Model-driven decision support system for estimating number of ambulances required during earthquake disaster relief operation

Most of human life has been encountered danger due to natural disasters nowadays. One of these natural disasters that endanger human lives and which causes lot of damages is earthquake. A proper emergency response after an earthquake happening is important and has high priority in earthquake emergency management to reduce number of damages. Decision making for critical resources in the phase of response, is one of the main concerns for managers. Ambulance, as one of the critical resource that can help to reduce earthquake losses and costs, needs to be planned. Confusion in the number of victims in the early stages of earthquake, access complexity to the required data of different organizations by the pressing time, complicated nature of estimation, diversity of models and limitation of time for decision making are the main problems associated with estimating ambulances during earthquake disaster which makes estimation too difficult. In addition, there is a call for research in determining the number of required ambulances during earthquake emergency management, due to high error in estimating the number of ambulances in the current methods, which leads to unnecessary expenses and thereby helping to ensure that disaster sites are not overcrowded with emergency workers impeding each other's effectiveness. Such complexity suggests the introduction of Decision Support System (DSS). More accurate estimation of the number of required ambulances using a decision support system can help managers to speed up the process of decision making and thus reducing error and costs. Since the number of ambulances needed during a disaster is directly proportional to the number of victims requiring hospital treatment and in order to reach the first objective of this study, factors determining the number of human casualties in earthquake disaster i.e. population, modified Mercalli, age, time, building occupancy and gender are selected as the most relevant factors which have high probability in creating human casualties. The collected data from various relevant sources is used in proposing the model of this research. After testing different approaches, Fuzzy rule-based approach is being used, after defining the rules for each aforementioned factors and optimization is conducted in order to minimize the error for estimating the number of human casualties. Finally, by using de Boer formula and obtained number of human casualties, the number of required ambulances is estimated accurately. The results indicate that the error is decreased by more than 50% in the proposed method. A prototype of Model-Driven Decision Support System was developed based on the proposed model that can be used to aid emergency response planners for their decision making process prior to take any action during earthquake emergency management

Emergency medical service response system performance in an urban South African setting: a computer simulation model

Includes bibliographical references.This study investigated the effects of different response strategies, vehicle location strategies and vehicle numbers on response times in a simulated Emergency Medical Services system. The simulation was a computer model using discrete-event simulation and the model was based on Western Cape Emergency Medical Services operations in Cape Town. The study objectives were to (i) create the simulation model, (ii) determine the best-performing combination of explanatory factors and (iii) determine the effect of increasing vehicle numbers on response time performance. The simulation model took into account incident arrival rates, incident and hospital spatial distributions, vehicle numbers and dispatch practices in the modelled system. Verification and validation of the simulation model utilised a combination of quantitative and qualitative methods. The validated simulation model was changed in two ways: (i) the response strategy was changed to either single or two-tier (the response model factor) and (ii) the vehicle location strategy was changed to either dynamic or static (the vehicle location factor). This yielded four individual models each representing one combination of these factors. Each simulation model was run for a simulated period of seven days. Output data were analysed using multivariate analysis of variance in order to identify differences in response time between the factor combinations. A single-tier model using dynamic vehicle locations produced the best response performance. This model was run repeatedly, increasing vehicle numbers incrementally with each run to assess the effect of increased vehicle numbers on response time performance. A doubling of vehicle numbers resulted in an 14% increase in the number of responses meeting the national performance target for high acuity incidents, while a seven-fold increase in vehicle numbers increased this to 15%. No further performance increases were seen beyond this with increased vehicle numbers. A 2% performance increase for lower acuity incidents was seen with the same increase in vehicle numbers. In the system modelled, increasing vehicle numbers should not be expected to realise anything more than small improvements in response time performance, at a high operational cost. Fine-grained dynamic deployment of vehicles in anticipation of system demand appears to be a more important determinant of response performance than vehicle numbers alone

