CN109829829B - Intelligent recommendation method and related device based on big data - Google Patents
Intelligent recommendation method and related device based on big data Download PDFInfo
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- CN109829829B CN109829829B CN201811527654.4A CN201811527654A CN109829829B CN 109829829 B CN109829829 B CN 109829829B CN 201811527654 A CN201811527654 A CN 201811527654A CN 109829829 B CN109829829 B CN 109829829B
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Abstract
The embodiment of the application discloses an intelligent recommendation method and a related device based on big data, wherein the method comprises the following steps: acquiring medical data of an object to be recommended from a medical database, wherein the medical data comprises disease names and treatment fees; inputting the medical data into a reimbursement arrangement combination model for processing, outputting at least two reimbursement arrangement combinations corresponding to the medical data, wherein the reimbursement arrangement combination model is used for selecting reimbursement arrangement combinations meeting the medical data of the object to be recommended, and each reimbursement arrangement combination comprises at least two reimbursement parameter sets; and inputting the medical data and at least two reimbursement arrangement combinations into a reimbursement recommendation model for processing, and outputting a target reimbursement arrangement combination, wherein the target reimbursement arrangement combination is the most reimbursement cost in the at least two reimbursement arrangement combinations. By adopting the embodiment of the application, the reimbursement arrangement combination with the highest reimbursement cost can be recommended to the user based on the medical data.
Description
Technical Field
The application relates to the technical field of medical insurance, in particular to an intelligent recommendation method and a related device based on big data.
Background
At present, with the improvement of living standard and the enhancement of insurance consciousness, more and more users can purchase a plurality of commercial insurance at different insurance companies, the reimbursement proportion and reimbursement capping cost of different commercial insurance for the same disease are also different, and reimbursement conflict may exist in reimbursement of the same disease at different insurance companies. Typically, a patient may go to multiple insurance companies for whom commercial insurance is purchased for multiple claims after treatment discharge. The prior art recommends reimbursement arrangement and combination to users based on the position of the insurance company, and has low pertinence and influences user experience.
Disclosure of Invention
The embodiment of the application provides an intelligent recommendation method and a related device based on big data, which are used for recommending reimbursement permutation and combination with the highest reimbursement cost to a user based on medical data.
In a first aspect, an embodiment of the present application provides an intelligent recommendation method based on big data, where the method includes:
acquiring medical data of an object to be recommended from a medical database, wherein the medical data comprises a disease name and treatment cost;
inputting the medical data into a reimbursement arrangement and combination model for processing, outputting at least two reimbursement arrangement and combination corresponding to the medical data, wherein the reimbursement arrangement and combination model is used for selecting reimbursement arrangement and combination meeting the medical data of the object to be recommended, and each reimbursement arrangement and combination comprises at least two reimbursement parameter sets;
And inputting the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model for processing, and outputting a target reimbursement arrangement combination, wherein the target reimbursement arrangement combination is the most reimbursement cost in the at least two reimbursement arrangement combinations.
In a second aspect, an embodiment of the present application provides an intelligent recommendation apparatus based on big data, the apparatus including:
an acquisition unit configured to acquire medical data of an object to be recommended from a medical database, the medical data including a disease name and a treatment cost;
the first determining unit is used for inputting the medical data into a reimbursement arrangement combination model for processing, outputting at least two reimbursement arrangement combinations corresponding to the medical data, wherein the reimbursement arrangement combination model is used for selecting reimbursement arrangement combinations meeting the medical data of the object to be recommended, and each reimbursement arrangement combination comprises at least two reimbursement parameter sets;
and the second determining unit is used for inputting the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model for processing, and outputting a target reimbursement arrangement combination, wherein the target reimbursement arrangement combination is the most reimbursement cost in the at least two reimbursement arrangement combinations.
In a third aspect, an embodiment of the present application provides a server, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in the method according to the first aspect of the embodiment of the present application.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program for execution by a processor to implement some or all of the steps described in the method according to the first aspect of the embodiments of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the method of the first aspect of the embodiments of the present application.
It can be seen that, in the embodiment of the present application, the server obtains medical data of an object to be recommended from a medical database, the medical data includes a disease name and a treatment cost, inputs the medical data into a reimbursement permutation and combination model to process, outputs at least two reimbursement permutations corresponding to the medical data, inputs the medical data and the at least two reimbursement permutations into the reimbursement recommendation model to process, outputs a target reimbursement permutation and combination, and the target reimbursement permutation and combination is the most reimbursement cost in the at least two reimbursement permutations and combinations. This realizes recommendation of the reimbursement arrangement combination with the highest reimbursement cost to the user based on the medical data.
These and other aspects of the application will be more readily apparent from the following description of the embodiments.
Drawings
In order to more clearly describe the embodiments of the present application or the technical solutions in the background art, the following description will describe the drawings that are required to be used in the embodiments of the present application or the background art.
FIG. 1A is a flow chart of a first intelligent recommendation method based on big data according to an embodiment of the present application;
FIG. 1B is a schematic diagram of an embodiment of the present application;
FIG. 1C is another schematic illustration provided by an embodiment of the present application;
FIG. 2 is a flow chart of a second intelligent recommendation method based on big data according to an embodiment of the present application;
FIG. 3 is a flow chart of a third intelligent recommendation method based on big data according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an intelligent recommendation device based on big data according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed description of the preferred embodiments
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
The following will describe in detail.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In the following, some terms used in the present application are explained for easy understanding by those skilled in the art.
(1) The terminals may include various handheld devices, vehicle mount devices, wearable devices, computing devices, or other processing devices connected to a wireless modem, as well as various forms of User Equipment (UE), mobile Station (MS), terminal devices (terminal devices), etc.
