CN113936782A - Emergency medical resource allocation optimization method considering position distribution - Google Patents
Emergency medical resource allocation optimization method considering position distribution Download PDFInfo
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Abstract
An emergency medical resource allocation optimization method considering position distribution belongs to the field of public event emergency resource management. The invention aims to solve the problem that the existing emergency medical resource allocation method does not consider factors such as position distribution and the like, so that the allocation is unreasonable. The method comprises the following steps: calculating the running time of the emergency ambulance, and taking the medical supply point corresponding to the path with the running time less than a preset running time threshold value as a primary effective medical supply point; taking the primary effective medical supply point with the rescue survival probability reaching the expected survival probability threshold as a secondary effective medical supply point according to the relation between the running time corresponding to the primary effective medical supply point and the rescue time constraint threshold; selecting all secondary effective medical supply points with time accessibility meeting a limit threshold as target supply points; and taking the target supply point with the largest space accessibility as a preferred point of the emergency medical resource configuration. The invention can improve the time-space accessibility and the utilization efficiency of the emergency medical resources.
Description
Technical Field
The invention relates to an emergency medical resource allocation optimization method considering position distribution, and belongs to the field of public event emergency resource management.
Background
The traditional medical service resource allocation and scheduling aims at realizing medical resource equalization and regional medical resource allocation fairness, and indexes such as thousands of bed numbers and thousands of professional doctors under a hierarchical diagnosis and treatment system are optimized by calculating the sum of medical services obtained in a certain region. The method is not enough for the fine research of the time-space accessibility centering on the emergency medical service, so that the configuration defect is gradually shown.
The most core problem of emergency medical service is service accessibility under specific time-space constraint conditions, the regional position of a medical institution and the real-time traffic state on a road network are key factors influencing the time-space accessibility of the urban emergency medical service, and the existing research lacks an important link contacting the actual road network in the process of medical resource allocation, so that the emergency medical service is not beneficial to rapidly rescuing patients and improving the rescue rate.
At present, the research on the allocation of emergency medical resources is mainly to adopt a static research method, which takes emergency service facilities as a center, sets the service radius of the emergency service facilities by considering the path, vehicles and population factors, and calculates the supply and demand conditions and the accessibility of the emergency medical services according to the population of the residents to complete the allocation of the resources. In practice, cross-regional movement of the population can cause changes in supply and demand for emergency services, and therefore the influence of cross-regional movement of the population on the supply and demand changes and the resulting time-varying characteristics of traffic impedance needs to be considered. In addition, the layout of the existing medical facilities is mostly considered from population density or population activity intensity, and due to the influence of separation of jobs and dwellings, the activity range of people is greatly increased, so that the problem that the current emergency medical resource radiation crowd has repetition or careless care is caused. How to consider the problem of emergency service demand change caused by different occupation distributions is still a new field.
Due to the fact that factors such as occupation distribution and the like are not considered in the fine research of space-time accessibility in resource allocation in the existing method, unreasonable phenomenon exists in emergency medical service resource allocation.
Disclosure of Invention
Aiming at the problem that the existing emergency medical resource allocation method does not consider factors such as position distribution and the like, so that the allocation is unreasonable, the invention provides an emergency medical resource allocation optimization method considering position distribution.
The invention relates to an emergency medical resource allocation optimization method considering occupation distribution, which comprises the following steps,
firstly, the method comprises the following steps: calculating the running time of the emergency ambulance from each medical supply point to the emergency medical supply point according to the paths between the emergency medical demand point and the N medical supply points, and taking the medical supply point corresponding to the path with the running time less than a preset running time threshold value as a primary effective medical supply point; n is a positive integer;
II, secondly: calculating the rescue survival probability of each primary effective medical supply point according to the relation between the running time corresponding to the primary effective medical supply point and the rescue time constraint threshold, and taking the primary effective medical supply point with the rescue survival probability reaching the expected survival probability threshold as a secondary effective medical supply point;
thirdly, the method comprises the following steps: determining the reachable opportunity of each secondary effective medical supply point to each emergency medical demand point according to the relation between the running time of the secondary effective medical supply point and the rescue time constraint threshold, and accumulating the reachable opportunity number of each secondary effective medical supply point to all the emergency medical demand points to obtain the time reachability of each secondary effective medical supply point to all the emergency medical demand points; selecting all secondary effective medical supply points with time accessibility meeting a limit threshold as target supply points;
fourthly, the method comprises the following steps: calculating the supply-demand ratio of each target supply point to the emergency medical demand points according to the medical service resource supply capacity of each target supply point and the total emergency medical demand of the emergency medical demand points obtained through prediction; accumulating and summing the supply-demand ratios of all the emergency medical demand points of each target supply point to obtain the space accessibility of all the emergency medical demand points of each target supply point; and taking the target supply point with the largest space accessibility as a preferred point of the emergency medical resource configuration.
According to the emergency medical resource allocation optimization method considering the occupational distribution of the invention,
the obtaining process of the running time comprises the following steps:
based on ArcGIS projection, projecting the position of the medical supply point to the nearest road section, and determining a path between the emergency medical demand point and the N medical supply points; the required travel time of the emergency vehicle from each medical supply point to the emergency medical demand point is calculated on a path basis.
