CN111190931A - Method and device for processing prescription data, electronic equipment and storage medium - Google Patents
Method and device for processing prescription data, electronic equipment and storage medium Download PDFInfo
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- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H20/13—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered from dispensers
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Abstract
The embodiment of the application provides a method and a device for processing prescription data, electronic equipment and a storage medium, wherein the method comprises the following steps: when a prescription generation request sent by a client is received, determining a target user identifier and analysis result information corresponding to the prescription generation request; determining target user attribute information corresponding to the target user identification; generating medicine list data according to the analysis result information and the target user attribute information, and sending the medicine list data to the client; when the confirmation message sent by the client side for the medicine list data is received, the prescription data for the medicine list data is generated, the automatic recommendation of the prescription matched with the diagnosis result is realized, the selection range of the medicine is expanded in an automatic matching mode, the fact that a doctor only selects the medicine in common use is avoided, the doctor does not need to repeatedly select the medicine in a medicine database for the same diagnosis result, and the working efficiency is improved.
Description
Technical Field
The present application relates to the field of information technologies, and in particular, to a method and an apparatus for processing prescription data, an electronic device, and a storage medium.
Background
With the development of scientific technology, after the doctor asks for a patient, the doctor can take a prescription for the patient through an electronic system.
In the prior art, a doctor can build a medicine database of the doctor, and after an inquiry technology, the doctor searches the medicine database through a diagnosis result or a medicine name according to the diagnosis result, searches for a medicine matched with an illness state, and adds the medicine to an electronic prescription.
However, this method has many disadvantages, and since the doctor can only select the medicine familiar to him/herself by searching in the medicine database built by the doctor himself/herself, actually, the medicine is various, and there is a limitation in selecting the medicine by the doctor.
Moreover, when a doctor takes an electronic prescription every time, the doctor needs to search in the medicine database, even if a patient has the same disease, the doctor needs to repeat the steps of searching, determining the dosage of the medicine and taking the prescription repeatedly, so that the repeated work is increased, and the work efficiency is reduced.
Disclosure of Invention
In view of the above problems, it is proposed to provide a method and apparatus for processing prescription data, an electronic device, and a storage medium, which overcome the above problems or at least partially solve the above problems, including:
a method of processing prescription data, the method comprising:
when a prescription generation request sent by a client is received, determining a target user identifier and analysis result information corresponding to the prescription generation request;
determining target user attribute information corresponding to the target user identification;
generating medicine list data according to the analysis result information and the target user attribute information, and sending the medicine list data to the client;
and generating prescription data aiming at the medicine list data when receiving a confirmation message sent by the client aiming at the medicine list data.
Optionally, the generating of drug inventory data according to the analysis result information and the target user attribute information includes:
generating first alternative medicine list information according to the analysis result information;
generating second alternative medicine list information according to the analysis result information and the target user attribute information;
and generating medicine list data by combining the first alternative medicine list information and the second alternative medicine list information.
Optionally, the generating of the drug list data by combining the first alternative drug list information and the second alternative drug list information includes:
if the first alternative medicine list information is completely the same as the second alternative medicine list information, generating medicine list data by adopting the first alternative medicine information or the second alternative medicine information;
optionally, the generating of the drug list data by combining the first alternative drug list information and the second alternative drug list information includes:
if the first alternative medicine list information is not identical to the second alternative medicine list information, determining medicine attribute information corresponding to different target medicine objects;
determining a medicine recommendation score corresponding to the target medicine object according to the medicine attribute information;
determining optimal alternative medicine list information from the first alternative medicine list information and the second alternative medicine list information according to the medicine recommendation score; the optimal alternative medicine list is the alternative medicine list with the highest sum of the medicine recommendation scores corresponding to the contained target medicine objects;
and generating the drug list data according to the optimal alternative drug list information.
Optionally, the drug attribute information includes multiple attribute types, and determining a drug recommendation score corresponding to the target drug object according to the drug attribute information includes:
determining a basic recommendation score corresponding to each attribute type according to the drug attribute information;
and according to the type weight corresponding to each attribute type, carrying out weighted summation on the basic recommendation scores to obtain the medicine recommendation score corresponding to the target medicine object.
Optionally, the method further comprises:
acquiring a historical drug modification record; wherein the historical drug modification record comprises a recommended first drug object identifier and a second drug object identifier replacing the first drug object identifier;
determining that a distinct attribute type exists for the first drug object and the second drug object;
and respectively adjusting the type weight corresponding to the attribute type aiming at the first medicine object and the second medicine object.
