CN111383123A - Clinical medical expense statistical method and device, storage medium and electronic equipment - Google Patents
Clinical medical expense statistical method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The present disclosure relates to a method for counting clinical medical expenses, a device for counting clinical medical expenses, a computer-readable storage medium, and an electronic apparatus. The statistical method for clinical medical expenses in the embodiment of the disclosure comprises the following steps: determining a plurality of clinical classifications corresponding to a target disease species; acquiring medical expense information of the multiple clinical types and acquiring state transition information of the various clinical types; and counting the clinical medical expenses of the target disease according to the medical expense information and the state transition information. According to the method, multiple influence factors such as the severity of the disease, the population distribution, the difference of diagnosis and treatment methods and the like are brought into the statistics of clinical medical expenses, so that the statistical result can truly reflect the complex disease diagnosis and treatment conditions, and the rationality and the interpretability of the statistical result are improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for counting clinical medical expenses, a computer-readable storage medium, and an electronic device.
Background
The medical insurance is an insurance which takes the body of the insured life as an insurance target and ensures that the insured life can be compensated for the expense or loss of the insured life when the insured life charges a certain amount of insurance fee. Different insurance types correspond to different insurance rates, and the insurance cost actually paid by the applicant can be calculated according to the application amount of the applicant and the corresponding insurance rates.
Generally, an insurance company can determine an insurance rate corresponding to an insurance type by counting clinical medical expenses for different disease categories. However, the conventional clinical medical expense statistical method can only count the overall expense of the disease species, neglects the influence factors such as the severity of the disease and the difference of the diagnosis and treatment method, and the statistical result hardly reflects the actual medical expense condition.
Therefore, how to obtain the medical expense statistical result under the influence of various complex factors is a problem to be solved urgently.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure is directed to a method, an apparatus, a computer-readable storage medium, and an electronic device for calculating clinical medical expenses, so as to overcome, at least to some extent, the technical problem that a medical expense calculation result cannot be obtained due to various complex factors due to limitations of related technologies.
According to one aspect of the present disclosure, a statistical method of clinical medical expenses is provided, which is characterized by comprising:
determining a plurality of clinical classifications corresponding to a target disease species;
acquiring medical expense information of the multiple clinical types and acquiring state transition information of the various clinical types;
and counting the clinical medical expenses of the target disease according to the medical expense information and the state transition information.
In an exemplary embodiment of the present disclosure, the acquiring medical expense information of the plurality of clinical classifications includes:
determining a plurality of clinical diagnosis and treatment paths respectively corresponding to the plurality of clinical classifications;
acquiring node diagnosis and treatment cost information of each diagnosis and treatment node on the clinical diagnosis and treatment path;
determining path diagnosis and treatment cost information of the clinical diagnosis and treatment path based on the node diagnosis and treatment cost information, and taking the path diagnosis and treatment cost information as corresponding clinical typing medical cost information.
In an exemplary embodiment of the present disclosure, the acquiring node diagnosis and treatment cost information of each diagnosis and treatment node located on the clinical diagnosis and treatment path includes:
establishing a target disease species database which is related to a target disease species and comprises a plurality of cases;
acquiring disease course diagnosis and treatment records and disease course diagnosis and treatment cost information of a plurality of cases corresponding to the same clinical typing from the target disease category database;
determining diagnosis and treatment nodes related to each case according to the course diagnosis and treatment record, and determining node diagnosis and treatment cost information of the diagnosis and treatment nodes related to each case according to the course diagnosis and treatment cost information;
and determining the node diagnosis and treatment cost information of each diagnosis and treatment node on the clinical diagnosis and treatment path according to the node diagnosis and treatment cost information of the diagnosis and treatment nodes related in each case.
In an exemplary embodiment of the disclosure, the establishing a target disease category database including a plurality of cases related to a target disease category includes:
acquiring case information of a plurality of cases related to a target disease type, wherein the case information comprises a course diagnosis and treatment record and course diagnosis and treatment cost information of the cases;
and establishing a target disease species database related to the target disease species based on the case information.
