CN107491644A - Based on the extensive wound strategy simulation system of system dynamics and method - Google Patents
Based on the extensive wound strategy simulation system of system dynamics and method Download PDFInfo
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Abstract
The invention provides one kind to be based on the extensive wound strategy simulation system of system dynamics, including：Subsystem occurs for extensive traumatic event, to simulate quantity and the condition of the injury caused by the extensive wound sick and wounded；Hospital's emergency medicine treatment strength subsystem, it is horizontal to simulating hospital emergency medicine treatment；Government emergency organizes and directs subsystem, to simulate the command decision ability that government emergency organizes and directs department；120 first-aid centre's subsystems, subsystem occurs with extensive traumatic event and government emergency is organized and directed subsystem and is connected with each other, to simulate pre hospital care strength and ability；Extensive wound sick and wounded's final result estimates subsystem, and subsystem, hospital's emergency medicine treatment strength subsystem and government emergency, which occurs, with extensive traumatic event organizes and directs subsystem and be connected, and is formed to simulate the final result of the extensive wound sick and wounded.Additionally provide and be based on the extensive wound strategy analogy method of system dynamics.
Description
Technical Field
The invention relates to a system and method for simulating large-scale wound emergency medical rescue based on system dynamics.
Background
With the acceleration of the industrialization process and the change of the social natural environment, although the social efficiency is greatly improved, the consequences are serious when an accident happens. Therefore, how to organize emergency medical rescue actions efficiently after a large-scale trauma event occurs has an important role in reducing the casualty rate. At present, according to the research of domestic and foreign documents, the casualty rate of the sick and wounded is mainly influenced by four aspects after a large-scale traumatic event: the first is the injury factor, including the injured part, the injured type, the injury mechanism, the severity of the injury and the like; pre-hospital emergency factors including the evacuation efficiency of the sick and wounded, the pre-hospital triage method, the pre-hospital time, the medical delivery tools and the like; thirdly, the factors of hospital treatment, including the capacity, timeliness, resources, management and the like of the hospital treatment; and fourthly, organizing and commanding factors comprising organizing and commanding efficiency, organizing and commanding execution rate, organizing and commanding response speed, organizing and commanding response force and the like. The key point of reducing the death rate of the sick and wounded after the occurrence of the large-scale trauma is to start from pre-hospital emergency factors and organization command factors, improve the timeliness of pre-hospital emergency and enhance the organization command efficiency and the execution rate.
In the practical process of reducing the death rate of the wounded after the occurrence of the large-scale trauma, the pre-hospital emergency time is shortened, the pre-hospital emergency efficiency is improved, and the command efficiency and the execution rate of the organization are enhanced, so that the emergency medical rescue efficiency of the large-scale trauma is improved, the death rate of the large-scale trauma is reduced, and the key is to construct an efficient emergency medical rescue system of the large-scale trauma. The Emergency Management System (EMS) in the United states, the Main Emergency Management (MEM) in the United kingdom, the Disaster Response System (DRS) in Japan and other systems achieve the aim of determining the optimal emergency medical rescue scheme in the shortest time to a certain extent, and effectively reduce the death rate of large-scale wounds. The American depends on a cloud service and a new system method of DIORAMA-II Architecture of triage and post delivery of positioning calculation, thereby effectively improving the efficiency of triage before hospital and medical post delivery; a set of new pre-hospital triage classification method established by Spain effectively improves the evacuation efficiency of the sick and wounded after a large-scale trauma event; the doctor-helicopter system constructed in 2007 in Japan effectively shortens the time for medical rescue efforts to reach the accident scene. However, China has insufficient emergency medical rescue research and experience for large-scale trauma.
In order to promote the reduction of the death rate of the large-scale trauma, qualitative analysis aiming at the pre-hospital emergency of the large-scale trauma, qualitative analysis aiming at the hospital rescue and qualitative analysis aiming at the emergency medical rescue strategy are formed, but the existing qualitative analysis of the large-scale trauma emergency medical rescue has the following defects:
for qualitative analysis of pre-hospital first aid, some analyze the effect of a triage method, some analyze the influence of triage on mortality, and some analyze the relation between pre-hospital time and mortality; for qualitative analysis of hospital treatment, some analyze clinical treatment methods, and some analyze application of auxiliary examination; the qualitative analysis of the rescue strategy mainly remains in the theoretical discussion of the first-aid strategy. These qualitative analyses are focused on unilateral analysis and discussion, and cannot simulate feedback influence and dynamic interaction relation among factors, and cannot perform system research combining system internal and external, structural factors and scientific factors, and surface factors and deep factors. The explanation of the integral composition and action relation of the large-scale wound emergency medical rescue system is insufficient, and the test basis of policy intervention 'target spot' and intervention strength for improving the large-scale wound emergency efficiency and reducing the large-scale wound death rate cannot be obtained.
The large-scale wound emergency medical rescue system has multiple feedbacks, is an extremely complex large system, is unreliable in intuition and subjective judgment, and is nearly infeasible in actual experiments, so that the system needs to turn to computer simulation based on system dynamics to establish a dynamic feedback model system to search factors influencing the death rate of the large-scale wound, and provide solution countermeasures and suggestions.
Disclosure of Invention
The invention aims to provide a large-scale wound emergency medical rescue simulation system and method based on system dynamics, which are used for establishing a dynamic feedback model system, searching factors influencing the death rate of large-scale wounds, and providing a solution and suggestion to solve the problem that the prior art cannot carry out actual tests on the large-scale wound emergency medical rescue system.