(2) A server, also called a server, is a device that provides computing services. The server comprises a processor, hard disk, memory, system bus, etc., similar to a general purpose computer architecture. In a network environment, the service types provided by the servers are different and are divided into file servers, database servers, application program servers, WEB servers and the like.
Embodiments of the present application are described in detail below.
Referring to fig. 1A, fig. 1A is a flow chart of a first intelligent recommendation method based on big data according to an embodiment of the present application, where the intelligent recommendation method based on big data includes:
step 101: the server obtains medical data of the object to be recommended from a medical database, wherein the medical data comprises disease names and treatment fees.
In one possible example, the server obtains medical data of an object to be recommended from a medical database, including:
the server receives indication information sent by the reimbursement recommendation management platform, wherein the indication information is used for indicating the server to acquire medical data of the object to be recommended;
the server sends request information to a server of a target medical institution, wherein the request information is used for the server of the target medical institution to feed back all medical data of the object to be recommended stored in a medical database of the server, and each medical data corresponds to one treatment time;
the server receives all medical data of the object to be recommended, which is sent by the server of the target medical institution aiming at the request information;
and the server takes the medical data with the latest treatment time in all the medical data as the medical data of the object to be recommended.
In one possible example, before the server obtains the medical data of the object to be recommended from the medical database, the method further comprises:
when the reimbursement arrangement combination recommendation function of the reimbursement recommendation management platform is in an on state, the reimbursement recommendation management platform displays the options of the object to be recommended, the medical institution options and the reimbursement arrangement combination recommendation button on a display interface of the reimbursement recommendation management platform;
If the clicking operation of the reimbursement arrangement combination recommendation button aiming at the object to be recommended in the target medical institution is detected, the reimbursement recommendation management platform sends indication information to the server, wherein the indication information is used for indicating the server to acquire medical data of the object to be recommended.
The object option to be recommended includes a name identifier, a name input box, a phone number identifier, a phone number input box, an identity number identifier and an identity number input box, and the medical institution option includes a region identifier, a region input box, a medical institution identifier and a medical institution input box, as shown in fig. 1B.
The server and the reimbursement recommendation management platform have a connection relationship.
For example, as shown in fig. 1C, when a click operation of the reimbursement arrangement combination recommendation button for an object to be recommended in a target medical institution is detected, the reimbursement recommendation management platform transmits instruction information to the server, the server receives the instruction information transmitted by the reimbursement recommendation management platform and transmits request information to the server of the target medical institution, and the server of the target medical institution receives the request information transmitted by the server and transmits 10 pieces of medical data of the object to be recommended to the server.
In one possible example, the server obtains medical data of an object to be recommended, including:
The method comprises the steps that a server sends first request information to a terminal of an object to be recommended, wherein the first request information is used for indicating the object to be recommended to input a disease name and treatment cost on a display interface of the terminal;
the server receives a first disease name and a first treatment expense sent by the terminal of the object to be recommended aiming at the first request information;
the server takes the first disease name and the first treatment expense as medical data of the object to be recommended.
Step 102: the server inputs the medical data into a reimbursement combination model for processing, outputs at least two reimbursement combinations corresponding to the medical data, and the reimbursement combination model is used for selecting reimbursement combinations meeting the medical data of the object to be recommended, and each reimbursement combination comprises at least two reimbursement parameter sets.
The reimbursement parameter set comprises M stepped reimbursement expense intervals, M reimbursement proportions, reimbursement capping expense and reimbursement labels, wherein each reimbursement proportion corresponds to one reimbursement expense interval, M is an integer greater than or equal to 2, and the reimbursement labels are used for representing other insurance companies with reimbursement conflicts in the disease names corresponding to the reimbursement parameter set.
The greater the intermediate value of the reimbursement cost interval, the greater the reimbursement proportion, for example, the first reimbursement cost interval is 1000-3000 yuan, and the second reimbursement cost interval is 3000-5000 yuan, and the first reimbursement proportion corresponding to the first reimbursement cost interval is smaller than the second reimbursement proportion corresponding to the second reimbursement cost interval.
In one possible example, the server inputs the medical data into a reimbursement permutation and combination model to process, outputs at least two reimbursement permutation and combinations corresponding to the medical data, and includes:
the server inputs the medical data into a reimbursement arrangement and combination model, wherein the reimbursement arrangement and combination model is pre-provided with a plurality of reimbursement parameter sets, and each reimbursement parameter set corresponds to an insurance company;
the server determines at least two reimbursement parameter sets corresponding to the medical data according to the corresponding relation between the prestored disease names and reimbursement parameter sets in the reimbursement permutation and combination model;
and the server executes the operation of planning reimbursement permutation and combination on the at least two reimbursement parameter sets to obtain at least two reimbursement permutation and combination corresponding to the at least two reimbursement parameter sets.
Wherein, the corresponding relation between the disease name and the reimbursement parameter set is shown in the following table:
TABLE 1
| Disease name | Reimbursement parameter set |
| Disease name 1 | Reimbursement parameter set 1 |
| Disease name 2 | Reimbursement parameter set 2 |
| Disease name 3 | Reimbursement parameter set 3 |
| Disease name 4 | Reimbursement parameter set 4 |
| ...... | ...... |
If the number of the at least two reimbursement parameter sets is 2, the number of the at least two reimbursement permutation and combination is 2; if the number of the at least two reimbursement parameter sets is 3, the number of the at least two reimbursement permutation and combination is 6; if the number of the at least two reimbursement parameter sets is P, and P is an integer greater than 3, the number of the at least two reimbursement parameter sets is: p× (P-1) × (P-2) × … ×1.