In an exemplary embodiment of the present disclosure, after establishing a target disease category database including a plurality of cases related to a target disease category, the method further includes:
preprocessing the data in the target disease species database;
wherein the preprocessing comprises data structuring processing, data normalization processing and/or data missing value processing.
In an exemplary embodiment of the present disclosure, the acquiring state transition information of various clinical classifications includes:
and acquiring the distribution proportion of various clinical classifications and the state transition probability of various clinical classifications.
In an exemplary embodiment of the disclosure, the counting clinical medical expenses of the target disease category according to the medical expense information and the state transition information includes:
generating a state transition matrix according to the state transition probabilities of the various clinical classifications;
and calculating to obtain a medical expense expected value by using the medical expense information, the distribution proportion and the state transition matrix, and taking the medical expense expected value as the clinical medical expense of the target disease species.
According to one aspect of the present disclosure, there is provided a statistical apparatus for clinical medical expenses, which is characterized by comprising:
an information acquisition module configured to determine a plurality of clinical classifications corresponding to a target disease category and acquire medical expense information of the plurality of clinical classifications;
a cost determination module configured to determine a medical cost expectation value of the target disease category according to the medical cost information;
a rate determination module configured to determine a medical insurance rate for the target disease category based on the expected medical cost value.
According to an aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which is characterized in that the computer program, when being executed by a processor, implements any of the above described statistical methods of clinical medical costs.
According to one aspect of the present disclosure, there is provided an electronic device characterized by comprising a processor and a memory; wherein the memory is for storing executable instructions of the processor, the processor being configured to perform any of the above described statistical methods of clinical medical spending via execution of the executable instructions.
In the statistical method for clinical medical expenses provided by the exemplary embodiment of the disclosure, by determining different clinical classifications of target disease species and medical expenses and state transition information of various clinical classifications, multiple influence factors such as severity of diseases, population distribution, differences of diagnosis and treatment methods and the like can be incorporated into statistics of clinical medical expenses, so that a statistical result can truly reflect complex diagnosis and treatment conditions of diseases, and the rationality and interpretability of the statistical result are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 schematically illustrates a flow chart of steps of a statistical method of clinical medical spending in an exemplary embodiment of the present disclosure.
Fig. 2 schematically shows a partial step flow chart of a statistical method of clinical medical spending in another exemplary embodiment of the present disclosure.
Fig. 3 schematically shows a partial step flow chart of a statistical method of clinical medical spending in another exemplary embodiment of the present disclosure.
Fig. 4 schematically shows a partial step flowchart of a statistical method of clinical medical spending in another exemplary embodiment of the present disclosure.
Fig. 5 shows a schematic diagram of a state transition matrix in an exemplary embodiment of the present disclosure.
Fig. 6 is a diagram illustrating a clinical medical expense calculation manner of a statistical method of clinical medical expenses in an application scenario according to an exemplary embodiment of the present disclosure.
Fig. 7 is a diagram illustrating a clinical typing status transition of a statistical method of clinical medical spending in an application scenario according to an exemplary embodiment of the present disclosure.
Fig. 8 schematically shows a block diagram of a statistical apparatus for clinical medical spending in an exemplary embodiment of the present disclosure.
Fig. 9 schematically illustrates a schematic diagram of a program product in an exemplary embodiment of the present disclosure.
Fig. 10 schematically illustrates a module diagram of an electronic device in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The exemplary embodiment of the present disclosure first provides a statistical method of clinical medical expenses, which can be used to count clinical medical expenses of various diseases under the influence of a plurality of complex factors. The statistics of clinical medical spending may provide a data basis for insurance companies to calculate and determine premium rates for various different types of medical insurance. Each type of medical insurance can pay for diagnosis and treatment costs of a single disease type, and can pay for diagnosis and treatment costs of multiple related disease types, which is not particularly limited in the present disclosure.
Referring to the step flow shown in fig. 1, the statistical method for clinical medical expenses provided by the present exemplary embodiment may mainly include the following steps:
step S110. determine a plurality of clinical classifications corresponding to the target disease species.