Modelling Emergency Medical Services

Emergency Medical Services (EMS) play a pivotal role in any healthcare organisation. Response and turnaround time targets are always of great concern for the Welsh Ambulance NHS Trust (WAST). In particular, the more rural areas in South East Wales consistently perform poorly with respect to Government set response standards, whilst delayed transfer of care to Emergency Departments (EDs) is a problem publicised extensively in recent years. Many Trusts, including WAST, are additionally moving towards clinical outcome based performance measures, allowing an alternative system-evaluation approach to the traditional response threshold led strategies, resulting in a more patient centred system. Three main investigative parts form this thesis, culminating in a suite of operational and strategic decision support tools to aid EMS managers. Firstly, four novel allocation model methods are developed to provide vehicle allocations to existing stations whilst maximising patient survival. A detailed simulation model then evaluates clinical outcomes given a survival based (compared to response target based) allocation, determining also the impact of the fleet, its location and a variety of system changes of interest to WAST (through ‘what-if?’ style experimentation) on entire system performance. Additionally, a developed travel time matrix generator tool, enabling the calculation and/or prediction of journey times between all pairs of locations from route distances is utilised within the aforementioned models. The conclusions of the experimentation and investigative processes suggest system improvements can in fact come from better allocating vehicles across the region, by reducing turnaround times at hospital facilities and, in application to South East Wales, through alternative operational policies without the need to increase resources. As an example, a comparable degree of improvement in patient survival is witnessed for a simulation scenario where the fleet capacity is increased by 10% in contrast to a scenario in which ideal turnaround times (within the target) occur