Specifically, the server performs an operation of formulating reimbursement permutation and combination on at least two reimbursement parameter sets, and an embodiment of obtaining at least two reimbursement parameter permutations and combinations corresponding to the at least two reimbursement parameter sets may be: if the number of the at least two reimbursement parameter sets is Q, and Q is an integer greater than or equal to 2, setting Q reimbursement arrangement and combination positions, wherein the Q reimbursement arrangement and combination positions are from position 1 to position Q; selecting any one of Q reimbursement parameter sets to be placed at a position 1, selecting any one of the rest (Q-1) reimbursement parameter sets to be placed at a position 2 until the rest 1 reimbursement parameter sets are selected to be placed at a position Q; to obtain [ Q X (Q-1) × (Q-2) × … ×1] reimbursement arrangements.
Step 103: and the server inputs the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model for processing, and outputs a target reimbursement arrangement combination, wherein the target reimbursement arrangement combination is the most reimbursement cost in the at least two arrangement combinations.
In one possible example, the server processes the medical data and the at least two reimbursement permutations into a reimbursement recommendation model, outputs a target reimbursement permutation, comprising:
the server inputs the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model, wherein the reimbursement recommendation model is used for determining reimbursement arrangement combinations corresponding to the maximum reimbursement expense in the at least two reimbursement arrangement combinations;
the server determines reimbursement fees of each reimbursement permutation and combination in the at least two reimbursement permutation and combinations according to a prestored reimbursement fee policy to obtain at least two reimbursement fees corresponding to the at least two reimbursement permutation and combinations;
and the server takes the reimbursement permutation and combination corresponding to the maximum reimbursement expense in the at least two reimbursement fees as a target reimbursement permutation and combination.
In one possible example, each reimbursement permutation includes N reimbursement parameter sets, where N is an integer greater than or equal to 2, and the server determines reimbursement fees for each reimbursement permutation of the at least two reimbursement permutations according to a pre-stored reimbursement fee policy, including:
A1: the server determines a first part of reimbursement fees corresponding to the ith reimbursement parameter set;
a2: the server judges whether a reimbursement conflict exists between the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets;
a3: if the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets do not have reimbursement conflict, the server analyzes the (i+1) th reimbursement parameter set to obtain M stepped reimbursement expense intervals and M first reimbursement proportions corresponding to the (i+1) th reimbursement parameter set, wherein each first reimbursement proportion corresponds to one reimbursement expense interval, and M is an integer greater than or equal to 2;
a4: the server determines M first treatment fees corresponding to the treatment fees, wherein each first treatment fee corresponds to a reimbursement fee interval;
a5: the server determines first part reimbursement fees corresponding to the (i+1) th reimbursement parameter set according to the M first treatment fees, the M first reimbursement proportions and a prestored reimbursement fee formula;
repeating the steps A2-A5 until i=N, and obtaining N first part reimbursement fees corresponding to the N reimbursement parameter sets; wherein i is an increasing integer with an initial value of 1 and 1 as an interval;
The server takes the sum of the N first part reimbursement fees as reimbursement fees of each reimbursement permutation and combination.
Specifically, an embodiment in which the server determines whether there is a reimbursement conflict between the (i+1) th reimbursement parameter set and the previous i reimbursement parameter set may be: resolving the reimbursement tag of the (i+1) reimbursement parameter set to obtain at least one first insurance company name corresponding to the reimbursement tag of the (i+1) reimbursement parameter set; acquiring the names of the second insurance companies corresponding to each reimbursement parameter set in the first i reimbursement parameter sets, and acquiring the names of the i second insurance companies corresponding to the first i reimbursement parameter sets; judging whether at least one first insurance company name exists in the i second insurance company names; if at least one first insurance company name exists in the i second insurance company names, determining that a reimbursement conflict exists between the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets.
Wherein, the reimbursement cost formula is:
S＝T 1 ×α 1 +T 2 ×α 2 +…+T m ×α m ；
wherein S is the first part reimbursement cost corresponding to the (i+1) th reimbursement parameter set, T 1 For the 1 st first treatment cost of the M first treatment costs, alpha 1 For the first reimbursement proportion corresponding to the 1 st first treatment expense, T 2 For the 2 nd first treatment cost of the M first treatment costs, alpha 2 For the first reimbursement proportion corresponding to the 2 nd first treatment cost, T m For the Mth first treatment cost of the M first treatment costs, alpha m A first reimbursement proportion corresponding to the Mth first treatment cost.
The embodiment of determining the first part of reimbursement charges corresponding to the ith reimbursement parameter set by the server is the same as the embodiment of determining the first part of reimbursement charges corresponding to the (i+1) th reimbursement parameter set by the server, and will not be described herein.
In one possible example, each reimbursement permutation includes N reimbursement parameter sets, where N is an integer greater than or equal to 2, and the server determines reimbursement fees for each reimbursement permutation of the at least two reimbursement permutations according to a pre-stored reimbursement fee policy, including:
b1: the server determines a second part of reimbursement fees corresponding to the ith reimbursement parameter set;
b2: the server judges whether a reimbursement conflict exists between the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets;
b3: if the (i+1) th reimbursement parameter set and the previous i reimbursement parameter set have reimbursement conflicts, the server determines whether the sum of the i second part reimbursement fees corresponding to the previous i reimbursement parameter sets is greater than or equal to the reimbursement capping fees of the (i+1) th reimbursement parameter set;
B4: if the sum of the i second part reimbursement fees is smaller than the reimbursement capping fees, the server takes the reimbursement fee difference value of the reimbursement capping fees and the sum of the i second part reimbursement fees as the second part reimbursement fees of the (i+1) th reimbursement parameter set;
repeating the steps B2-B4 until i=N, and obtaining N second part reimbursement fees corresponding to the N reimbursement parameter sets; wherein i is an increasing integer with an initial value of 1 and 1 as an interval;
the server takes the sum of the N second part reimbursement fees as reimbursement fees of each reimbursement permutation and combination.