Taking a clinical medical overhead system for a target disease category as an example, the step first determines a plurality of clinical classifications corresponding to the target disease category. The different clinical classifications of disease usually imply different treatment regimens and medical costs. For example, acute lymphoblastic leukemia can be classified into three clinical classifications of low-risk, medium-risk and high-risk, and different treatment schemes are generally used for the three clinical classifications of acute lymphoblastic leukemia, which correspondingly generate different medical expenses.
And S120, acquiring medical expense information of various clinical types and acquiring state transition information of various clinical types.
Still taking acute lymphoblastic leukemia as an example, low-risk acute lymphoblastic leukemia, medium-risk acute lymphoblastic leukemia and high-risk acute lymphoblastic leukemia can use different treatment schemes, and correspondingly different medical expenses can be generated, and the three clinical types have certain transition probability. For the multiple clinical classifications determined in step S110, this step will obtain medical cost information for various clinical classifications, and will also obtain state transition information for various clinical classifications.
And S130, counting the clinical medical expenses of the target disease according to the medical expense information and the state transition information. According to the medical expense information and the state transition information acquired in step S120, the expected medical expense value of the target disease category may be determined as a clinical medical expense statistical result of the target disease category. The expected medical cost value is actually an estimate of the total medical cost of each clinical type of the target disease, and for example, the expected medical cost value of each clinical type may be calculated by weighting the medical cost of each clinical type according to the disease proportion of different clinical types in the whole target disease as a weight value, based on the state transition probability between each clinical type, and the result of the weighted calculation may be used as the expected medical cost value of the target disease. The expected medical cost value of the target disease type may be calculated using any other formula or model, and this is not particularly limited in the present exemplary embodiment.
In application scenarios involving the field of insurance, the statistics of the present exemplary embodiment may be used to determine an insurance rate for medical insurance corresponding to the target disease category. For example, the medical insurance rate corresponding to the expected value of the medical expense can be obtained by inquiring according to the corresponding relation between the preset expected value of the medical expense and the medical insurance rate; or according to a preset formula or model, taking the expected value of the medical expense as an input parameter, and calculating to obtain a corresponding medical insurance rate; in addition, after the expected value of the medical expense is determined, the medical insurance rate of the target disease species can be determined by combining various parameters such as the incidence rate of the target disease species, the market interest rate and the company expense rate; this is not a particular limitation of the present exemplary embodiment.
In the method for counting clinical medical expenses provided by the exemplary embodiment, by determining different clinical classifications of target disease species and medical expenses of various clinical classifications, multiple influence factors such as severity of diseases, population distribution and differences of diagnosis and treatment methods can be brought into the process of counting clinical medical expenses, the association degree of statistical results with disease species information and diagnosis and treatment information is improved, and further the rationality and interpretability of the statistical results of clinical medical expenses are improved.
Referring to the flow of steps shown in fig. 2, in another exemplary embodiment of the present disclosure, the acquiring medical expense information of a plurality of clinical classifications in step S120 may further include the steps of:
and S210, determining a plurality of clinical diagnosis and treatment paths respectively corresponding to a plurality of clinical typing.
In step S110, a plurality of clinical classifications corresponding to the target disease type are first determined, and in this step, a clinical diagnosis path corresponding to each clinical classification can be determined accordingly. The clinical diagnosis and treatment path mainly refers to a series of clinical diagnosis and treatment processes formed by aiming at a clinical typing, a plurality of stages may be involved in the processes, and each stage can be correspondingly used as a diagnosis and treatment node. Generally, different clinical classifications correspond to different clinical paths, and the different clinical paths may include a plurality of the same clinical nodes. For example, the clinical treatment for the low-risk acute lymphoblastic leukemia, which is a clinical classification, is divided into eight stages, i.e., trial treatment, induced remission, early intensive treatment, consolidation treatment, delayed intensive treatment, intermediate maintenance treatment, secondary intensive treatment, and maintenance treatment, and each treatment stage is a diagnosis node on the clinical diagnosis path.
And S220, acquiring node diagnosis and treatment cost information of each diagnosis and treatment node on the clinical diagnosis and treatment path.