Optionally, the method further comprises:
receiving a drug modification request sent by the client aiming at the drug list data;
and modifying the drug list data according to the drug modification request.
A device for processing prescription data, the device comprising:
the system comprises a generation request receiving module, a processing module and a processing module, wherein the generation request receiving module is used for determining a target user identifier and analysis result information corresponding to a prescription generation request when the prescription generation request sent by a client is received;
the user attribute information determining module is used for determining the target user attribute information corresponding to the target user identification;
the drug list data generation module is used for generating drug list data according to the analysis result information and the target user attribute information and sending the drug list data to the client;
and the prescription data generating module is used for generating the prescription data aiming at the medicine list data when receiving the confirmation message sent by the client aiming at the medicine list data.
An electronic device comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, the computer program, when executed by the processor, implementing the steps of the method of processing prescription data as described above.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of processing prescription data as described above.
The embodiment of the application has the following advantages:
in the embodiment of the application, when a prescription generation request sent by a client is received, a target user identifier and analysis result information corresponding to the prescription generation request are determined, then target user attribute information corresponding to the target user identifier can be determined, medicine list data are generated by adopting the analysis result information and the target user attribute information, the medicine list data are sent to the client, and when a confirmation message sent by the client for the medicine list data is received, the prescription data for the medicine list data are generated, so that the automatic recommendation of the prescription matched with a diagnosis result is realized, the selection range of medicines is expanded by an automatic matching mode, a doctor is prevented from selecting only in common medicines, the same diagnosis result is selected without repeatedly selecting in a medicine database by the doctor, and the working efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description of the present application will be briefly introduced below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a flow chart of the steps of a method of processing prescription data as provided in one embodiment of the present application;
FIG. 2 is a flow chart of steps of another method of processing prescription data as provided by an embodiment of the present application;
FIG. 3 is a flow chart of the generation of a prescription slip as provided by an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a device for processing prescription data according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a flowchart illustrating steps of a method for processing prescription data provided by an embodiment of the present application is shown, and may be applied to a server, and specifically includes the following steps:
as an example, the analysis result information may include: disease diagnostic information, such as diagnostic results obtained by a physician after analyzing the condition of a patient; the prescription generation request may be a request generated during an online interrogation.
In practical application, a doctor user can obtain analysis result information of a patient after analyzing the condition of the patient, the doctor user can input the analysis result information and a target user identifier of the patient into a client, and the client can generate a prescription generation request containing the analysis result information and the target user identifier in response to the operation of the doctor user and send the request to a server.
Upon receiving the prescription generation request, the server may determine the target user identification and analysis result information from the prescription generation request.
In one example, the prescription generating request may be a request generated during an on-line interrogation, such as when a doctor interrogates a patient via a PC or mobile terminal; alternatively, the request may be generated during an offline inquiry procedure, for example, when a doctor diagnoses a disease for a patient in a hospital, the request may be generated by a client installed in a PC to send a prescription to a server.
as an example, the target user attribute information may include any one or more of:
gender information, age information, occupation information.
In the concrete implementation, when a patient seeks a doctor, user attribute information, such as sex information, age information, professional information, academic information, home address information and the like, can be input in advance through a login client; or, the user attribute information can be input into the client by the staff when the hospital registers for treatment. When the user attribute information is input, the client can also generate a user identifier aiming at the user attribute information, and send the user identifier and the user attribute information to the server. After receiving the user identifier and the user attribute information, the server may generate a relationship list, and record a corresponding relationship between the user identifier and the user attribute information. After the target user identifier is determined, the server may determine user attribute information corresponding to the target user identifier.
103, generating medicine list data according to the analysis result information and the target user attribute information, and sending the medicine list data to the client;
as an example, the drug inventory data may include one or more drug information for treating a disease corresponding to the analysis result information.
After acquiring the target user attribute information, the server side can analyze the target user attribute information by adopting the analysis result information and the target user attribute information, acquire one or more kinds of medicine information after analysis, generate a medicine data list, and send the medicine list data to the client side.
And 104, generating the prescription data aiming at the medicine list data when receiving the confirmation message sent by the client aiming at the medicine list data.