According to the emergency medical resource allocation optimization method considering the occupational distribution, the rescue survival probability calculation method comprises the following steps:
in the formula, rho is rescue survival probability, titRepresents the running time from the medical supply point i to the emergency medical demand point j, and T is the rescue time constraint threshold.
According to the emergency medical resource allocation optimization method considering the occupational distribution, the running time tijIncluding time delays due to road congestion.
According to the emergency medical resource allocation optimization method considering the occupation distribution, the time accessibility calculation method comprises the following steps:
in the formula AjtTime accessibility of the secondary available medical supply point j to all the emergency medical demand points, n is the total number of emergency medical demand points, OjNumber of opportunities for secondary effective medical delivery point j, f (t)ij) For running timetijCorresponding reachable opportunities.
According to the emergency medical resource allocation optimization method considering the occupational distribution, the calculation method of the space accessibility comprises the following steps:
in the formula AjsSpatial accessibility, R, to all Emergency medical Requirements for target supply Point jjSupply-to-demand ratio, S, for target supply point j to each emergency medical demand pointjThe medical service resource supply capacity of the target supply point j, and D is the total emergency medical demand of the emergency medical demand point obtained by prediction.
According to the emergency medical resource allocation optimization method considering job distribution, provided by the invention, the total emergency medical requirement D is taken as the total emergency service demand TES of the target area, and the target area is taken as an emergency medical requirement point, then:
predicting and obtaining the total emergency service demand TES of the target area according to the frequent emergency service demand TUES and the sudden emergency service demand SUES;
predicting the frequent emergency service demand TUES of the target area according to the frequent emergency material demand TUET and the frequent emergency medical personnel demand TUED;
wherein the frequent emergency material demand TUET is determined by the product of the population size TOP of the target area, the age composition coefficient RA and the medical material allocation base RMT;
the frequent emergency medical personnel demand TUED is determined by the product of the number of emergency vehicles TOA in the target area and the medical personnel arming base RMD;
predicting the emergency service demand SUES of the target area according to the emergency material demand SUET and the emergency medical staff demand SUED;
the SUET is determined by the product of the frequency RS of disaster occurrence in the target area, the area coefficient RZ, the balance coefficient JHR and the product of the RSA multiplication of the single emergency task material allocation cardinality;
the sudden emergency medical staff demand SUED is determined by the product of the disaster occurrence frequency RS of the target area, the area coefficient RZ, the balance coefficient JHR and the single emergency task staff allocation cardinality RSD.
According to the emergency medical resource allocation optimization method considering the occupation distribution, the calculation method of the population size TOP comprises the following steps:
TOP＝INTER(AP，P0)，
wherein AP is the oral growth; p0 is the initial population value;
the method for calculating the population growth amount AP comprises the following steps:
AP＝P0×(RAP-DP)，
wherein RAP represents population growth rate and DP represents mortality;
the method for calculating the TOA of the emergency ambulance comprises the following steps:
TOA＝INTER(AA，A0)，
wherein AA is the growth of the emergency ambulance, and A0 is the initial number of the emergency ambulance;
the method for calculating the emergency ambulance growth amount AA comprises the following steps:
AA＝A0×(RAA-RAD)，
in the formula, RAA is the growth rate of the emergency ambulance, and RAD is the breakage rate of the emergency ambulance.
According to the emergency medical resource allocation optimization method considering the occupational distribution, the calculation method of the region coefficient RZ comprises the following steps:
in the formula HαIs the density of a dangerous source, P0For the economic loss ratio, α is the first elastic coefficient, and β is the second elastic coefficient.
According to the emergency medical resource allocation optimization method considering the occupational distribution of the invention,
α+β＝1；
the values of the first elastic coefficient alpha and the second elastic coefficient beta are both 0.5.
The invention has the beneficial effects that: based on the conditions of capacity limitation and emergency response time constraint, the method considers the commuting influence caused by the occupation distribution, combines the refined time-space accessibility research of the actual road network, establishes an emergency medical resource allocation optimization model, and converts the resource allocation problem with uncertain requirements into the resource allocation problem with determined requirements, thereby optimizing the emergency service requirement change caused by different occupation distributions and improving the time-space accessibility and the utilization efficiency of emergency medical resources.