For the clinical diagnosis and treatment path determined in step S210, node diagnosis and treatment cost information of each diagnosis and treatment node on the clinical diagnosis and treatment path is obtained in this step. This information may be obtained by big data analysis and calculation of medical data, or may be calculated in conjunction with charging criteria or recommendations provided by a clinical medical professional or medical institution. Still taking the low-risk acute lymphoblastic leukemia as an example, the step will acquire the diagnosis and treatment cost information of each node of the eight diagnosis and treatment nodes.
And S230, determining path diagnosis and treatment cost information of the clinical diagnosis and treatment path based on the node diagnosis and treatment cost information, and taking the path diagnosis and treatment cost information as corresponding clinical typing medical cost information.
According to the node diagnosis and treatment cost information of each diagnosis and treatment node acquired in step S220, path diagnosis and treatment cost information of a clinical diagnosis and treatment path can be determined in this step. For example, the clinical diagnosis and treatment path of the low-risk acute lymphoblastic leukemia includes eight diagnosis and treatment nodes, and the diagnosis and treatment costs of the nodes of the eight diagnosis and treatment nodes are accumulated to be used as the path diagnosis and treatment costs of the complete clinical diagnosis and treatment path. In addition, the weight information of each diagnosis and treatment node can be determined according to the information such as the importance degree, selectivity and replaceability of each diagnosis and treatment node, and then the path diagnosis and treatment cost is calculated according to the preset weight. The path diagnosis and treatment cost information determined in the step can be used as corresponding clinical typing medical cost information.
In the method for counting clinical medical expenses provided by the exemplary embodiment, various clinically typed clinical diagnosis and treatment paths are decomposed into a plurality of diagnosis and treatment nodes, so that a uniform expense calculation basis can be formed, different clinical typing of the same target disease species has more referential values in the aspect of medical expense statistics, and the accuracy and the reasonability of the clinical medical expense statistical result are further improved.
Referring to the step flow shown in fig. 3, on the basis of the above exemplary embodiment, step s220, acquiring node diagnosis and treatment cost information of each diagnosis and treatment node located on a clinical diagnosis and treatment path may further include the following steps:
step S310, establishing a target disease category database which is related to a target disease category and comprises a plurality of cases.
In order to accurately obtain the related cost information of different clinical classifications, different clinical diagnosis and treatment paths and different diagnosis and treatment nodes, a target disease category database which is related to a target disease category and comprises a plurality of cases can be established in advance. For example, the step may first collect case information of a plurality of cases related to the target disease type, where the case information includes a course diagnosis and treatment record and course diagnosis and treatment cost information of each case; then, a target disease category database related to the target disease category is established based on the case information. For example, for insurance rate calculation at leukemia risk, case information relating to leukemia may be collected and then a specialized leukemia database built.
And S320, acquiring the course diagnosis and treatment records and the course diagnosis and treatment cost information of a plurality of cases corresponding to the same clinical typing from the target disease species database.
Based on the target disease category database established in step S310, this step can obtain the course clinical records and course clinical cost information of a plurality of cases corresponding to the same clinical classification. The course diagnosis record of a certain case may reflect the complete diagnosis process of the case, and the diagnosis process may involve some or all diagnosis nodes on the corresponding clinical diagnosis path. The medical treatment cost information can reflect the expense items of the case in the whole medical treatment process and the corresponding expense data.
And S330, determining diagnosis and treatment nodes related to each case according to the course diagnosis and treatment records, and determining node diagnosis and treatment cost information of the diagnosis and treatment nodes related to each case according to the course diagnosis and treatment cost information.
According to the course diagnosis and treatment record of each case obtained in step S320, the diagnosis and treatment node in the clinical diagnosis and treatment path related to each case can be determined in this step. Due to differences in case-specific situations, there may also be differences between cases in the number and types of diagnosis nodes. According to the course diagnosis and treatment cost information of each case acquired in step S320, node diagnosis and treatment cost information of diagnosis and treatment nodes related to each case can be determined.
And S340, determining the node diagnosis and treatment cost information of each diagnosis and treatment node on the clinical diagnosis and treatment path according to the node diagnosis and treatment cost information of the diagnosis and treatment nodes related in each case.