After receiving the drug list data, the client can display one or more types of drug information recorded in the drug list data in a display interface, a doctor user can check the drugs recommended by the drug list data and judge whether the recommended drug types or drug doses need to be modified, the doctor user can click an operation key to confirm that the drug list data is correct under the condition that modification is not needed, and the client can generate a confirmation message and send the confirmation message to the server in response to user operation.
After receiving the confirmation message sent by the client for the drug manifest data, the server may generate the prescription data for the drug manifest data.
In an example, after the prescription data is generated, the server can send the data to the client of the doctor user, and the doctor user sends the prescription determined by the prescription data to the user through mail, short message or fax; or, the server can also acquire the contact information of the user, such as a mobile phone number and a mailbox address, from the user attribute information and send the prescription to the patient.
In an embodiment of the present application, the method may further include the steps of: receiving a drug modification request sent by the client aiming at the drug list data; and modifying the drug list data according to the drug modification request.
In practical application, after receiving the drug list data, the client may display one or more types of drug information recorded in the drug list data in a display interface, and after viewing the drug information, a doctor user may modify the drug information recorded in the drug list data, such as modifying a drug type or a drug dose.
After receiving the drug modification request, the server can obtain the modified drug information in the request, and modify the drug list data according to the drug modification request.
In the embodiment of the application, when a prescription generation request sent by a client is received, a target user identifier and analysis result information corresponding to the prescription generation request are determined, then target user attribute information corresponding to the target user identifier can be determined, medicine list data are generated by adopting the analysis result information and the target user attribute information, the medicine list data are sent to the client, and when a confirmation message sent by the client for the medicine list data is received, the prescription data for the medicine list data are generated, so that the automatic recommendation of the prescription matched with a diagnosis result is realized, the selection range of medicines is expanded by an automatic matching mode, a doctor is prevented from selecting only in common medicines, the same diagnosis result is selected without repeatedly selecting in a medicine database by the doctor, and the working efficiency is improved.
Referring to fig. 2, a flowchart illustrating steps of another method for processing prescription data provided by an embodiment of the present application is shown, and may be applied to a server, and specifically includes the following steps:
in practical application, a doctor user can make the same or different prescriptions for the same disease, and on the basis, a plurality of pieces of historical diagnosis information can be stored in the server, the historical diagnosis information can include the corresponding relation between the analysis result information and the prescription information, and one or more pieces of alternative medicine information which can be adopted when aiming at one piece of analysis result information are recorded. By storing a plurality of pieces of historical diagnosis information, the server side can form a database with huge data size and stored with rare cases or a plurality of common cases.
After receiving the analysis result information, the server may query the corresponding one or more types of prescription information using the analysis result information, and determine alternative drug information from the prescription information, and then, the server may generate first alternative drug list information using the alternative drug information corresponding to the analysis result information.
in a particular implementation, a doctor can prescribe different prescriptions for different patient specific physical conditions for the same ailment, for example, in the case of a common cold, the drugs available to an average adult, child or pregnant woman are not the same. Based on the information, the server can also store the corresponding relation among the analysis result information, the user attribute information and the prescription information.
After the target user attribute information is obtained, the server side can use the analysis result information and the target user attribute information to inquire one or more corresponding prescription information and determine alternative medicine information from the prescription information, and after the alternative medicine information is obtained, the server side can use the alternative medicine information corresponding to the analysis result information and the target user attribute information to generate second alternative medicine list information.
For example, after a doctor diagnoses a patient, the analysis result information is that the patient has hypercholesterolemia, and the target user attribute of the patient is a 50-year-old male driver. After the analysis result information and the target user attribute are determined, a doctor can input the information into a client and send the information to a server to search for alternative medicine list information, after the analysis result information and the target user attribute are received, the server can search for prescription information which is provided by the doctor when a 50-year-old male driver has hypercholesterolemia from a plurality of pieces of prestored historical diagnosis information, after a plurality of pieces of prescription information are obtained, the server can count the occurrence times of the alternative medicine information in all the prescription information, and generate a second alternative medicine list by adopting one or more pieces of alternative medicine information with the highest occurrence times.
after the second candidate medicine list information is obtained, the server may compare the first candidate medicine list information with the second candidate medicine list information, and generate medicine list data according to a comparison result after the comparison.
In an embodiment of the present application, step 205 may include the following sub-steps:
substep 11, if the first candidate drug list information is completely the same as the second candidate drug list information, generating drug list data by using the first candidate drug information or the second candidate drug information;
specifically, the server may compare whether the first candidate drug list information is identical to the second candidate drug list information, for example, whether the drug type, the drug manufacturer, and the drug dose are identical, and if the first candidate drug list information is identical to the second candidate drug list information, the server may generate the drug list data by using the first candidate drug information or the second candidate drug information.