Drawings
FIG. 1 is a flow chart of an emergency medical resource allocation optimization method of the present invention in view of occupational distribution;
FIG. 2 is a flow chart for obtaining total emergency service demand;
FIG. 3 is a diagram of an emergency services demand association;
figure 4 is a population scale sub-system diagram.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
First embodiment, referring to fig. 1, the present invention provides an emergency medical resource allocation optimization method considering occupation distribution, including,
firstly, the method comprises the following steps: calculating the running time of the emergency ambulance from each medical supply point to the emergency medical supply point according to the paths between the emergency medical demand point and the N medical supply points, and taking the medical supply point corresponding to the path with the running time less than a preset running time threshold value as a primary effective medical supply point; n is a positive integer;
II, secondly: calculating the rescue survival probability of each primary effective medical supply point according to the relation between the running time corresponding to the primary effective medical supply point and the rescue time constraint threshold, and taking the primary effective medical supply point with the rescue survival probability reaching the expected survival probability threshold as a secondary effective medical supply point;
thirdly, the method comprises the following steps: determining the reachable opportunity of each secondary effective medical supply point to each emergency medical demand point according to the relation between the running time of the secondary effective medical supply point and the rescue time constraint threshold, and accumulating the reachable opportunity number of each secondary effective medical supply point to all the emergency medical demand points to obtain the time reachability of each secondary effective medical supply point to all the emergency medical demand points; selecting all secondary effective medical supply points with time accessibility meeting a limit threshold as target supply points;
fourthly, the method comprises the following steps: calculating the supply-demand ratio of each target supply point to the emergency medical demand points according to the medical service resource supply capacity of each target supply point and the total emergency medical demand of the emergency medical demand points obtained through prediction; accumulating and summing the supply-demand ratios of all the emergency medical demand points of each target supply point to obtain the space accessibility of all the emergency medical demand points of each target supply point; and taking the target supply point with the largest space accessibility as a preferred point of the emergency medical resource configuration.
Further, the obtaining of the runtime includes:
based on ArcGIS projection, projecting the position of a medical supply point to the nearest road section, turning the street data plane to a point, taking a geometric center as an emergency medical demand point, seeking a path meeting conditions based on a road network, and determining paths between the emergency medical demand point and N medical supply points; the required travel time of the emergency vehicle from each medical supply point to the emergency medical demand point is calculated on a path basis.
Let i Hospital as the starting point and name N0If it arrives at the demand point N in the road network1At least k road sections need to be passed, then N is called0And N1Is contiguous of order k, k being denoted as N1Relative to N0Adjacent gradient values of (a). In N0——N1On the path, the emergency ambulance is classified according to the road type, the time variation of the accessibility index is considered, and for the simplicity of calculation, the operation time of the emergency ambulance on the adjacent-order path can be calculated according to the arrangement of the k value from small to large. And calculating the running time of the paths between all the demand points and the supply points, and taking the paths with the running time less than a preset running time threshold value as effective paths.
Still further, the rescue survival probability calculation method comprises the following steps:
in the formula, rho is rescue survival probability, tijRepresents the running time from the medical supply point i to the emergency medical demand point j, and T is the rescue time constraint threshold.
Emergency medical response time threshold constraints: regarding the survival probability of the wounded, the relation between the average survival probability of the wounded and the time for the wounded to obtain rescue is estimated based on the rescue success rate of the hospital under the condition of emergency, and a function of the survival probability and the time is constructed. The rescue time constraint threshold T is the maximum rescue time constraint value, and can be 30min, 60 min, 120 min and 180min according to the types of the wounded.
According to the rescue survival probability rho, if the injured person with the serious injury can be rescued within 30 minutes, the survival rate is 87.5 percent, the rescue time is reduced to 50 percent when the rescue time is 60 minutes, and the rescue rate is rapidly reduced to 25 percent when the rescue time is 80 minutes. Considering that the research object is emergency medical service, the invention can select the rescue rate of 87.5 percent, and the time constraint is 30 min.
Still further, the running time tijIncluding time delays due to road congestion.
Considering the influence of peak commuting, when actually rescuing, the speed delay needs to be considered. The analysis may be performed on weekdays and off-weekdays, on-peak time periods, and off-peak time periods.
(1) Actual speed during off-peak hours
Since emergency vehicles have the highest right of way, they may be considered free-flow speed travel during off-peak hours. Specific vehicle speeds are referenced to road design specifications, as in table 1.
TABLE 1 urban road grade and design speed
(2) Peak time period actual time calculation:
the influence of emergency service rescue time caused by occupation distribution needs to be considered in the peak time period. The congestion coefficient of the peak time period can be read by using a hundred-degree map, and the speed can be corrected. The traffic state is divided into 4 states of serious congestion, slow running and smooth traffic, the priority traffic right of emergency service is considered, and the weight coefficient can be selected to be 0.8, 0.7 and 0.7 of the road congestion index.
And further, the service performance of each point is evaluated by opportunity accumulated reachability, and scheduling is considered to be more optimal due to higher reachability. The method mainly comprises the calculation of the number of activities which can be reached by a certain interest point in a specific cost function under the consideration of spatial correlation characteristics, namely corresponding to a starting point-end point path network in a given traffic cost range. The opportunity accumulation reachability model considers the space correlation and the time variability characteristics, selects time cost as an index, measures time reachability, measures the space correlation by using the number of opportunities reaching interest points of work and the like, and evaluates the scheduling excellent condition according to the reachability.
Calculating the time reachability of the feed point:
time threshold constraints are defined in the cumulative-of-opportunity reachability model beyond which time period no longer an opportunity is considered to obtain the corresponding service or considered unreachable. Accumulating the number of reachable opportunities that the medical resource service providing place, namely the supply point j meets the time constraint to obtain the time reachability A of the supply point j within the time threshold constraintjt：
The calculation method of the time reachability includes:
in the formula AjtTime accessibility of the secondary available medical supply point j to all the emergency medical demand points, n is the total number of emergency medical demand points, OjThe number of opportunities of the secondary effective medical supply point j is the number of emergency medical resources (hospitals) in the region where the secondary effective medical supply point j meets the time threshold constraint; f (t)ij) For the running time tijCorresponding reachable opportunities.