In step S330, node diagnosis and treatment cost information of diagnosis and treatment nodes involved in each case may be determined, and each case may have a difference in diagnosis and treatment node and node diagnosis and treatment cost information. In other words, the same diagnosis node on the clinical diagnosis path may correspond to different node diagnosis costs in different cases. As a preferred embodiment, this step may determine the node diagnosis and treatment cost of each diagnosis and treatment node located on the clinical diagnosis and treatment path by averaging the node diagnosis and treatment costs corresponding to the same diagnosis and treatment node in each case. Of course, in other exemplary embodiments, any other calculation method may be adopted, and the present disclosure is not limited to this specifically.
Preferably, after the step s310 of establishing the target disease category database including a plurality of cases related to the target disease category, the exemplary embodiment may further include the steps of: preprocessing data in a target disease species database; wherein the preprocessing comprises data structuring processing, data normalization processing and/or data missing value processing. The data structuring processing is to extract relevant content and information of disease diagnosis and treatment from medical texts or medical images by using a natural language structuring technology, the data normalization processing is to convert the same content information into a standard format and unit, and the data missing value processing is to complement incomplete data caused by mechanical or artificial reasons in a database in a deleting or interpolating mode. By preprocessing the database, the accuracy and effectiveness of the basic data can be improved.
On the basis of the above exemplary embodiment, the obtaining of the state transition information of various clinical classifications in step S120 may include obtaining distribution ratios of various clinical classifications and state transition probabilities of various clinical classifications.
The plurality of clinical classifications corresponding to the same target disease species have different distribution ratios, and the distribution ratio can be the disease ratios of different clinical classifications calculated according to historical medical data; in addition, the distribution ratio of the number of patients with different clinical classifications in the whole patient population of the target disease species can be calculated, and the exemplary embodiment is not particularly limited to this. In addition, there is a certain probability that multiple clinical classifications corresponding to the same target disease species will undergo state transitions. For example, low-risk acute lymphoblastic leukemia, intermediate-risk acute lymphoblastic leukemia, and high-risk acute lymphoblastic leukemia all have a certain transition probability with each other.
On this basis, referring to the flow of steps shown in fig. 4, step s130, the step of counting the clinical medical expenses of the target disease category according to the medical expense information and the state transition information may further include the following steps:
and S410, generating a state transition matrix according to the state transition probabilities of various clinical classifications.
For example, for A, B, C state transition probabilities for three clinical classifications, a state transition matrix of three rows and three columns as shown in FIG. 5 may be generated. Wherein t isAARepresenting the probability that the clinical type A does not have a state transition, tABRepresents the probability of transition from clinical typing A to clinical typing B, and tBAThen represents the probability of transition from clinical typing B to clinical typing a. By analogy, the other parameters in the matrix represent the state transition probabilities between the corresponding clinical typing, respectively.
And S420, calculating to obtain a medical expense expected value by using the medical expense information, the distribution proportion and the state transition matrix, and taking the medical expense expected value as clinical medical expenses of the target disease species.
According to the acquired medical expense information, the distribution ratio and the state transition matrix generated in step S410, the expected value of the medical expense of the target disease species can be determined in this step. For example, in this step, a markov chain model is established using a state transition matrix, and a desired medical cost value of the target disease type, which is a clinical medical cost of the target disease type, can be calculated by combining distribution ratios of various clinical classifications and medical cost information.
It should be noted that although the above exemplary embodiments describe the various steps of the methods of the present disclosure in a particular order, this does not require or imply that these steps must be performed in that particular order, or that all of the steps must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
The statistical method for clinical medical spending provided in the above exemplary embodiment is described in detail below with reference to a specific application scenario. The application scene mainly utilizes medical big data to classify the special disease data based on clinical typing and counts clinical medical expenses, thereby determining special severe risk rates.
1) Data source
The data for calculating the medical expenses come from electronic medical records in domestic hospitals, and a target disease species special disease database is established according to the target disease risk. For example, to calculate the rate for leukemia risk, a leukemia specific disease library is created.