For example, the first alternative drug manifest information includes drug object A1、B1、C1The second alternative drug list information includes drug object A2、B2、C2Wherein A is1、B1、C1And A2、B2、C2The type, manufacturer and dosage of the medicine are the same, the server can adopt A1、B1、C1Or A2、B2、C2Drug inventory data is generated.
Substep 12, if the first candidate drug list information is not identical to the second candidate drug list information, determining drug attribute information corresponding to different target drug objects;
in practical application, if the first candidate drug list information is not identical to the second candidate drug list information, the server may obtain target drug objects that are different from the first candidate drug list information and the second candidate drug list information, and determine drug attribute information of the target drug objects.
Substep 13, determining a medicine recommendation score corresponding to the target medicine object according to the medicine attribute information;
after acquiring the drug attribute information, the server may calculate the drug recommendation score of the target drug object using one or more drug attribute information.
In an embodiment of the present application, the drug attribute information may include a plurality of attribute types, and the sub-step 13 may include the sub-steps of:
substep 131, determining a basic recommendation score corresponding to each attribute type according to the medicine attribute information;
as an example, the property types may include one or more of:
the system comprises a pharmacodynamic attribute, an inventory attribute, a price attribute, a cognition attribute, a profit attribute, an affinity attribute and a risk attribute.
The pharmacodynamic profile can determine the therapeutic effect of the target drug subject, e.g., within one week, within three weeks, within half a year, etc.
The inventory attributes may determine the current inventory status of the target drug object, such as in a full inventory status, an out of inventory status, or a severely out of inventory status.
The price attribute may determine price gaps of the target drug object among the same type of drugs, such as moderate price, high price, and low price.
The awareness attribute may determine the degree to which the target drug object is approved by the user in the sales marketplace, such as widely approved or not approved. Taking the bezoar antihypertensive pill as an example, two manufacturers of Tongrentang and Daren Tang are both producing, but users are more cognizant of the products of Tongrentang, and the cognition attribute of the Tongrentang bezoar antihypertensive pill can be set as wide recognition.
The profit attribute may determine a profit level that the vendor may obtain when selling the target drug object; the affinity attribute may determine the affinity of the relationship between the server manufacturer or producer and the target drug object seller; the risk profile may determine the extent of side effects of the pharmaceutical subject.
In practical application, a list can be preset for different drug objects, the basic recommendation scores of the attribute information of the drug objects are recorded, and the drug objects and the basic recommendation scores of the attribute information are stored in a database of a server. For example, the base recommendation scores of the drug efficacy attribute, the stock quantity attribute, and the cognition degree attribute of a may be set to 7, 6, and 3, respectively.
After the drug attribute information is obtained, the server side can determine one or more attribute types in the drug attribute information, and query in a preset list to determine the basic recommendation score of each attribute type.
In one example, the base recommendation score for a price attribute may be set to a negative number, and the absolute value of the base recommendation score for a price attribute may be negatively related to the probability that the drug object is selected, reflecting the negative impact of the price.
The basic recommendation score of the inventory attribute is in positive correlation with the probability of the selected medicine object, when the inventory state is a serious shortage state, the risk that delivery cannot be timely caused by shortage of inventory exists, and the basic recommendation score of the inventory attribute can be correspondingly reduced.
For example, the server manufacturer may sign a sales agreement with the drug vendor of a, and when the sales amount exceeds a preset value, the server manufacturer may obtain a bonus, and at this time, the basic recommendation score of the affinity attribute may be increased.
And a substep 132, performing weighted summation on the basic recommendation scores according to the type weight corresponding to each attribute type, so as to obtain a medicine recommendation score corresponding to the target medicine object.
In practical application, different attribute types may have different type weights, and the server may store the correspondence between the attribute types and the type weights. By type weight, the importance of the attribute type in calculating the drug recommendation score can be determined. For example, when the effect of the medicine is emphasized, the weight of the attribute of the medicine can be set to the highest value of all types of weights, such as 50%; the profit attribute or the closeness attribute may be set to 40% when the sales profit is valued.
After determining the basic recommendation score, the server may further search for a type weight corresponding to the attribute type, and adopt the type weight of each attribute type to perform weighted summation on the basic recommendation score to obtain a drug recommendation score of the target drug object.