Further, if the sum of the i second partial reimbursement costs is greater than or equal to the reimbursement capping cost, the server determines that the second partial reimbursement cost of the (i+1) th reimbursement parameter set is zero.
It can be seen that, in the embodiment of the present application, the server obtains medical data of an object to be recommended from a medical database, the medical data includes a disease name and a treatment cost, inputs the medical data into a reimbursement permutation and combination model to process, outputs at least two reimbursement permutations corresponding to the medical data, inputs the medical data and the at least two reimbursement permutations into the reimbursement recommendation model to process, outputs a target reimbursement permutation and combination, and the target reimbursement permutation and combination is the most reimbursement cost in the at least two reimbursement permutations and combinations. This realizes recommendation of the reimbursement arrangement combination with the highest reimbursement cost to the user based on the medical data.
In one possible example, the server processes the medical data and the at least two reimbursement permutations into a reimbursement recommendation model, and after outputting the target reimbursement permutations, the method further comprises:
the server acquires insurance companies corresponding to each reimbursement parameter set in at least two reimbursement parameter sets included in the target reimbursement arrangement combination;
the server determines an insurance company permutation and combination corresponding to the target reimbursement permutation and combination;
and the server sends the reimbursement fees corresponding to the insurance company permutation and combination and the target permutation and combination to the terminal of the object to be recommended and/or at least one terminal with binding relation with the terminal of the object to be recommended.
The position of the reimbursement parameter set in the target reimbursement arrangement combination is the position of the insurance company corresponding to the reimbursement parameter set in the insurance company arrangement combination.
Referring to fig. 2, fig. 2 is a flow chart of a second big data based intelligent recommendation method according to an embodiment of the present application, where the big data based intelligent recommendation method includes:
step 201: the server obtains medical data of the object to be recommended from a medical database, wherein the medical data comprises disease names and treatment fees.
Step 202: the server inputs the medical data into a reimbursement combination model, wherein the reimbursement combination model is pre-provided with a plurality of reimbursement parameter sets, and each reimbursement parameter set corresponds to one insurance company.
Step 203: and the server determines at least two reimbursement parameter sets corresponding to the medical data according to the corresponding relation between the prestored disease names and reimbursement parameter sets in the reimbursement permutation and combination model.
Step 204: and the server executes the operation of planning reimbursement permutation and combination on the at least two reimbursement parameter sets to obtain at least two reimbursement permutation and combination corresponding to the at least two reimbursement parameter sets.
Step 205: and the server inputs the medical data and the at least two reimbursement permutation and combination into a reimbursement recommendation model, wherein the reimbursement recommendation model is used for determining reimbursement permutation and combination corresponding to the maximum reimbursement expense in the at least two reimbursement permutation and combination.
Step 206: and the server determines reimbursement fees of each reimbursement permutation and combination in the at least two reimbursement permutation and combinations according to a prestored reimbursement fee policy to obtain at least two reimbursement fees corresponding to the at least two reimbursement permutation and combinations.
Step 207: and the server takes the reimbursement permutation and combination corresponding to the maximum reimbursement expense in the at least two reimbursement fees as a target reimbursement permutation and combination.
It should be noted that, the specific implementation of each step of the method shown in fig. 2 may be referred to the specific implementation of the foregoing method, which is not described herein.
Referring to fig. 3, fig. 3 is a flow chart of a third intelligent recommendation method based on big data according to an embodiment of the present application, where the intelligent recommendation method based on big data includes:
step 301: the server obtains medical data of the object to be recommended from a medical database, wherein the medical data comprises disease names and treatment fees.
Step 302: the server inputs the medical data into a reimbursement combination model, wherein the reimbursement combination model is pre-provided with a plurality of reimbursement parameter sets, and each reimbursement parameter set corresponds to one insurance company.
Step 303: and the server determines at least two reimbursement parameter sets corresponding to the medical data according to the corresponding relation between the prestored disease names and reimbursement parameter sets in the reimbursement permutation and combination model.
Step 304: and the server executes the operation of planning reimbursement permutation and combination on the at least two reimbursement parameter sets to obtain at least two reimbursement permutation and combination corresponding to the at least two reimbursement parameter sets.
Step 305: and the server inputs the medical data and the at least two reimbursement permutation and combination into a reimbursement recommendation model, wherein the reimbursement recommendation model is used for determining reimbursement permutation and combination corresponding to the maximum reimbursement expense in the at least two reimbursement permutation and combination.
Step 306: and the server determines reimbursement fees of each reimbursement permutation and combination in the at least two reimbursement permutation and combinations according to a prestored reimbursement fee policy to obtain at least two reimbursement fees corresponding to the at least two reimbursement permutation and combinations.
Step 307: and the server takes the reimbursement permutation and combination corresponding to the maximum reimbursement expense in the at least two reimbursement fees as a target reimbursement permutation and combination.
Step 308: and the server acquires insurance companies corresponding to each reimbursement parameter set in at least two reimbursement parameter sets included in the target reimbursement arrangement combination.
Step 309: and the server determines an insurance company permutation and combination corresponding to the target reimbursement permutation and combination.
Step 310: and the server sends the reimbursement fees corresponding to the insurance company permutation and combination and the target permutation and combination to the terminal of the object to be recommended and/or at least one terminal with binding relation with the terminal of the object to be recommended.
It should be noted that, the specific implementation of each step of the method shown in fig. 3 may refer to the specific implementation of the foregoing method, which is not described herein.