2) Disease-specific database data preprocessing
The special disease database data preprocessing mainly comprises structuring, normalization and missing value processing. The structuring refers to extracting information required by disease medical expenses from medical texts or medical images by using a natural language structuring technology, for example, extracting clinical diagnosis and treatment plan information of a patient from diagnosis and treatment process descriptions of medical records in hospital. Normalization refers to converting the same content information into standard format and units, such as acute lymphocytic leukemia, which is also the diagnosis name, different source writing (may be ALL, acute gonorrhea, etc.), and the same name or code. The missing value processing means that data incompleteness caused by mechanical or human reasons in the database is supplemented in a deleting or interpolating mode, for example, the hospitalization cost field information of some leukemia patients is missing, and the leukemia patients need to be rejected.
3) Clinical typing of target disease species
Different clinical typing of diseases generally means different treatment schemes and medical expenses, and different clinical typing marks are carried out on target diseases by using well-processed disease related variables, so that medical expenses of different clinical typing of diseases can be calculated conveniently. For example, acute lymphoblastic leukemia is clinically classified into low-risk, medium-risk and high-risk groups, different treatment schemes are provided for the three clinically classified acute lymphoblastic leukemias on MIMS medication guidelines, and by referring to the rules about the clinical classification of acute lymphoblastic leukemia in the diagnosis and treatment suggestions of acute lymphoblastic leukemia, a discriminant model of the clinical classification of acute lymphoblastic leukemia is established by using disease-related variables corresponding to the rules, so that the clinical classification marking of acute lymphoblastic leukemia patients in a special disease bank is realized.
4) Clinical diagnosis and treatment path disassembly for different clinical types of target disease
The patient medical record information recorded in the target disease species special disease database only records the disease diagnosis and treatment process segments of the patients, but not the whole-flow diagnosis and treatment process of each patient, and the cost information of different clinical typing patients in the special disease database is directly adopted to calculate the medical cost of the target disease species, so that the cost is underestimated. The diagnosis and treatment paths of different clinical types of target diseases are determined by combining the MIMS medication guide and clinical medical experts, the clinical diagnosis and treatment process record information of the patients in the special disease library is utilized, and a clinical diagnosis and treatment path disassembly model is established by adopting disease related variables corresponding to the diagnosis and treatment paths, so that the target disease clinical diagnosis and treatment paths are marked. For example, the clinical treatment of the low-risk group acute lymphoblastic leukemia patient is divided into eight stages of test treatment, induction remission, early strengthening treatment, delayed strengthening treatment, intermediate maintenance treatment, strengthening treatment again and maintenance treatment, and disease variables corresponding to the eight stages of clinical treatment are adopted to mark the eight stages, so that the dismantling of the diagnosis and treatment path of the low-risk group acute lymphoblastic leukemia is realized.
5) Clinical medical expense calculation based on target disease clinical diagnosis and treatment path
The diagnosis record of each time of seeing a doctor of the patient in the target disease special disease library can be marked as the corresponding clinical typing through the item 3), and the treatment record of each time of seeing a doctor of the patient in the target disease special disease library can be marked as the corresponding clinical diagnosis and treatment path through the item 4); when calculating the medical expense of the target disease species, firstly calculating the medical expense of different clinical types of the target disease species; because the medical history information of each patient is not the record of the whole disease course, when calculating the medical expenses of different clinical diagnosis and treatment types, the medical expenses corresponding to each time of treatment of each patient, namely the medical expenses of different clinical diagnosis and treatment paths of each patient, are firstly counted, and then the average medical expenses of the same clinical diagnosis and treatment paths of all the patients are calculated, so that the medical expenses of each clinical diagnosis and treatment path can be obtained, and the sum of the expenses of each clinical diagnosis and treatment path is the medical expenses of the clinical diagnosis and treatment type corresponding to the target disease type. After medical expenses of different clinical classifications of the target disease species are obtained, an expected value of the medical expenses of the target disease species can be calculated according to the disease proportion of the target disease species in the different clinical classifications (in addition, the expected value can also be combined with the state transition relation among the different clinical classifications).