For example, the base recommendation scores of the drug efficacy attribute, the inventory attribute, the price attribute, the cognition attribute, the profit attribute and the affinity attribute of the a drug are 7, 6, -5, 4, 3 and 3, and the corresponding type weights are 50%, 5%, 10%, 15% and 10%, respectively, so that the drug recommendation score of the a drug can be finally calculated to be 4.45.
Of course, a person skilled in the art may select one or more attribute types to calculate the drug recommendation score of the target drug object according to actual needs, and the number of the selected attribute types is not limited in the embodiment of the present application.
Substep 14, determining optimal candidate drug list information from the first candidate drug list information and the second candidate drug list information by using the drug recommendation score; the optimal alternative medicine list is the alternative medicine list with the highest sum of the medicine recommendation scores corresponding to the contained target medicine objects;
after determining the drug recommendation score of the target drug object, the server may calculate a sum of drug recommendation scores of the target drug object in the first candidate drug list information and the second candidate drug list information, respectively, and determine, as optimal candidate drug list information, candidate drug list information with the highest sum of drug recommendation scores from the first candidate drug list information and the second candidate drug list information.
For example, the first candidate drug list information includes drug A, B, C, D, the second candidate drug list information includes drug information A, B, E, F, the server may determine C, D, E, F as the target drug object, and calculate that the drug recommendation scores C, D, E, F are 3.55, 4.85, 4.15, and 3.65, respectively, and since the sum of the drug recommendation scores of C and D is 8.4 and the sum of the drug recommendation scores of E and F is 7.8, the server may determine the first candidate drug list information as the optimal candidate drug list information.
And a substep 15, generating the drug list data according to the optimal candidate drug list information, and sending the drug list data to the client.
After the optimal candidate medicine list information is obtained, the server side can generate medicine list data by adopting the optimal candidate medicine list information, and the medicine list data is sent to the client side.
after receiving the drug list data, the client can display one or more types of drug information recorded by the drug list data in a display interface, a doctor user can check the drugs recommended by the drug list data, after the drugs are confirmed to be correct, the doctor user can click an operation key to confirm that the drug list data are correct, the client can generate a confirmation message in response to the user operation, and the confirmation message is sent to the server.
After receiving the confirmation message sent by the client for the drug manifest data, the server may generate the prescription data for the drug manifest data.
after the prescription data is generated, the server side can obtain a historical medicine modification record, and a first medicine object identification recommended in the medicine list data generated by the server side and a second medicine object identification replacing the first medicine object identification after modification can be included in the historical medicine modification record.
In practical application, a doctor user checks drug list data sent by a server through a client, the drug list data may include one or more recommended drug objects, after checking, the doctor user may replace a first drug object recommended by the drug list data with a second drug object, in response to an operation of the doctor user, the client may send a drug modification request to the server, where the request may include a first drug object identifier and a second drug object identifier replacing the first drug object identifier, and after receiving the drug modification request, when the server modifies the drug list data, the server may record in a history drug modification record that the first drug object identifier is replaced with the second drug object identifier.
after obtaining the first drug object identifier and the second drug object identifier, the server may determine the first drug object and the second drug object, obtain the attribute types of the first drug object and the second drug object, and determine the attribute types that have differences.
For example, after obtaining the attribute types of the first and second drug objects, the server may further obtain a base recommendation score of the attribute types, and determine, in a quantitative manner, where the attribute types of the first and second drug objects are different.
After determining that there are different attribute types, the server may adjust the type weights of the attribute types of the first drug object and the second drug object, respectively.
For example, in the historical drug modification record, when an a drug is recommended for 20 times, the a drug is replaced with a B drug, and after the server acquires the attribute types and the basic recommendation scores of the attribute types of the a drug and the B drug, the attribute types of the a drug and the B drug that are different are determined to be a price attribute and a pharmacodynamic attribute, wherein the basic recommendation score of the price attribute of the a drug is-5, and the basic recommendation score of the pharmacodynamic attribute is 7; the basic recommendation score of the price attribute of the medicine B is-2, and the basic recommendation score of the pharmacodynamic attribute is 4; and the server determines through analysis that when the doctor user selects the medicine A or the medicine B, the influence of the price attribute on the selection result is greater than the influence of the drug effect attribute on the selection result.