In order to achieve the above object, the present invention provides a large-scale wound emergency medical rescue simulation system based on system dynamics, comprising:
the large-scale wound event generation subsystem is used for simulating the quantity and the wound condition of large-scale wound patients;
a hospital emergency medical treatment power subsystem connected to the large-scale trauma event generation subsystem for simulating a hospital emergency medical treatment power level;
the government emergency organization command subsystem is used for simulating the command decision capability of a government emergency organization command department;
120 a first aid center subsystem interconnected with the large-scale trauma event generation subsystem and the government emergency organization command subsystem for simulating pre-hospital first aid strength and capacity;
and the large-scale wound patient outcome prediction subsystem is connected with the large-scale wound event generation subsystem, the hospital emergency medical treatment and treatment power subsystem and the government emergency organization command subsystem and is used for simulating the outcome constitution of the large-scale wound patient.
Preferably, the large scale trauma event generating subsystem comprises the following simulated variables: the number of trauma patients, the number of light trauma patients, the number of middle trauma patients and the number of heavy trauma patients are simulated according to the simulation variables to obtain the emergency call demand and the hospitalization medical demand;
the hospital emergency medical treatment power subsystem comprises: the emergency treatment capacity and the inpatient wound treatment capacity of the current hospital are updated in real time, and the emergency treatment capacity and the inpatient medical treatment capacity of the hospital are determined by acquiring the emergency treatment demand and the inpatient medical treatment demand and judging the supply-demand ratio of the emergency treatment and the inpatient medical treatment according to the emergency treatment capacity and the inpatient wound treatment capacity of the current hospital;
the government emergency organization command subsystem comprises execution variables: organizing the command execution rate, wherein the command execution rate is used for influencing the emergency treatment efficiency of the 120 emergency treatment center subsystem;
120 the emergency center subsystem includes the observation variables: according to the pre-hospital time timeliness grade, simulating pre-hospital emergency strength and capacity under the influence of the emergency call demand, the in-hospital medical demand, the hospital emergency medical treatment capacity and the organization command execution rate;
the large-scale wound sick and wounded outcome prediction subsystem comprises simulation parameters: and determining the light injury mortality, the middle injury mortality and the heavy injury mortality according to the pre-hospital emergency strength and ability, the emergency call demand, the hospitalization medical demand, the hospital emergency medical treatment ability and the organization command execution rate.
Preferably, the large-scale traumatic event occurrence subsystem further comprises the following parameters: the method comprises the following steps of obtaining a light injury medical demand, an on-site death risk judgment factor, an on-site death risk increment and an on-site first-aid effective rate, wherein the light injury medical demand is obtained according to the initial number of light injury patients and the demand increment, and the heavy injury medical demand is obtained by correcting the number of the heavy injury patients through the on-site death risk judgment factor, the on-site death risk increment and the on-site first-aid effective rate, and is equal to the number of middle injury patients;
in the subsystem of the hospital emergency medical treatment power, parameters influencing the hospital emergency medical treatment power comprise: influence of treatment ability parameters, fixed asset values, labor numbers and labor indexes;
the government emergency organization command subsystem further comprises the following parameters: the requirement judgment is carried out, wherein the requirement judgment is carried out according to an information acquisition rate, a requirement judgment difference and a standard judgment condition, the organization command execution rate is judged according to the requirement, and the information acquisition rate, the requirement judgment difference and the standard judgment condition are used for determining;
the 120 emergency center subsystem further includes the following parameters: the on-site waiting time, the triage first-aid time and the post-delivery en-route time, wherein the pre-hospital time is determined by the on-site waiting time, the triage first-aid time and the post-delivery en-route time;
in the large-scale wound patient outcome estimation subsystem, the light injury mortality is influenced by the light injury mortality to be estimated, the light injury complication proportion, the light injury proportion and the complication risk increment; the mortality rate of the middle-injured patients is influenced by the estimated mortality rate of the middle-injured patients, the proportion of complications of the middle-injured patients, the proportion of the middle-injured patients and the increment of the risk of the complications; the rate of severe injury mortality is affected by the rate of severe injury mortality to be estimated, the rate of severe injury complications, the rate of severe injury and the incremental risk of complications.
Preferably, the input and output variables of the system include: flow rate variables, auxiliary variables, initial variables, and constants; wherein, the flow bit variable comprises: demand judgment, medical delivery plan, medical delivery decision and organization command condition; the flow rate variables include: information acquisition rate, information processing rate, plan completion rate and decision execution condition; the auxiliary variables include: information acquisition rate, information processing rate, plan completion rate, decision execution condition and the like; the initial variables include: the method comprises the following steps of (1) obtaining an initial value of the effective rate of the on-site emergency treatment, an initial value of the number of the available ambulances, an initial value of demand judgment, an initial value of a medical delivery plan, an initial value of a medical delivery decision and an initial value of an organization command condition; the constants include: hospitalization medical demand rate, light injury medical demand judgment factor, light injury proportion, population base number and population coverage rate of large-scale trauma events.
The invention also provides a large-scale wound emergency medical rescue simulation method based on system dynamics, which is used for building the system and comprises the following steps:
s1: determining main variables of the system, wherein the variables comprise: flow rate variables, auxiliary variables, initial variables, and constants;
s2: establishing a main functional relationship of the system based on system dynamics, comprising:
flow position variable mathematical equation:
LEV(t)＝LEV(t-Δt)+Δt×[R1(t-Δt)-R2(t-Δt)]Δt＞0；
LEV(t)|t＝0＝LEV(t0)；
wherein R is1(t-Δt)、R2(t- Δ t) inflow rate and outflow rate, respectively;
flow rate variable mathematical equation:
RAT(t)＝f1[LEV(t),A(t),RAT1(t-Δt)]；
wherein LEV (t) indicates that the right flow position variable of the equation should be the value at time t; a (t) represents that the right side of the equation contains an auxiliary variable which is a value at the time t; RAT (radio access technology)1(t- Δ t) represents the value at the right side of the equation at which the flow rate-containing variable should be t- Δ t; the determination of the above t and t-delta t depends on the sequence of the simulation time variable calculation;
the number of patients with mild injury (1-mild injury medical requirement judgment factor + mild injury medical requirement increment);
the in-situ mortality rate is (1-effective rate of first aid on site) × (risk judgment factor of in-situ death + increment of risk of in-situ death) × proportion of heavy injury;
the light injury medical requirement judgment factor and the in-situ death risk judgment factor are real numbers between 0 and 1;
s3, writing the functional relation and the variable into a system, and assigning values to input variables of the system to obtain a large-scale wound emergency medical rescue simulation system;
and S4, debugging the large-scale wound emergency medical rescue simulation system.
