomenon, or process being represented.
2.4 Process Optimisation 2.4.1 What is Process Optimisation? Process optimisation involves the adjustment a processes to optimise some specified set of parameters without violating certain constraints. The most common goals of process optimisation are 15
to minimise cost and maximise throughput or efficiency. This is one of the major quantitative tools in industrial decision making. When optimising a process, the goal is to maximise one or more of the process specifications, while keeping all others within their constraints. This can be done by using a process mining tool, discovering the critical activities and bottlenecks, and acting specifically on these (Biegler 2010).
2.4.2 Areas of Process Optimisation There are generally three parameters that can be adjusted to affect optimal performance (Biegler 2010). They are:
Equipment Optimisation This parameter verifies that the existing equipment is being used to its fullest advantage by examining operating data to identify equipment bottlenecks.
Operating Procedures Although operating procedures may vary widely in different industries, this parameter is often improved through automation when these procedures create bottlenecks.
Control Optimisation In certain systems, there are several control loops responsible for controlling parts of the processes. If these control loops are not properly designed and tuned, the processes do not run optimally. This can make the system more expensive to operate, and cause equipment to wear prematurely. For the control loops to run efficiently, the quick identification of problems is important, therefore this parameter carries large significance.
2.5 Conclusion After conducting in-depth research on the areas of knowledge related to the emergency medical services in Trinidad & Tobago, a firm understanding of the concepts involved has been attained. The gaps in the literature reviewed are mostly limited to information concerning the EMS in Trinidad & Tobago, as well as methods of optimising the EMS system. The knowledge gained from this research will be used to guide the development of the system optimisation strategies and the final optimised model.
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3.0 Methodology 3.1 Methodology Steps I. System Research and Problem Definition
• Carry out extensive research on topics relevant to the project to create a comprehensive understanding of present literature
• Analyse and evaluate the existing EMS system
II. Simulation of EMS System
• Develop accurate model for entire system
• Simulate system model using Arena
• Verify system model simulation using Arena
• Validate system model with data provided
III. Optimisation Analysis
• Define system needs an metrics and establish the relative importance of each
• Develop several optimisation strategies which could be used to reduce inefficiencies and maximise system effectiveness
• Create new system models corresponding to each optimisation strategy
• Carry out extensive simulations of proposed system optimisation strategies, and compare the data produced to initial evaluation results
• Demonstrate improvements made on the system as a result of the optimisation strategies
• Speculate the public health implications of these system changes
• Discuss the significance of extra expenses and/or savings produced by the optimised system
• Suggest further methods of improving the system outside of optimisation
3.2 Justification of Methodology 3.2.1 System Research and Problem Definition
This first step is essential to the project as it clarifies the problem that needs to be solved, why it needs to be solved and who the solution will benefit. This requires extensive research to familiarise the information relevant to the issue, essentially putting the problem in context. Once an adequate understanding of the context is achieved, the existing conditions surrounding the system can then be analysed and evaluated.
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3.2.2 Simulation of EMS System
The next step of this project serves to evaluate the strategies proposed in the previous step. Before the strategies can be simulated, however, the system model must be developed to mimic the realworld model of the EMS system as closely as possible. The system simulation will be first created, then verified using a collection of techniques. Once the behaviour of the system is verified, the system will be validated using data provided, as well as through expert examination.
3.2.3 Optimisation Analysis
This final step begins with determining the system’s needs and metrics, after which evaluation can establish relative importance. From the information gathered, several strategies can then be proposed, with each strategy targeting one or many areas of inefficiency. Models corresponding to each proposed optimisation strategy will then be created. These optimised models will then be simulated extensively, with the results being carefully documented. Hybrid models will also be created to investigate the effect of combined strategies, this will be done using the product design and development process. Data collected from the simulations can then be used to compare the strategies to give an idea of the impact each change creates. The predicted impact of each strategy will then be broken down by identifying quantifiable improvements demonstrated through the system simulations. The real-world effects of these improvements on public health can then be speculated, giving an idea of how effectively the problems presented can be solved. The added expenses or savings of each strategy have to then be considered relative to the financial constraints of an EMS business when justifying their implementation. After the most appropriate strategies have been identified, it would be useful to explore the improvement of this system further through more exhaustive research, detailed simulation and through the testing of other optimisation strategies.
3.3 Methods of Research
• Collecting data on the company’s operations through a series of interviews (see Appendix, Interview 2)
• Collecting data on user experiences with the Service through interviews (see Appendix, Interview 3)
• Reviewing any information on the system provided by the company and its employees (see Appendix, Interview 1)
• Reviewing past literature on topics covered in this study
• Collecting relevant information on the hospitals’ relationship with the company
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3.4 Model Verification Techniques 3.4.1 Structured Walk-Through
The model will be analysed, step-by-step, to create a logical understanding of the system and its many elements. This will assist in discovering possible logic or procedural errors, while identifying elements that have not been fully considered, in order to create as accurate a representation of the intended model as possible (Zimmerman 2000).
3.4.2 Deterministic Verification
The presence of random variables can sometimes make it difficult to determine if a model is operating as expected or required (Zimmerman 2000). The delay and scheduling variables within the model will be replaced with constant, deterministic values to determine if the model is behaving as intended. It is only after the behaviour of the model is verified that random variables can be introduced.
3.5 Model Validation Techniques 3.5.1 Expert Intuition
The model will be examined by an individual or group of individual with an in depth knowledge of the system (Zimmerman 2000). The inspection will be done as a step by step process, with careful attention to model output and behaviour. If the model is not initially validated, it will be adjusted accordingly and resubmitted for validation. It is only after this technique is completed, that other methods of model validation will be utilised.
3.5.2 Real System Measurements
Comparison of a simulation with the system it is based on is often the most reliable method of model validation (Zimmerman 2000). In this instance, however, it is made particularly difficult by the company’s confidentiality policy, and as such, no numerical data will be directly provided for the research. However, more informal and potentially less reliable data has been provided through employees such as operators and EMT’s who interact directly with the system on a daily basis. This data will be compared with the assumptions, input values, output values, and workloads of the simulation to determine if the system has been accurately represented and produces realistic outputs.
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3.6 Model Refinement Process
A modified form of Ulrich & Eppinger’s product design and development process will be used from the generation of optimisation strategies and their respective models to the creation of the final concept (Ulrich and Eppinger 2012). The modified process will be as follows:
Figure 4: Modified Product Design & Development Process
3.6.1 Identify System Needs
The system’s needs have been gathered through research and stated in the form of issues in the problem statement, however, the relative importance of each issue will need to be established.
3.6.2 Establish System Metrics
The system’s metrics and requirements will need to be established in order to both guide the development of optimised models and gauge their relative effectiveness. This will be represented in the form of a list of metrics table.
3.6.3 Generate Optimised Models
Optimisation strategies created from the consideration of the system’s needs will be used to develop optimised versions of the original system model.
3.6.4 Model Testing & Refinement
Each optimised model will be tested and the output recorded in detail. The models may be narrowed down using a concept-screening matrix, after which they can be further refined through
the
adjustment of relevant variables and the combination of certain strategies.
3.6.5 Select Final Model
The final model will be selected through the use of a concept-scoring matrix, using the weighted sum or ratings to determine a model’s overall ranking. The model with the highest ranking will be chosen as the final optimised model.
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4.0 The Existing System 4.1 Dispatch Procedure
The dispatch procedure commences when an emergency call is made to the national ambulance service. The call is typically made through the designated 811 toll-free hotline, however, the elderly or those living in rural areas may call the police, fire, or Amalgamated Security Services (911) hotline because the correct number is not known off-hand. If the wrong hotline is called, the call is forwarded to the GMRTT dispatch centre, after which, information on the emergency is collected and processed. Once the relevant details including the location of the incident are collected, the emergency is classified into one of 6 possible ranks, those being: Bravo 1, Bravo 2, Bravo 3, Bravo 4, Bravo 5 and Bravo 6, in order of highest to lowest priority.
• Bravo 1: Very serious, life-threatening emergencies eg. cardiac or respiratory arrest
• Bravo 2: Serious, life-threatening emergencies eg. chest pains or strokes
• Bravo 3: Serious, possibly life-threatening emergencies eg. slow strokes
• Bravo 4: Possibly life-threatening emergencies eg. breathing issues
• Bravo 5: Not serious or life-threatening emergencies eg. animal bites
• Bravo 6: Minor injuries or illnesses eg. viral illnesses
Once ranked, the emergencies are placed in a queue, with higher rankings always receiving treatment before lower rankings, and in some cases rerouting ambulances already en route to an emergency to one with a higher ranking. When an emergency reaches the top of the queue, the dispatcher, using the system, identifies the closest available ambulance in the area. The emergency’s details are then communicated through the Mobile Data Terminal (MDT) system, as well as radio, phone calls, and instant messaging systems provided through cell phones equipped with military grade security. The ambulances are equipped with Global Positioning Systems (GPS) and directed using location data provided by the caller, however in the case that the caller was unable to provide the information, their location can be obtained from the call data.
Depending on the level of the emergency, an ambulance equipped with Advanced Life Support (ALS) capabilities may be dispatched, otherwise an ambulance with Basic Life Support (BLS) is dispatched. BLS ambulances are staffed with two Emergency Medical Technicians (EMT) with one being the driver of the vehicle, as in this system there is no dedicated ambulance driver. These ambulances are outfitted with oxygen equipment, airway management equipment, gauze to stop bleeding, and an Automated External Defibrillator (AED). ALS ambulances on the other hand are typically staffed by at 21
least one paramedic and one EMT, though they may sometimes be staffed by two paramedics. They contain more medicine than the BLS ambulances, and are outfitted with all the same equipment, with the exception of the Philips HeartStart MRx Defibrillator which is a more advanced alternative to the AED.