Still further, the spatial reachability of the feed point is calculated: the calculation method of the spatial reachability comprises the following steps:
in the formula AjsSpatial accessibility, R, to all Emergency medical Requirements for target supply Point jjSupply-to-demand ratio, S, for target supply point j to each emergency medical demand pointjThe medical service resource supply capacity of the target supply point j, and D is the total emergency medical demand of the emergency medical demand point obtained by prediction.
A two-step mobile search method is applied:
the method is based on network analysis in the GIS to obtain the distance and time from the demand point to the supply point, and sets a search threshold value to enable the search process to be calculated in the search domain, thereby overcoming the limit of a fixed boundary.
In the emergency services resource allocation scenario studied by the present invention, with the two-step mobile search method, locations with distances less than a threshold are considered to have similar reachability, while locations with distances greater than the threshold are not reachable. And when the response time exceeds the specified time threshold remainder, the rescue is considered to fail and is counted as unreachable. The distance constraint is the product of the city trunk speed standard and the time threshold.
In the first step of searching: starting from each supply point, searching all demand points in the limit travel time, and selecting the point with the maximum time accessibility as the candidate point set of the second step of search. And in the second step of searching, searching the supply points in the searching radius by taking each accident occurrence point in the alternative point set as a center, and finally selecting the point with the largest space accessibility as a result point.
The process of constructing the emergency medical resource configuration model comprises the following steps:
1. the basic condition assumption is that:
(1) the same type of road part in the road network is regarded as a road section, and only the travel mode of the emergency ambulance is considered.
(2) The emergency service response time studied in the present embodiment is the time of passage of the vehicle on the road.
(3) Medical resources include emergency vehicles and the number of associated physician personnel.
2. Emergency medical demand assignment
According to the regional emergency service prediction method, data can be obtained through calculation of a system dynamics model, namely, the regional risk coefficient is adjusted according to the prediction result of the demand of emergency medical resources, and further, the demand of each regional emergency service is determined.
When the space of the emergency service requirement is distributed, an emergency medical service requirement point needs to be established:
the emergency medical requirement point is a specific point in a city where an emergency medical requirement exists, namely an accident occurrence point, and can be represented by i. It is believed that there are n demand points within a study area in a certain city. According to the basic principle of division of the occupational distribution community, the geometric center point of the street is used as an emergency medical demand point.
3. Emergency medical resource scheduling
After the emergency medical resource allocation model allocates the demands, the emergency service resource allocation problem can be converted into a resource allocation problem determined by the demands. For the characteristics of emergency medical treatment, there are several following goals and principles when resource allocation is performed:
(1) the accessibility range of the emergency medical service resources is the widest, so that the medical service resources cannot be excessively concentrated or dispersed under the effective resource condition, and the regional demand of the emergency medical resources needs to be fully considered.
(2) Emergency medical services meet timeliness requirements, and emergency vehicle paths that exceed a time threshold are not considered valid paths. The determination of the time threshold needs to be combined with the actual urban rescue standard. The emergency medical resource configuration results should minimize the total time for all emergency vehicles in the system to reach each point of demand.
(3) The increase in emergency resources cannot exceed the maximum capacity of the hospital class without changing the existing hospital configuration and class. In practice, the hospital emergency medical service supply capacity is related to the turnover rate of the hospital, the number of beds, the number of physicians, the number of emergency vehicles, the hospital grade, and the like. The present invention interprets the supply capacity of a hospital as the capacity of the emergency ambulance in the hospital to provide service.
For the analysis of the above objectives and principles, an emergency tender dispatch model can be established as follows:
f1＝min∑i∈I∑j∈Jtijxijf(tij)，
in the formula VjFor hospital SjTransport capacity of the emergency ambulance; x is the number ofijTransport capacity for emergency vehicles from hospital j to i;
in the emergency medical resource allocation model, a first objective function f1In order to ensure that the total travel time of the emergency ambulance is minimum. Second objective function f2The utility model aims to realize the maximum utilization of the turnover service capacity of the emergency ambulance in the hospital and minimize the idle capacity of the supply capacity of the emergency ambulance in the hospital.
The two objective functions specify the space-time constraints of the crash cart, while giving the requirement of maximum coverage of the emergency coverage. Objective function f1The method shows that the supply capacity of the hospital is limited, the allocation level of the emergency ambulance cannot exceed the maximum supply capacity of the hospital, and the emergency service response decision is facilitated to avoid the phenomenon that the hospital is crowded. Objective function f2The emergency medical service system is meant to cover the whole regional emergency service requirement, and has the characteristics of emergency medical service 'mandatory' and 'low economy'.
Designing an algorithm based on a reachability restriction resource configuration model:
as can be seen from the modeling process described above, f (t)ij) A variable of 0-1, and a vehicle path weight greater than a time constraint is considered 0. After the OD cost matrix is solved by utilizing ArcGIS, the objective function can be converted into a static traffic distribution problem. Because the emergency time is the shortest and the hospital idle resources are the least, expected values can be given based on the cost matrix. Therefore, the model can be solved through an ideal point method, and the effect of the solution is measured by comparing the difference between the actual value and the expected value.