Preferably, the flow bit variables include: demand judgment, medical delivery plan, medical delivery decision and organization command condition; the flow rate variables include: information acquisition rate, information processing rate, plan completion rate and decision execution condition; the auxiliary variables include: information acquisition rate, information processing rate, plan completion rate, decision execution condition and the like; the initial variables include: the method comprises the following steps of (1) obtaining an initial value of the effective rate of the on-site emergency treatment, an initial value of the number of the available ambulances, an initial value of demand judgment, an initial value of a medical delivery plan, an initial value of a medical delivery decision and an initial value of an organization command condition; the constants include: hospitalization medical demand rate, light injury medical demand judgment factor, light injury proportion, population base number, population coverage rate of large-scale trauma event and the like.
Preferably, the step S1 includes: and firstly, performing simulation debugging by using trial variables in the variation range of each variable, and determining the variable if the system behavior does not have obvious variation exceeding the preset range in the debugging.
Preferably, the step S1 includes: for the initial variables, three approaches are taken: firstly, historical data are fitted; secondly, initializing the simulation system at a balance position; thirdly, a special increasing or declining process is used as an initialization process.
The invention has the following beneficial effects:
(1) establishing a dynamic feedback simulation system based on system dynamics, searching factors influencing the death rate of the large-scale wound, and providing solution countermeasures and suggestions;
(2) the system dynamics model of the large-scale wound emergency medical rescue system is constructed by establishing five subsystems and enabling input variables and output variables of the five subsystems to mutually influence, mutually restrict and interact.
Drawings
FIG. 1 is a system configuration diagram of the present invention;
FIG. 2 is a diagram of the large scale trauma event generating subsystem of the present invention;
FIG. 3 is a schematic diagram of a hospital emergency medical treatment force subsystem according to the present invention;
FIG. 4 is a block diagram of a government emergency organization command subsystem of the present invention;
FIG. 5 is a diagram showing the construction of a 120 emergency treatment center subsystem according to the present invention;
FIG. 6 is a structural diagram of a large-scale wound patient outcome estimation subsystem of the present invention;
FIG. 7 is a diagram illustrating a cause analysis of the preferred embodiment;
FIG. 8 is a graph showing the analysis of the results of the preferred embodiment;
FIG. 9 is a flow chart of a simulation method of the present invention;
FIG. 10 is a graph of mortality trend results for simulation of different scale traumatic events in accordance with a preferred embodiment;
FIG. 11 is a graph of the mortality results from a trauma of the current scale at trauma events under simulation of different interventions in accordance with a preferred embodiment;
FIG. 12 is a graph of pre-hospital time and timeliness ratings results for a trauma event of current scale under simulation of different interventions in accordance with a preferred embodiment;
FIG. 13 is a graph of the time-to-first-aid trend of triage for a wound event of current scale under simulation of different interventions in accordance with a preferred embodiment.
Detailed Description
While the embodiments of the present invention will be described and illustrated in detail with reference to the accompanying drawings, it is to be understood that the invention is not limited to the specific embodiments disclosed, but is intended to cover various modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking specific embodiments as examples with reference to the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
As shown in fig. 1, the present embodiment provides a large-scale wound emergency medical rescue simulation system based on system dynamics, including:
a large-scale trauma event generation subsystem 10 for simulating the number and trauma of large-scale trauma patients;
a hospital emergency medical treatment power subsystem 20 connected to the large-scale trauma event generating subsystem 10 for simulating a hospital emergency medical treatment power level;
a government emergency organization command subsystem 30 for simulating the command decision capability of the government emergency organization command department;
120 a first aid center subsystem 40 interconnected with said large scale trauma event generation subsystem 10 and said government emergency organization command subsystem 30 for simulating pre-hospital emergency strength and capacity;
the large-scale wound patient outcome prediction subsystem 50 is connected with the large-scale wound event generation subsystem 10, the hospital emergency medical treatment force subsystem 20 and the government emergency organization command subsystem 30 and is used for simulating the outcome constitution of the large-scale wound patient.
As shown in fig. 2, the large-scale trauma event generation subsystem 10 is mainly used for simulation of trauma generation, the main simulation parameter is the number of trauma patients, and the main simulation parameter of the subsystem is the number of trauma patients, and specifically mainly includes the following simulation variables: the number of the trauma patients, the number of the light trauma patients, the number of the medium trauma patients and the number of the heavy trauma patients are simulated according to the simulation variables to obtain the emergency call demand and the hospitalization medical demand. The number of the trauma patients in the subsystem is determined by the population base number and the population coverage rate of large-scale trauma events, and then the number of the patients with light injury, medium injury and heavy injury is determined according to the proportion of the light injury, the medium injury and the heavy injury. In addition, the large-scale trauma event generating subsystem further comprises the following parameters: the method comprises the following steps of obtaining a light injury medical demand according to the initial number of light injury patients and the demand increment, obtaining a severe injury medical demand after correcting the number of severe injury patients according to the light injury medical demand, the on-site death risk judgment factor, the on-site death risk increment and the on-site emergency efficiency, wherein the middle injury medical demand is equal to the number of middle injury patients. During specific execution, part of the patients with slight injury have no medical requirements and are mainly influenced by the judgment factor of the medical requirements for slight injury, but the injury of part of the patients with slight injury is worsened along with the increase of time, and the medical requirements for slight injury are increased and reflected as increment of the medical requirements for slight injury, so that the quantity of the medical requirements for slight injury consists of initial requirement and requirement increment; the system assumes that the patients with moderate injury are all in medical need; among the seriously injured patients, partial critically ill patients have the in-situ death risk, and the in-situ death rate is influenced by in-situ death risk judgment factors, in-situ death risk increment and on-site first aid efficiency; the effective rate of the on-site first aid is determined by the initial value of the effective rate of the on-site first aid and the rate of the on-site first aid; the medical requirement of the seriously injured patients is the number of the seriously injured patients excluding the number of dead patients in the field; finally, the actual medical demand is the sum of the light injury medical demand, the number of patients with intermediate injury, and the heavy injury medical demand, which is further divided into emergency medical demand and hospitalization medical demand.