4.2 On-Site Procedure As soon as the ambulances arrives, emergency care is given on scene using the S.A.M.P.L.E. first aid procedure:
• Signs & Symptoms: anything observed by the patient or EMT/Paramedic
• Allergies: environmental allergies, allergies to medication
• Medications: identification of any medication being taken
• Past Medical History: any relevant medical information
• Last Oral Intake: last intake of food or drink
• Events Leading Up to the Illness or Injury: what happened in the time leading up to incident
In the case of wounds, the patient is treated with compression and bandages, otherwise, the patient’s blood pressure and vital signs are assessed. After this basic first aid, the patient is loaded into the ambulance and transported to the nearest or most appropriate hospital.
4.3 Hospital Handover & Re-Entry Procedure After collecting a patient, the ambulance staff communicates with the relevant hospital, indicating its approach. On arrival, the ambulance staff is met by nurses who assist in the offloading and transportation of the patient, as well as the collecting of important medical information and documentation. In some instances the patient may refuse treatment after arrival, in these cases, they are instructed to sign a document indicating that they ignored medical advice, after which the ambulance and hospital staff are relieved of any legal responsibility.
Once the patient is handed over, the data on the entire dispatch is recorded and utilised by the ambulance staff when submitting the daily report at the end of their shift. The ambulance is then cleaned and directed to a strategic location identified by the EMS system, after which it will wait for the next dispatch instruction. Ambulance locations are determined by a variety of factors including population density, historical demand, and geography. This ensures that there is always an ambulance within a certain time range of any location in Trinidad & Tobago.
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4.4 Major Delays The time taken to arrive at any location may be influenced by traffic, especially in the instance where motorists refused to clear a path for an ambulance in transit, something which is apparently common. This may be especially limiting in the event that a patient has to be transported to a specialised hospital located in a distant area. The ambulance staff may also experience delays when accessing a patient at the scene of an accident. Sometimes assistance may be required to get to the patient’s physical location, and other times, the patient may be in the midst of a dangerous confrontation where the ambulance staff has to wait until the police can eliminate the threat. Another delay arrises if a hospital emergency room is full, and hospital beds are unavailable or there are a limited amount of doctors attending to patients. This situation can significantly elongate the hospital handover time and prevent the ambulance from becoming available in a timely fashion.
4.5 Demand The EMS system may experience high demand based on location and time of day. The areas of high demand are:
• The area of East Port of Spain, which has the highest demand
• The suburb of Maraval in Port of Spain
• The town of Carenage • The town of Sangre Grande
• The city of San Fernando
• The town of Princes Town
• Along the Uriah Butler Highway
The peak times identified are typically 5 a.m. to 9 a.m., which is often when the loved ones of patients discover that they are in need of emergency care, as well as during the periods of 7 a.m. to 8 a.m. when motorists rushing to work may be involved in accidents. Demand may also stay high between 9 a.m. until around midday when patients notice that their blood pressure is high. Between 1 p.m. and 5 p.m. is identified as being the period of lowest demand.
4.6 Standards The EMS provided by GMRTT adheres to two main regulations. The first regulation concerns call waiting time for the emergency hotline, dictating that no caller should wait more than 1 minute before speaking to an operator. The second regulation concerns the time taken to arrive at the scene of an 23
emergency, referred to as ‘Shoot Time’. This time will vary due to the differences in distance for each emergency, but is used by the company as a means of judging the efficiency of the service in each instance. Any discrepancy with the shoot time is required to be fully explained in the daily report at the end of each shift.
4.7 Budget According to the Ministry of Health, GMRTT has been awarded a five year contract valued at $30.8 million per quarter or $92 million annually (Wayow 2016). These funds are mainly used to cover the costs of maintenance and operation of their ambulance fleet, the payment of emergency staff, and the payment of administrative staff. GMRTT also have the responsibility of purchasing new ambulances, and are contractually obligated to have a fleet of 74 ambulances at all times, though they have never met this requirement (La Rose 2015). It was implied that the bulk of the budget goes towards maintaining the fleet with another considerable portion assigned to the purchasing of new vehicles.
4.7 Conclusion In conclusion, GMRTT operates much like the typical EMS provider. Their dispatch procedure and employment of the Bravo system allows for the prioritisation of the most sensitive emergencies, and the use of the S.A.M.P.L.E. procedure on-site ensures consistent and comprehensive first aid. Emergency responses are often predictably delayed by traffic, though issues with patient accessibility and hospital capacity can also contribute to these delays. The service has its greatest demand in the morning, and emergencies are typically concentrated in areas of high population, as well as in areas occupied by the elderly. The company has a collection of important, though varying standards, and though it has been awarded the long-term contract for providing the emergency service in Trinidad & Tobago, it has yet to meet the contractually obligated number of ambulances present in its fleet.
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5.0 The System Model 5.1 Model of the Existing System
The system model was created using the Arena simulation software with the data inputted being provided by employees of GMRTT. Due to the company’s confidentiality policy, the data had to be collected through secondary means — that being, through a series of interviews with the relevant staff (see Appendix, Interviews 1-2). The data provided is therefore an estimation of the system’s properties rather than an exact measurement. This source of inaccuracy has been mitigated through the use of schedules, uniform and triangular distributions, and rough probabilities.
Schedules, primarily used for call generation in this model, define exactly how many entities are created within a certain period. The time between calls is split equally among the number of calls per hour and does not vary. This allows the model to imitate the time-based demand of the real system, ensuring that the increase in load during certain periods is properly simulated, giving more realistic results (see Appendix, Table 18).
In uniform distributions, all values between a defined maximum and minimum value have an equal probability of occurring. This means that certain variables in the model are indiscriminate, which is the ideal distribution when limited data is available. Triangular distributions on the other hand define the upper and lower limit of possible values, much like in the uniform distribution, but has a point of high probability density representing the most popular or most likely value. This is inherently more reliable than the uniform distribution, but it requires more information to be provided (see Appendix, Table 19).
Stating the likelihood of an incident occurring in the real world is naturally as difficult as predicting the future, thankfully these likelihoods can be defined through probabilities. Without years of numerical data being accumulated, analysed, and made easily accessible, calculating the probability of an event in this system would be unrealistic. It is for this reason that rough estimations will have to be made (see Appendix, Table 20).
Using these principles, the following model was developed:
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Figure 5: Model of the Existing EMS System Created in Arena
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5.2 Assumptions Several assumptions were made in the creation of this model to limit its complexity and account for the lack of information available. These assumptions are:
1. No more and no less calls other than the total number defined will occur within a 24 hour period.
2. The number of ambulances in the system remains constant over the span of one week.
3. If a telephone number other than the 811 hotline is incorrectly called, the caller will be forwarded to the correct dispatch centre shortly after.
4. If the correct telephone number is called, the caller will successfully reach an operator.
5. The call does not become disconnected at any time.
6. The caller will have to wait before reaching an operator.
7. There are no prank calls or calls that do not warrant dispatch.
8. Emergencies of higher priorities are always attended to before emergencies of lower priorities.
9. ALS ambulances are dispatched to serve lower priority level emergencies in addition to higher level emergencies.
10.The ambulance successfully delivers the patient to the hospital.
11.After an ambulance has delivered a patient, it will become available again.
5.3 Model Verification 5.3.1 Structured Walk-Through
The first method of model verification as mentioned before will be the structured walk-through. The model is explained in its logical steps below:
Figure 6: The Ambulances Enter the System
At the beginning of the simulation, exactly 34 ambulances are created and made available for use in the system, this coincides with the fact that there are always at least 34 ambulances on the road at any time (see Appendix, Interview 2). After the ambulances are introduced, they are assigned with an attribute marking them available and sent to await dispatch instructions in a queue. This process is assumed to happen with no time delay.
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Figure 7: Calls Are Generated
Calls are then generated at a frequency coinciding with the total number of calls scheduled to that specific hour. Whenever a call is made, a ‘patient’ entity is created, this entity is used in the tracking of ‘wait times’ and ‘transfer times’ within the system. If a call is made, it will contact the right number about 80% of the time, and will proceed without delay to wait anywhere between 15 seconds and 1 minute to speak to an operator. If a call is made to the wrong number, often the police or fire department, it will eventually be forwarded to the correct hotline where it will also wait to speak to an operator.
Figure 8: The Information is Processed and the Emergency Classified
When a call finally reaches the operator, a little over a minute is taken to collect the information related to the emergency. After all the information is collected, the dispatcher then classifies the emergency into one of six priority groups, the likelihood of each classification being defined by their different probabilities (see Appendix, Table 20). The emergency then joins a queue to wait until an ambulance becomes available. Depending on the emergency’s priority, it may move directly to the 28
front of the queue and will be the next emergency attended to. This queue is governed by a condition stating that it will release the emergency at the head of the queue if there is at least one ambulance in the queue waiting for dispatch.
Figure 9: Dispatch Signal is Sent
Assuming there is at least one ambulance waiting for dispatch, a signal is sent to the ambulance queue indicating that an ambulance should be released to attend to the next emergency. The next emergency in the emergency queue will then check if the same condition is met, and if so, will trigger the signal, repeating the process until there are no more ambulances available for dispatch.
Figure 10: Ambulance Enters Dispatched State
Once a signal is sent to the ambulance dispatch queue and an ambulance is released, it is assigned a new attribute marking it unavailable, and placed in a queue indicating its occupation.
Figure 10: Ambulance Travels to Scene, Treats Patient, Travels to Hospital
As the ambulance is dispatched, its makes its way to the scene of the incident where it provides limited on-scene treatment, after which it transports the patient to the hospital.
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Figure 11: Ambulance Hands Over Patient
When the ambulance arrives at the hospital, there is a delay in handing over the patient. This delay is followed by whatever cleaning and documentation needs to be done, after which the ambulance goes back on the road and becomes available for dispatch once more. As the ambulance becomes available, it sends a signal to the queue containing dispatched ambulances.
Figure 12: Ambulance Rejoins Dispatch Queue
Once the queue containing the occupied ambulances receives the appropriate signal, an ambulance is released. The ambulance is marked unavailable and sent to the ambulance dispatch queue to await instructions once more.
5.3.2 Deterministic Verification
For this method of verification, all variables within the model have been replaced with constant values. The absence of variables will allow the model to be tested for accurate and appropriate behaviour through simulation. Theoretical calculations concerning the operation and outputs of the model will be presented and consequently compared with the outputs produced by the simulation in Arena. It is only after both the simulated outputs and the theoretical calculations have been proven to be equal that the model can be considered verified with this technique.
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The constants used in the verification model are as follows:
• Make Call: 1 entity generated per hour, maximum of 150 entities generated
• Ambulance Enter System: 34 entities generated
• Call Successful: True = 0% probability
• Wrong Number: 30 seconds delay
• Call Waiting: 30 seconds delay
• Emergency Information Processed: 1 minute delay
• Time to Scene: 15 minutes delay
• Service on Scene: 10 minutes delay
• Time to Hospital: 10 minutes delay
• Hospital Handover: 20 minutes delay
• ReEntry Time: 5 minutes delay
In addition, the ‘Wait til Ambulance Available’ queue was set to ‘first in first out’ (no prioritisation), which ensures that the wait for an ambulance to become available for dispatch remains constant. Considering these changes, the theoretical calculation for the operation of the model are presented below.
Calculations: Wait Time1 = Wrong Number + Call Waiting + Emergency Information Processed + Time to Scene
Wait Time = 0.5 + 0.5 + 1 + 15
:.Wait Time = 17 min
Transfer Time2 = Service on Scene + Time to Hospital + Hospital Handover
Transfer Time = 10 + 10 + 20
:.Transfer Time = 40 min
Other Time3 = ReEntry Time
:.Other Time = 5 min
Total Time4 = Wait Time + Transfer Time + Other Time
1
‘Wait Time’ is the time taken between calling a number and the ambulance arriving on scene.
2
‘Transfer Time’ is the time taken between arriving on scene and handing over a patient to the hospital.
3
‘Other Time’ is any time that is not included in ‘Wait Time’ or ‘Transfer Time’.
4
‘Total Time’ is the time it takes for the ‘patient’ entity to exit the system.
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Total Time = 17 + 40 + 5
:.Total Time = 62 min
Assumptions: Ambulance (Number In) = 34
Ambulance (Number Out) = 0
Patient (Number In) = 150
Patient (Number Out) = 150
Results: The model was simulated for a total of 168 hours (7 days) and the output compared to the theoretical calculations.
Measured Times
Theoretical
Wait Time/min
Simulation
17
Average 17.0000
Minimum 17.0000
Maximum 17.0000
Transfer Time/min
40
40.0000
40.0000
40.0000
Other Time/min
5
5.0000
5.0000
5.0000
Total Time/min
62
62.0000
62.0000
62.0000
Table 1: Theoretical Calculations vs Arena Simulated Output of Measured Times
Entity Ambulances
Number In 34.00
Number Out 0.00
Patients
150.00
150.00
Table 2: Arena Simulated Output of Number of Entities In & Out
Conclusion: It is clear from comparison of the simulated and calculated values that all are exactly equal to the values that were theoretically calculated. The model is therefore verified in accordance with this technique.
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5.3 Model Validation 5.3.1 Expert Intuition
The model was carefully inspected and analysed by a dispatch operator and a paramedic who are both currently employed by GMRTT, after which it was deemed accurate. The model can therefore be considered validated through expert intuition.
5.3.2 Real System Measurements
In order to measure all aspects of the model, system measurements were obtained from two main sources. The first source of data was five individuals who had recently used the ambulance service and had made note of certain time intervals, this data was collected through interviews (see Appendix, Interview 3). The second source of data was a paramedic who had measured the time intervals of three emergency responses over the course of one day in cooperation with this project.
Limitations:
The first set of data can be assumed to contain some amount of error due to the lack of purposeful or reliable time measurement and the emotional nature of the situation, which can affect objective judgement. The second set of data can also be assumed to be relatively accurate, but may contain some amount of error due to the nature of the paramedic’s job and the resulting inability to measure the time with high accuracy.
Observations:
Wait Time/min Location Emergency Called Correct Number
Individual 1 Individual 2 Individual 3 Individual 4 Individual 5 Average 30 30 60 30 20 34.00 St. Anns Chaguanas Paramin St. Augustine Bethel Childbirth Chest Pains Sports Injury Fainting Injury yes
yes
no
no
yes
0.40
Table 3: The Time Taken Between Placing Call & the Arrival of an Ambulance
Time
Emergency 1 Time to Scene/min 19 Service on Scene/min 15 Time to Hospital/min 22 Hospital Handover/min 21 Re-Entry Time/min 7
Measurement Emergency 2 Emergency 3 20 27 11 14 19 14 22 15 5 5
Average 22 13.33 18.33 19.33 5.67
Table 4: Measured Time Taken for Time Intervals
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Results:
The following results were obtained from simulating the model for 168 hours (7 days).
Time Wait Time/min
Average
Minimum
Maximum
29.5981
6.9459
106.22
Table 5: Arena Simulated Wait Time
Time
Average
Minimum
Maximum
Time to Scene/min
Hospital Handover/min
16.6679 12.4999 19.8928 25.0430
5.2183 10.0092 10.0031 15.1297
29.4848 14.9991 29.9928 39.8083
Re-Entry Time/min
7.4738
5.0001
9.9990
Service on Scene/min Time to Hospital/min
Table 6: Arena Simulated Time Intervals
Conclusion: From analysis of the observed measurements with the output generated by simulating the model, a few inferences can be made. Firstly, the average ‘Wait Time’ of the simulated model was about 5 minutes faster than the real world averages, though it should be noted that all reading were within the maximum and minimum range of the simulation. Due to the small difference in averages considering the large range of possible values, the potentially inflated values given by each individual interviewed, and the higher probability of calling the wrong number than specified in the model, it can be assumed that the section of the model related to ‘Wait Time’ performs similarly to the real-world system. In addition, the average values of the simulated times in Table 6 were all very close to those of the measured times in Table 4 with the exception of the time to scene and hospital handover time. All measured times were also within the minimum and maximum ranges of the simulated model. It can therefore be concluded that the model performs closely to the real-world system overall and can be considered valid.
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6.0 System Model Optimisation 6.1 Identify System Needs
In order for an optimised model to be developed, the needs of the system have to first be identified and prioritised. The needs of all stakeholders including the Ministry of Health, GMRTT, and the country at large have been considered. The interpreted needs are therefore as follows:
Need No. 1
The EMS system
2
The EMS system
3
The EMS system
4
The EMS system
5 6
The EMS system The EMS system
7
The EMS system
Need
Assigned Importance Rating
has a short ‘Wait Time’ has a short time to definitive care is able to serve many patients per day has a relatively high quality of care is not expensive to operate is not expensive to maintain does not require a high initial investment
5 4 5 3 5 5 5
Table 7: The System Needs
Key - 5: “Very High Importance”, 4: “High Importance”, 3: “Important”, 2: “Low Importance”, 1: “Very Low Importance”
Being able to serve many patients per day and having a short ‘Wait Time’ are the two of most important needs, as the main objective of any emergency medical service is to serve anyone who needs assistance, and respond in as timely a manner as possible. The lack of these two needs has been especially evident in this particular system, as they are referenced multiple times in Section 1.1 and have been established as the basis of this research project. The time to definitive care, the cost of initial investment and the expense of operating and maintaining the system are also of relatively high importance. This is due to the fact that the time to definitive care often affects the outcome of a medical emergency (Samra, Qin and He 2014), and as such often determines the effectiveness of an EMS system. In addition, both the Ministry of Health and GMRTT are limited by budget constraints and have to approach the operation of the EMS as a business in order for it to remain viable. The quality of care, though the lowest in relative importance among the identified needs, is still of significance, and is therefore rated as ‘Important’. The quality of care given to a patient has to be considered at any level of the healthcare system, but though the EMS may provide this care up to a certain point, the most important treatment often occurs at dedicated facilities outside of this system.
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6.2 Establish System Metrics
Once the needs of the system have been identified, it is then necessary to establish metrics in order to judge if and how well an optimisation strategy improves upon the existing system. For a strategy to be considered, it should perform as well as or better than the existing system in the conceptscreening process.
Metric Need
Metric
Imp.
Units
3
Percentage of ‘patients’ out
5
%
2
1
3
1
Maximum ‘Wait Time’ Average ‘Wait Time’
5 5
min min
4
1
Minimum ‘Wait Time’
3
min
5
4
6
2
7
2
8
2
9
1
10
1
11
1
3 4 4 3 3 3 2
quality (rel.) min min min min min min
12
1,3
3
people
13
1,3
2
people
14
1,3
2
people
15
7
Quality of Care Maximum ‘Transfer Time’ Average ‘Transfer Time’ Minimum ‘Transfer Time’ Maximum wait until ambulance available Average wait until ambulance available Minimum wait until ambulance available Maximum number of people waiting until ambulance available Average number of people waiting until ambulance available Minimum number of people waiting until ambulance available Cost of implementation
5
cost (rel.)
16
5,6
Cost of operation
5
cost (rel.)
No.
No.
1
Table 8: The System Metrics
Key - 5: “Very High Importance”, 4: “High Importance”, 3: “Important”, 2: “Low Importance”, 1: “Very Low Importance”
The first metric of percentage ‘patients’ out refers to the patient entity (see Section 5.3.1) that is used to track the movement of the emergency through the system. The percentage of entities that manage to leave the system at the end of the 7 day simulation is indicative of how effective the EMS system is, as a smaller percentage of patients receiving definitive care within a given time period limits the 36
amount of individuals who can benefit from the system. This is an issue, as the main priority of any EMS system is to provide the service to all who need it.
The next three metrics of maximum, average, and minimum ‘Wait Time’ measure the time it takes between placing a call and the arrival of the ambulance. This metric was preferred over the time from completing the emergency call because it included other factors such as call success and call wait time, making it more comprehensive. A shorter average wait time indicates that the ambulance arrives much sooner for the average case, a difference that may be life saving, and a reduction in the maximum wait time limits potential danger of worse case scenarios. This metric can be used as a measure of the system’s efficiency, as it relates the time resource usage to the ‘arrival on scene’ goal.
The quality of care given by a particular model measures the level of professional oversight at every stage of the system, with the assumption being that a higher quality of care will limit complications during on scene treatment or while in transit. This measurement is relative and will range from ‘low’ and ‘medium’ to ‘high’.
The maximum, average and minimum ‘Transfer Time’ are metrics that specifically measure a patient’s time to definitive care. The importance of this time has been previously discussed, and it is just as important to the outcome of an emergency as the ‘Wait Time’.
The maximum, average and minimum wait until an ambulance becomes available is another measure of how efficiently the system is working. A large value implies that patients are having to wait longer for an ambulance to become available to attend to them. This metric is included in the calculation of ‘Wait Time’, but serves as an important sub-metric to identify the location of bottlenecks within the system. The maximum, average and minimum number of people waiting until an ambulance becomes available acts as a supporting metric to this, giving even more insight into the areas that may limit the system, and the extent of the effects.
The cost of implementation and cost of operation are the final set of metrics which are measured in the relative values of ‘low’ and ‘medium’ and ‘high’. The metric is relative due to the difficulty involved in accurately evaluating the costs involved with this particular system. These measurements are some of the most important, as they represent the real world consequences of a proposed system, balancing the perceived benefits or drawbacks of a model that may have been implied by previous metrics. It is important to note however, that this metric only considers the expenses of the EMS provider and therefore disregards any extra expenses directly associated with the operation of government-run hospitals and health centres.
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6.3 Optimisation Strategies
Now that both the needs of the system and the units by which it will be measured have been established, it is possible to propose strategies for its improvement. Each strategy will take into consideration all factors surrounding the system and present a plausible solution. These strategies will be modelled and simulated with Arena, and the outputted measurements will be compared with the existing system. The strategies that demonstrate significant improvement over the original system will then be further developed until an ideal, final model is created.
In developing the optimisation strategies, all areas of inefficiency previously identified were considered. These areas include: instances of the wrong number being called during emergencies (see Section 4.1), the unreasonably long hospitable handover times (see Section 1.1), the lack of available ambulances (see Section 1.1), and traffic (see Section 4.4). As a result, three strategies were proposed, each strategy attempting to minimise the identified inefficiencies in its own way. These strategies are as follows:
6.3.1 Strategy 1: Public Education This first strategy may prove effective as prior investigation has made it apparent that many individuals are not completely certain of how to get into contact with Trinidad & Tobago’s emergency medical services. As mentioned before (see Section 4.1), many users of this service do not know the telephone number for the emergency hotline, often calling the police or fire services. This issue is made worse by the lack of clarity in the information provided by the government online, as the information presented online by one official government website lists the fire service as the main provider of ambulance services ("Citizen" 2017), while the website for the country’s North West Regional Health Authority (NWRHA) also instructs callers to contact a number other than the 811 hotline whenever in need of an ambulance ("The North West Regional Health Authority" 2017). This is a major issue as there have been instances of citizens being forced to rely on ambulances provided by the fire services or nearby hospitals which are not obligated to operate outside of their typical responsibilities. This either results in service of much lower quality than required being provided, or the loss of critical time before receiving emergency care, both instances putting the sick and injured at risk. Better education of the public through campaigns that promote awareness and consistent information being presented online can provide further clarity and reduce the possibility of error in this first stage of the EMS system. This strategy directly addresses the first area of inefficiency identified, it will be simulated by assuming a 95% success rate when contacting the emergency services, as closer to 100% may be too difficult or expensive to attain in the short term.
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6.3.2 Strategy 2: On-Scene Treatment The Franco-German model of care in which physicians are a major part of the EMS system demonstrates the benefits of treatment on-scene (see Section 2.1.5). Time to definitive care in this system is much less due to the presence of fully qualified physicians, which results in fewer people being transported to hospitals, avoiding traffic delays altogether. In the case that transportation is necessary, this system may also allow the patient to bypass the emergency department altogether and be warded immediately. This would alleviate the present bottleneck in Trinidad & Tobago’s hospital system, as the typically long waiting time for handover has to do with the availability of beds in the emergency department. To model this particular strategy, it will be assumed that a much longer time will be spent on scene, as the location of the patient is to function as the hospital, and the ambulance as the makeshift emergency room. This model effectively brings the doctor directly to the patient, but comes at the expense of purchasing specialised equipment for multiple vehicles and while hiring qualified physicians alongside paramedics. This strategy addresses the second, third and fourth area of inefficiency identified, as it allows the system to often avoid both the hospital handover delay and traffic altogether, while likely making ambulances available at a much faster available due to the reduced transfer and handover time.
6.3.3 Strategy 3: Health Centres Many moderately equipped health centres and clinics have existed in Trinidad & Tobago for decades, being provided through the Regional Health Authorities to serve all citizens ("The Ministry Of Health" 2017). It has been possible to receive adequate treatment for less severe emergencies without the need to go to the hospital, often on a walk-in basis — though the same level of service that would be expected in a hospital may not be offered. These centres can be used as another means of lightening the load on the emergency department of hospitals by facilitating the administration of sufficient treatment to minor cases without the need of transportation to a major hospital. This would allow the ambulances to re-enter the available state at a much faster rate, increasing the overall efficiency of the system. Though only a handful of district health facilities operate on a 24-hour basis, increased funding from the Ministry of Health would allow certain health centres to operate around the clock, thereby becoming a realistic alternative to hospital treatment. In fact, health centres have already been used for auxiliary support as a means of lightening the load on overcrowded hospitals (Webb 2018). The idea of providing 24/7 access to a health centre has been presented to the Ministry of Health in the past, but it was not considered as it served too few people to be cost effective (Sookraj 2012). This strategy, however, serves the entire country and will likely improve the lives of many. The strategy of health centres mainly targets the second area of inefficiency by allowing ambulances to take patients to facilities with much shorter handover times, and likely allowing them to become available much faster, thus also improving on the third area of inefficiency.
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6.4 Optimised Models
The system model corresponding to each strategy has been developed from the original unoptimised model with some key changes.
6.4.1 Strategy 1: Public Education
Figure 13: Model of Strategy 1 Created in Arena
This first model is identical in structure to the original model, however, the value for the decide module ‘Call Successful’ has been changed, making the probability of truth equal to 95% (see Appendix, Table 21) to appropriately reflect this particular optimisation strategy.
6.4.2 Strategy 2: On-Scene Treatment
Figure 14: Model of Strategy 2 Created in Arena
40
The second model has physical differences in its structure as well as differences in its operation in order to represent the strategy of on-scene treatment. The first change, which cannot be seen, is in the lengthening of the value for the ‘Service on Scene’ module to a minimum of 20 minutes and a maximum of 40 minutes (see Appendix, Table 22) in accordance with the Franco-German model of care. This model aims to provide patients with emergency care equivalent to that which would have been given in an emergency room and therefore requires considerably more time on scene (see Section 2.1.5).
Figure 15: Ambulance Offers On-Scene Treatment
After the patient receives care, a decide module is used to determine if the patient still needs to go to the hospital. If the patient is a ‘Bravo 4’ emergency or above, the answer is always yes, otherwise, the ambulance has completed its job and is able to rejoin the queue to wait for dispatch. A patient below ‘Bravo 4’ that is treated in this system can be assumed to be effectively treated, and if any other treatment is necessary, can be safely transported to a medical centre by other means. Once the patient is transported to the hospital, another decide module is used to determine if it is still necessary for the patient to be handed over to the emergency room or if they can be admitted directly to the hospital ward (see Figure 2). If the emergency has been classified as a ‘Bravo 6’, it is assumed that the patient is in critical condition and will likely require further emergency care, otherwise the patient will bypass the emergency room. Admitting a patient to the hospital ward can be assumed to require less handover time than admitting them to the emergency room due to the relative competition for ER hospital beds, therefore the value for handover time is less (see Appendix, Table 22).
41
6.4.3 Strategy 3: Health Centres
Figure 16: Model of Strategy 3 Created in Arena
This model is also structurally different to the original system model, though it shares some similarities with the model of Strategy 2.
Figure 17: Ambulance Takes Patient to Health Centre
The main difference in this model occurs after the patient has been collected. In this strategy, there is the option of taking the patient to a Health Centre instead of directly to the hospital depending on the severity of the emergency. The decide module will send the ambulance to the hospital if the patient is classified as a ‘Bravo 4’ or above, otherwise, the patient can be treated at a health centre. The key difference between a health centre and a hospital is that there are several times more possible locations densely populated throughout the twin islands (see Appendix, Figure 25). This would result in a shorter average travel time to the next available centre, as well as a shorter handover time considering the difference in demand relative to a hospital emergency room. This assumption has therefore been included in the model (see Appendix, Table 23).
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6.4.4 Strategy 4: Artificial Delay
Figure 18: Model of the Artificial Delay Strategy Created in Arena
During the testing and simulation of the previous strategies, it became apparent that artificial delays introduced into the system in the form of longer wait times produced notable improvements. In particular, it was noted that increasing the minimum and maximum time taken to collect and process information from a caller by 30 seconds reduced the overall time to definitive care. This can be translated to the real world by instructing operators to collect more information from callers or provide more information relevant to the emergency. As such, the call would likely not last long enough to become an annoyance or hindrance to the situation, while creating a small delay that appears to improve the efficiency of this system (see Appendix, Table 24).
6.5 Performance of Optimised Models
Each model was simulated using the Arena software, and the outputted values compared to benchmarks set by the original model. All models scored similarly for metric 1, with the leader only improving on the original system by 0.12%. There was a surprisingly drastic reduction in time for metric 2, however, as Strategy 2 managed to reduce the maximum wait time by almost 70 minutes, while Strategies 1 and 4 both saw a negative increase to this ‘very important’ metric. All strategies managed to decrease their average wait times, with Strategy 2 making the most improvements once more. From these initial simulations, it becomes apparent that Strategies 1 and 4 fail to equal or improve upon the original model in maximum ‘Wait Time’, one of the most important metrics. In addition, their improvement on the other four most important metrics were minimal or none at all. These relationships have been represented below in the form of a concept-screening matrix.
43
Existing Strategy Strategy Strategy Strategy System 1 Values 2 Values 3 Values 4 Values Values
Metric No.
Metric
Imp.
Units
1
Percentage of ‘patients’ out
5
%
99.36
99.18
99.48
99.31
99.22
2
Maximum ‘Wait Time’
5
min
106.22
109.44
39.3159
70.5748
121.89
3
Average ‘Wait Time’
5
min
29.5981
26.5086
18.9705
21.3593
27.9638
4
Minimum ‘Wait Time’
3
min
6.9459
6.9509
7.3536
6.9834
7.7705
5
Quality of Care
3
quality (rel.)
medium
medium
high
medium
medium
6
Maximum ‘Transfer Time’ Average ‘Transfer Time’ Minimum ‘Transfer Time’
4
min
81.4330
82.3256
96.8615
78.1647
80.9594
4
min
57.4424
57.5507
42.2807
42.4521
57.7036
3
min
37.5455
37.6033
25.0036
26.2334
36.8849
7 8 9
Maximum wait until ambulance available
3
min
86.4235
86.3589
14.3834
46.5033
101.12
10
Average wait until ambulance available
3
min
10.8965
8.0421
0.2305
2.5728
8.8075
11
Minimum wait until ambulance available
2
min
0.00
0.00
0.00
0.00
0.00
12
Maximum number of people waiting until ambulance available
3
people
37.0000
35.0000
9.0000
22.0000
43.0000
13
Average number of people waiting until ambulance available
2
people
2.8712
2.1549
0.0613
0.7029
2.3565
14
Minimum number of people waiting until ambulance available
2
people
0.00
0.00
0.00
0.00
0.00
15
Cost of implementation
5
low
medium
high
low
low
16
Cost of operation
5
low
low
medium
low
low
cost (rel.) cost (rel.)
Table 9: The Existing Model Vs Optimised Models
Key - 5: “Very High Importance”, 4: “High Importance”, 3: “Important”, 2: “Low Importance”, 1: “Very Low Importance”
44
Metric No. 1 2 3 4 5 6
Existing System
Metric
Percentage of ‘patients’ out Maximum ‘Wait Time’ Average ‘Wait Time’ Minimum ‘Wait Time’ Quality of Care Maximum ‘Transfer Time’
Strategy 1 Strategy 2 Strategy 3 Strategy 4
0
-
+
-
-
0 0 0 0
+ 0
+ + +
+ + 0
+ 0
0
-
-
+
+
7
Average ‘Transfer Time’
0
-
+
+
-
8
Minimum ‘Transfer Time’
0
-
+
+
+
0
+
+
+
-
0
+
+
+
+
0
0
0
0
0
0
+
+
+
-
0
+
+
+
+
0
0
0
0
0
0 0 0 16 0 0 3
0 5 4 7 -2 5 no
10 2 4 6 1 yes
9 3 4 5 2 yes
0 0 5 5 6 -1 4 no
9 10 11 12
13
14 15 16
Maximum wait until ambulance available Average wait until ambulance available Minimum wait until ambulance available Maximum number of people waiting until ambulance available Average number of people waiting until ambulance available Minimum number of people waiting until ambulance available Cost of implementation Cost of operation Sum of +’s Sum of 0’s Sum of -’s Net Score Rank Continue?
Table 10: Concept-Screening Matrix for Optimised Models
Key - + : “Better Than”, 0 : “Same As”, - : “Worse Than”
Both Strategies 1 and 4 have produced net scores lower than the existing system and will consequently be excluded from further consideration. The remaining strategies will be further improved upon until an optimal model for this system is created.
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6.6 Model Testing & Refinement
It was determined that in order to improve upon the two remaining strategies while capitalising on the observed benefits of Strategies 1 and 4, hybrid models incorporating the qualities of the eliminated models should be tested for viability. Combining Strategies 2 and 3 was not considered due to the elements of Strategy 2 that make Strategy 3 redundant.
6.6.1 Strategy 2 Model Development
Figure 18: Strategy 2 Hybrid Model Created in Arena
Strategy 2 was first combined with Strategy 1 by increasing the call success probability to 95%. It was then combined with Strategy 4 by lengthening the upper and lower limits of information process time by 30 seconds. A final hybrid model was created by combining Strategy 2 with both Strategies 1 and 4. The results of simulating these hybrid models are shown below.
Metric No.
Metric
2 3 4
Percentage of ‘patients’ out Maximum ‘Wait Time’ Average ‘Wait Time’ Minimum ‘Wait Time’
5
Quality of Care
1
6 7
Maximum ‘Transfer Time’ Average ‘Transfer Time’
Units
Strategy 2 Strategy Values 2+1 Values
Strategy 2+4 Values
Strategy 2+1+4 Values
%
99.48
99.58
99.32
99.42
min min min quality (rel.)
39.3159 18.9705 7.3536
43.4003 19.0782 6.7370
44.7907 19.6198 7.6537
37.7976 19.5361 7.8448
high
high
high
high
min
96.8615
99.39
99.11
98.0560
min
42.2807
41.8127
42.4546
42.1098
46
8 9 10 11 12
13
14
Minimum ‘Transfer Time’ Maximum wait until ambulance available Average wait until ambulance available Minimum wait until ambulance available Maximum number of people waiting until ambulance available Average number of people waiting until ambulance available Minimum number of people waiting until ambulance available
15
Cost of implementation
16
Cost of operation
min
25.0036
25.0067
25.0279
25.0020
min
14.3834
18.6187
19.0753
13.1853
min
0.2305
0.4961
0.4403
0.4465
min
0.00
0.00
0.00
0.00
people
9.0000
13.0000
11.0000
8.0000
people
0.0613
0.1292
0.1162
0.1231
people
0.00
0.00
0.00
0.00
high
high
high
high
medium
medium
medium
medium
cost (rel.) cost (rel.)
Table 11: The Strategy 2 Model Vs Hybrid Models
In order to determine the overall best performing model of the four, a concept-scoring matrix was created. All metrics are included with their relevant weightings, with the exception of the metrics that remained the same for all four models. Ratings are determined by using the model’s numerical rank for each metric, starting from 4 as the highest rank to 1 as the lowest.
Metric No.
Weight
1 5 2 5 3 5 4 3 6 4 7 4 8 3 9 3 10 3 12 3 13 2 Total Score Rank
Strategy 2 Values
Rating 3 3 4 3 4 2 3 3 4 3 4 130 1
Wt. 15 15 20 9 16 8 9 9 12 9 8
Strategy 2+1 Values
Rating 4 2 3 4 1 4 2 2 1 1 1
Wt. 20 10 15 12 4 16 6 6 3 3 2 97 3
Strategy 2+4 Values
Rating 1 1 1 2 2 1 1 1 3 2 3
Wt. 5 5 5 6 8 4 3 3 9 6 6 60 4
Strategy 2+1+4 Values
Rating 2 4 2 1 3 3 4 4 2 4 2 113 2
Wt. 10 20 10 3 12 12 12 12 6 12 4
Table 12: Concept-Scoring Matrix for Strategy 2 Hybrid Models
47
As can be seen in Table 11, The original Strategy 2 model has proven to be the most ideal when compared to its hybrids models. The original model has the shortest average ‘wait time’, shortest maximum ‘transfer time’, shortest average wait until ambulance becomes available, and the least average number of people waiting in the queue for an ambulance, all while being a close second for many of the other metrics.
6.6.2 Strategy 3 Model Development
Figure 19: Strategy 3 Hybrid Model Created in Arena
Strategy 3 was also combined with Strategy 1, increasing the call success probability to 95%, and with Strategy 4, lengthening the upper and lower limits of information process time by 30 seconds. A final hybrid model was created by combining Strategy 3 with both Strategies 1 and 4. The results of simulating these hybrid models are shown below.
Metric No.
Metric
2 3 4
Percentage of ‘patients’ out Maximum ‘Wait Time’ Average ‘Wait Time’ Minimum ‘Wait Time’
5
Quality of Care
1
6 7 8
Maximum ‘Transfer Time’ Average ‘Transfer Time’ Minimum ‘Transfer Time’
Units
Strategy 3 Strategy Values 3+1 Values
Strategy 3+4 Values
Strategy 3+1+4 Values
%
99.31
99.07
99.48
99.46
min min min quality (rel.)
70.5748 21.3593 6.9834
49.6599 19.3276 6.9459
55.6414 19.6893 6.7749
63.0206 21.1618 7.3829
medium
medium
medium
medium
min
78.1647
80.7075
77.4696
83.4840
min
42.4521
42.2459
38.7216
38.7772
min
26.2334
26.8568
21.2613
21.0016 48
Maximum wait until ambulance available Average wait until ambulance available Minimum wait until ambulance available Maximum number of people waiting until ambulance available Average number of people waiting until ambulance available Minimum number of people waiting until ambulance available
9 10 11 12
13
14 15
Cost of implementation
16
Cost of operation
min
46.5033
24.0455
28.8483
36.2580
min
2.5728
0.7881
1.0596
1.9080
min
0.00
0.00
0.00
0.00
people
22.0000
13.0000
19.0000
22.0000
people
0.7029
0.2108
0.2816
0.5243
people
0.00
0.00
0.00
0.00
low
medium
low
low
low
low
low
low
cost (rel.) cost (rel.)
Table 13: The Strategy 3 Model Vs Hybrid Models
A concept-scoring matrix was once more created to rank these models. All metrics are included with their relevant weightings, with the exception of the metrics that remained the same for all four models. Ratings are determined by using the model’s numerical rank for each metric, starting from 4 as the highest rank to 1 as the lowest.
Metric No.
Weight
5 5 5 3 4 4 3 3 3 3 2 5
1 2 3 4 6 7 8 9 10 12 13 15
Total Score Rank
Strategy 3 Values
Rating 2 1 1 2 3 1 2 1 1 1 1 2 69 4
Wt. 10 5 5 6 12 4 6 3 3 3 2 10
Strategy 3+1 Values
Rating 1 4 4 3 2 2 1 4 4 4 4 1 122 2
Wt. 5 20 20 9 8 8 3 12 12 12 8 5
Strategy 3+4 Values
Rating 4 3 3 4 4 4 3 3 3 3 3 2 146 1
Wt. 20 15 15 12 16 16 9 9 9 9 6 10
Strategy 3+1+4 Values
Rating 3 2 2 1 1 3 4 2 2 1 2 2
Wt. 15 10 10 3 4 12 12 6 6 3 4 10 95 3
Table 14: Concept-Scoring Matrix for Strategy 3 Hybrid Models
49
Table 13 demonstrates that the combination of Strategy 3 with Strategy 4 has proven to be overwhelmingly beneficial. Though the Strategy 3 and 1 hybrid appears to dominate many of the metrics, it was the importance of each metric that saw the winning strategy accumulate the highest score in the concept-scoring matrix. The Strategy 3 and 4 hybrid has the highest percentage of patients served, shortest minimum ‘wait time’, maximum ‘transfer time’, and average transfer time, while producing the second best values in every other metric.
6.7 Final Model Selection
Now that each model has been fully developed, the final model can be selected. This selection will be done using the concept-scoring method demonstrated in the previous section. The model with the highest score will be accepted as the final model.
Strategy 2 Values
Strategy 3+4 Values
% min min min quality (rel.) min min min min min min
99.48 39.3159 18.9705 7.3536
99.48 55.6414 19.6893 6.7749
high
medium
96.8615 42.2807 25.0036 14.3834 0.2305 0.00
77.4696 38.7216 21.2613 28.8483 1.0596 0.00
people
9.0000
19.0000
people
0.0613
0.2816
people
0.00
0.00
high
low
medium
low
Metric No.
Metric
Units
1 2 3 4
Percentage of ‘patients’ out Maximum ‘Wait Time’ Average ‘Wait Time’ Minimum ‘Wait Time’
5
Quality of Care
6 7 8 9
Maximum ‘Transfer Time’ Average ‘Transfer Time’ Minimum ‘Transfer Time’ Maximum wait until ambulance available Average wait until ambulance available Minimum wait until ambulance available Maximum number of people waiting until ambulance available Average number of people waiting until ambulance available Minimum number of people waiting until ambulance available
10 11 12 13 14 15
Cost of implementation
16
Cost of operation
cost (rel.) cost (rel.)
Table 15: The Strategy 3 Model Vs Hybrid Models
All metrics are included with their relevant weightings, with the exception of the metrics that remained the same for both models. Ratings are determined by using the model’s numerical rank for each metric, starting from 2 as the highest rank to 1 as the lowest.
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Strategy 2 Values Metric No.
Weight
2 3 4 5 6 7 8 9 10 12 13 15 16
5 5 3 3 4 4 3 3 3 3 2 5 5 Total Score
Weighted Score 10 10 3 6 4 4 3 6 6 6 4 5 5
Rating 2 2 1 2 1 1 1 2 2 2 2 1 1
Strategy 3+4 Values
Weighted Score 5 5 6 3 8 8 6 3 3 3 2 10 10
Rating 1 1 2 1 2 2 2 1 1 1 1 2 2
72
72
Table 16: Concept-Scoring Matrix for the Final Models
This method has unwittingly produced equal scores for the two models, implying that a different method of selection is necessary. Using real-world reasoning, it may be possible to discern the most appropriate and realistic model to proceed with. The difficulties faced by the providers of emergency medical services are mainly related to budget constraints, the lack of doctors and beds in emergency rooms, and delays encountered when transporting patients to the hospital.
The Strategy 3 and 4 hybrid addresses the first issue much more effectively than Strategy 2, as it is significantly cheaper to implement and operate for GMRTT. In the case of Strategy 2, GMRTT would have the responsibility of purchasing and maintaining specialised equipment for their ambulances, while hiring or facilitating doctors on-board their ambulances and restocking ambulances with more plentiful and costly supplies on a regular basis — all responsibilities which carry significant short-term and long-term expenses. ‘Strategy 3+4’ on the other hand would be utilising already existing resources in the form of currently operating health centres, while not requiring the purchase and maintenance of extra equipment or the hiring of new employees (at least on the part of GMRTT). Implementing Strategy 2 requires either the training of specialised paramedics or the hiring of doctors for each ambulance, a requirement that puts further strain on the second difficulty faced by the providers of EMS. Though it alleviates the third difficulty by not having to transport most patients to the hospital, in the case of human resource reallocation, having less doctors available in the hospitals could be just as damaging, while hiring extra doctors may be unrealistic for both the RHA and GMRTT. Strategy 2 also opens GMRTT up to much more liability, as they will have to assume responsibility for all patients treated on scene. It is for these reasons that ‘Strategy 3+4’ will be the final model chosen, and will be referred to as ‘the optimised system’ going forward.
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7.0 The Optimised System 7.1 Description of the Optimised System The optimised system model developed in chapter 6 has been shown to be the most appropriate of all the potential models proposed. The system begins much like the original system described in Section 5.3.1 and operates identically up to the point that the emergency information is being reported. An artificially created delay is implemented in the system, extending the original time taken to record and process information by 30 seconds, with a minimum of 1.5 minutes and a maximum of 2 minutes (see Appendix, Table 25). The emergency is then classified and placed in a queue to wait until an ambulance becomes available, much like the original system. The ambulance makes its way to the scene of the emergency and gives basic treatment on-scene, after which it begins transporting the patient to the nearest point of definitive care. In this system, the patient may be taken to the hospital or to a health centre depending on the classification of the emergency. The patient is then handed over, and the ambulance is allowed to re-enter the available state.
7.2 Optimised System Performance Analysis
The differences in structure and operation between the existing system and optimised system are the source of the improvements made on the EMS system, the extent of these improvements is shown in the table below.
Metric No.
Metric
Units
Existing System Values
1
Percentage of ‘patients’ out
%
99.36
99.48
0.12%
2
Maximum ‘Wait Time’ Average ‘Wait Time’ Minimum ‘Wait Time’ Quality of Care Maximum ‘Transfer Time’ Average ‘Transfer Time’ Minimum ‘Transfer Time’ Maximum wait until ambulance available Average wait until ambulance available
min min min quality (rel.) min min min
106.22 29.5981 6.9459 medium 81.4330 57.4424 37.5455
55.6414 19.6893 6.7749 medium 77.4696 38.7216 21.2613
47.62% 33.48% 2.46% 4.87% 32.59% 43.37%
min
86.4235
28.8483
66.62%
min
10.8965
1.0596
90.28%
3 4 5 6 7 8 9 10
Optimised System Values
Percent Change
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11 12
13
14 15 16
Minimum wait until ambulance available Maximum number of people waiting until ambulance available Average number of people waiting until ambulance available Minimum number of people waiting until ambulance available Cost of implementation Cost of operation
min
0.00
0.00
0.00%
people
37.0000
19.0000
48.65%
people
2.8712
0.2816
90.19%
people
0.00
0.00
0.00%
cost (rel.) cost (rel.)
low low
low low
-
Table 17: The Existing System Model Vs The Optimised System Model
Each metric was plotted on a bar chart comparing the readings outputted from simulating the existing system and the optimised system in Arena. Metrics in which the values remained the same were not plotted due to redundancy.
7.2.1 Percentage of ‘patients’ Out
Existing System
99.36%
99.48%
Optimised System
99.34%
99.38%
99.42%
99.46%
99.5%
Percentage of ‘patients’ Out Figure 20: Percent of ‘patients’ Out for Existing System Vs Optimised System
Figure 20 illustrates the difference in the percentage of patient entities that leave the system when simulated for 168 hours. This particular metric is representative of the system’s effectiveness as defined in Section 2.3.3. It can be seen that the utilisation of artificial delays and health centres results in a significant increase in the number of patients that receive definitive care within a certain time period.
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7.2.2 Maximum, Average & Minimum ‘Wait Time’
Maximum
Average
Minimum
106.22
Existing System
55.64
Optimised System Existing System Optimised System Existing System Optimised System
29.60 19.69 6.95 6.77 20
40
60
80
100
120
‘Wait Time’ (minutes) Figure 21: Maximum, Average & Minimum ‘Wait Time’ for Existing System Vs Optimised System
Figure 21 illustrates the differences in the maximum, average and minimum ‘Wait Times’ for both models when simulated for 168 hours. It can be seen that the optimised system was able to drastically reduce the maximum ‘Wait Time’, while also reducing the average ‘Wait Time’ by more than a third of the original value. From the equation for ‘Total Time’ in Section 5.3.2, it can be inferred that as the ‘Wait Time’ decreases, the the overall time a patient spends in the EMS system reduces, allowing more patients to receive definitive care within the period, and hence increasing the percentage of ‘patient’ entities that leave the system.
7.2.3 Maximum, Average & Minimum ‘Transfer Time’
Maximum
Average
Minimum
81.43 77.47
Existing System Optimised System Existing System Optimised System Existing System Optimised System
21.26 15
38.72 37.55 30
57.44
45
60
75
90
‘Transfer Time’ (minutes) Figure 22: Maximum, Average & Minimum ‘Transfer Time’ for Existing System Vs Optimised System
Figure 22 illustrates the differences in the maximum, average and minimum ‘Transfer Times’ for both models when simulated for 168 hours. It can be seen that the optimised system only produced a marginal improvement in the maximum value, however both the average and minimal values were 54
shortened significantly. This is likely due to the shorter distance needed to travel to health centres as opposed to hospitals in this system. ‘Transfer Time’ also contributes to the total time patients spend in the EMS system, implying that a reduction in this time also increases the percentage of ‘patient’ entities that leave the system.
7.2.4 Maximum & Average Wait Until Ambulance Available
86.42
Existing System Maximum
28.85
Optimised System Existing System Average Optimised System
10.90 1.06 15
30
45
60
75
90
Wait Until Ambulance Available (minutes) Figure 23: Maximum & Average Wait Until Ambulance Available
Figure 23 illustrates the differences in the maximum and average wait until an ambulance becomes available for both models when simulated for 168 hours. It can be seen that the optimised system has reduced both the maximum and average wait time, and in the case of the average time, reduces the time by more than 90%. The wait until an ambulance becomes available is one of the metrics used to calculate ‘Wait Time’, therefore an reduction in this metric further improves a patient’s ‘Wait Time’ and by extension, the ‘Total Time’ and percentage of ‘patients’ out.
7.2.5 Maximum & Average Number of People Waiting Until Ambulance Available
37.00
Existing System Maximum
19.00
Optimised System Existing System Average Optimised System
2.87 0.28 6.667
13.333
20
26.667
33.333
40
Number Of People Waiting Until Ambulance Available Figure 24: Maximum & Average Number of People Waiting Until Ambulance Available
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Figure 24 illustrates the differences in the maximum and average number of people waiting until an ambulance becomes available for both models when simulated for 168 hours. This metric serves as a sub-metric to the wait time until an ambulance becomes available and gives further insight into the time spent in the emergency queue, a factor that is both a bottleneck itself as well as an indicator of other bottlenecks within the system. The optimised system manages to both limit the maximum number of emergencies waiting in the queue and reduce the average number to almost 0, implying that it is unlikely for there to be even 1 person waiting in the queue at any time — a significant improvement.
7.3 Discussion of Performance Improvements
The source of the improvements made on the system lie in the effect of the two strategies implemented. Introducing the option of health centres impacts the system’s ‘Transfer Time’, as the centres create many more options for an ambulance transporting patients, and they are often much closer to a location than the nearest hospital (Sookraj 2012). This reduction in travel time limits the maximum ‘Transfer Time’ that may occur in the worse case scenario of this system, giving even the most distant and rural emergencies a reasonable time to definitive care. A reduction in ‘Transfer Time’ also implies that ambulances will become available at a much faster rate, thereby reducing the amount of time emergencies spend waiting for attention, and consequently limiting the number of people in the queue at any time. This is further assisted by the artificial delay which allows the system to serve emergencies already in the queue before adding more, effectively preventing a large back-up of emergencies. This can be seen in the notable difference in the maximum and average number of people in the queue, as well as the time spent in the queue. The effect of the queue has such significance due to the way it prioritises emergencies. A set of emergencies could be waiting for an ambulance to become available for a long period, however, as soon as an emergency of much higher priority joins the queue, it immediately moves to the top. This further elongates the wait of the emergencies already in the queue and contributes to a much higher average and maximum ‘Wait Time’ within the system, as the queue time is one of the major constituents that make up this metric. Though artificial delays naturally lengthen the ‘Wait Time’, in this case by 30 seconds, it is small enough to be considered negligible. The negative effects of the delay cannot be seen, as the maximum and average ‘Wait Time’ are reduced considerably by the effects of the shorter wait in the ambulance queue. Shorter ‘Transfer Times’ and ‘Wait Times’ mean the total time a patient spends in the system also becomes much shorter. These incremental reductions in time allow for more patients to be served within a certain time period, and as a result, a greater percentage of ‘patient’ entities leave the optimised system. It is clear that the strategies implemented in this system work both individually and synergistically to reduce the effects of bottlenecks present in the system, resulting in an overall improvement in the system’s performance and efficiency.
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7.3 Potential Impact of the Optimised System
If implemented, this system will have far reaching effects on all its stakeholders. Politically, it may receive some amount of government pushback due to the increase in funding needed to operate certain health centres on a 24-hour basis. Political leaders may however see the optimised system as an opportunity to demonstrate their ability to enact meaningful change that benefits all people, increasing their chance of re-election.
Economically, the government could potentially increase taxes to support the funding of this system. This will mainly affect the common person, however a much more effective EMS may convince individuals that private health insurance is unnecessary, thereby reducing their expenses, and even increasing disposable incomes, potentially encouraging economic growth. The increase in work hours at these health centres may also provide new job opportunities and have a positive effect on the national employment rate.
Socially, more confidence in the system’s ability to serve citizens will allow individuals to worry less about health issues and emergencies, increasing the country’s overall quality of life. The implementation of 24-hour health centres will not only serve to improve the EMS system, but also any community they are present in as well.
Legally, the health and safety standards of each health centre would have to be maintained, and in some cases improved, in order to provide adequate treatment to patients without the need to go to the hospital. Laws and regulations may also be put in place to ensure the adoption of this system by any company contracted by the government in the future.
Environmentally, the reduction in travel time for the ambulances both reduces the average fuel usage of each ambulance and limits the exhaust fumes emitted by each vehicle, two changes that may positively impact the environment.
7.4 Conclusion
The objectives of this project were achieved whereby:
• The Emergency Medical Service provided Global Medical Response of Trinidad & Tobago (GMRTT) was investigated and described. The existing system was investigated and described in this project through the use of existing literature and interviews. Through this investigation, major areas of inefficiency and limitations within the system were identified.
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• A model was designed to accurately represent the EMS system in Trinidad & Tobago. This was done using the information provided during the investigation and verified by comparing simulated outputs with measurements obtained from GMRTT employees and users of the service.
• Changes were proposed to improve the average wait time of the system by at least 20%. These came in the form of optimisation strategies, and were simulated in Arena using modified versions of the original model. The initial optimisation strategies were ‘public education’, ‘on-scene treatment’, and ‘health centres’, though, ‘artificial delays’ was added after further testing of the system.
• Improvements on the system were demonstrated as a result of the changes made to optimise the system. Each strategy produced its own set of improvements, though some produced more significant improvements than others. The most impactful models were further developed and combined with other strategies until an ideal model combining the third and fourth strategies was created. The optimised model was able to reduce the system’s average ‘Wait Time’ by 33.48%.
• The impact of these changes on the system and the country at large was evaluated. The optimised model showed noticeable improvements in how efficiently and effectively the system operates, these changes could save lives if implemented. The political, economical, social, legal and environmental effects of this proposed system were also discussed.
As a result of the modelling and simulation of this system, the main conclusions are as follows:
i. The use of the ‘Public Education’ and ‘Artificial Delay’ strategies on their own do not produce a significant improvement on the system.
ii. The use of ‘On-Scene Treatment’ is extremely effective in reducing ambulance wait times, however, implementing and facilitating its operation is expensive.
iii. The use of ‘Health Centres’ is very effective in reducing ambulance wait times when combined with ‘Artificial Delays’ and is more financially viable than ‘On-Scene Treatment’, making it the ideal strategy for optimising this system.
iv. There is a strong relationship between the average length of time a patient spends in the ambulance queue and the overall efficiency of the system.
v. A reduction in the total time spent attending to an emergency increases the amount of emergencies that can be attended to within a certain period.
7.5 Recommendations for Future Studies
Due to the lack of time, resources and data afforded to this project, the research on this topic was limited to some degree. These limitations provide the opportunity for further development of this topic in future studies. Areas of potential development include the following:
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1. Further Research & Data Collection
The extent and accuracy of this project’s research and data collection were both significantly limited due to the contracted EMS provider’s confidentiality policy. Further research into issues affecting the system could include internal issues, which were not explored in this project, as well as organisational issues, which have to do with the Trinidadian healthcare system as a whole. More comprehensive data collection over longer periods of time will increase the accuracy of the system model, making its simulated outputs a much more reliable means of judging and improving upon the real-world system.
2. More Detailed Modelling The detail of the system model created in Section 5 had to be limited due to the lack of time and information. A more in-depth model including the operation of the dispatch call centre, the locations strategy of the ambulances, and the movement of ambulances in and out of the system would contribute to the accuracy of the system model, and potentially assist in the identification of other areas of inefficiency. In addition, it would allow the model to simulate more detailed and specific situations, giving the researcher further insight into the workings of this system.
3. Geographically Specific Modelling Although GMRTT serves the entirety of Trinidad & Tobago, each island has geographical areas of varying demographics, populations, and economic activities. As a result, certain areas produce a higher demand for this service (see Section 4.5) and may require operation strategies that are more tailored to the specific area. It would be helpful to model and optimise the operation of the EMS in these areas once more data is made available, as to potentially increase the efficiency of the overall system.
4. Other Optimisation Strategies Though four optimisation strategies were tested in this project, many other strategies could be proposed and evaluated for effectiveness. One such strategy could be the use of roaming health clinics which would effectively take the health centre to patients with less serious emergencies, much like strategy 2. They could also be strategically placed throughout the country to act as a midpoint between hospitals and the location of an emergency. Another possible strategy could be the partial implementation of the Franco-German model of care where less serious emergencies are treated on scene and not transported, while others are transported as usual. This would eliminate the necessity of purchasing specialised ambulance equipment and the hiring of medical practitioners, as only basic first aid would be required.
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5. System Stress Testing Whereas this study chooses to focus on the improvement of the existing system under normal conditions, it would be helpful for further studies to explore the effects of unexpectedly high service usage (like in the case of a major incident), and the extent to which the system will be able to handle it. This type of stress testing is useful in any system, and will allow the providers of this service to be adequately prepared for the worst-case scenario that they may face. This is especially important considering the nature of this system, as an EMS system that is unable to respond as effectively as possible in times of crisis may result in a mass casualty situation. It is therefore imperative that this system is continually tested so that preemptive action may be taken to limit the loss of lives in these circumstances.
6. Ambulance Utilisation This system can also be tested and optimised for maximum ‘ambulance utilisation’. The principle behind this metric is based on the fact that unused ambulances that are utilising resources (in the form of vehicle & employee costs) reduce the overall efficiency of the system. An EMS system that allocates financial resources to areas that do not increase its effectiveness also limits its own potential for improvement through the purchase of better equipment and the hiring of more employees. For this reason, establishing an optimal ratio of ambulances present in the system to the effectiveness of the system is essential to the improvement of the EMS, though it may appear that less ambulances are being utilised.
7. Establishment of Industry Standards Though GMRTT may abide by its own standards (that themselves vary from time to time), it would be helpful to establish a set of performance standards for this particular industry that will both increase this company’s accountability and dictate the performance of future contractors. Set standards for metrics such as average ‘Wait Time’ and number of people in the ambulance queue can provide a benchmark for performance from which the body governing emergency medical services can assess the efficiency of a system. These standards can also help in identifying when and where an issue in the system is occurring in the case that a particular metric falls below an acceptable limit. The establishment of standards will also give the Ministry of Health a realistic basis from which to renew or terminate a company’s contract, as it would provide a means by which a company’s performance can be unbiasedly judged.
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Appendix Interview 1 - Description of the System This interview was performed between Mr. McGibbon and three emergency medical technicians currently employed by GMRTT. All answers were recorded to their knowledge and transcribed by Mr. McGibbon. Some of the questions asked were pre-prepared, while others were follow-up, contextual questions. See Section 4.0 for the information gained from these questions.
1. What are the stages of the EMS system?
2. Describe the dispatch procedure.
3. Why is the wrong hotline often called?
4. How are emergencies processed?
5. How are different emergencies treated?
6. What is the ambulance location strategy used?
7. What level of qualification do ambulance staff have?
8. What medical equipment is present on each ambulance?
9. How do the ambulance drivers find locations?
10.Describe the on-site procedure.
11.How does communication occur within the system?
12.Describe the hospital handover procedure.
13.Describe the re-entry procedure.
14.What are the major delays faced in this system?
15.Where are the geographical areas of greatest demand?
16.What are the peak service usage hours in this system?
17.What are the standards followed in this system?
18.What are the accountability measures within the system?
19.What are GMRTT’s major expenses?
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Interview 2 - Model Data Collection This interview was specifically performed with the intention of gathering data for the creation of the system model. The first section of this interview was directed towards an emergency medical dispatcher possessing 3 years experience, who is currently employed by GMRTT. The second section of this interview was directed towards an emergency medical technician possessing 5 years experience, who is currently employed by GMRTT. All answers were recorded to their knowledge and transcribed by Mr. McGibbon. Some of the question asked could not be answered and were therefore omitted in the results. The type of distribution used in the modelling of each variable reflects this lack of information.
Emergency Medical Dispatcher 1. How many calls does the emergency hotline receive every hour? (See Table 18) 2. How often do people call the wrong hotline? (See Table 20)
3. What is the minimum, average and maximum time taken for an emergency to be forwarded to your hotline? (See Table 19)
4. What is the minimum, average and maximum time a caller waits before speaking to an operator? (See Table 19)
5. What is the minimum, average and maximum time spent speaking to an operator? (See Table 19) 6. What is the likelihood of each Bravo classification? (See Table 20) Emergency Medical Technician
1. How many ambulances are on the road at any given time? (34 ambulances)
2. What is the minimum, average and maximum time taken to arrive at the scene of an emergency? (See Table 19)
3. What is the minimum, average and maximum time spent at the scene of an emergency? (See Table 19)
4. What is the minimum, average and maximum time taken to arrive at the hospital? (See Table 19) 5. What is the minimum, average and maximum time taken to hand over patients to the hospital? (See Table 19) 6. What is the minimum, average and maximum time taken for an ambulance to become available after handing over a patient? (See Table 19)
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Interview 3 - Model Validation This interview was performed between Mr. McGibbon and five individuals who had used the services of GMRTT within the past two years. The data from these interviews were used in the validation of the system model (see Section 5.3.2) and as such only the time data was required, however, extra information was collected to give better context of each incident. The answers were tabulated and presented in Table 3.
1. How much time passed between the initial placing of the call to the arrival of the ambulance?
2. Where was the emergency located?
3. What was the nature of the emergency?
4. Was the 811 hotline called?
Schedules - Existing System
Interval 12 am - 1 am 1 am - 2 am 2 am - 3 am 3 am - 4 am 4 am - 5 am 5 am - 6 am 6 am - 7am 7 am - 8 am 8 am - 9 am 9 am - 10 am 10 am - 11 am 11 am - 12 pm
Make a Call Average # of Calls Interval 15 12 pm - 1 pm 15 1 pm - 2 pm 15 2 pm - 3 pm 15 3 pm - 4 pm 15 4 pm - 5 pm 30 5 pm - 6 pm 30 6 pm - 7 pm 30 7 pm - 8 pm 30 8 pm - 9 pm 15 9 pm - 10 pm 15 10 pm - 11 pm 15 11 pm - 12 am
Average # of Calls 15 7 7 7 7 15 15 15 15 15 15 15
Table 18: Existing System Model Call Arrival Schedule
Delays - Existing System
Module Wrong Number Call Waiting Emergency Information Processed Time to Scene Service on Scene Time to Hospital Hospital Handover Re-Entry Time
Minimum 30 s 15 s
Average 30 s
Maximum 60 s 60 s
Distribution Uniform Triangular
1 min
-
1.5 min
Uniform
5 min 10 min 10 min 15 min 5 min
15 min 20 min -
30 min 15 min 30 min 40 min 10 min
Triangular Uniform Uniform Triangular Uniform
Table 19: Existing System Model Delay Times
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Decisions - Existing System
Module
Probability 80% 20% 5% 10% 10% 35% 20% 20%
Call Successful
Dispatch Decision
Description Yes No Bravo 1 Bravo 2 Bravo 3 Bravo 4 Bravo 5 Bravo 6
Table 20: Existing System Model Decisions & Probabilities
Decisions - Strategy 1
Module
Probability 95% 5% 5% 10% 10% 35% 20% 20%
Call Successful
Dispatch Decision
Description Yes No Bravo 1 Bravo 2 Bravo 3 Bravo 4 Bravo 5 Bravo 6
Table 21: Optimisation Strategy 1 System Model Decisions & Probabilities
Delays - Strategy 2
Module Wrong Number Call Waiting Emergency Information Processed Time to Scene Service on Scene Time to Hospital Hospital Handover
ER Hospital Handover Re-Entry Time
Minimum 30 s 15 s
Average 30 s
Maximum 60 s 60 s
Distribution Uniform Triangular
1 min
-
1.5 min
Uniform
5 min 20 min 10 min
15 min -
30 min 40 min 30 min
Triangular Uniform Uniform
15 min
20 min
40 min
Triangular
10 min 5 min
15 min -
20 min 10 min
Triangular Uniform
Table 22: Optimisation Strategy 2 System Model Delay Times
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Delays - Strategy 3
Module Wrong Number Call Waiting Emergency Information Processed Time to Scene Service on Scene Time to Hospital Time to PreHospital Hospital Handover PreHospital Handover Re-Entry Time
Minimum 30 s 15 s
Average 30 s
Maximum 60 s 60 s
Distribution Uniform Triangular
1 min
-
1.5 min
Uniform
5 min 20 min 10 min 5 min 15 min 5 min 5 min
15 min 10 min 20 min 10 min -
30 min 40 min 30 min 15 min 40 min 15 min 10 min
Triangular Uniform Uniform Triangular Triangular Triangular Uniform
Table 23: Optimisation Strategy 3 System Model Delay Times
Delays - Strategy 4
Module Wrong Number Call Waiting Emergency Information Processed Time to Scene Service on Scene Time to Hospital Hospital Handover Re-Entry Time
Minimum 30 s 15 s
Average 30 s
Maximum 60 s 60 s
Distribution Uniform Triangular
1.5 min
-
2 min
Uniform
5 min 20 min 10 min 15 min 5 min
15 min 20 min -
30 min 40 min 30 min 40 min 10 min
Triangular Uniform Uniform Triangular Uniform
Table 24: Optimisation Strategy 4 System Model Delay Times
Delays - Optimised System
Module Wrong Number Call Waiting Emergency Information Processed Time to Scene Service on Scene Time to Hospital Time to PreHospital Hospital Handover PreHospital Handover Re-Entry Time
Minimum 30 s 15 s
Average 30 s
Maximum 60 s 60 s
Distribution Uniform Triangular
1.5 min
-
2 min
Uniform
5 min 20 min 10 min 5 min 15 min 5 min 5 min
15 min 10 min 20 min 10 min -
30 min 40 min 30 min 15 min 40 min 15 min 10 min
Triangular Uniform Uniform Triangular Triangular Triangular Uniform
Table 25: Optimised System Model Delay Times
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Figure 25: A Map of Trinidad & Tobago Showing the Locations of Health Centres & Hospitals ("The Ministry Of Health - Trinidad And Tobago" 2017)
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Glossary • definitive care - the point at which all treatment required at that time has occurred.
• efficiency - the amount of resources utilised in completing a task i.e. Wait Time. • effectiveness - the degree to which an objective is successfully achieved i.e. Percentage of ‘patients’ out.
• E.M.S. - Emergency Medical Services
• E.M.T. - Emergency Medical Technician
• G.M.R.T.T. - Global Medical Response of Trinidad & Tobago
• R.H.A. - Regional Health Authorities
• Total Time - the time it takes for the ‘patient’ entity to exit the system.
• Transfer Time - the time taken between arriving on scene and handing over a patient to the hospital.
• Other Time - any time that is not included in ‘Wait Time’ or ‘Transfer Time’.
• Wait Time - the time taken between calling a number and the ambulance arriving on scene.
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