The foregoing description of the embodiments of the present application has been presented primarily in terms of a method-side implementation. It will be appreciated that the big data based intelligent recommendation apparatus, in order to implement the above-mentioned functions, includes a hardware structure and/or a software module for executing the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
According to the embodiment of the application, the intelligent recommendation device based on big data can be divided into the functional units according to the method example, for example, each functional unit can be divided corresponding to each function, and two or more functions can be integrated into one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an intelligent recommendation device based on big data according to an embodiment of the present application, where the intelligent recommendation device 400 based on big data includes a processing unit 401, a storage unit 402, and a communication unit 403, and the processing unit 401 includes an obtaining unit, a first determining unit, and a second determining unit, where:
an acquisition unit configured to acquire medical data of an object to be recommended from a medical database, the medical data including a disease name and a treatment cost;
the first determining unit is used for inputting the medical data into a reimbursement arrangement combination model for processing, outputting at least two reimbursement arrangement combinations corresponding to the medical data, wherein the reimbursement arrangement combination model is used for selecting reimbursement arrangement combinations meeting the medical data of the object to be recommended, and each reimbursement arrangement combination comprises at least two reimbursement parameter sets;
And the second determining unit is used for inputting the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model for processing, and outputting a target reimbursement arrangement combination, wherein the target reimbursement arrangement combination is the most reimbursement cost in the at least two reimbursement arrangement combinations.
It can be seen that, in this example, medical data of an object to be recommended is acquired from a medical database, the medical data includes a disease name and a treatment cost, the medical data is input into a reimbursement arrangement combination model for processing, at least two reimbursement arrangement combinations corresponding to the medical data are output, the medical data and the at least two reimbursement arrangement combinations are input into the reimbursement recommendation model for processing, a target reimbursement arrangement combination is output, and the target reimbursement arrangement combination is the most reimbursement cost in the at least two reimbursement arrangement combinations. This realizes recommendation of the reimbursement arrangement combination with the highest reimbursement cost to the user based on the medical data.
In one possible example, in acquiring medical data of an object to be recommended from a medical database, the above-mentioned acquisition unit is specifically configured to:
receiving indication information sent by a reimbursement recommendation management platform, wherein the indication information is used for indicating a server to acquire medical data of the object to be recommended;
Transmitting request information to a server of a target medical institution, wherein the request information is used for the server of the target medical institution to feed back all medical data of the object to be recommended stored in a medical database of the server, and each medical data corresponds to one treatment time;
receiving all medical data of the object to be recommended, which is sent by a server of the target medical institution aiming at the request information;
and taking the medical data with the latest treatment time in all the medical data as the medical data of the object to be recommended.
In one possible example, in the aspect of inputting the medical data into the reimbursement arrangement and combination model for processing and outputting at least two reimbursement arrangement and combinations corresponding to the medical data, the first determining unit is specifically configured to:
inputting the medical data into a reimbursement arrangement and combination model, wherein the reimbursement arrangement and combination model is pre-provided with a plurality of reimbursement parameter sets, and each reimbursement parameter set corresponds to an insurance company;
determining at least two reimbursement parameter sets corresponding to the medical data according to the corresponding relation between the prestored disease names and reimbursement parameter sets in the reimbursement permutation and combination model;
and executing the operation of planning reimbursement permutation and combination on the at least two reimbursement parameter sets to obtain at least two reimbursement permutation and combination corresponding to the at least two reimbursement parameter sets.
In one possible example, the second determining unit is specifically configured to, in processing the medical data and the at least two reimbursement arrangement combinations by inputting the reimbursement recommendation model and outputting the target reimbursement arrangement combinations:
inputting the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model, wherein the reimbursement recommendation model is used for determining reimbursement arrangement combinations corresponding to the maximum reimbursement expense in the at least two reimbursement arrangement combinations;
determining reimbursement fees of each reimbursement permutation and combination in the at least two reimbursement permutation and combinations according to a prestored reimbursement fee policy to obtain at least two reimbursement fees corresponding to the at least two reimbursement permutation and combinations;
and taking the reimbursement permutation and combination corresponding to the maximum reimbursement expense in the at least two reimbursement fees as a target reimbursement permutation and combination.
In one possible example, each reimbursement permutation includes N reimbursement parameter sets, where N is an integer greater than or equal to 2, and the second determining unit is specifically configured to:
A1: determining a first part of reimbursement cost corresponding to the ith reimbursement parameter set;
a2: judging whether a reimbursement conflict exists between the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets;
a3: if the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets do not have reimbursement conflict, analyzing the (i+1) th reimbursement parameter set to obtain M stepped reimbursement expense sections and M first reimbursement proportions corresponding to the (i+1) th reimbursement parameter set, wherein each first reimbursement proportion corresponds to one reimbursement expense section, and M is an integer greater than or equal to 2;
a4: determining M first treatment fees corresponding to the treatment fees, wherein each first treatment fee corresponds to a reimbursement fee interval;
a5: determining a first part reimbursement cost corresponding to the (i+1) th reimbursement parameter set according to the M first treatment costs, the M first reimbursement proportions and a prestored reimbursement cost formula;
repeating the steps A2-A5 until i=N, and obtaining N first part reimbursement fees corresponding to the N reimbursement parameter sets; wherein i is an increasing integer with an initial value of 1 and 1 as an interval;
and taking the sum of the N first part reimbursement fees as reimbursement fees of each reimbursement permutation and combination.