Based on the method, the server can increase the type weight of the basic recommendation score of the price attribute of the A medicine, decrease the type weight of the basic recommendation score of the efficacy attribute, and reduce the medicine recommendation score of the A medicine; then, the type weight of the basic recommendation score of the price attribute of the B medicine can be reduced, the type weight of the basic recommendation score of the pharmacodynamic attribute can be increased, and the medicine recommendation score of the B medicine can be increased.
Of course, in practical applications, the historical drug modification record may further include dose adjustment for the drug subject, for example, the dose of the pediatric fermented soybean and fructus forsythiae heat-clearing granules is adjusted from one box to two boxes.
Or, the server may further record a recommendation success rate of the drug object, for example, the child fermented soybean and forsythia heat-clearing granules are recommended for 10 times in total in the record, 3 times are replaced, the success rate is 70%, the server may count the recommendation success rate of the drug object according to a preset time interval, and when the recommendation success rate is smaller than a preset threshold, another drug object may be used to replace the original drug object, for example, acetaminophen is used to replace the child fermented soybean and forsythia heat-clearing granules. Through continuously acquiring historical medicine modification and according to historical medicine modification records, the server side can have learning capacity, and can continuously update recommended medicine list data so as to push more accurate medicine list data to the client side of a doctor user.
In the embodiment of the application, when a prescription generation request sent by a client is received, first candidate medicine list information and second candidate medicine list information are generated, medicine list data are generated by combining the first candidate medicine list information and the second candidate medicine list information, when a confirmation message sent by the client for the medicine list data is received, the prescription data for the medicine list information are generated, then, a historical medicine modification record comprising a first medicine object identifier and a second medicine object identifier can be obtained, the attribute types of the first medicine object and the second medicine object which are different are determined, the type weights corresponding to the attribute types are adjusted respectively for the first medicine object and the second medicine object, the automatic generation of the prescription by combining a diagnosis result and patient information is realized, and the modification record of the medicine list data by a doctor is obtained, the type weight of the medicine is adaptively adjusted when the medicine is selected, the generation strategy of the prescription can be continuously optimized, and the accuracy of medicine selection and the working efficiency of doctors are improved.
In order to enable those skilled in the art to better understand the above steps, the following is an example to illustrate the embodiments of the present application, but it should be understood that the embodiments of the present application are not limited thereto.
Referring to FIG. 3, a flow chart for generating a prescription slip is provided for an embodiment of the present application.
A 50-year-old male driver, who is diagnosed to determine hypercholesterolemia (i.e., analysis result information), can input the analysis result information and target user attribute information "50 years", "male" and "driver" to the client, and in response to a user operation, the client can transmit "hypercholesterolemia", "50 years", "male" and "driver" to the server.
After the analysis result information and the target user attribute information are obtained, the server can search the prescription in the database for the hypercholesterolemia, and count to obtain a vacancy option of the three medicines by mainly opening the three medicines when the hypercholesterolemia is treated.
Then, the server can calculate the three medicines A with the most occurrence times in the prescription for the hypercholesterolemia1、B1、C1And generates A1、B1、C1Corresponding first alternative drug manifest data. In addition, the server can also determine the prescription for treating a 50-year-old male driver aiming at the prescription of 'hypercholesterolemia', and calculate the three medicines A with the most occurrence times2、B2、C2And generates A2、B2、C2Corresponding second alternative drug manifest data.
When A is1、B1、C1And A2、B2、C2When the data is completely the same, the server can select A1、B1、C1Or A2、B2、C2Generating a drug data list when A1、B1、C1And A2、B2、C2When not the same, the server can determine C1And C2Different, the acquired drug attribute information such as inventory attribute, closeness attribute, drug effect attribute, cognition attribute and success rate attribute is acquired, the basic recommendation scores of attribute types and attribute types are acquired at the same time, and C is calculated1And C2The recommended value of the medicine is calculated as C1A recommended score of the drug is higher than C2So the server adopts A1、B1、C1Drug manifest data are generated: ezetimibe, rosuvastatin and simvastatin.
After the medicine data list is generated, the server side can send the medicine list data to the client side, after a doctor checks the medicine list data, the ezetimibe in the medicine list data is replaced by the atorvastatin calcium, and the modified medicine list data is sent to the server side.