Still further, with reference to fig. 2 and fig. 3, taking the total emergency medical requirement D as the total emergency service requirement TES of the target area, and the target area as the emergency medical requirement point, then:
predicting and obtaining the total emergency service demand TES of the target area according to the frequent emergency service demand TUES and the sudden emergency service demand SUES;
predicting the frequent emergency service demand TUES of the target area according to the frequent emergency material demand TUET and the frequent emergency medical personnel demand TUED;
wherein the frequent emergency material demand TUET is determined by the product of the population size TOP of the target area, the age composition coefficient RA and the medical material allocation base RMT;
the frequent emergency medical personnel demand TUED is determined by the product of the number of emergency vehicles TOA in the target area and the medical personnel arming base RMD;
predicting the emergency service demand SUES of the target area according to the emergency material demand SUET and the emergency medical staff demand SUED;
the SUET is determined by the product of the frequency RS of disaster occurrence in the target area, the area coefficient RZ, the balance coefficient JHR and the product of the RSA multiplication of the single emergency task material allocation cardinality;
the sudden emergency medical staff demand SUED is determined by the product of the disaster occurrence frequency RS of the target area, the area coefficient RZ, the balance coefficient JHR and the single emergency task staff allocation cardinality RSD.
The present embodiment may include the steps of:
performing job distribution and emergency medical service characteristic analysis;
the invention researches and analyzes the characteristics of high timeliness, unpredictability and the like of emergency medical resources, combines the influence of population factors, job distribution, emergency probability and the like on emergency medical configuration, analyzes job distribution conditions through indexes such as employment living ratio, independent indexes, space matching degree and the like, considers the difference of the job living ratio of different areas, analyzes job distribution and emergency medical service characteristics, and provides a prophase theoretical basis for establishing a demand prediction system.
Establishing an emergency medical demand model system;
and (3) establishing a system dynamics system boundary and dividing the emergency service demand system boundary, converting key variables into horizontal variables in the model system diagram 3 according to the causal relationship and feedback loops among all subsystems on the basis of researching a causal loop of the emergency medical demand system, dividing all other variables into rate variables, constants and auxiliary variables according to a preset rule, generating an emergency medical demand model system diagram, establishing a demand prediction model of emergency medical resources considering occupational distribution, and carrying out validity verification by using Vensims.
Still further, the method for calculating the population size TOP comprises the following steps:
TOP＝INTER(AP，P0)，
wherein AP is the oral growth; p0 is the initial population value;
the method for calculating the population growth amount AP comprises the following steps:
AP＝P0×(RAP-DP)，
wherein RAP represents population growth rate and DP represents mortality;
the method for calculating the TOA of the emergency ambulance comprises the following steps:
TOA＝INTER(AA，A0)，
wherein AA is the growth of the emergency ambulance, and A0 is the initial number of the emergency ambulance;
the method for calculating the emergency ambulance growth amount AA comprises the following steps:
AA＝A0×(RAA-RAD)，
in the formula, RAA is the growth rate of the emergency ambulance, and RAD is the breakage rate of the emergency ambulance.
Still further, the method for calculating the area coefficient RZ comprises the following steps:
in the formula HαIs the density of a dangerous source, P0For the economic loss ratio, α is the first elastic coefficient, and β is the second elastic coefficient.
Analyzing the characteristics of the occupational distribution and the emergency medical service:
analysis of influence of position distribution:
the measure of the size of the occupational balance is divided into the balance of quantity and the balance of quality, the balance of quantity is measured by employment living ratio, the balance of quality is measured by independent index of Thomas, and the relationship between the occupational space gathering place and the living gathering place is measured by space matching degree. The invention selects the independent index to analyze the position distribution.
Employment living ratio:
the employment living ratio is the ratio of employment posts to population number in a certain area, and is a static index. When the employment occupancy ratio value is between [0.8,1.2], the area is considered to be in place balance.
The duty ratio in the target area is expressed by a balance coefficient JHR:
j is the number of posts in the target area, and H is the number of people in the target area.
Independent indices:
the independent index is also called the dynamic occupancy distribution ratio, which is the ratio of the number of people working in the study area to those living in the study area and those working to foreign places in the study area. The index represents the self-sufficient capacity of employment and accommodation in the area of study.
In the formula of BRiIs an independent index in region i, IJiNumber of people working and living in area i, OHiThe number of people working in the area i to the foreign area.
Spatial matching degree:
the space matching degree indicates the degree of space matching between the residential work place and the residential place. Collecting data under the scale of traffic cells by investigation, performing space unit aggregation analysis on street scale or larger area, and utilizing space dislocation index SMIijTo measure values over a larger scale. The larger the SMI value is, the higher the spatial misalignment between the residence and the working place under the clustering space unit is.
SMI in the formulaijRepresenting the spatial dislocation index of the i area and the j industry; pjRepresenting j total employment population; n is the number of regions; e.g. of the typeijThe employment post number of the i area j industry; ejThe total employment post number of j industry; pijThe employment population number of the i area j industry.