In one possible example, each reimbursement permutation includes N reimbursement parameter sets, where N is an integer greater than or equal to 2, and the second determining unit is specifically configured to:
b1: determining a second part of reimbursement cost corresponding to the ith reimbursement parameter set;
b2: judging whether a reimbursement conflict exists between the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets;
b3: if the (i+1) th reimbursement parameter set and the previous i reimbursement parameter set have reimbursement conflicts, determining whether the sum of i second part reimbursement fees corresponding to the previous i reimbursement parameter sets is greater than or equal to reimbursement capping fees of the (i+1) th reimbursement parameter set;
b4: if the sum of the i second part reimbursement fees is smaller than the reimbursement capping fees, taking a reimbursement fee difference value of the sum of the reimbursement capping fees and the i second part reimbursement fees as the second part reimbursement fees of the (i+1) th reimbursement parameter set;
repeating the steps B2-B4 until i=N, and obtaining N second part reimbursement fees corresponding to the N reimbursement parameter sets; wherein i is an increasing integer with an initial value of 1 and 1 as an interval;
And taking the sum of the N second part reimbursement fees as reimbursement fees of each reimbursement permutation and combination.
In one possible example, the processing unit 401 further includes:
the first acquisition unit is used for acquiring insurance companies corresponding to each reimbursement parameter set in at least two reimbursement parameter sets included in the target reimbursement arrangement combination;
a third determining unit, configured to determine an insurance company permutation and combination corresponding to the target reimbursement permutation and combination;
and the sending unit is used for sending the reimbursement fees corresponding to the insurance company permutation and combination and the target permutation and combination to the terminal of the object to be recommended and/or at least one terminal with a binding relationship with the terminal of the object to be recommended.
The processing unit 401 may be a processor or a controller (for example, may be a central processing unit (Central Processing Unit, CPU), a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an Application-specific integrated controller (Application-Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof), the storage unit 402 may be a memory, and the communication unit 403 may be a transceiver, a transceiver controller, a radio frequency chip, a communication interface, or the like.
Referring to fig. 5, in accordance with the embodiments shown in fig. 1A, 2 and 3, fig. 5 is a schematic structural diagram of a server according to an embodiment of the present application, the server includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the following steps:
acquiring medical data of an object to be recommended from a medical database, wherein the medical data comprises a disease name and treatment cost;
inputting the medical data into a reimbursement arrangement and combination model for processing, outputting at least two reimbursement arrangement and combination corresponding to the medical data, wherein the reimbursement arrangement and combination model is used for selecting reimbursement arrangement and combination meeting the medical data of the object to be recommended, and each reimbursement arrangement and combination comprises at least two reimbursement parameter sets;
and inputting the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model for processing, and outputting a target reimbursement arrangement combination, wherein the target reimbursement arrangement combination is the most reimbursement cost in the at least two reimbursement arrangement combinations.
It can be seen that, in this example, the server acquires medical data of an object to be recommended from the medical database, the medical data includes a disease name and a treatment cost, inputs the medical data into the reimbursement permutation and combination model to process, outputs at least two reimbursement permutation and combinations corresponding to the medical data, inputs the medical data and the at least two reimbursement permutation and combination into the reimbursement recommendation model to process, outputs a target reimbursement permutation and combination, and the target reimbursement permutation and combination is the most reimbursement cost in the at least two reimbursement permutation and combination. This realizes recommendation of the reimbursement arrangement combination with the highest reimbursement cost to the user based on the medical data.
In one possible example, in acquiring medical data of an object to be recommended from a medical database, the program comprises instructions specifically for:
receiving indication information sent by a reimbursement recommendation management platform, wherein the indication information is used for indicating a server to acquire medical data of the object to be recommended;
transmitting request information to a server of a target medical institution, wherein the request information is used for the server of the target medical institution to feed back all medical data of the object to be recommended stored in a medical database of the server, and each medical data corresponds to one treatment time;
Receiving all medical data of the object to be recommended, which is sent by a server of the target medical institution aiming at the request information;
and taking the medical data with the latest treatment time in all the medical data as the medical data of the object to be recommended.
In one possible example, in the aspect of inputting the medical data into the reimbursement arrangement combination model for processing and outputting at least two reimbursement arrangement combinations corresponding to the medical data, the program includes instructions specifically for executing the steps of:
inputting the medical data into a reimbursement arrangement and combination model, wherein the reimbursement arrangement and combination model is pre-provided with a plurality of reimbursement parameter sets, and each reimbursement parameter set corresponds to an insurance company;
determining at least two reimbursement parameter sets corresponding to the medical data according to the corresponding relation between the prestored disease names and reimbursement parameter sets in the reimbursement permutation and combination model;
and executing the operation of planning reimbursement permutation and combination on the at least two reimbursement parameter sets to obtain at least two reimbursement permutation and combination corresponding to the at least two reimbursement parameter sets.
In one possible example, in processing the medical data and the at least two reimbursement permutations into a reimbursement recommendation model and outputting a target reimbursement permutation, the program includes instructions specifically for:
Inputting the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model, wherein the reimbursement recommendation model is used for determining reimbursement arrangement combinations corresponding to the maximum reimbursement expense in the at least two reimbursement arrangement combinations;
determining reimbursement fees of each reimbursement permutation and combination in the at least two reimbursement permutation and combinations according to a prestored reimbursement fee policy to obtain at least two reimbursement fees corresponding to the at least two reimbursement permutation and combinations;
and taking the reimbursement permutation and combination corresponding to the maximum reimbursement expense in the at least two reimbursement fees as a target reimbursement permutation and combination.