After receiving the modified drug list data, the server side can generate a final prescription by using the modified drug list data, and after acquiring the attribute types and basic recommendation scores of the attribute types of ezetimibe and atorvastatin calcium, determining that the attribute types of ezetimibe and atorvastatin calcium which are different are price attributes and pharmacodynamic attributes, wherein the basic recommendation score of the price attribute of the ezetimibe is-5, and the basic recommendation score of the pharmacodynamic attribute is 7; the basic recommendation score of the atorvastatin calcium price attribute is-2, and the basic recommendation score of the pharmacodynamic attribute is 4; after the server side is analyzed, the influence of the price attribute on the selection result is larger than the influence of the drug effect attribute on the selection result when the doctor user selects the ezetimibe or the atorvastatin calcium.
Based on the method, the server can increase the type weight of the basic recommendation score of the ezetimibe price attribute, decrease the type weight of the basic recommendation score of the pharmacodynamic attribute and reduce the drug recommendation score of the ezetimibe; then, the type weight of the basic recommendation score of the atorvastatin calcium price attribute can be adjusted down, the type weight of the basic recommendation score of the pharmacodynamic attribute can be adjusted up, and the medicine recommendation score of the atorvastatin calcium can be increased.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.
Referring to fig. 4, a schematic structural diagram of a processing apparatus for prescription data provided by an embodiment of the present application is shown, which can be applied to a server, and specifically includes the following modules:
a generation request receiving module 401, configured to determine, when a prescription generation request sent by a client is received, a target user identifier and analysis result information corresponding to the prescription generation request;
a user attribute information determining module 402, configured to determine target user attribute information corresponding to the target user identifier;
a drug list data generating module 403, configured to generate drug list data according to the analysis result information and the target user attribute information, and send the drug list data to the client;
a prescription data generating module 404, configured to generate prescription data for the drug list data when receiving a confirmation message sent by the client for the drug list data.
In an embodiment of the present application, the drug inventory data generating module 403 includes:
a first alternative medicine list information generating submodule for generating according to the analysis result information
First alternative drug manifest information;
the second alternative medicine list information generating submodule is used for generating second alternative medicine list information according to the analysis result information and the target user attribute information;
and the combining submodule is used for combining the first alternative medicine list information and the second alternative medicine list information to generate medicine list data.
In an embodiment of the present application, the combining sub-module may include:
a first data generating unit, configured to generate drug list data by using the first candidate drug information or the second candidate drug information if the first candidate drug list information is completely the same as the second candidate drug list information;
in an embodiment of the present application, the combining sub-module may further include:
a second data generating unit, configured to determine, if the first candidate drug list information is not identical to the second candidate drug list information, drug attribute information corresponding to different target drug objects;
a medicine recommendation score determining unit, configured to determine, according to the medicine attribute information, a medicine recommendation score corresponding to the target medicine object;
an optimal candidate drug list information determining unit, configured to determine optimal candidate drug list information from the first candidate drug list information and the second candidate drug list information by using the drug recommendation score; the optimal alternative medicine list is the alternative medicine list with the highest sum of the medicine recommendation scores corresponding to the contained target medicine objects;
and the third data generation unit is used for generating the medicine list data according to the optimal alternative medicine list information.
In an embodiment of the present application, the drug attribute information includes a plurality of attribute types, and the drug recommendation score determining unit includes:
a basic recommendation score determining subunit, configured to determine, according to the medicine attribute information, a basic recommendation score corresponding to each attribute type;
and the weighted sum subunit is used for carrying out weighted sum on the basic recommendation scores according to the type weight corresponding to each attribute type to obtain the medicine recommendation score corresponding to the target medicine object.
In an embodiment of the present application, the apparatus further includes:
the acquisition module is used for acquiring historical medicine modification records; wherein the historical drug modification record comprises a recommended first drug object identifier and a second drug object identifier replacing the first drug object identifier;
an attribute type distinction determination module for determining an attribute type in which the first drug object and the second drug object are distinguished;
and the adjusting module is used for adjusting the type weight corresponding to the attribute type respectively aiming at the first medicine object and the second medicine object.
In an embodiment of the present application, the apparatus further includes:
a drug modification request receiving module, configured to receive a drug modification request sent by the client for the drug list data;
and the modification module is used for modifying the drug list data according to the drug modification request.
In an embodiment of the present application, the analysis result information includes: disease diagnosis information, target user attribute information including any one or more of:
gender information, age information, occupation information.
In an embodiment of the present application, the prescription generation request is a request generated during an online inquiry.