As shown in fig. 6: the target disease species D was labeled as n clinical typing D1、D2、…DnWherein D is1The m clinical paths for type A patients are labeled as P1、P2、…Pm. Wherein the clinical diagnosis and treatment path for the first patient in X times is P1、P3、…PmCorresponding medical expense is C11、C13、…C1X. By analogy, the medical cost of all clinic diagnosis and treatment paths of A patients can be obtained, and the medical cost is marked as P1The average medical cost of (2) is used as the clinical diagnosis path P1The same reasoning gives P2、…PmMedical cost of (1), then1The medical cost of (A) is a complete clinical pathway (P)1+P2+…Pm) Medical cost C of1. By the same method, obtain D2、…DnMedical cost C of2、…Cn。
There is usually a state transition between the clinical classifications of the target disease species, and as shown in fig. 7, it is necessary to calculate the medical cost of the target disease species based on the different clinical classification ratios after the transition between the different states. Assume the probability of a transition between any two clinical classifications i and j is tijAccordingly, a clinical typing transition matrix can be determined, and then the medical cost of the target disease species is the expected cost value corresponding to the state transition matrix.
6) Rate calculation for target disease type serious disease risk
Item 5) gives the medical cost of the target disease species, and the rate of the serious disease of the target disease species can be determined by combining the incidence rate of the target disease species, the market interest rate and the company expense rate.
In the application scene, the medical treatment cost of the severe disease is relatively accurately calculated by using the medical big data, and the reasonable determination of the insurance fee of the severe disease is facilitated.
In an exemplary embodiment of the present disclosure, a statistical apparatus of clinical medical expenses is also provided. As shown in fig. 8, the statistical device 800 for clinical medical expenses may mainly include: a typing determination module 810, an information acquisition module 820, and an overhead statistics module 830. Wherein the typing determination module 810 is configured to determine a plurality of clinical classifications corresponding to a target disease category; the information acquisition module 820 is configured to acquire medical expense information of a plurality of clinical classifications and acquire state transition information of the various clinical classifications; the cost statistic module 830 is configured to count the clinical medical costs of the target disease category according to the medical cost information and the state transition information.
The specific details of the clinical medical cost statistic device are described in detail in the corresponding clinical medical cost statistic method, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In an exemplary embodiment of the present disclosure, there is also provided a computer readable storage medium having a computer program stored thereon, which when executed by a processor, can implement the above-mentioned statistical method of clinical medical expenses of the present disclosure. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code; the program product may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, or a removable hard disk, etc.) or on a network; when the program product is run on a computing device (which may be a personal computer, a server, a terminal apparatus, or a network device, etc.), the program code is configured to cause the computing device to perform the method steps in the above exemplary embodiments of the disclosure.
Referring to fig. 9, a program product 900 for implementing the above method according to an embodiment of the present disclosure may employ a portable compact disc read only memory (CD-ROM) and include program code, and may run on a computing device (e.g., a personal computer, a server, a terminal device, or a network device, etc.). However, the program product of the present disclosure is not limited thereto. In the exemplary embodiment, the computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium.
The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device over any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), etc.; alternatively, the connection may be to an external computing device, such as through the Internet using an Internet service provider.
In an example embodiment of the present disclosure, there is also provided an electronic device comprising at least one processor and at least one memory for storing executable instructions of the processor; wherein the processor is configured to perform the method steps in the above-described exemplary embodiments of the disclosure via execution of the executable instructions.
The electronic apparatus 1000 in the present exemplary embodiment is described below with reference to fig. 10. The electronic device 1000 is only one example and should not bring any limitations to the functionality or scope of use of embodiments of the present disclosure.
Referring to FIG. 10, an electronic device 1000 is shown in the form of a general purpose computing device. The components of the electronic device 1000 may include, but are not limited to: at least one processing unit 1010, at least one memory unit 1020, a bus 1030 that couples various system components including the processing unit 1010 and the memory unit 1020, and a display unit 1040.
Wherein the storage unit 1020 stores program code which may be executed by the processing unit 1010 such that the processing unit 1010 performs the method steps in the above-described exemplary embodiments of the present disclosure.