Emergency medical services resource characteristics:
emergency service resources are a general term for various resources required in emergency procedures. The emergency service resource studied by the embodiment belongs to emergency medical service resources, and mainly comprises the number of emergency vehicles in a hospital and the number of emergency medical care personnel in the hospital. Emergency medical services have several characteristics:
(1) high aging property
The emergency medical service resource is different from a general emergency service resource and has strong timeliness, and the emergency service resource must arrive at a specified place within a specified time. Thus, the present embodiment takes into account the time constraints of emergency medical resource accessibility when studying emergency medical service resource configurations.
(2) Structural property
Emergency medical services resources are a series of supporting facilities, and for medical emergency services, emergency service requirements include emergency ambulance vehicles, emergency number of beds, corresponding professional doctors, and the like. The number of the main emergency vehicles and the number of the emergency medical personnel researched by the embodiment are regarded as the success of the rescue when the emergency medical personnel arrive at the site at the first time.
(3) Low economical efficiency
Emergency medical services are generally set for specific hazard sources, are generally used less frequently, and are often of lower practical utility. The primary goal of emergency resources is to cover all ranges of demand points, and the secondary is to minimize system internal scheduling time. In the process of researching emergency resources, although the cost is also considered, the system scheduling is optimal under the condition of meeting the first two targets, and an optimization model with maximized benefits is not established independently. The present embodiment uses this characteristic, and does not need to consider the economy too much.
(4) Homogeneity of the mixture
Different from general medical services, a patient can select different hospitals to receive rescue services according to own habits and payment capacity, and emergency services always give priority to the nearest hospitals to rescue the patient. Therefore, when the emergency service resource demand model is established, individual demand differences do not need to be considered, and research can be carried out on an mesoscopic level.
(5) Unpredictability
Emergencies are difficult to predict the size, time, and duration of an emergency outbreak due to their complex evolutionary mechanisms, and medical emergency services should have a high range of coverage. In the embodiment, when the sudden emergency service resource demand prediction is researched, the random value between the maximum value and the minimum value of the disaster accident rate within six years is adopted for measurement.
Model assumptions are proposed and system boundaries are partitioned:
1. proposing system dynamics model assumptions
The invention assumes that the development of the considered emergency service requirement is comprehensively influenced by a plurality of factors, and in order to make the constructed system dynamics model more logical and intuitive, the following basic assumptions are provided for some system relations and related parameters in the model:
the emergency service requirement is a dynamic, complex, comprehensive and multi-industry crossed system project, and the involved influence factors are numerous, so that certain secondary important influence factors are excluded from the model in the specific model construction process for ensuring the simplicity of the model and the acquirability of related data.
Secondly, in the simulation time range, social economy and population are developed stably, the development situation of medical services in the whole market is good, the level of medical care personnel is high, and meanwhile, the scale of a hospital is kept unchanged for a short time and is slowly increased for a long time.
And thirdly, the occupation distribution balance index is not changed greatly in a short period under the influence of policies.
2. Partitioning emergency service demand system boundaries
The emergency service system researched by the invention is an emergency medical service system, the emergency medical requirement system is divided into a frequent emergency medical requirement subsystem and an emergent emergency medical requirement system, and the respective basic subsystems of the two are a human mouth subsystem and an emergency ambulance use subsystem. The structure and the incidence relation among all the subsystems are connected by applying concepts and variables in a medical emergency demand chain; in setting the system time boundary, the runtime system boundary of the emergency medical need system model may be defined, for example, to 2015 to 2023, taking into account the type of historical data and the sensitivity of the emergency medical need to various contextual variables.
Establishing an incidence relation graph of an emergency medical requirement model:
the method comprises the following steps that a model system is a key step for generating a DYNAMO equation, on the basis of researching a causal loop of an emergency medical demand system, key variables are converted into horizontal variables in a flow diagram of the model system according to causal relations and feedback loops among subsystems, all other variables are divided into rate variables, constants and auxiliary variables according to preset rules, and parameter sizes or parameter ranges are determined for the variables respectively.
In the constructed emergency medical demand model system, fig. 3, the economic total amount, the number of the permanent population and the holding capacity of the emergency ambulance are main horizontal variables; the economic total growth rate, the population growth rate, the emergency ambulance growth rate and the like are rate variables; the rejection rate of the emergency ambulance is a constant, and variables such as the occupational balance coefficient and the like are auxiliary variables. With population scale subsystems as in figure 4.
Each chain in fig. 3 has a causal relationship, which can be divided into positive feedback that promotes an increase in the variable pointed to by the arrow and negative feedback that promotes a decrease in the variable pointed to by the arrow. As can be seen from FIG. 3, controlling the occupancy balance factor under the assumed conditions may reduce the risk of impact from regional emergencies, thereby reducing the need for emergency services in the region.
The population growth rate and the balance coefficient of position and residence are respectively table functions, and the numerical values of the table functions are set to be larger first and smaller second in consideration of the urgency of emergency development in a research area. And carrying out simulation analysis on the data for many times by using Vensim to obtain a proper parameter value.
Analyzing and establishing a decision model:
the following system dynamics equations may be established based on the above analysis of the existing emergency services system and the system dynamics flow diagram:
establishing a DYNAMOO equation comprising two state equations:
TOP＝INTER(AP，P0)，
TOA＝INTER(AA，A0)。
auxiliary equation: TES is TUES + SUES,
TUES＝TUET+TUED，
SUES＝SUET+SUED，
TUET＝TOP×RA×RMT，
TUED＝TOA×RMD，
SUET＝RS×RZ×JHR×RSA，
SUED＝RS×RZ×JHR×RSD；
rate variable equation:
AA＝A0×(RAA-RAD)，
AP＝P0×(RAP-DP)。
the relevant parameter variables in the emergency service demand feedback loop and equation are illustrated as follows:
TABLE 2 meanings of the variables of the parameters
The main model parameter calibration method comprises the following steps:
(1) frequency of occurrence of regional emergency:
the emergencies studied by the invention refer to fire accidents, traffic accidents and geological disaster accidents, and the accidents are influenced by various factors, and the emergencies occur randomly within a period of time rather than show a trend of increasing or decreasing along with the change of time series. After the research area is divided into traffic cells, the number of regional emergencies is found to be randomly distributed between the maximum value and zero by comparing historical data of the past year.
(2) The area coefficient is as follows:
an emergency event occurs in a certain area and is a random event. However, once an accident occurs, the same level of accident type has different effects in different regions, and the scale of emergency service requirements is also different. And carrying out risk division on the regions by the emergency.
And the risk assessment of the urban emergency service area is carried out from the aspects of danger occurrence frequency, density and economic strength and weakness coefficients. And (3) applying a Kobub-Douglas production function, taking the density of the hazard source, the age coefficient and the economic index as input, taking the comprehensive risk evaluation as output, constructing a comprehensive vulnerability evaluation model, and obtaining the comprehensive vulnerability evaluation for the regional accident emergency risk by using the following formula.
As an example, α + β ═ 1.
As an example, the first elastic coefficient α and the second elastic coefficient β both take a value of 0.5.
(3) Balance coefficient:
the scene variables in the invention are measured by the independent indexes, and are calculated according to the calculation formula of the independent indexes and by combining the employment population number and the work number of each area released by the human resource guarantee bureau of research area.
(4) Determination of coefficient weights:
the emergency events corresponding to the emergency services researched by the invention mainly comprise fire accidents, geological disaster accidents and public health accidents. According to the research object, the characteristics of less data parameters and high data quantity precision are considered, an objective weighting evaluation method is adopted, the weight of each index is calculated by a variation coefficient method, and the weight of each index is obtained by calculating the statistical characteristics of the average value, the standard deviation and the like of the parameters.
Considering different dimensions of each index in an evaluation index system, each index is standardized by using the following formula to obtain the variation coefficient of each index, and the value of the variation coefficient is used for measuring the difference degree of each index.
In the formula WmThe standard deviation coefficient of the m index; sigmamThe standard deviation of the m index; a. themIs the average of the m-th index.
And determining the weight of each index according to the following formula, wherein the larger the standard deviation coefficient is, the larger the weight of the index is:
in the formula wmRepresenting the weight.
And (3) validity verification:
and (3) completing the construction of a system dynamics model by using Vensim software, and determining the unit and the range of various parameters and variables. In order to avoid errors in the system structure, the causal relationship and the logic quantity relationship among the variables of the model, and further influence the predicted structure, the simulation conditions of the model need to be verified.
(1) Internal operation inspection:
the Vensim system is provided with a model detection module and a unit detection module, and can check both the established model and the established dimension. The dimension of each variable in the detection target requirement model is appropriate, equation operation can be performed, and each equation is guaranteed to have practical significance.
(2) And (3) checking the validity:
whether the model can truly reflect the modeling target or not is a key concern. After the internal inspection is completed, the results are compared with actual historical data by means of system simulation. Because the selection subjectivity of many parameters of the system dynamics model is strong, the model is corrected while the parameters are corrected by using a sensitivity analysis method. The sensitivity can be obtained from the following equation.
In the formula SLRepresents the sensitivity of the state variable L; l istRepresents a state variable L; Δ LtIs the variation of the state variable L in the time period of delta t; u shapetIs a parameter U under a state variable L; delta UtIs the amount of change in the parameter U over the period of at.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (10)
1. An emergency medical resource allocation optimization method considering position distribution is characterized by comprising the following steps of,
firstly, the method comprises the following steps: calculating the running time of the emergency ambulance from each medical supply point to the emergency medical supply point according to the paths between the emergency medical demand point and the N medical supply points, and taking the medical supply point corresponding to the path with the running time less than a preset running time threshold value as a primary effective medical supply point; n is a positive integer;
II, secondly: calculating the rescue survival probability of each primary effective medical supply point according to the relation between the running time corresponding to the primary effective medical supply point and the rescue time constraint threshold, and taking the primary effective medical supply point with the rescue survival probability reaching the expected survival probability threshold as a secondary effective medical supply point;
thirdly, the method comprises the following steps: determining the reachable opportunity of each secondary effective medical supply point to each emergency medical demand point according to the relation between the running time of the secondary effective medical supply point and the rescue time constraint threshold, and accumulating the reachable opportunity number of each secondary effective medical supply point to all the emergency medical demand points to obtain the time reachability of each secondary effective medical supply point to all the emergency medical demand points; selecting all secondary effective medical supply points with time accessibility meeting a limit threshold as target supply points;
fourthly, the method comprises the following steps: calculating the supply-demand ratio of each target supply point to the emergency medical demand points according to the medical service resource supply capacity of each target supply point and the total emergency medical demand of the emergency medical demand points obtained through prediction; accumulating and summing the supply-demand ratios of all the emergency medical demand points of each target supply point to obtain the space accessibility of all the emergency medical demand points of each target supply point; and taking the target supply point with the largest space accessibility as a preferred point of the emergency medical resource configuration.