As shown in FIG. 3, the hospital emergency medical treatment force subsystem 20 is primarily connected to the large-scale trauma event generation subsystem through emergency and hospitalization requirements, which include: the emergency treatment capacity and the inpatient wound treatment capacity of the current hospital are updated in real time, and the emergency treatment capacity and the inpatient medical treatment capacity of the hospital are determined by acquiring the emergency treatment demand and the inpatient medical treatment demand and judging the supply-demand ratio of the emergency treatment and the inpatient medical treatment according to the emergency treatment capacity and the inpatient wound treatment capacity of the current hospital. In the subsystem of the hospital emergency medical treatment power, the parameters influencing the hospital emergency medical treatment power comprise: influence of treatment ability parameters, fixed asset values, labor numbers and labor indexes; wherein the emergency treatment and hospitalization treatment abilities are influenced by treatment ability parameters, fixed asset values, labor numbers and labor indexes; the manpower number is determined by the manpower distribution condition, the manpower distribution rate, the manpower distribution smooth cycle and the per-capita output rate; the per-person output rate is influenced by the total income and the hospital market income; the hospital market income is determined by the average cost and the number of patients; the number of patients is affected by the number of patients, the number of annual patients and the number of annual patients.
As shown in fig. 4, the government emergency organization command subsystem includes the execution variables: and organizing the command execution rate, wherein the command execution rate influences the emergency efficiency of the emergency center subsystem 120. The government emergency organization command subsystem also comprises the following parameters: and the requirement judgment is carried out by judging the information acquisition rate, the requirement judgment difference and the standard judgment condition, the organization command execution rate is judged by the requirement, and the information acquisition rate, the requirement judgment difference and the standard judgment condition are used for determining the requirement. Wherein, the information acquisition rate is mainly influenced by the efficiency of the information system; furthermore, a medical delivery plan is determined by combining the demand judgment and the information processing rate, and is influenced by the plan condition difference and the standard plan condition; similarly, the medical delivery plan and the plan completion rate jointly act on the medical delivery decision and are influenced by the decision condition difference and the standard decision condition; the decision execution condition is further influenced by the post-medical decision, and the organization command condition is determined together with the organization condition difference and the standard organization condition; and finally, determining the execution rate of the organization command according to the organization command condition and the time factor.
As shown in fig. 5, the 120 emergency center subsystem further includes an observation variable: and simulating pre-hospital emergency strength and capacity under the influence of the emergency call demand, the in-hospital medical demand, the hospital emergency medical treatment capacity and the organization command execution rate according to the pre-hospital time timeliness grade. The pre-hospital time timeliness grade in this embodiment represents the timeliness degree of the pre-hospital time, and is divided into 5 grades, less than or equal to 30 minutes, and the best timeliness is 1 grade; 30-60 minutes, and the timeliness grade is 2; 60-120 minutes, and the timeliness grade is 3; 120-180 minutes, and the timeliness level is 4; greater than 180 minutes, with a timeliness rating of 5. The 120 first aid center subsystem also includes the following parameters: the pre-hospital time is determined by the on-site waiting time, the triage first aid time and the post-delivery transit time. Wherein the pre-hospital time is determined by the on-site waiting time, the triage first-aid time and the post-delivery transit time; the field waiting time is influenced by the execution rate of the organization command and the standard field waiting time; the triage emergency time is determined by standard triage emergency time, the number of actually called medical staff and the actual medical demand; the number of actually called medical staff is determined according to the number of the called medical staff and the execution rate of the organization command; the back-delivery on-the-way time is influenced by the standard back-delivery on-the-way time, the number of actually called ambulances and the actual medical demand; the actual number of ambulance calls is determined by the organization command execution rate, the number of ambulance calls, the initial number of ambulance calls, and the snapshot rate.
As shown in fig. 6, the large-scale trauma victim outcome estimation subsystem further comprises simulation parameters: and determining the light injury mortality, the middle injury mortality and the heavy injury mortality according to the pre-hospital emergency strength and ability, the emergency call demand, the hospitalization medical demand, the hospital emergency medical treatment ability and the organization command execution rate. In the large-scale wound patient outcome estimation subsystem, the light injury mortality is influenced by the light injury mortality to be estimated, the light injury complication proportion, the light injury proportion and the complication risk increment; the mortality rate of the middle-injured patients is influenced by the estimated mortality rate of the middle-injured patients, the proportion of complications of the middle-injured patients, the proportion of the middle-injured patients and the increment of the risk of the complications; the rate of severe injury mortality is affected by the rate of severe injury mortality to be estimated, the rate of severe injury complications, the rate of severe injury and the incremental risk of complications. The mortality rate to be estimated for the light injury, the mortality rate to be estimated for the middle injury and the mortality rate to be estimated for the heavy injury are respectively determined according to the death risk estimation for the light injury, the death risk estimation for the middle injury and the heavy injury, the risk increment for the treatment capacity and the risk increment for the timeliness; wherein the rescue ability risk increment is influenced by hospital trauma rescue ability assessment and time; the timeliness risk increment is affected by the pre-hospital time, timeliness level and time.
The five subsystems mutually influence and act through data and logic relations among the parameters. The large-scale wound incident generation subsystem and the hospital emergency medical treatment power subsystem are connected in series through jointly evaluating the treatment capacity of the hospital through the medical demand and the medical supply-demand ratio; the government emergency organization command subsystem and the 120 emergency center subsystem are connected in series by jointly determining the pre-hospital time through the organization command execution rate; the large-scale trauma event generation subsystem is connected in series with the 120 emergency treatment center subsystem by the fact that the actual medical need affects the amount of time before the hospital. Finally, the large-scale wound emergency medical rescue system takes the large-scale wound patient outcome estimation subsystem as a core, and externally expresses the dynamic change of the system through the parameter of the wound death rate in the subsystem; the large-scale wound patient outcome estimation subsystem is influenced by other subsystems together and is connected with the large-scale wound event generation subsystem in series through the light, medium and heavy wound proportion; the system is connected in series with a hospital emergency medical treatment power subsystem through the wound treatment capacity; connected in series by pre-hospital time and timeliness ratings.
For example: in the 120 first aid center subsystem, the variable "on-site waiting time" is given by the formula: the standard field waiting time/organization command execution rate, the 'organization command execution rate' in the formula belongs to the government emergency organization command subsystem, and the 'standard field waiting time' belongs to the 120 emergency center subsystem. The formula of the organization command execution rate in the above formula is: SMOOTH (organization command case, Time + 0.01); the "standard field latency" is a constant: for 12 minutes. The formula of the organization command condition in the above formula is: INTEG (IF THEN ELSE (tissue status Difference > decision execution case, + tissue status Difference), 0.4). By analogy, quantitative relations exist between variables in the model.
Further, the input and output variables of the system include: flow rate variables, auxiliary variables, initial variables, and constants; wherein,
the flow bit variables include: demand judgment, medical delivery plan, medical delivery decision and organization command condition;
the flow rate variables include: information acquisition rate, information processing rate, plan completion rate and decision execution condition;
the auxiliary variables include: information acquisition rate, information processing rate, plan completion rate, decision execution condition and the like;
the initial variables include: the method comprises the following steps of (1) obtaining an initial value of the effective rate of the on-site emergency treatment, an initial value of the number of the available ambulances, an initial value of demand judgment, an initial value of a medical delivery plan, an initial value of a medical delivery decision and an initial value of an organization command condition;
the constants include: hospitalization medical demand rate, light injury medical demand judgment factor, light injury proportion, population base number and population coverage rate of large-scale trauma events.
The embodiment also provides a large-scale wound emergency medical rescue simulation method based on system dynamics, as shown in fig. 9, for establishing the system, wherein the simulation method comprises the following steps:
s1: determining main variables of the system, wherein the variables comprise: flow rate variables, auxiliary variables, initial variables, and constants; wherein,
the flow bit variables include: demand judgment, medical delivery plan, medical delivery decision and organization command condition;
the flow rate variables include: information acquisition rate, information processing rate, plan completion rate and decision execution condition;
the auxiliary variables include: information acquisition rate, information processing rate, plan completion rate, decision execution condition and the like;
the initial variables include: the method comprises the following steps of (1) obtaining an initial value of the effective rate of the on-site emergency treatment, an initial value of the number of the available ambulances, an initial value of demand judgment, an initial value of a medical delivery plan, an initial value of a medical delivery decision and an initial value of an organization command condition;
the constants include: hospitalization medical demand rate, light injury medical demand judgment factor, light injury proportion, population base number, population coverage rate of large-scale trauma event and the like.
Furthermore, emergency medical rescue system models are numerous and not readily ascertainable due to large-scale trauma. Variable selection must be integrated with model operation. The model determines system variables through a simulation experiment method, the variables are roughly tried to carry out model debugging in a variation range of the variables, and the variables are determined when model behaviors have no significant variation. Therefore, step S1 specifically includes the following execution modes: and firstly, performing simulation debugging by using trial variables in the variation range of each variable, and determining the variable if the system behavior does not have obvious variation exceeding the preset range in the debugging.
The system variables established in step S1 include a flow rate variable, an auxiliary variable, an initial variable, a constant, and the like. To simplify the model parameters, those parameters that do not change significantly with time are also approximated as constant values. For the initial variables, step S1 takes three approaches: firstly, historical data are fitted; secondly, initializing the simulation system at a balance position; thirdly, a special increasing or declining process is used as an initialization process. Considering that the determination of the initial variable has a large influence on the system behavior, some necessary technical processes are performed on data with large fluctuation in the actual system, and the average value of the time periods is selected.
The model parameter estimation adopts the following method:
(1) determining parameters by applying statistical data and survey data;
(2) some conventional mathematical methods such as economic metrology, grey prediction, etc.;
(3) carrying out analogy calculation by using the causal relationship among the factors in the model;
(4) and (5) evaluating by an expert.
S2: establishing a main functional relation of the system based on system dynamics, which specifically comprises the following steps:
the problem is solved based on system dynamics, a quantitative simulation model is finally established to obtain a simulation system on the basis of qualitative analysis, and the qualitative analysis of the concept model and the logic model lays a foundation for establishing the system dynamics quantitative simulation system. The mathematical equation of the system dynamics variables is used for establishing a quantitative model which can be simulated on a computer, and therefore, the mathematical equation must meet the necessary condition for simulation. Thus establishing the functional relationship described above. And the equations are established as follows:
flow position variable mathematical equation:
LEV(t)＝LEV(t-Δt)+Δt×[R1(t-Δt)-R2(t-Δt)]Δt＞0；
LEV(t)|t＝0＝LEV(t0)；
wherein R is1(t-Δt)、R2(t- Δ t) inflow rate and outflow rate, respectively;
flow rate variable mathematical equation:
RAT(t)＝f1[LEV(t),A(t),RAT1(t-Δt)]；
wherein, LEV (t) represents that the right flow position variable of the equation should be a value at the time t; a (t) represents that the right side of the equation contains an auxiliary variable which is a value at the time t; RAT (radio access technology)1(t- Δ t) represents the value at the right side of the equation at which the flow rate-containing variable should be t- Δ t; the above determination of the times t and t- Δ t depends on the order of the simulation time variable calculations.