Naturalistic decision making in emergency ambulance command and control

This paper reports on a field study into the nature of decision making in the command and control of emergency ambulances at the London Ambulance Service (LAS). This paper will describe how real-time decisions are made by emergency medical dispatchers and the decision strategies they invoke as they assess the situation, plan and co-ordinate the dispatch of emergency ambulances. A cognitive task analysis approach known as the Critical Decision Method (Hoffman et al., 1998; Klein et al., 1989) was used in the study. The study showed that decision making in emergency ambulance command and control involves four major processes---assessment of the situation, assessment of resources, planning, and co-ordinating and control. These four processes function within an awareness of goings-on in and around the sectors that the dispatchers operate in. This awareness is referred to as situation awareness and is being reported elsewhere (Wong & Blandford, submitted). The decision making process resembles the decision making described by naturalistic decision making models (see (Zsambok & Klein, 1997) for an extensive discussion on the topic) and is an extension of the Integrated Decision Model (Wong, 1999). The study also suggested that a lot of effort was directed at understanding and assessing the situation and in maintaining a constant awareness of the situation. These observations have significant implications for the design of information systems for command and control purposes. These implications will be discussed separately in another paper. The paper will first introduce the domain of EMD at the LAS, then explain how the Critical Decision Method was used in the data collection and in the data anlaysis. It will then describe how decisions are made, particularly during major incidents, and then discuss the implications of those findings for the design of command and control systems.UnpublishedClawson, Jeff J., & Dernocoeur, Kate Boyd. (1998). Principles of Emergency Medical Dispatch. (2ed ed.). Salt Lake City, Utah: Priority Press, The National Academy of Emergency Medical Dispatch. Crandall, Beth, & Getchell-Reiter, Karen. (1993). Critical decision method: A technique for eliciting concrete assessment indicators from the intuition of NICU nurses. Advances in ursing Science, 16(1), 42-51. Drillings, Michael, & Serfaty, Daniel. (1997). Naturalistic Decision Making in Command and Control. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (71-80). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Finkelstein, Anthony , & Dowell, John. (1996). A Comedy of Errors: the London Ambulance Service case study, Proceedings of the 8th International Workshop on Software Specification & Design IWSSD-8 (2-4): IEEE CS Press. Flin, Rhona, Slaven, Georgina, & Stewart, Keith. (1996). Emergency decision making in the offshore oil and gas industry. Human Factors, 38(2), 262-277. Glaser, Barney G., & Strauss, Anselm L. (1968). The discovery of grounded theory: Strategies for qualitative research. London: Weidenfeld and Nicolson. Hajdukiewicz, John R., Burns, Catherine M., Vicente, Kim J., & Eggleston, Robert G. (1999). Work Domain Analysis for Intentional Systems Proceedings of Human Factors and Ergonomics Society 43rd Annual Meeting 1999, 333-337. Henderson, Shane G., & Mason, Andrew J. (1999). Estimating ambulance requirements in Auckland, New Zealand Proceedings of Winter Simulation Conference on Winter simulation: Simulation: a bridge to the future, 1670-1674. Hoffman, Robert R., Crandall, Beth, & Shadbolt, Nigel. (1998). Use of the Critical Decision Method to elicit expert knowledge: A case study in the methodology of Cognitive Task Analysis. Human Factors, 40(2), 254-276. Kaempf, George L., Klein, Gary, Thordsen, Marvin, & Wolf, Steve. (1996). Decision making in complex naval command and control environments. Human Factors(Special Issue),. Kaempf, George L., Wolf, Steve, & Miller, Thomas E. (1993). Decision making in the AEGIS Combat Information Center Proceedings of Human Factors and Ergonomics Society 37th Annual Meeting, 1107-1111. Klein, Gary A. (1993). A Recognition-Primed Decision (RPD) Model of Rapid Decision Making. In G. A. Klein, J. Orasanu, R. Calderwood, & C. E. Zsambok (Eds.), Decision Making in Action: Models and Methods . Norwood, NJ: Ablen Publishing Corp. Klein, Gary A. (1999). Sources of Power: How people make decisions. Cambridge, MA: The MIT Press. Klein, Gary A., Calderwood, Roberta, & Macgregor, Donald. (1989). Critical decision method for eliciting knowledge. IEEE Transactions on Systems, Man and Cybernetics, 19(3), 462-472. Lipshitz, Raanan, & Ben Shaul, Orit. (1997). Schemata and mental models in Recognition-Primed Decision Making. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (293-303). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. McCarthy, John C., Wright, Peter C., Healey, Patrick, Dearden, Andrew, & Harrison, Michael D. (1997). Locating the scene: The particular and the general in contexts for ambulance control, Proceedings of the international ACM SIGGROUP conference on Supporting group work: the integration challenge GROUP 97 Conference (101-110). Phoenix, AZ: ACM Press. Orasanu, Judith, & Fischer, Ute. (1997). Finding decisions in natural environments: The view from the cockpit. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (343-357). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Pascual, Raphael, & Henderson, Simon. (1997). Evidence of naturalistic decision making in military command and control. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (217-226). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Simon, H.A. (1977). The new science of management decision. Englewood Cliffs, NJ: Prentice Hall. Stokes, Alan F., Kemper, Kenneth, & Kite, Kirsten. (1997). Aeronautical decision making: Cue recognition, and expertise under time pressure. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (183-196). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Wong, B.L. William. (1999). The Integrated Decision Model in Emergency Dispatch Management and its Implications for Design. In J. Scott & B. Delgarno (Eds.), Proceedings of the Ninth Australian Conference on Computer-Human Interaction OzCHI'99, 28 - 20 November 1999 (98-104). Charles Sturt University, Wagga Wagga, Australia: CHISIG, Ergonomics Society of Australia. Wong, B.L. William. (2000). The Integrated Decision Model in Emergency Dispatch Management and its Implications for Design. Australian Journal of Information Systems, 2(7), 95-107. Wong, B.L. William, & Blandford, Ann. (submitted). Situation awareness in Emergency Medical Dispatch. . Wong, William B.L., O'Hare, David, & Sallis, Philip J. (1996). A Goal-Oriented Approach for Designing Decision Support Displays in Dynamic Environments. In J. Grundy & M. Apperley (Eds.), Proceedings of OzCHI '96, The Sixth Australian Computer Human Interaction Conference, November 24-27, 1996, Hamilton, New Zealand, 78-85. Wong, William B.L., Sallis, Philip J., & O'Hare, David. (1997). Eliciting information portrayal requirements: Experiences with the Critical Decision Method. In H. Thimbleby, B. O'Conaill, & P. Thomas (Eds.), People and Computers XII, HCI '97 Conference of the British Computer Society Special Interest Group on Human-Computer Interaction (397-415). University of West England, Bristol, UK: Springer. Wong, William B.L., Sallis, Philip J., & O'Hare, David. (1998). The Ecological Approach to interface design: Applying the Abstraction Hierarchy to intentional domains. In P. Calder & B. Thomas (Eds.), Designing the Future: Proceedings of the Eighth Australian Conference on Computer-Human Interaction OzCHI'98 (144-151). Adelaide, Australia: IEEE Computer Society Press. Zhu, Zhiwei, McKnew, Mark A., & Lee, Jim. (1992). Effects of time-varied arrival rates: an investigation in emergency ambulance service systems; Proceedings of Conference on Winter simulation, 1180 - 1186. Zsambok, Caroline E. (1997). Naturalistic Decision Making: Where are we now? In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (3-16). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Zsambok, Caroline E., & Klein, Gary (Eds.). (1997). Naturalistic Decision Making. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers

Naturalistic decision making in emergency ambulance command and control

This paper reports on a field study into the nature of decision making in the command and control of emergency ambulances at the London Ambulance Service (LAS). This paper will describe how real-time decisions are made by emergency medical dispatchers and the decision strategies they invoke as they assess the situation, plan and co-ordinate the dispatch of emergency ambulances. A cognitive task analysis approach known as the Critical Decision Method (Hoffman et al., 1998; Klein et al., 1989) was used in the study. The study showed that decision making in emergency ambulance command and control involves four major processes---assessment of the situation, assessment of resources, planning, and co-ordinating and control. These four processes function within an awareness of goings-on in and around the sectors that the dispatchers operate in. This awareness is referred to as situation awareness and is being reported elsewhere (Wong & Blandford, submitted). The decision making process resembles the decision making described by naturalistic decision making models (see (Zsambok & Klein, 1997) for an extensive discussion on the topic) and is an extension of the Integrated Decision Model (Wong, 1999). The study also suggested that a lot of effort was directed at understanding and assessing the situation and in maintaining a constant awareness of the situation. These observations have significant implications for the design of information systems for command and control purposes. These implications will be discussed separately in another paper. The paper will first introduce the domain of EMD at the LAS, then explain how the Critical Decision Method was used in the data collection and in the data anlaysis. It will then describe how decisions are made, particularly during major incidents, and then discuss the implications of those findings for the design of command and control systems.UnpublishedClawson, Jeff J., & Dernocoeur, Kate Boyd. (1998). Principles of Emergency Medical Dispatch. (2ed ed.). Salt Lake City, Utah: Priority Press, The National Academy of Emergency Medical Dispatch. Crandall, Beth, & Getchell-Reiter, Karen. (1993). Critical decision method: A technique for eliciting concrete assessment indicators from the intuition of NICU nurses. Advances in ursing Science, 16(1), 42-51. Drillings, Michael, & Serfaty, Daniel. (1997). Naturalistic Decision Making in Command and Control. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (71-80). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Finkelstein, Anthony , & Dowell, John. (1996). A Comedy of Errors: the London Ambulance Service case study, Proceedings of the 8th International Workshop on Software Specification & Design IWSSD-8 (2-4): IEEE CS Press. Flin, Rhona, Slaven, Georgina, & Stewart, Keith. (1996). Emergency decision making in the offshore oil and gas industry. Human Factors, 38(2), 262-277. Glaser, Barney G., & Strauss, Anselm L. (1968). The discovery of grounded theory: Strategies for qualitative research. London: Weidenfeld and Nicolson. Hajdukiewicz, John R., Burns, Catherine M., Vicente, Kim J., & Eggleston, Robert G. (1999). Work Domain Analysis for Intentional Systems Proceedings of Human Factors and Ergonomics Society 43rd Annual Meeting 1999, 333-337. Henderson, Shane G., & Mason, Andrew J. (1999). Estimating ambulance requirements in Auckland, New Zealand Proceedings of Winter Simulation Conference on Winter simulation: Simulation: a bridge to the future, 1670-1674. Hoffman, Robert R., Crandall, Beth, & Shadbolt, Nigel. (1998). Use of the Critical Decision Method to elicit expert knowledge: A case study in the methodology of Cognitive Task Analysis. Human Factors, 40(2), 254-276. Kaempf, George L., Klein, Gary, Thordsen, Marvin, & Wolf, Steve. (1996). Decision making in complex naval command and control environments. Human Factors(Special Issue),. Kaempf, George L., Wolf, Steve, & Miller, Thomas E. (1993). Decision making in the AEGIS Combat Information Center Proceedings of Human Factors and Ergonomics Society 37th Annual Meeting, 1107-1111. Klein, Gary A. (1993). A Recognition-Primed Decision (RPD) Model of Rapid Decision Making. In G. A. Klein, J. Orasanu, R. Calderwood, & C. E. Zsambok (Eds.), Decision Making in Action: Models and Methods . Norwood, NJ: Ablen Publishing Corp. Klein, Gary A. (1999). Sources of Power: How people make decisions. Cambridge, MA: The MIT Press. Klein, Gary A., Calderwood, Roberta, & Macgregor, Donald. (1989). Critical decision method for eliciting knowledge. IEEE Transactions on Systems, Man and Cybernetics, 19(3), 462-472. Lipshitz, Raanan, & Ben Shaul, Orit. (1997). Schemata and mental models in Recognition-Primed Decision Making. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (293-303). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. McCarthy, John C., Wright, Peter C., Healey, Patrick, Dearden, Andrew, & Harrison, Michael D. (1997). Locating the scene: The particular and the general in contexts for ambulance control, Proceedings of the international ACM SIGGROUP conference on Supporting group work: the integration challenge GROUP 97 Conference (101-110). Phoenix, AZ: ACM Press. Orasanu, Judith, & Fischer, Ute. (1997). Finding decisions in natural environments: The view from the cockpit. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (343-357). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Pascual, Raphael, & Henderson, Simon. (1997). Evidence of naturalistic decision making in military command and control. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (217-226). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Simon, H.A. (1977). The new science of management decision. Englewood Cliffs, NJ: Prentice Hall. Stokes, Alan F., Kemper, Kenneth, & Kite, Kirsten. (1997). Aeronautical decision making: Cue recognition, and expertise under time pressure. In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (183-196). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Wong, B.L. William. (1999). The Integrated Decision Model in Emergency Dispatch Management and its Implications for Design. In J. Scott & B. Delgarno (Eds.), Proceedings of the Ninth Australian Conference on Computer-Human Interaction OzCHI'99, 28 - 20 November 1999 (98-104). Charles Sturt University, Wagga Wagga, Australia: CHISIG, Ergonomics Society of Australia. Wong, B.L. William. (2000). The Integrated Decision Model in Emergency Dispatch Management and its Implications for Design. Australian Journal of Information Systems, 2(7), 95-107. Wong, B.L. William, & Blandford, Ann. (submitted). Situation awareness in Emergency Medical Dispatch. . Wong, William B.L., O'Hare, David, & Sallis, Philip J. (1996). A Goal-Oriented Approach for Designing Decision Support Displays in Dynamic Environments. In J. Grundy & M. Apperley (Eds.), Proceedings of OzCHI '96, The Sixth Australian Computer Human Interaction Conference, November 24-27, 1996, Hamilton, New Zealand, 78-85. Wong, William B.L., Sallis, Philip J., & O'Hare, David. (1997). Eliciting information portrayal requirements: Experiences with the Critical Decision Method. In H. Thimbleby, B. O'Conaill, & P. Thomas (Eds.), People and Computers XII, HCI '97 Conference of the British Computer Society Special Interest Group on Human-Computer Interaction (397-415). University of West England, Bristol, UK: Springer. Wong, William B.L., Sallis, Philip J., & O'Hare, David. (1998). The Ecological Approach to interface design: Applying the Abstraction Hierarchy to intentional domains. In P. Calder & B. Thomas (Eds.), Designing the Future: Proceedings of the Eighth Australian Conference on Computer-Human Interaction OzCHI'98 (144-151). Adelaide, Australia: IEEE Computer Society Press. Zhu, Zhiwei, McKnew, Mark A., & Lee, Jim. (1992). Effects of time-varied arrival rates: an investigation in emergency ambulance service systems; Proceedings of Conference on Winter simulation, 1180 - 1186. Zsambok, Caroline E. (1997). Naturalistic Decision Making: Where are we now? In G. K. Caroline E. Zsambok (Ed.), Naturalistic Decision Making (3-16). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Zsambok, Caroline E., & Klein, Gary (Eds.). (1997). Naturalistic Decision Making. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers