Evaluation method for performing configuration decision for radiation equipment

Abstract

The invention provides an evaluation method for configuration decision making for radiation equipment, and belongs to the technical field of medical treatment, and the method comprises the steps: firstly constructing a scene configuration decision evaluation system of the radiation equipment, then collecting purchase application information filled and reported by a plurality of hospitals through the evaluation system, and carrying out the preliminary evaluation of the purchase application information, according to real-world data of the medical equipment, the resource allocation condition of the medical equipment is quantitatively evaluated by using an informatization means, and the evaluation method is suitable for allocation decision evaluation of the radiation equipment of the medical conjunct and is suitable for two conventional scenes of adding equipment and updating equipment. The method does not contain special scenes such as first-time purchase application, provides auxiliary decision support and data support for large-scale medical equipment configuration of a medical institution, optimizes structural configuration of the medical equipment and reasonable configuration of financial funds, and finally achieves the purposes of improving quality and efficiency and improving the doctor-seeing satisfaction degree of patients.

Description

Technical Field

[0001] The invention belongs to the field of medical technology, and in particular relates to an evaluation method for making configuration decisions for radiation equipment. Background Art

[0002] Medical equipment is an auxiliary tool for medical diagnosis, which can help doctors make accurate judgments on the condition and is welcomed by many hospitals. With the development of science and technology, medical equipment is constantly updated, which has greatly helped to improve the medical environment for patients. The hospital's investment in medical equipment continues to grow. While improving the level of diagnosis and treatment, the waste caused by blindly purchasing equipment has become more serious, especially large medical equipment. Due to its large capital investment and high operating costs, more sufficient and effective demonstration should be carried out when purchasing. While meeting the actual clinical needs, the sustainable development of the hospital should be taken into account to achieve the optimal allocation of medical resources. The role of medical equipment in hospital diagnosis and treatment activities is very important. Ensuring the normal use of medical equipment and meeting clinical needs is the basic requirement for medical equipment configuration and management, and it is also the key and difficulty.

[0003] In the process of medical equipment configuration and selection, there are problems such as immature configuration evaluation standards, large differences in equipment utilization rates, vague audit basis, insufficient sources of configuration and usage evaluation data, and lack of information-based evaluation methods, which may lead to decision-making deviations, resulting in low equipment utilization rates, or even idleness, resulting in waste of resources. Traditional medical equipment configuration is dominated by clinical demands and empirical estimates, supplemented by simple economic forecasts. The lack of real and reliable data makes it impossible to reasonably correct and adjust the configuration plan of medical equipment. Summary of the invention

[0004] The purpose of the present invention is to provide an evaluation method for configuration decisions of radiation equipment, aiming to solve the problem that the configuration of medical equipment in the prior art is dominated by clinical demands and empirical estimates, supplemented by simple economic forecasts, and lacks real and reliable data, resulting in the inability to reasonably correct and adjust the configuration plan of medical equipment.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An evaluation method for configuration decision of radiological equipment comprises the following steps:

[0007] S1. Build a scenario configuration decision-making evaluation system for radiation equipment;

[0008] S2. Use the evaluation system to collect purchase application information submitted by multiple hospitals;

[0009] S3. Use the evaluation system to conduct a preliminary evaluation of the purchase application information and generate preliminary evaluation data;

[0010] S4. Use the evaluation system to obtain equipment operation monitoring data and calculate the increase demand index of multiple hospitals and the update demand index of each equipment;

[0011] S5. Set overall budget goals;

[0012] S6. Organize the preliminary evaluation data of a certain type of equipment procurement application from multiple hospitals, equipment operation monitoring data, increase demand assessment data, and update demand assessment data, and build a linear programming model for analysis.

[0013] As a preferred solution of the present invention, the configuration decision evaluation system includes a procurement application module, an equipment operation status monitoring module, an increase demand evaluation module, an update demand evaluation module, a procurement decision module and an auxiliary decision analysis report module, wherein the auxiliary decision analysis report module is used to analyze the overall expected target situation, the target situation after decision evaluation, and the application and decision details of each hospital.

[0014] As a preferred solution of the present invention, the purchase application module is used to collect the purchase application information of each hospital and perform a preliminary evaluation on the purchase application information. The purchase application module is provided with a purchase application information unit and a purchase application evaluation unit.

[0015] As a preferred solution of the present invention, the procurement application information includes application type, equipment classification, brand and model of the equipment to be purchased, original value of the equipment to be purchased, funds to be applied for, personnel support, installation conditions support and procurement budget execution status.

[0016] As a preferred solution of the present invention, the preliminary evaluation steps of the purchase application evaluation unit are specifically as follows:

[0017] S301. Obtaining a certain purchase application information submitted by a single hospital;

[0018] S302, matching the background knowledge base to obtain the threshold and discrimination rules of the corresponding information;

[0019] S303: Determine whether a certain information reported meets the preset threshold judgment rule. The judgment result has the following two situations A and B:

[0020] A: If a certain information reported meets the preset threshold judgment rules, it is determined that the application information reported has passed the preliminary assessment;

[0021] B: If a certain information reported does not meet the preset threshold judgment rules, it is determined that the application information reported has not passed the preliminary assessment;

[0022] S304: For other items of application information of a single hospital, the above steps S301-S303 are sequentially judged. When all the information reported passes the preliminary evaluation, the procurement application information of the hospital passes the preliminary evaluation.

[0023] S305. According to the above steps S301-S304, determine whether the procurement application information of other hospitals has passed the preliminary evaluation.

[0024] As a preferred solution of the present invention, the equipment operation status monitoring module monitors the overall equipment operation status and the operation status of a single device in each hospital through Internet of Things collection and information system fusion technology, and generates overall equipment operation status data and single equipment operation status data.

[0025] As a preferred solution of the present invention, the overall budget target set in step S5 includes an annual overall budget amount target value, an annual planned equipment number target value, an annual expected average reservation days target value, and an annual expected single-unit average monthly revenue growth ratio target value.

[0026] As a preferred solution of the present invention, the increased demand evaluation module is used to calculate the increased demand index and increased demand ranking of each hospital. The increased demand calculation method is as follows:

[0027] S401, construct evaluation matrix X:

[0028] Assume that there are n hospitals to be evaluated, m evaluation indicators, and the evaluation matrix is ​​X. The element x in matrix X is ij (1≤i≤n,1≤j≤m), represents the jth evaluation index value of the i-th evaluation object;

[0029] S402, construct the forward matrix Y:

[0030] The element in the forward matrix Y is y ij (1≤i≤n,1≤j≤m), the m evaluation indicators are positively transformed, and the positive transformation formula is as follows:

[0031] For high-quality indicators, y ij =x ij ; 1≤i≤n,1≤j≤m;

[0032] For the low-quality index y ij =max(x 1j ,x 2j ,…,x nj )-x ij ; 1≤i≤n,1≤j≤m;

[0033] S403, construct a dimensionless evaluation matrix Z:

[0034] The dimensionless evaluation matrix Z has elements z ij (1≤i≤n,1≤j≤m), the calculation formula is as follows,

[0035] S404, determine the optimal solution Z + 、The worst solution Z - :

[0036]

[0037] S405, calculate the distance from each evaluation object to the optimal solution and the worst solution:

[0038] Define the distances from the i-th (i=1,2,…,n) evaluation object to the optimal solution and the worst solution as

[0039]

[0040]

[0041] Among them, j represents a certain evaluation index, m represents the number of evaluation indicators (columns), W j represents the weight value of the jth indicator, represents the optimal solution data for the jth indicator, represents the worst solution data for the jth indicator, z ij It represents the corresponding data of the jth indicator of an evaluation object i;

[0042] S406. Calculate the increased demand index of each evaluation object. The calculation method is as follows:

[0043]

[0044] Where: 0≤S i ≤100, and S i The bigger The smaller it is, the closer it is to the optimal value;

[0045] S i The larger the value is, the greater the increase in demand index of the evaluation object is, and the intensity of the hospital's increase in equipment demand is similar to S i Directly proportional.

[0046] As a preferred solution of the present invention, the update requirement evaluation module is used to calculate the update requirement index and update requirement ranking of the single equipment of each hospital based on the operation data of all single equipment of each hospital. The update requirement calculation formula is as follows:

[0047]

[0048] Where:

[0049] y ij —Update demand index of the jth device in the i-th hospital;

[0050] a ij —The service life of the jth device in the i-th hospital;

[0051] b ij —Annual revenue of the jth device in the i-th hospital;

[0052] c ij —Annual number of failures of the jth device in the i-th hospital;

[0053] d ij —The proportion of annual maintenance and repair costs of the jth device in the i-th hospital;

[0054] A—The minimum service life of similar equipment allowed to be scrapped;

[0055] B—average annual revenue of similar equipment;

[0056] C—average annual failure times of similar equipment;

[0057] D—The proportion of annual repair and maintenance costs of similar equipment.

[0058] As a preferred solution of the present invention, in step S6, a linear programming model is constructed using the procurement decision module for analysis, and the calculation method is as follows:

[0059] Objective function: Constraints: in,

[0060]

[0061] α ij is an integer, β ij is an integer;

[0062] Where:

[0063] α ij —The i-th hospital plans to approve the addition of the j-th device of a certain type;

[0064] β ij —The jth piece of equipment of a certain type that the i-th hospital intends to approve for renewal;

[0065] t i —The i-th hospital intends to apply for the j-th piece of equipment of a certain type that has passed the preliminary evaluation of the purchase application information;

[0066] s i—The jth device of a certain type that the i-th hospital intends to apply for and update and that has passed the preliminary evaluation of the purchase application information;

[0067] m—the total number of equipment of a certain type that is increased and updated in the decision-making body's budget;

[0068] y—the total amount of funds budgeted for a certain type of equipment to be increased and updated by the decision-making body;

[0069] n—the total number of hospitals managed by the decision-making body;

[0070] a i —The number of certain types of equipment currently available in the i-th hospital;

[0071] b i —The average daily workload of a certain type of equipment in the i-th hospital;

[0072] c i —The average daily working hours of a certain type of equipment in the i-th hospital;

[0073] e i —The average number of working days per month of a certain type of equipment in the i-th hospital;

[0074] f i —The average appointment time for a certain type of equipment in the i-th hospital;

[0075] g i —The average monthly revenue of a certain type of equipment in the i-th hospital;

[0076] h i —The average monthly main cost of a certain type of equipment in the i-th hospital;

[0077] k i —Ranking of the increased demand for a certain type of equipment in the i-th hospital;

[0078] l i —Update demand index of a certain type of equipment in the i-th hospital;

[0079] p ij —The amount of funds requested for the additional purchase of the jth piece of a certain type of equipment for the i-th hospital and which has passed the preliminary evaluation of the purchase application information;

[0080] q ij —The proposed amount of funds to be applied for after the purchase application information is initially evaluated for the j-th equipment of a certain type in the i-th hospital;

[0081] ε ij — The original value of the j-th piece of equipment of a certain type to be purchased by the i-th hospital and preliminarily evaluated through the purchase application information;

[0082] θ ij—The original value of the j-th piece of equipment of a certain type in the i-th hospital that is to be purchased and updated and preliminarily evaluated through the purchase application information;

[0083] u—the threshold of the average daily workload of a single device of a certain type, usually the 40th percentile;

[0084] v—the threshold of the average daily working hours of a certain type of equipment, usually the 40th percentile;

[0085] w—the threshold of the average number of reservation days for a certain type of equipment, usually the median;

[0086] x—the update demand index threshold of a certain type of equipment, usually the 90th percentile;

[0087] γ—the threshold of the increase in demand ranking of a certain type of equipment;

[0088] δ—depreciation period of a certain type of equipment;

[0089] M—the threshold for the number of additional devices of a certain type that a single hospital can apply for;

[0090] N—the threshold number of updated equipment of a certain type that a single hospital can apply for;

[0091] A—the expected average number of days for reservation for a certain type of equipment;

[0092] B—The expected average monthly net revenue growth rate of a certain type of equipment.

[0093] Compared with the prior art, the present invention has the following beneficial effects:

[0094] 1. The present invention uses information technology to quantitatively evaluate the resource allocation of medical equipment based on the real-world data of medical equipment. The evaluation method is applicable to the evaluation of radiological equipment configuration decisions of medical alliances, and is applicable to the two conventional scenarios of adding equipment and updating equipment, but does not include special scenarios such as the first application for purchase. The evaluation method includes a procurement application module, an equipment operation status monitoring module, an increase demand assessment module, an update demand assessment module, a procurement decision module, and an auxiliary decision analysis report module; under the conditions of meeting the annual planned number of equipment, the annual expected average number of appointment days, and the annual expected average monthly revenue growth rate of a single unit, the annual overall budget amount is the lowest; it provides auxiliary decision support and data support for the configuration of large medical equipment in medical institutions, optimizes the structural configuration of medical equipment and the reasonable allocation of financial funds, and ultimately achieves the purpose of improving quality and efficiency and enhancing patient satisfaction with medical treatment.

[0095] 2. The present invention constructs a scenario configuration decision evaluation system for radiation equipment, collects purchase application information submitted by multiple hospitals and conducts preliminary evaluation, obtains equipment operation monitoring data to calculate the increase demand index and update demand index, sets the overall budget target, and organizes relevant data to construct a linear programming model for analysis, so as to make more scientific and reasonable configuration decisions for radiation equipment;

[0096] 3. The present invention provides an information-based evaluation method, which makes decisions based on real and reliable data, avoiding decision-making deviations caused by reliance on clinical demands, empirical estimates, and simple economic forecasts in traditional configuration methods; through preliminary evaluation of procurement application information and analysis of equipment operation monitoring data, it is possible to more accurately understand the hospital's demand for radiation equipment and the actual operating conditions of the equipment, thereby reasonably correcting and adjusting the configuration plan of medical equipment, improving the utilization rate of equipment, and reducing idle waste; setting an overall budget target and constructing a linear programming model for analysis helps to meet the needs of the hospital while taking into account the sustainable development of the hospital and achieving the optimal allocation of medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0098] Figure 1 A system block diagram of an evaluation method for making configuration decisions for radiation equipment according to the present invention;

[0099] Figure 2 A schematic flow chart of an evaluation method for making configuration decisions for radiation equipment according to the present invention;

[0100] Figure 3 The present invention is an evaluation flow chart of an evaluation method for making configuration decisions for radiation equipment. DETAILED DESCRIPTION

[0101] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0102] Example 1

[0103] See also Figure 1-Figure 3 , the present invention provides the following technical solutions:

[0104] An evaluation method for configuration decision of radiological equipment comprises the following steps:

[0105] S1. Build a scenario configuration decision-making evaluation system for radiation equipment;

[0106] The configuration decision evaluation system includes a purchase application module, an equipment operation status monitoring module, an increase demand evaluation module, an update demand evaluation module, a purchase decision module, and an auxiliary decision analysis report module;

[0107] S2. Use the evaluation system to collect purchase application information submitted by multiple hospitals;

[0108] S3. Use the evaluation system to conduct a preliminary evaluation of the purchase application information and generate preliminary evaluation data;

[0109] S4. Use the evaluation system to obtain equipment operation monitoring data and calculate the increase demand index of multiple hospitals and the update demand index of each equipment;

[0110] S5. Set overall budget goals;

[0111] S6. Organize the preliminary evaluation data of a certain type of equipment procurement application from multiple hospitals, equipment operation monitoring data, increase demand assessment data, and update demand assessment data, and build a linear programming model for analysis.

[0112] In a specific embodiment of the present invention, the evaluation method of the present invention is applicable to the evaluation of the configuration decision of the radiological equipment of the medical alliance, and is applicable to the two conventional scenarios of adding equipment and updating equipment, and does not include special scenarios such as the first application for purchase. The configuration decision evaluation system in the evaluation method includes a purchase application module, an equipment operation status monitoring module, an increase demand evaluation module, an update demand evaluation module, a purchase decision module and an auxiliary decision analysis report module. Under the conditions of meeting the annual planned number of equipment, the annual expected average number of appointment days, and the annual expected average monthly revenue growth rate of a single unit, the annual overall budget amount is the lowest, with the purpose of achieving accurate allocation of financial funds, reasonable allocation of medical equipment, improving the use efficiency of equipment, shortening the patient examination appointment time, and improving patient satisfaction with the treatment;

[0113] The purchase application module includes the purchase application information reporting unit and the purchase application information preliminary evaluation unit of each hospital. The purchase application information mainly includes the application type (addition or update), equipment classification (such as scientific research type, clinical scientific research type and clinical practical type), brand and model of the equipment to be purchased, the original value of the equipment to be purchased, the funds to be applied for, the personnel support, the installation conditions, the execution of the purchase budget in the past three years, etc.; the purchase application information preliminary evaluation module matches the background knowledge base and the preset threshold according to the reported information, and evaluates whether the equipment classification, brand and model of the equipment to be purchased, the original value of the equipment to be purchased, the funds to be applied for, the personnel support, the installation conditions, the execution of the purchase budget in the past three years, the number of applied equipment, and the service life of the equipment meet the requirements;

[0114] The equipment operation status monitoring module monitors the overall equipment operation status and the operation status of each hospital's single equipment through IoT collection and information system integration technology. The overall equipment operation status monitoring indicators mainly include the number of equipment, the average daily workload of a single equipment, the average daily working time of a single equipment, the average daily startup time of a single equipment, the average number of working days per month, the average number of appointment days, the average monthly income of a single equipment, the average monthly main cost of a single equipment, etc. The single equipment operation status monitoring indicators mainly include the equipment activation years, the number of annual failures, the average daily workload, the average daily working time, the average daily startup time, the average monthly working days, the average monthly income, the average monthly main cost, etc.

[0115] The increase demand evaluation module uses big data analysis and statistical analysis technology to calculate the increase demand index and increase demand ranking of each hospital based on the operation status of a certain type of equipment in each hospital. The larger the increase demand index, the stronger the increase demand. The increase demand ranking is arranged in descending order from large to small according to the index, and the hospital with the largest increase demand index ranks first.

[0116] The update need evaluation module uses big data analysis and statistical analysis technology to calculate the update need index and update need ranking of each hospital's single equipment based on the operation status of all single equipment in each hospital. The larger the update need index, the stronger the update need. The update need ranking is arranged in descending order from large to small according to the index, and the one with the largest update need index ranks first.

[0117] The purchasing decision module uses operations research techniques to construct a linear programming model, determine the objective function, set constraints, and analyze the solution model based on the preliminary evaluation of each hospital's equipment application, equipment operation monitoring, increased demand evaluation, and updated demand evaluation.

[0118] The auxiliary decision analysis report module includes the overall expected target situation, the target situation after decision evaluation, and the application and decision details of each hospital. Among them, the overall expected target situation includes the annual overall budget amount, the annual planned number of equipment, the annual expected average number of appointment days, the annual expected average monthly revenue growth rate of each unit, etc.; the target situation after decision evaluation includes the budget amount forecast value, the planned number of equipment forecast value (divided into the number of increased equipment, the number of updated equipment, and the number of equipment with tiered classification), the average appointment days forecast value, the average monthly revenue growth rate forecast value of each unit, the name of the hospital that passed the decision evaluation and the equipment purchase situation (including application type, equipment tiered classification, brand model, budget amount, etc.), the number of equipment applied for, the number and ratio of approved equipment, the number of hospitals that applied, The number and proportion of hospitals that passed the application; the application and decision details of each hospital include the hospital name, application type, equipment classification, equipment brand and model, original value of equipment, application funds, proposed approved funds, personnel support and evaluation results, installation conditions and evaluation results, procurement budget execution in the past three years and evaluation results, preliminary evaluation results of application information, procurement decision support evaluation results, increase in demand index and ranking and threshold evaluation results, update of demand index and ranking and threshold evaluation results, average daily workload and threshold evaluation results of a single unit, average daily working hours and threshold evaluation results of a single unit, average number of appointment days and threshold evaluation results, average appointment days forecast, average monthly revenue forecast of a single unit, total number of evaluation items, number of evaluation items that passed and proportion, etc.

[0119] Specifically, the preliminary evaluation steps of the purchase application evaluation unit are as follows:

[0120] S301. Obtaining a certain purchase application information submitted by a single hospital;

[0121] S302, matching the background knowledge base to obtain the threshold and discrimination rules of the corresponding information;

[0122] S303: Determine whether a certain information reported meets the preset threshold judgment rule. The judgment result has the following two situations A and B:

[0123] A: If a certain information reported meets the preset threshold judgment rules, it is determined that the application information reported has passed the preliminary assessment;

[0124] B: If a certain information reported does not meet the preset threshold judgment rules, it is determined that the application information reported has not passed the preliminary assessment;

[0125] S304: For other items of application information of a single hospital, the above steps S301-S303 are sequentially judged. When all the information reported passes the preliminary evaluation, the procurement application information of the hospital passes the preliminary evaluation.

[0126] S305. According to the above steps S301-S304, determine whether the procurement application information of other hospitals has passed the preliminary evaluation.

[0127] Specifically, the increased demand evaluation module is used to calculate the increased demand index and increased demand ranking of each hospital. The increased demand evaluation module uses big data analysis and statistical analysis technology to calculate the increased demand index and increased demand ranking of each hospital according to the operation status of a certain type of equipment in each hospital; the larger the increased demand index, the stronger the increased demand, and the increased demand ranking is arranged in descending order from large to small according to the index, and the hospital with the largest increased demand index ranks first;

[0128] The core indicators in the scenario of increasing the number of radiological equipment mainly include the average daily working hours of a single device, the average daily workload of a single device, the average number of appointment days, etc. The calculation method for increasing the demand is as follows:

[0129] S401, construct evaluation matrix X:

[0130] Assume that there are n hospitals to be evaluated, m evaluation indicators, and the evaluation matrix is ​​X. The element x in matrix X is ij (1≤i≤n,1≤j≤m), represents the jth evaluation index value of the i-th evaluation object;

[0131] S402, construct the forward matrix Y:

[0132] The element in the forward matrix Y is y ij (1≤i≤n,1≤j≤m), the m evaluation indicators are positively transformed, and the positive transformation formula is as follows:

[0133] For high-quality indicators, y ij =x ij ; 1≤i≤n,1≤j≤m;

[0134] For the low-quality index y ij =max(x 1j ,x 2j ,…,x nj )-x ij ; 1≤i≤n,1≤j≤m;

[0135] S403, construct a dimensionless evaluation matrix Z:

[0136] The dimensionless evaluation matrix Z has elements z ij (1≤i≤n,1≤j≤m), the calculation formula is as follows,

[0137] S404, determine the optimal solution Z + 、The worst solution Z - :

[0138]

[0139] S405, calculate the distance from each evaluation object to the optimal solution and the worst solution:

[0140] Define the distances from the i-th (i=1,2,…,n) evaluation object to the optimal solution and the worst solution as

[0141]

[0142]

[0143] Among them, j represents a certain evaluation index, m represents the number of evaluation indicators (columns), W j represents the weight value of the jth indicator, represents the optimal solution data for the jth indicator, represents the worst solution data for the jth indicator, z ij It represents the corresponding data of the jth indicator of an evaluation object i;

[0144] S406. Calculate the increased demand index of each evaluation object. The calculation method is as follows:

[0145]

[0146] Where: 0≤S i ≤100, and S i The bigger The smaller it is, the closer it is to the optimal value;

[0147] S i The larger the value is, the greater the increase in demand index of the evaluation object is, and the intensity of the hospital's increase in equipment demand is similar to S i Directly proportional.

[0148] Specifically, the update need evaluation module calculates the update need index and update need ranking of the single equipment in each hospital according to the operation status of all single equipment in each hospital using big data analysis and statistical analysis technology; the larger the update need index, the stronger the update need, and the update need ranking is arranged in descending order from large to small according to the index, and the one with the largest update need index ranks first;

[0149] The core indicators in the radiation equipment update scenario mainly include service life, annual failure times, annual revenue, annual repair and maintenance cost ratio, etc. The update demand calculation method is as follows:

[0150]

[0151] Where:

[0152] yij —Update demand index of the jth device in the i-th hospital;

[0153] a ij —The service life of the jth device in the i-th hospital;

[0154] b ij —Annual revenue of the jth device in the i-th hospital;

[0155] c ij —Annual number of failures of the jth device in the i-th hospital;

[0156] d ij —The proportion of annual maintenance and repair costs of the jth device in the i-th hospital;

[0157] A—The minimum service life of similar equipment allowed to be scrapped;

[0158] B—average annual revenue of similar equipment;

[0159] C—average annual failure times of similar equipment;

[0160] D—The proportion of annual repair and maintenance costs of similar equipment.

[0161] Specifically, in step S6, a linear programming model is constructed using the procurement decision module for analysis. The procurement decision module uses operations research techniques to construct a linear programming model, determine the objective function, set constraints, and analyze the solution model based on the preliminary evaluation of a certain type of equipment application in each hospital, the equipment operation monitoring, the increase in demand evaluation, and the update of the demand evaluation. The calculation method is as follows:

[0162] Objective function: Constraints: in,

[0163]

[0164] α ij is an integer, β ij is an integer;

[0165] Where:

[0166] α ij —The i-th hospital plans to approve the addition of the j-th device of a certain type;

[0167] β ij —The jth piece of equipment of a certain type that the i-th hospital intends to approve for renewal;

[0168] t i—The i-th hospital intends to apply for the j-th piece of equipment of a certain type that has passed the preliminary evaluation of the purchase application information;

[0169] s i —The jth device of a certain type that the i-th hospital intends to apply for and update and that has passed the preliminary evaluation of the purchase application information;

[0170] m—the total number of equipment of a certain type that is increased and updated in the decision-making body's budget;

[0171] y—the total amount of funds budgeted for a certain type of equipment to be increased and updated by the decision-making body;

[0172] n—the total number of hospitals managed by the decision-making body;

[0173] a i —The number of certain types of equipment currently available in the i-th hospital;

[0174] b i —The average daily workload of a certain type of equipment in the i-th hospital;

[0175] c i —The average daily working hours of a certain type of equipment in the i-th hospital;

[0176] e i —The average number of working days per month of a certain type of equipment in the i-th hospital;

[0177] f i —The average appointment time for a certain type of equipment in the i-th hospital;

[0178] g i —The average monthly revenue of a certain type of equipment in the i-th hospital;

[0179] h i —The average monthly main cost of a certain type of equipment in the i-th hospital;

[0180] k i —Ranking of the increased demand for a certain type of equipment in the i-th hospital;

[0181] l i —Update demand index of a certain type of equipment in the i-th hospital;

[0182] p ij —The amount of funds requested for the additional purchase of the jth piece of a certain type of equipment for the i-th hospital and which has passed the preliminary evaluation of the purchase application information;

[0183] q ij —The proposed amount of funds to be applied for after the purchase application information is initially evaluated for the j-th equipment of a certain type in the i-th hospital;

[0184] ε ij— The original value of the j-th piece of equipment of a certain type to be purchased by the i-th hospital and preliminarily evaluated through the purchase application information;

[0185] θ ij —The original value of the j-th piece of equipment of a certain type in the i-th hospital that is to be purchased and updated and preliminarily evaluated through the purchase application information;

[0186] u—the threshold of the average daily workload of a single device of a certain type, usually the 40th percentile;

[0187] v—the threshold of the average daily working hours of a certain type of equipment, usually the 40th percentile;

[0188] w—the threshold of the average number of reservation days for a certain type of equipment, usually the median;

[0189] x—the update demand index threshold of a certain type of equipment, usually the 90th percentile;

[0190] γ—the threshold of the increase in demand ranking of a certain type of equipment;

[0191] δ—depreciation period of a certain type of equipment;

[0192] M—the threshold for the number of additional devices of a certain type that a single hospital can apply for;

[0193] N—the threshold number of updated equipment of a certain type that a single hospital can apply for;

[0194] A—the expected average number of days for reservation for a certain type of equipment;

[0195] B—The expected average monthly net revenue growth rate of a certain type of equipment.

[0196] Specifically, the present invention also provides a process for the practical application of an evaluation method for making configuration decisions for radiation equipment, and the specific steps are as follows:

[0197] Ⅰ: Each hospital fills in the purchase application information:

[0198] Each hospital fills in the purchase application information in the purchase application information module of the purchase application module. The purchase application information mainly includes the application type (addition or update), equipment classification (such as scientific research type, clinical research type and clinical practical type), brand and model of the equipment to be purchased, the original value of the equipment to be purchased, the funds to be applied for, the personnel support, the installation conditions support, and the execution of the purchase budget in the past three years;

[0199] II: Conduct a preliminary assessment of each hospital's procurement application information:

[0200] The purchase application information of each hospital is preliminarily evaluated through the purchase application information preliminary evaluation module of the purchase application module. The evaluation principle is as follows: the purchase application information preliminary evaluation matches the background knowledge base and the preset threshold according to the filled information, and evaluates whether the equipment ladder classification, the brand model of the equipment to be purchased, the original value of the equipment to be purchased, the funds to be applied for, the personnel support, the installation conditions, the execution of the purchase budget in the past three years, the number of equipment applied for, and the service life of the equipment meet the requirements;

[0201] III: Obtain equipment operation monitoring data, calculate the increase demand index of each hospital and the update demand index of each device in each hospital:

[0202] Obtain the overall equipment operation data in the equipment operation monitoring module, such as the average daily working hours of a single unit, the average daily workload of a single unit, the average number of appointment days and other indicator data, and calculate the increased demand index and ranking of each hospital according to the increased demand calculation method; the larger the increased demand index, the stronger the increased demand, and the increased demand ranking is arranged in descending order from large to small according to the index, and the one with the largest increased demand index ranks first;

[0203] Obtain the operation data of a single device in the equipment operation monitoring module, such as service life, annual number of failures, annual income, annual repair and maintenance cost ratio, and other indicators, and calculate the update demand index and ranking of each device in each hospital according to the update demand calculation method; the larger the update demand index, the stronger the update demand, and the update demand ranking is arranged in descending order from large to small according to the index, and the device with the largest update demand index ranks first;

[0204] IV: Set overall budget targets, such as the annual overall budget amount, the annual planned number of equipment, the annual expected average number of appointment days, the annual expected average monthly revenue growth rate of a single unit, etc.

[0205] Set overall budget targets, such as the annual overall budget amount, the annual planned number of equipment, the annual expected average number of appointment days, the annual expected average monthly revenue growth rate of a single unit, and other target values;

[0206] V: Organize the preliminary evaluation data of equipment procurement applications of various hospitals, equipment operation monitoring data, add demand evaluation data, update demand evaluation data, build a linear programming model, determine the objective function, set constraints, and analyze and solve the model;

[0207] Organize the preliminary evaluation data of each hospital's equipment procurement application (such as the number of equipment applied for by each hospital and the number of equipment that passed the preliminary evaluation, the amount of funds applied for, etc.), equipment operation monitoring data (such as the number of equipment in each hospital, the average daily workload of each equipment, the average daily working time of each equipment, the average number of appointment days, the average monthly income of each equipment, the average monthly main cost of each equipment, etc.), increase the demand assessment data (such as the increase demand index and ranking of each hospital, etc.), update the demand assessment data (such as the update demand index and ranking of each equipment in each hospital, etc.), use operations research technology to construct a linear programming model, determine the objective function, set constraints, and use methods such as branch and bound method or genetic algorithm to analyze and solve the model; the principle of the model is as follows: under the conditions of meeting the annual planned number of equipment, the annual expected average appointment days, and the annual expected average monthly income growth rate of each equipment, the annual overall budget amount is the lowest, the purpose is to achieve accurate allocation of financial funds, reasonable allocation of medical equipment, improve the efficiency of equipment use, shorten patient examination appointment time, and improve patient satisfaction with medical treatment;

[0208] VI: Output auxiliary decision analysis report:

[0209] According to the model solution results, the auxiliary decision analysis report module is used to output the auxiliary decision analysis report in the form of WORD or PDF; the auxiliary decision analysis report includes the overall expected target situation, the target situation after decision evaluation, the application and decision details of each hospital, etc. Among them, the overall expected target situation includes the annual overall budget amount, the annual planned number of equipment, the annual expected average number of appointment days, the annual expected average monthly revenue growth rate of a single unit, etc.; the target situation after decision evaluation includes the budget amount forecast value, the planned number of equipment forecast value, the average appointment days forecast value, the average monthly revenue growth rate of a single unit, etc. The predicted value of average revenue growth ratio, the names of hospitals that have passed the decision-making assessment and the equipment purchase status, the number and ratio of equipment that have passed, the number and ratio of hospitals that have passed, etc.; the application and decision details of each hospital include the name of the hospital, application type, equipment ladder classification, equipment brand and model, original value of the equipment, application funds, proposed approved funds, preliminary evaluation results of application information, procurement auxiliary decision evaluation results, increase in demand index and ranking and threshold evaluation results, update of demand index and ranking and threshold evaluation results, average appointment days predicted value, average monthly revenue predicted value of a single unit, number and ratio of items that have passed the evaluation, etc.;

[0210] The present invention quantitatively evaluates the resource allocation of medical equipment based on the real-world data of medical equipment by means of information technology. The evaluation method is applicable to the evaluation of radiological equipment configuration decisions of medical alliances, and is applicable to the two conventional scenarios of adding equipment and updating equipment, but does not include special scenarios such as the first application for purchase. The evaluation method includes a procurement application module, an equipment operation status monitoring module, an increase demand assessment module, an update demand assessment module, a procurement decision module and an auxiliary decision analysis report module; under the conditions of meeting the annual planned number of equipment, the annual expected average number of appointment days, and the annual expected average monthly revenue growth rate of a single unit, the annual overall budget amount is the lowest; it provides auxiliary decision support and data support for the configuration of large medical equipment in medical institutions, optimizes the structural configuration of medical equipment and the reasonable allocation of financial funds, and ultimately achieves the purpose of improving quality and efficiency and enhancing patient satisfaction with medical treatment.

[0211] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating configuration decisions for radiation equipment, characterized in that: The steps include: S1. Build a scenario configuration decision-making evaluation system for radiation equipment; S2. Use the evaluation system to collect purchase application information submitted by multiple hospitals; S3. Use the evaluation system to conduct a preliminary evaluation of the purchase application information and generate preliminary evaluation data; S4. Use the evaluation system to obtain equipment operation monitoring data and calculate the increase demand index of multiple hospitals and the update demand index of each equipment; S5. Set overall budget goals; S6. Organize the preliminary evaluation data of a certain type of equipment procurement application from multiple hospitals, equipment operation monitoring data, increase demand assessment data, and update demand assessment data, and build a linear programming model for analysis.

2. The evaluation method for configuration decision of radiation equipment according to claim 1, characterized in that: The configuration decision evaluation system includes a procurement application module, an equipment operation status monitoring module, an increase demand evaluation module, an update demand evaluation module, a procurement decision module and an auxiliary decision analysis report module, wherein the auxiliary decision analysis report module is used to analyze the overall expected target situation, the target situation after decision evaluation, and the application and decision details of each hospital.

3. The method for evaluating configuration decisions for radiation equipment according to claim 2, characterized in that: The purchase application module is used to collect purchase application information from various hospitals and to perform a preliminary evaluation on the purchase application information. The purchase application module is provided with a purchase application information unit and a purchase application evaluation unit.

4. The evaluation method for configuration decision of radiation equipment according to claim 3, characterized in that: The procurement application information includes application type, equipment classification, brand and model of equipment to be purchased, original value of equipment to be purchased, funds to be applied for, personnel support, installation conditions and procurement budget execution status.

5. The method for evaluating configuration decisions for radiation equipment according to claim 4, characterized in that: The preliminary evaluation steps of the purchase requisition evaluation unit are as follows: S301. Obtaining a certain purchase application information submitted by a single hospital; S302, matching the background knowledge base to obtain the threshold and discrimination rules of the corresponding information; S303: Determine whether a certain information reported meets the preset threshold judgment rule. The judgment result has the following two situations A and B: A: If a certain information reported meets the preset threshold judgment rules, it is determined that the application information reported has passed the preliminary assessment; B: If a certain information reported does not meet the preset threshold judgment rules, it is determined that the application information reported has not passed the preliminary assessment; S304: For other items of application information of a single hospital, the above steps S301-S303 are sequentially judged. When all the information reported passes the preliminary evaluation, the procurement application information of the hospital passes the preliminary evaluation. S305. According to the above steps S301-S304, determine whether the procurement application information of other hospitals has passed the preliminary evaluation.

6. The evaluation method for configuration decision of radiation equipment according to claim 5, characterized in that: The equipment operation status monitoring module monitors the overall equipment operation status and the operation status of individual equipment in each hospital through Internet of Things collection and information system fusion technology, and generates overall equipment operation status data and individual equipment operation status data.

7. The method for evaluating configuration decisions for radiation equipment according to claim 6, characterized in that: The overall budget target set in step S5 includes an annual overall budget amount target value, an annual planned equipment number target value, an annual expected average reservation days target value, and an annual expected single-unit average monthly revenue growth rate target value.

8. The method for evaluating configuration decisions for radiation equipment according to claim 7, characterized in that: The increased demand evaluation module is used to calculate the increased demand index and increased demand ranking of each hospital. The increased demand calculation method is as follows: S401, construct evaluation matrix X: Assume that there are n hospitals to be evaluated, m evaluation indicators, and the evaluation matrix is ​​X. The element x in matrix X is ij (1≤i≤n,1≤j≤m), represents the jth evaluation index value of the i-th evaluation object; S402, construct the forward matrix Y: The element in the forward matrix Y is y ij (1≤i≤n,1≤j≤m), the m evaluation indicators are positively transformed, and the positive transformation formula is as follows: For high-quality indicators, y ij =x ij ; 1≤i≤n,1≤j≤m; For the low-quality index y ij =max(x 1j ,x 2j ,…,x nj )-x ij ; 1≤i≤n,1≤j≤m; S403, construct a dimensionless evaluation matrix Z: The dimensionless evaluation matrix Z has elements z ij (1≤i≤n,1≤j≤m), the calculation formula is as follows, S404, determine the optimal solution Z + 、The worst solution Z - : S405, calculate the distance from each evaluation object to the optimal solution and the worst solution: Define the distances from the i-th (i=1,2,…,n) evaluation object to the optimal solution and the worst solution as Among them, j represents a certain evaluation index, m represents the number of evaluation indicators (columns), W j represents the weight value of the jth indicator, represents the optimal solution data for the jth indicator, represents the worst solution data for the jth indicator, z ij It represents the corresponding data of the jth indicator of an evaluation object i; S406. Calculate the increased demand index of each evaluation object. The calculation method is as follows: Where: 0≤S i ≤100, and S i The bigger The smaller it is, the closer it is to the optimal value; S i The larger the value is, the greater the increase in demand index of the evaluation object is, and the intensity of the hospital's increase in equipment demand is similar to S i Directly proportional.

9. The method for evaluating configuration decisions for radiation equipment according to claim 8, characterized in that: The update requirement evaluation module is used to calculate the update requirement index and update requirement ranking of each hospital's single equipment based on the operation data of all single equipment in each hospital. The update requirement calculation formula is as follows: Where: y ij —Update demand index of the jth device in the i-th hospital; a ij —The service life of the jth device in the i-th hospital; b ij —Annual revenue of the jth device in the i-th hospital; c ij —Annual number of failures of the jth device in the i-th hospital; d ij —The proportion of annual maintenance and repair costs of the jth device in the i-th hospital; A—The minimum service life of similar equipment allowed to be scrapped; B—average annual revenue of similar equipment; C—average annual failure times of similar equipment; D—The proportion of annual repair and maintenance costs of similar equipment.

10. The method for evaluating configuration decisions for radiation equipment according to claim 9, characterized in that: In step S6, a linear programming model is constructed using the procurement decision module for analysis, and the calculation method is as follows: Objective function: Constraints: in, α ij is an integer, β ij is an integer; Where: α ij —The i-th hospital plans to approve the addition of the j-th device of a certain type; β ij —The jth piece of equipment of a certain type that the i-th hospital intends to approve for renewal; t i —The i-th hospital intends to apply for the j-th piece of equipment of a certain type that has passed the preliminary evaluation of the purchase application information; s i —The jth device of a certain type that the i-th hospital intends to apply for and update and that has passed the preliminary evaluation of the purchase application information; m—the total number of equipment of a certain type that is increased and updated in the decision-making body's budget; y—the total amount of funds budgeted for a certain type of equipment to be increased and updated by the decision-making body; n—the total number of hospitals managed by the decision-making body; a i —The number of certain types of equipment currently available in the i-th hospital; b i —The average daily workload of a certain type of equipment in the i-th hospital; c i —The average daily working hours of a certain type of equipment in the i-th hospital; e i —The average number of working days per month of a certain type of equipment in the i-th hospital; f i —The average appointment time for a certain type of equipment in the i-th hospital; g i —The average monthly revenue of a certain type of equipment in the i-th hospital; h i —The average monthly main cost of a certain type of equipment in the i-th hospital; k i —Ranking of the increased demand for a certain type of equipment in the i-th hospital; l i —Update demand index of a certain type of equipment in the i-th hospital; p ij —The amount of funds requested for the additional purchase of the jth piece of a certain type of equipment for the i-th hospital and which has passed the preliminary evaluation of the purchase application information; q ij —The proposed amount of funds to be applied for after the purchase application information is initially evaluated for the j-th equipment of a certain type in the i-th hospital; ε ij — The original value of the j-th piece of equipment of a certain type to be purchased by the i-th hospital and preliminarily evaluated through the purchase application information; θ ij —The original value of the j-th piece of equipment of a certain type in the i-th hospital that is to be purchased and updated and preliminarily evaluated through the purchase application information; u—the threshold of the average daily workload of a single device of a certain type, usually the 40th percentile; v—the threshold of the average daily working hours of a certain type of equipment, usually the 40th percentile; w—the threshold of the average number of reservation days for a certain type of equipment, usually the median; x—the update demand index threshold of a certain type of equipment, usually the 90th percentile; γ—the threshold of the increase in demand ranking of a certain type of equipment; δ—depreciation period of a certain type of equipment; M—the threshold for the number of additional devices of a certain type that a single hospital can apply for; N—the threshold number of updated equipment of a certain type that a single hospital can apply for; A—the expected average number of days for reservation for a certain type of equipment; B—The expected average monthly net revenue growth rate of a certain type of equipment.

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