The number of patients with mild injury (1-mild injury medical requirement judgment factor + mild injury medical requirement increment);
the in-situ mortality rate is (1-effective rate of first aid on site) × (risk judgment factor of in-situ death + increment of risk of in-situ death) × proportion of heavy injury;
the light injury medical requirement judgment factor and the in-situ death risk judgment factor are real numbers between 0 and 1;
s3, writing the functional relation and the variable into a system, and assigning values to input variables of the system to obtain a large-scale wound emergency medical rescue simulation system;
and S4, debugging the large-scale wound emergency medical rescue simulation system.
In step S2, the method further includes:
(1) establishment of emergency treatment and hospitalization wound treatment capacity:
according to the previous research results, the main influencing factors of the emergency treatment and hospitalization wound treatment capacity are the treatment capacity level, the emergency treatment and hospitalization manpower condition and the fixed asset condition, and the following equations are provided in the simulation system:
emergency trauma treating ability parameter emergency manpower index fixed asset value ^ (1-emergency manpower index)/10000
Hospitalized wound curing capacity parameter hospitalized manpower index fixed resource value ^ (1-hospitalized manpower index)/10000
The emergency and hospitalization human indices are real numbers between 0 and 1, and are derived using a table function of site survey data. The size of the influencing factors of manpower and fixed assets is also obtained by combining national health service survey data, expert consultation and site survey.
(2) Establishment of wound healing capacity assessment:
the wound treatment capacity is determined by the emergency treatment capacity and the hospitalization treatment capacity together, and the influence of the emergency treatment capacity on the overall wound treatment capacity is larger according to the historical data and the current condition investigation of large-scale wound emergency medical rescue. The emergency and hospitalization capacities are classified into a first grade and a second grade, which are respectively represented by 1 and 2, according to whether the medical supply quantity of the emergency and the hospitalization can satisfy the medical demand. If the emergency treatment and the hospitalization treatment capacity are both in the first grade, the wound treatment capacity is in the first grade; if the emergency treatment capacity grade is a first grade, and the hospitalization treatment capacity is a second grade, the wound treatment capacity is a second grade; if the emergency treatment capacity grade is a second grade and the hospitalization treatment capacity is a first grade, the wound treatment capacity is a third grade; if the emergency and hospitalization capacity ratings are both a second rating, the wound treatment capacity is a fourth rating. Thus, the function of the wound healing capacity assessment is established as: IF THEN ELSE (emergency treatment ability grade value ═ 1,1, IF THEN ELSE (emergency treatment ability grade value < hospitalization ability grade value, 2, IF THEN ELSE (emergency treatment ability grade value > hospitalization ability grade value, 3, 4))). Similarly, the pre-hospital time timeliness level, similarly available: pre-hospital time timeliness rating IF THEN ELSE (pre-hospital time 30,1, IF THEN ELSE (pre-hospital time 60,2, IF THEN ELSE (pre-hospital time 120,3, IF THEN ELSE (pre-hospital time 180, 4, 5)))).
(3) Establishing an initial value function:
the raw data used in this example were collected from national sanitation statistics yearbook in 2015, statistical yearbook in 2016, data from the committee of Shanghai City, and data from the field investigation of large-scale traumatic events in Shanghai City. And (4) carrying out statistical analysis on the data of different years, and estimating by adopting a moving average method. And supplementing and estimating the data missing part and the qualitative data part by combining an expert consulting method and a literature review.
Determination of annual diagnosis rate
Since no careful study of revenue predictions is required here, on the other hand, the annual visit rate in the model is considered constant for simplicity of the model. According to the 2014 national health statistics yearbook, the two-week diagnosis rate of residents is 15.4%. The annual diagnosis rate of residents is 0.154 x 26, based on a total of 52 weeks per year. Other rates, such as annual prevalence, etc., can be obtained in the same way.
② determination of the number of medical staff to be called
Currently, the number of emergency medical personnel in Shanghai is 982, and there are 16 administrative areas. According to historical data of large-scale traumatic emergency medical rescue in Shanghai city, the emergency strength of 2.5 administrative districts is averagely called in one rescue operation. Thus, the number of callable medical staff is determined from an average of 2.5 administrative districts, which is 153.44 people. Other numbers, such as the number of available ambulances, etc., are equally available.
The step S3 specifically includes writing out the main functions and variables in the simulation system as shown in table 1:
TABLE 1 Large Scale trauma Emergency medical rescue model Primary function and variables
In step S4, when the large-scale trauma emergency medical rescue simulation system is debugged, the Vensim DSS software is used to operate the model system in this embodiment: and setting the variables and defining a variable equation on a VensimS (hardware simulation system) simulation platform, linking feedback loops between the variables, and debugging the simulation system.
The debugged simulation system further comprises the following steps of further analyzing the interrelation among variables:
and analyzing factors influencing each variable and the interrelation among the factors by using the functions of the reason tree and the fruiting tree of the constructed large-scale wound emergency medical rescue model. Through the analysis of the cause tree, variables acting on selected variables can be enumerated and all variables of the last level of the influencing factor of a given variable can be traced, so that the cause tree of the given variable constitutes a subsystem, and the external actions of the variables determine the change of the given variable. Fig. 7 is a graph of analysis of the reason tree for the execution rate of the organization command. The fruit tree analysis can enumerate the acting variables for the selected variables; then, for these variables, the variables whose action is to be performed are listed again; and analogizing in sequence, and tracing back in a forward direction step by step until one step of a given variable appears. The result tree for a given variable thus constitutes a subsystem representing the ultimate effect of the given variable on the overall system. FIG. 8 is a fruit tree analysis diagram of the execution rate of the organization command.
The simulation system can be applied to the simulation and intervention research for carrying out large-scale wound emergency medical rescue:
referring to fig. 10, 11, 12 and 13, the constructed large-scale wound emergency medical rescue model is used to simulate the change trend of the mortality rate of the wound events of different scales (fig. 10), and simulate the change trends of the wound mortality rate of the current scale (fig. 11), the pre-hospital time timeliness level (fig. 12) and the triage emergency time (fig. 13), wherein a curve 1 is the current actual result, and curves 2, 3 and 4 are the test results. The influence of the change of the number of ambulances, the change of the number of emergency medical personnel and the change of the execution rate of the organization command on the observation indexes is observed, so that the key factors influencing the death rate of the trauma are determined. For example, FIG. 12 Curve 2: the number of the adjustable ambulances is reduced by 50 percent, and the selective adjustment rate is reduced by 50 percent; curve 3: the number of the adjustable ambulances is increased by 20 percent, and the selective adjustment rate is increased by 20 percent; curve 4: the number of the adjustable ambulances is increased by 50 percent, and the selective adjustment rate is increased by 50 percent. Simulation finds that increasing the number of ambulances can quickly and obviously improve the timeliness grade of the pre-hospital time within 24 hours; reducing the number of ambulances significantly reduces the level of pre-hospital time and timeliness. The results demonstrate that increasing the number of ambulances reasonably can increase the level of promptness in time before hospital, thereby indirectly reducing the mortality of trauma.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to make modifications or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (8)
1. A large-scale wound emergency medical rescue simulation system based on system dynamics is characterized by comprising:
the large-scale wound event generation subsystem is used for simulating the quantity and the wound condition of large-scale wound patients; a hospital emergency medical treatment power subsystem connected to the large-scale trauma event generation subsystem for simulating a hospital emergency medical treatment power level;
the government emergency organization command subsystem is used for simulating the command decision capability of a government emergency organization command department; 120 a first aid center subsystem interconnected with the large-scale trauma event generation subsystem and the government emergency organization command subsystem for simulating pre-hospital first aid strength and capacity;
and the large-scale wound patient outcome prediction subsystem is connected with the large-scale wound event generation subsystem, the hospital emergency medical treatment and treatment power subsystem and the government emergency organization command subsystem and is used for simulating the outcome constitution of the large-scale wound patient.
2. The system dynamics-based large-scale trauma emergency medical rescue simulation system of claim 1,
the large scale trauma event generating subsystem includes the following simulated variables: the number of trauma patients, the number of light trauma patients, the number of middle trauma patients and the number of heavy trauma patients are simulated according to the simulation variables to obtain the emergency call demand and the hospitalization medical demand;
the hospital emergency medical treatment power subsystem comprises: the emergency treatment capacity and the inpatient wound treatment capacity of the current hospital are updated in real time, and the emergency treatment capacity and the inpatient medical treatment capacity of the hospital are determined by acquiring the emergency treatment demand and the inpatient medical treatment demand and judging the supply-demand ratio of the emergency treatment and the inpatient medical treatment according to the emergency treatment capacity and the inpatient wound treatment capacity of the current hospital;
the government emergency organization command subsystem comprises execution variables: organizing the command execution rate, wherein the command execution rate is used for influencing the emergency treatment efficiency of the 120 emergency treatment center subsystem;
120 the emergency center subsystem includes the observation variables: according to the pre-hospital time timeliness grade, simulating pre-hospital emergency strength and capacity under the influence of the emergency call demand, the in-hospital medical demand, the hospital emergency medical treatment capacity and the organization command execution rate;
the large-scale wound sick and wounded outcome prediction subsystem comprises simulation parameters: and determining the light injury mortality, the middle injury mortality and the heavy injury mortality according to the pre-hospital emergency strength and ability, the emergency call demand, the hospitalization medical demand, the hospital emergency medical treatment ability and the organization command execution rate.
3. The system dynamics-based large-scale trauma emergency medical rescue simulation system of claim 2,
the large scale trauma event generation subsystem further comprises the following parameters: the method comprises the following steps of obtaining a light injury medical demand, an on-site death risk judgment factor, an on-site death risk increment and an on-site first-aid effective rate, wherein the light injury medical demand is obtained according to the initial number of light injury patients and the demand increment, and the heavy injury medical demand is obtained by correcting the number of the heavy injury patients through the on-site death risk judgment factor, the on-site death risk increment and the on-site first-aid effective rate, and is equal to the number of middle injury patients;
in the subsystem of the hospital emergency medical treatment power, parameters influencing the hospital emergency medical treatment power comprise: influence of treatment ability parameters, fixed asset values, labor numbers and labor indexes;
the government emergency organization command subsystem further comprises the following parameters: the requirement judgment is carried out, wherein the requirement judgment is carried out according to an information acquisition rate, a requirement judgment difference and a standard judgment condition, the organization command execution rate is judged according to the requirement, and the information acquisition rate, the requirement judgment difference and the standard judgment condition are used for determining;
the 120 emergency center subsystem further includes the following parameters: the on-site waiting time, the triage first-aid time and the post-delivery en-route time, wherein the pre-hospital time is determined by the on-site waiting time, the triage first-aid time and the post-delivery en-route time;
in the large-scale wound patient outcome estimation subsystem, the light injury mortality is influenced by the light injury mortality to be estimated, the light injury complication proportion, the light injury proportion and the complication risk increment; the mortality rate of the middle-injured patients is influenced by the estimated mortality rate of the middle-injured patients, the proportion of complications of the middle-injured patients, the proportion of the middle-injured patients and the increment of the risk of the complications; the rate of severe injury mortality is affected by the rate of severe injury mortality to be estimated, the rate of severe injury complications, the rate of severe injury and the incremental risk of complications.
4. The system dynamics-based large-scale wound emergency medical rescue model building system according to claim 1, wherein input and output variables of the system include: flow rate variables, auxiliary variables, initial variables, and constants; wherein,
the flow bit variables include: demand judgment, medical delivery plan, medical delivery decision and organization command condition;
the flow rate variables include: information acquisition rate, information processing rate, plan completion rate and decision execution condition;
the auxiliary variables include: information acquisition rate, information processing rate, plan completion rate, decision execution condition and the like;
the initial variables include: the method comprises the following steps of (1) obtaining an initial value of the effective rate of the on-site emergency treatment, an initial value of the number of the available ambulances, an initial value of demand judgment, an initial value of a medical delivery plan, an initial value of a medical delivery decision and an initial value of an organization command condition;
the constants include: hospitalization medical demand rate, light injury medical demand judgment factor, light injury proportion, population base number and population coverage rate of large-scale trauma events.
5. A large-scale trauma emergency medical rescue simulation method based on system dynamics, for establishing a system according to any one of claims 1 to 4, comprising the steps of:
s1: determining main variables of the system, wherein the variables comprise: flow rate variables, auxiliary variables, initial variables, and constants;
s2: establishing a main functional relationship of the system based on system dynamics, comprising:
flow position variable mathematical equation:
LEV(t)＝LEV(t-Δt)+Δt×[R1(t-Δt)-R2(t-Δt)]Δt＞0；
LEV(t)|t＝0＝LEV(t0)；
wherein R is1(t-Δt)、R2(t- Δ t) inflow rate and outflow rate, respectively;
flow rate variable mathematical equation:
RAT(t)＝f1[LEV(t),A(t),RAT1(t-Δt)]；
wherein LEV (t) indicates that the right flow position variable of the equation should be the value at time t; a (t) represents that the right side of the equation contains an auxiliary variable which is a value at the time t; RAT (radio access technology)1(t- Δ t) represents the value at the right side of the equation at which the flow rate-containing variable should be t- Δ t; the determination of the above t and t-delta t depends on the sequence of the simulation time variable calculation;
the number of patients with mild injury (1-mild injury medical requirement judgment factor + mild injury medical requirement increment);
the in-situ mortality rate is (1-effective rate of first aid on site) × (risk judgment factor of in-situ death + increment of risk of in-situ death) × proportion of heavy injury;
the light injury medical requirement judgment factor and the in-situ death risk judgment factor are real numbers between 0 and 1;
s3, writing the functional relation and the variable into a system, and assigning values to input variables of the system to obtain a large-scale wound emergency medical rescue simulation system;
and S4, debugging the large-scale wound emergency medical rescue simulation system.
6. The large-scale wound emergency medical rescue simulation method based on system dynamics of claim 5, wherein,
the flow bit variables include: demand judgment, medical delivery plan, medical delivery decision and organization command condition;
the flow rate variables include: information acquisition rate, information processing rate, plan completion rate and decision execution condition;
the auxiliary variables include: information acquisition rate, information processing rate, plan completion rate, decision execution condition and the like;
the initial variables include: the method comprises the following steps of (1) obtaining an initial value of the effective rate of the on-site emergency treatment, an initial value of the number of the available ambulances, an initial value of demand judgment, an initial value of a medical delivery plan, an initial value of a medical delivery decision and an initial value of an organization command condition;
the constants include: hospitalization medical demand rate, light injury medical demand judgment factor, light injury proportion, population base number, population coverage rate of large-scale trauma event and the like.
7. The large-scale wound emergency medical rescue simulation method based on system dynamics of claim 5, wherein the step S1 includes: and firstly, performing simulation debugging by using trial variables in the variation range of each variable, and determining the variable if the system behavior does not have obvious variation exceeding the preset range in the debugging.
8. The large-scale wound emergency medical rescue simulation method based on system dynamics of claim 5, wherein the step S1 includes: for the initial variables, three approaches are taken: firstly, historical data are fitted; secondly, initializing the simulation system at a balance position; thirdly, a special increasing or declining process is used as an initialization process.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108899079A (en) * | 2018-06-27 | 2018-11-27 | 中国人民解放军第二军医大学 | The mobilization system and method for civilian hospital |
| CN114334101A (en) * | 2021-09-28 | 2022-04-12 | 中国人民解放军总医院第三医学中心 | Large-scale sports event emergency medical rescue commanding and dispatching system taking plan system as support |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103279806A (en) * | 2013-05-03 | 2013-09-04 | 中国人民解放军第二军医大学 | Optimized decision method for conveying sick and wounded rescued through disaster medicine |
| CN103761366A (en) * | 2013-12-31 | 2014-04-30 | 中国人民解放军第二军医大学 | Hospital and community interaction model building system and method based on system dynamics |
-
2017
- 2017-08-21 CN CN201710718850.9A patent/CN107491644A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103279806A (en) * | 2013-05-03 | 2013-09-04 | 中国人民解放军第二军医大学 | Optimized decision method for conveying sick and wounded rescued through disaster medicine |
| CN103761366A (en) * | 2013-12-31 | 2014-04-30 | 中国人民解放军第二军医大学 | Hospital and community interaction model building system and method based on system dynamics |
Non-Patent Citations (1)
| Title |
|---|
| 刘旭: "抗震救灾医疗后送系统实证与建模研究", 《中国博士学位论文全文数据库 医药卫生科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108899079A (en) * | 2018-06-27 | 2018-11-27 | 中国人民解放军第二军医大学 | The mobilization system and method for civilian hospital |
| CN114334101A (en) * | 2021-09-28 | 2022-04-12 | 中国人民解放军总医院第三医学中心 | Large-scale sports event emergency medical rescue commanding and dispatching system taking plan system as support |
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