The memory unit 1020 may include readable media in the form of volatile memory units, such as a random access memory unit 1021(RAM) and/or a cache memory unit 1022, and may further include a read-only memory unit 1023 (ROM).
The electronic device 1000 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that allow a user to interact with the electronic device 1000, and/or with any devices (e.g., router, modem, etc.) that allow the electronic device 1000 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interfaces 1050. Also, the electronic device 1000 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) through the network adapter 1060. As shown in FIG. 10, the network adapter 1060 may communicate with other modules of the electronic device 1000 via the bus 1030. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software may be referred to herein generally as a "circuit," module "or" system.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments, and the features discussed in connection with the embodiments are interchangeable, if possible. In the above description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
Claims (10)
1. A statistical method for clinical medical expenses is characterized by comprising the following steps:
determining a plurality of clinical classifications corresponding to a target disease species;
acquiring medical expense information of the multiple clinical types and acquiring state transition information of the various clinical types;
and counting the clinical medical expenses of the target disease according to the medical expense information and the state transition information.
2. The statistical method for clinical medical expenses according to claim 1, wherein the acquiring medical expense information of the plurality of clinical classifications comprises:
determining a plurality of clinical diagnosis and treatment paths respectively corresponding to the plurality of clinical classifications;
acquiring node diagnosis and treatment cost information of each diagnosis and treatment node on the clinical diagnosis and treatment path;
determining path diagnosis and treatment cost information of the clinical diagnosis and treatment path based on the node diagnosis and treatment cost information, and taking the path diagnosis and treatment cost information as corresponding clinical typing medical cost information.
3. The method according to claim 2, wherein the obtaining node diagnosis cost information of each diagnosis node located on the clinical diagnosis path comprises:
establishing a target disease species database which is related to a target disease species and comprises a plurality of cases;
acquiring disease course diagnosis and treatment records and disease course diagnosis and treatment cost information of a plurality of cases corresponding to the same clinical typing from the target disease category database;
determining diagnosis and treatment nodes related to each case according to the course diagnosis and treatment record, and determining node diagnosis and treatment cost information of the diagnosis and treatment nodes related to each case according to the course diagnosis and treatment cost information;
and determining the node diagnosis and treatment cost information of each diagnosis and treatment node on the clinical diagnosis and treatment path according to the node diagnosis and treatment cost information of the diagnosis and treatment nodes related in each case.
4. The statistical method for clinical medical spending according to claim 3, wherein the establishing a target disease category database including a plurality of cases related to a target disease category comprises:
acquiring case information of a plurality of cases related to a target disease type, wherein the case information comprises a course diagnosis and treatment record and course diagnosis and treatment cost information of the cases;
and establishing a target disease species database related to the target disease species based on the case information.
5. The statistical method of clinical medical spending according to claim 3, wherein after establishing a target disease category database including a plurality of cases related to a target disease category, the method further comprises:
preprocessing the data in the target disease species database;
wherein the preprocessing comprises data structuring processing, data normalization processing and/or data missing value processing.
6. The statistical method for clinical medical spending according to any one of claims 1-5, wherein the obtaining of state transition information of various clinical classifications comprises:
and acquiring the distribution proportion of various clinical classifications and the state transition probability of various clinical classifications.
7. The method of claim 6, wherein the step of counting the clinical medical expenses of the target disease according to the medical expense information and the state transition information comprises:
generating a state transition matrix according to the state transition probabilities of the various clinical classifications;
and calculating to obtain a medical expense expected value by using the medical expense information, the distribution proportion and the state transition matrix, and taking the medical expense expected value as the clinical medical expense of the target disease species.
8. A statistical apparatus for clinical medical spending, comprising:
a typing determination module configured to determine a plurality of clinical classifications corresponding to a target disease category;
an information acquisition module configured to acquire medical expense information of the plurality of clinical classifications and acquire state transition information of the various clinical classifications;
and the expense counting module is configured to count the clinical medical expense of the target disease category according to the medical expense information and the state transition information.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the statistical method of clinical medical costs according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor;
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the statistical method of clinical medical spending of any one of claims 1-7 via execution of the executable instructions.
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