In one possible example, each reimbursement permutation includes N reimbursement parameter sets, N being an integer greater than or equal to 2, the program including instructions specifically for performing the following steps in determining reimbursement fees for each of the at least two reimbursement permutation based on a pre-stored reimbursement fee policy:
a1: determining a first part of reimbursement cost corresponding to the ith reimbursement parameter set;
a2: judging whether a reimbursement conflict exists between the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets;
a3: if the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets do not have reimbursement conflict, analyzing the (i+1) th reimbursement parameter set to obtain M stepped reimbursement expense sections and M first reimbursement proportions corresponding to the (i+1) th reimbursement parameter set, wherein each first reimbursement proportion corresponds to one reimbursement expense section, and M is an integer greater than or equal to 2;
A4: determining M first treatment fees corresponding to the treatment fees, wherein each first treatment fee corresponds to a reimbursement fee interval;
a5: determining a first part reimbursement cost corresponding to the (i+1) th reimbursement parameter set according to the M first treatment costs, the M first reimbursement proportions and a prestored reimbursement cost formula;
repeating the steps A2-A5 until i=N, and obtaining N first part reimbursement fees corresponding to the N reimbursement parameter sets; wherein i is an increasing integer with an initial value of 1 and 1 as an interval;
and taking the sum of the N first part reimbursement fees as reimbursement fees of each reimbursement permutation and combination.
In one possible example, each reimbursement permutation includes N reimbursement parameter sets, N being an integer greater than or equal to 2, the program including instructions specifically for performing the following steps in determining reimbursement fees for each of the at least two reimbursement permutation based on a pre-stored reimbursement fee policy:
b1: determining a second part of reimbursement cost corresponding to the ith reimbursement parameter set;
b2: judging whether a reimbursement conflict exists between the (i+1) th reimbursement parameter set and the previous i reimbursement parameter sets;
B3: if the (i+1) th reimbursement parameter set and the previous i reimbursement parameter set have reimbursement conflicts, determining whether the sum of i second part reimbursement fees corresponding to the previous i reimbursement parameter sets is greater than or equal to reimbursement capping fees of the (i+1) th reimbursement parameter set;
b4: if the sum of the i second part reimbursement fees is smaller than the reimbursement capping fees, taking a reimbursement fee difference value of the sum of the reimbursement capping fees and the i second part reimbursement fees as the second part reimbursement fees of the (i+1) th reimbursement parameter set;
repeating the steps B2-B4 until i=N, and obtaining N second part reimbursement fees corresponding to the N reimbursement parameter sets; wherein i is an increasing integer with an initial value of 1 and 1 as an interval;
and taking the sum of the N second part reimbursement fees as reimbursement fees of each reimbursement permutation and combination.
In one possible example, the above-described program further includes instructions for performing the steps of:
acquiring insurance companies corresponding to each reimbursement parameter set in at least two reimbursement parameter sets included in the target reimbursement arrangement combination;
determining an insurance company permutation and combination corresponding to the target reimbursement permutation and combination;
And sending reimbursement fees corresponding to the insurance company permutation and combination and the target permutation and combination to the terminal of the object to be recommended and/or at least one terminal with a binding relationship with the terminal of the object to be recommended.
The embodiment of the present application also provides a computer storage medium storing a computer program, where the computer program is executed by a processor to implement part or all of the steps of any one of the methods described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any one of the methods described in the method embodiments above. The computer program product may be a software installation package.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will appreciate, modifications will be made in the specific implementation and application scope in accordance with the idea of the present application, and the above description should not be construed as limiting the present application.
Claims (8)
1. An intelligent recommendation method based on big data is characterized by comprising the following steps:
acquiring medical data of an object to be recommended from a medical database, wherein the medical data comprises a disease name and treatment cost;
Inputting the medical data into a reimbursement arrangement and combination model for processing, outputting at least two reimbursement arrangement and combination corresponding to the medical data, wherein the reimbursement arrangement and combination model is used for selecting reimbursement arrangement and combination meeting the medical data of the object to be recommended, and each reimbursement arrangement and combination comprises at least two reimbursement parameter sets; comprising the following steps: inputting the medical data into a reimbursement arrangement and combination model, wherein the reimbursement arrangement and combination model is pre-provided with a plurality of reimbursement parameter sets, and each reimbursement parameter set corresponds to an insurance company; determining at least two reimbursement parameter sets corresponding to the medical data according to the corresponding relation between the prestored disease names and reimbursement parameter sets in the reimbursement permutation and combination model; performing the operation of planning reimbursement permutation and combination on the at least two reimbursement parameter sets to obtain at least two reimbursement permutation and combination corresponding to the at least two reimbursement parameter sets;
inputting the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model for processing, outputting a target reimbursement arrangement combination, wherein the target reimbursement arrangement combination is the most reimbursement cost in the at least two reimbursement arrangement combinations, and the method comprises the following steps: inputting the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model, wherein the reimbursement recommendation model is used for determining reimbursement arrangement combinations corresponding to the maximum reimbursement expense in the at least two reimbursement arrangement combinations; determining reimbursement fees of each reimbursement permutation and combination in the at least two reimbursement permutation and combinations according to a prestored reimbursement fee policy to obtain at least two reimbursement fees corresponding to the at least two reimbursement permutation and combinations; and taking the reimbursement permutation and combination corresponding to the maximum reimbursement expense in the at least two reimbursement fees as a target reimbursement permutation and combination.
2. The method of claim 1, wherein the obtaining medical data of the object to be recommended from the medical database comprises:
receiving indication information sent by a reimbursement recommendation management platform, wherein the indication information is used for indicating a server to acquire medical data of the object to be recommended;
transmitting request information to a server of a target medical institution, wherein the request information is used for the server of the target medical institution to feed back all medical data of the object to be recommended stored in a medical database of the server, and each medical data corresponds to one treatment time;
receiving all medical data of the object to be recommended, which is sent by a server of the target medical institution aiming at the request information;
and taking the medical data with the latest treatment time in all the medical data as the medical data of the object to be recommended.