2. The method of claim 1, wherein the emergency medical resource allocation optimization method considering occupational distribution,
the obtaining process of the running time comprises the following steps:
based on ArcGIS projection, projecting the position of the medical supply point to the nearest road section, and determining a path between the emergency medical demand point and the N medical supply points; the required travel time of the emergency vehicle from each medical supply point to the emergency medical demand point is calculated on a path basis.
3. The method of claim 2, wherein the emergency medical resource allocation optimization method considering occupational distribution,
the rescue survival probability calculation method comprises the following steps:
in the formula, rho is rescue survival probability, tijRepresents the running time from the medical supply point i to the emergency medical demand point j, and T is the rescue time constraint threshold.
4. The method of claim 3, wherein the runtime t is a running time tijIncluding time delays due to road congestion.
5. The method of claim 4, wherein the emergency medical resource allocation optimization method considering occupational distribution,
the calculation method of the time reachability includes:
in the formula AjtTime accessibility of the secondary available medical supply point j to all the emergency medical demand points, n is the total number of emergency medical demand points, OjNumber of opportunities for secondary effective medical delivery point j, f (t)ij) For the running time tijCorresponding reachable opportunities.
6. The method of claim 5, wherein the emergency medical resource allocation optimization method considering occupational distribution,
the calculation method of the spatial reachability comprises the following steps:
in the formula AjsSpatial accessibility, R, to all Emergency medical Requirements for target supply Point jjSupply-to-demand ratio, S, for target supply point j to each emergency medical demand pointjThe medical service resource supply capacity of the target supply point j, and D is the total emergency medical demand of the emergency medical demand point obtained by prediction.
7. The method of claim 6, wherein the emergency medical resource allocation optimization considering occupational distribution,
with the total demand D of emergent medical treatment as the total emergency service demand TES of target area, the target area is as emergent medical treatment demand point, then:
predicting and obtaining the total emergency service demand TES of the target area according to the frequent emergency service demand TUES and the sudden emergency service demand SUES;
predicting the frequent emergency service demand TUES of the target area according to the frequent emergency material demand TUET and the frequent emergency medical personnel demand TUED;
wherein the frequent emergency material demand TUET is determined by the product of the population size TOP of the target area, the age composition coefficient RA and the medical material allocation base RMT;
the frequent emergency medical personnel demand TUED is determined by the product of the number of emergency vehicles TOA in the target area and the medical personnel arming base RMD;
predicting the emergency service demand SUES of the target area according to the emergency material demand SUET and the emergency medical staff demand SUED;
the SUET is determined by the product of the frequency RS of disaster occurrence in the target area, the area coefficient RZ, the balance coefficient JHR and the product of the RSA multiplication of the single emergency task material allocation cardinality;
the sudden emergency medical staff demand SUED is determined by the product of the disaster occurrence frequency RS of the target area, the area coefficient RZ, the balance coefficient JHR and the single emergency task staff allocation cardinality RSD.
8. The method of claim 7, wherein the emergency medical resource allocation optimization method considering occupational distribution,
the calculation method of the population size TOP comprises the following steps:
TOP＝INTER(AP，P0)，
wherein AP is the oral growth; p0 is the initial population value;
the method for calculating the population growth amount AP comprises the following steps:
AP＝P0×(RAP-DP)，
wherein RAP represents population growth rate and DP represents mortality;
the method for calculating the TOA of the emergency ambulance comprises the following steps:
TOA＝INTER(AA，A0)，
wherein AA is the growth of the emergency ambulance, and A0 is the initial number of the emergency ambulance;
the method for calculating the emergency ambulance growth amount AA comprises the following steps:
AA＝A0×(RAA-RAD)，
in the formula, RAA is the growth rate of the emergency ambulance, and RAD is the breakage rate of the emergency ambulance.
9. The method of claim 8, wherein the emergency medical resource allocation optimization method considering occupational distribution,
the method for calculating the area coefficient RZ comprises the following steps:
in the formula HαIs the density of a dangerous source, P0For the economic loss ratio, α is the first elastic coefficient, and β is the second elastic coefficient.
10. The method of optimizing an emergency medical resource allocation according to a distribution of positions according to claim 9,
α+β＝1；
the values of the first elastic coefficient alpha and the second elastic coefficient beta are both 0.5.
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