In the embodiment of the application, when a prescription generation request sent by a client is received, a target user identifier and analysis result information corresponding to the prescription generation request are determined, then target user attribute information corresponding to the target user identifier can be determined, medicine list data are generated by adopting the analysis result information and the target user attribute information, the medicine list data are sent to the client, and when a confirmation message sent by the client for the medicine list data is received, the prescription data for the medicine list data are generated, so that the automatic recommendation of the prescription matched with a diagnosis result is realized, the selection range of medicines is expanded by an automatic matching mode, a doctor is prevented from selecting only in common medicines, the same diagnosis result is selected without repeatedly selecting in a medicine database by the doctor, and the working efficiency is improved.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
An embodiment of the present application also provides an electronic device, which may include a processor, a memory, and a computer program stored on the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for processing the prescription data as described above.
An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps of the method for processing prescription data as described above.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The method and the device for processing prescription data, the electronic device and the storage medium provided by the invention are introduced in detail, specific examples are applied in the text to explain the principle and the implementation of the application, and the description of the above embodiments is only used to help understand the method and the core ideas of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method of processing prescription data, the method comprising:
when a prescription generation request sent by a client is received, determining a target user identifier and analysis result information corresponding to the prescription generation request;
determining target user attribute information corresponding to the target user identification;
generating medicine list data according to the analysis result information and the target user attribute information, and sending the medicine list data to the client;
and generating prescription data aiming at the medicine list data when receiving a confirmation message sent by the client aiming at the medicine list data.
2. The method of claim 1, wherein generating drug manifest data based on the analysis result information and the target user attribute information comprises:
generating first alternative medicine list information according to the analysis result information;
generating second alternative medicine list information according to the analysis result information and the target user attribute information;
and generating medicine list data by combining the first alternative medicine list information and the second alternative medicine list information.
3. The method of claim 2, wherein generating drug manifest data in conjunction with the first alternative drug manifest information and the second alternative drug manifest information comprises:
and if the first alternative medicine list information is completely the same as the second alternative medicine list information, generating medicine list data by adopting the first alternative medicine information or the second alternative medicine information.
4. The method of claim 2 or 3, wherein generating drug manifest data in conjunction with the first alternative drug manifest information and the second alternative drug manifest information comprises:
if the first alternative medicine list information is not identical to the second alternative medicine list information, determining medicine attribute information corresponding to different target medicine objects;
determining a medicine recommendation score corresponding to the target medicine object according to the medicine attribute information;
determining optimal alternative medicine list information from the first alternative medicine list information and the second alternative medicine list information according to the medicine recommendation score; the optimal alternative medicine list is the alternative medicine list with the highest sum of the medicine recommendation scores corresponding to the contained target medicine objects;
and generating the drug list data according to the optimal alternative drug list information.
5. The method of claim 4, wherein the drug property information comprises a plurality of property types, and wherein determining the drug recommendation score for the target drug object according to the drug property information comprises:
determining a basic recommendation score corresponding to each attribute type according to the drug attribute information;
and according to the type weight corresponding to each attribute type, carrying out weighted summation on the basic recommendation scores to obtain the medicine recommendation score corresponding to the target medicine object.
6. The method of claim 5, further comprising:
acquiring a historical drug modification record; wherein the historical drug modification record comprises a recommended first drug object identifier and a second drug object identifier replacing the first drug object identifier;
determining that a distinct attribute type exists for the first drug object and the second drug object;
and respectively adjusting the type weight corresponding to the attribute type aiming at the first medicine object and the second medicine object.
7. The method of claim 1, further comprising:
receiving a drug modification request sent by the client aiming at the drug list data;
and modifying the drug list data according to the drug modification request.
8. An apparatus for processing prescription data, said apparatus comprising:
the system comprises a generation request receiving module, a processing module and a processing module, wherein the generation request receiving module is used for determining a target user identifier and analysis result information corresponding to a prescription generation request when the prescription generation request sent by a client is received;
the user attribute information determining module is used for determining the target user attribute information corresponding to the target user identification;
the drug list data generation module is used for generating drug list data according to the analysis result information and the target user attribute information and sending the drug list data to the client;
and the prescription data generating module is used for generating the prescription data aiming at the medicine list data when receiving the confirmation message sent by the client aiming at the medicine list data.
9. An electronic device comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, the computer program, when executed by the processor, implementing the steps of the method of processing prescription data as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the method of processing prescription-data as claimed in any one of claims 1 to 7.
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Application publication date: 20200522 |