3. The method of claim 1, wherein each reimbursement permutation includes N reimbursement parameter sets, N being an integer greater than or equal to 2, the determining reimbursement costs for each of the at least two reimbursement permutation according to a pre-stored reimbursement cost policy comprising:
A1: determining a first part of reimbursement cost corresponding to the ith reimbursement parameter set;
a2: judging whether a reimbursement conflict exists between the (i+1) reimbursement parameter set and the (i) reimbursement parameter set;
a3: if no reimbursement conflict exists between the i+1th reimbursement parameter set and the previous i reimbursement parameter sets, analyzing the i+1th reimbursement parameter set to obtain M stepped reimbursement expense intervals and M first reimbursement proportions corresponding to the i+1th reimbursement parameter set, wherein each first reimbursement proportion corresponds to one reimbursement expense interval, and M is an integer greater than or equal to 2;
a4: determining M first treatment fees corresponding to the treatment fees, wherein each first treatment fee corresponds to a reimbursement fee interval;
a5: determining a first part reimbursement cost corresponding to the (i+1) th reimbursement parameter set according to the M first treatment costs, the M first reimbursement proportions and a prestored reimbursement cost formula;
repeating the steps A2-A5 until i=N, and obtaining N first part reimbursement fees corresponding to the N reimbursement parameter sets; wherein i is an increasing integer with an initial value of 1 and 1 as an interval;
and taking the sum of the N first part reimbursement fees as reimbursement fees of each reimbursement permutation and combination.
4. The method of claim 1, wherein each reimbursement permutation includes N reimbursement parameter sets, N being an integer greater than or equal to 2, the determining reimbursement costs for each of the at least two reimbursement permutation according to a pre-stored reimbursement cost policy comprising:
b1: determining a second part of reimbursement cost corresponding to the ith reimbursement parameter set;
b2: judging whether a reimbursement conflict exists between the (i+1) reimbursement parameter set and the (i) reimbursement parameter set;
b3: if the i+1th reimbursement parameter set and the previous i reimbursement parameter set have reimbursement conflicts, determining whether the sum of i second part reimbursement fees corresponding to the previous i reimbursement parameter sets is greater than or equal to reimbursement capping fees of the i+1th reimbursement parameter set;
b4: if the sum of the i second part reimbursement fees is smaller than the reimbursement capping fees, taking a reimbursement fee difference value of the sum of the reimbursement capping fees and the i second part reimbursement fees as the second part reimbursement fees of the (i+1) th reimbursement parameter set;
repeating the steps B2-B4 until i=N, and obtaining N second part reimbursement fees corresponding to the N reimbursement parameter sets; wherein i is an increasing integer with an initial value of 1 and 1 as an interval;
And taking the sum of the N second part reimbursement fees as reimbursement fees of each reimbursement permutation and combination.
5. The method of claim 3 or 4, wherein the inputting the medical data and the at least two reimbursement arrangements into the reimbursement recommendation model is performed, and wherein after outputting the target reimbursement arrangements, the method further comprises:
acquiring insurance companies corresponding to each reimbursement parameter set in at least two reimbursement parameter sets included in the target reimbursement arrangement combination;
determining an insurance company permutation and combination corresponding to the target reimbursement permutation and combination;
and sending reimbursement fees corresponding to the insurance company permutation and combination and the target reimbursement permutation and combination to the terminal of the object to be recommended and/or at least one terminal with a binding relationship with the terminal of the object to be recommended.
6. Intelligent recommendation device based on big data, characterized in that it is adapted to perform the method according to any of claims 1-5, said device comprising:
an acquisition unit configured to acquire medical data of an object to be recommended from a medical database, the medical data including a disease name and a treatment cost;
the first determining unit is used for inputting the medical data into a reimbursement arrangement combination model for processing, outputting at least two reimbursement arrangement combinations corresponding to the medical data, wherein the reimbursement arrangement combination model is used for selecting reimbursement arrangement combinations meeting the medical data of the object to be recommended, and each reimbursement arrangement combination comprises at least two reimbursement parameter sets;
And the second determining unit is used for inputting the medical data and the at least two reimbursement arrangement combinations into a reimbursement recommendation model for processing, and outputting a target reimbursement arrangement combination, wherein the target reimbursement arrangement combination is the most reimbursement cost in the at least two reimbursement arrangement combinations.
7. A server comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-5.
8. A computer readable storage medium for storing a computer program for execution by a processor to implement the method of any one of claims 1-5.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997020279A1 (en) * | 1995-11-30 | 1997-06-05 | Inter*Act Systems, Incorporated | Method and system for presenting customized promotional offers |
| CN106777998A (en) * | 2016-12-19 | 2017-05-31 | 邹春秋 | A kind of medical system big data processing unit and method |
| CN106875030A (en) * | 2016-12-14 | 2017-06-20 | 武汉默联股份有限公司 | Commercial health insurance directly pays for intelligent recommendation system and method online |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030018496A1 (en) * | 2001-06-29 | 2003-01-23 | Siemens Medical Solutions Health Services Corporation | System and user interface for use in billing for services and goods |
| WO2011085040A1 (en) * | 2010-01-05 | 2011-07-14 | Abbott Diabetes Care Inc. | System and method for managing medical data and facilitating reimbursement for health care |
-
2018
- 2018-12-13 CN CN201811527654.4A patent/CN109829829B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997020279A1 (en) * | 1995-11-30 | 1997-06-05 | Inter*Act Systems, Incorporated | Method and system for presenting customized promotional offers |
| CN106875030A (en) * | 2016-12-14 | 2017-06-20 | 武汉默联股份有限公司 | Commercial health insurance directly pays for intelligent recommendation system and method online |
| CN106777998A (en) * | 2016-12-19 | 2017-05-31 | 邹春秋 | A kind of medical system big data processing unit and method |
Non-Patent Citations (1)
| Title |
|---|
| 报销比例应该适当;陈仰东;;中国医疗保险(第06期);第22-23页 * |
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