Medication reminder list generation method and related devices

Abstract

The application discloses a medicine prompt list generation method and a related device thereof, and the method comprises the following steps: determining a target patient and corresponding target medicine information; based on the target medicine information, matching a target medicine knowledge item from a pre-set structured medicine knowledge base; based on a pre-set prompt extraction rule, performing text screening processing on the target medicine knowledge item to obtain target prompt data; and according to a pre-set list template, performing layout combination processing on the target prompt data to generate a target medicine prompt list. Through the above method steps, the corresponding medicine knowledge content can be automatically matched according to the medicine information of the target patient, and the prompt content related to the medicine use instructions, precautions and the like can be arranged into a standardized list, thereby reducing the workload of manually searching and arranging the medicine prompt content, improving the medicine prompt list generation efficiency, and reducing the risk of missing or inconsistent expression of the medicine prompt content.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method, apparatus, and computer device for generating a drug alert list. Background Technology

[0002] In clinical nursing and patient medication management, nursing staff typically need to provide patients with relevant medication information based on their medication orders, types of medications, and usage requirements. This information usually includes the medication name, dosage, method of administration, precautions, procedures for missed doses, and adverse reaction information. For patients who need to carry multiple medications, relying solely on verbal explanations can be difficult for them to recall and verify later. Therefore, creating a medication information list that is easy for patients to view and retain is of practical value.

[0003] Current medication alert lists typically rely on nurses manually reviewing drug instructions, internal medication guidelines, or existing materials, or are compiled through verbal explanations, handwritten notes, or simple printed lists. This method suffers from problems such as fragmented drug information sources, time-consuming searches, and the potential for omissions. Furthermore, different nurses may have varying understandings, organizational habits, and expression methods for the same drug, leading to inconsistencies in the completeness, standardization, and comprehensibility of the generated alerts. For patients with numerous medications and complex alerts, manual compilation increases the repetitive workload for nurses, further impacting the efficiency of medication alert list generation.

[0004] Therefore, there is an urgent need for a method to generate drug alert lists that can improve the efficiency of drug alert list generation, reduce the risk of omissions in drug alert content, and enhance the standardization and consistency of alert content. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, and readable storage medium for generating a medication reminder list to address the aforementioned technical problems, thereby resolving the issues of low efficiency due to manual processing, easy omissions in medication reminders, inconsistencies in content generated by different nursing staff, and inconvenience for patients to verify the information later.

[0006] A method for generating a drug alert list, the method comprising: Identify the target patients and their corresponding target medication information; Based on the target medication information, target medication knowledge entries are matched from a preset structured drug knowledge base; Based on preset prompt extraction rules, the target drug knowledge items are subjected to text filtering processing to obtain target prompt data; Based on the preset list template, the target prompt data is formatted and combined to generate a target medication prompt list.

[0007] Optionally, determining the target patient and the corresponding target medication information includes: Obtain the patient information of the target patient and the corresponding medication prescription for the target patient; Based on the medication prescription, the target medication is selected in the medication selection interface; Based on the selected target drug, determine the target medication information corresponding to the target patient.

[0008] Optionally, the step of matching target drug knowledge entries from a preset structured drug knowledge base based on the target drug information includes: The target drug information is parsed to obtain at least one target drug name; The target drug name is matched with the drug name field in the preset structured drug knowledge base to obtain the target drug knowledge entry corresponding to the target drug name. The target drug knowledge entry includes usage and dosage information, precautions information and drug identification auxiliary information associated with the target drug name.

[0009] Optionally, the step of matching the target drug name with the drug name field in the preset structured drug knowledge base to obtain the target drug knowledge entry corresponding to the target drug name includes: Based on the target drug name, determine the corresponding target drug code; The target drug code is matched with the drug code field in the preset structured drug knowledge base to obtain the target drug knowledge entry.

[0010] Optionally, the step of performing text filtering on the target drug knowledge entries based on preset prompt extraction rules to obtain target prompt data includes: Extract the usage and dosage field and at least one precaution field from the target drug knowledge entry; The usage and dosage field is determined as the core medication instructions text, and the at least one precaution field is merged into a key reminder text. If a "Friendly Reminder" field exists in the target drug knowledge entry, extract the "Friendly Reminder" field to obtain supplementary reminder text; If the adverse reaction field exists in the target drug knowledge entry, the adverse reaction prompt text is filtered from the adverse reaction field based on the preset prompt extraction rules; The target prompt data is generated based on the core medication instructions text, key reminder text, and extracted supplementary reminder text and / or adverse reaction reminder text.

[0011] Optionally, generating the target prompt data based on the core medication instructions text, key reminder text, supplementary reminder text, and adverse reaction reminder text includes: Obtain the patient language field in the target drug knowledge entry that corresponds to the core drug instructions text and / or key reminder text; If the patient language field exists, the patient-friendly text corresponding to the patient language field will be determined as the target display text; In the absence of the patient language field, the corresponding core medication instructions text and / or key reminder text will be determined as the target display text. Based on the target display text, the target prompt data is generated.

[0012] Optionally, the step of formatting and combining the target prompt data according to a preset list template to generate a target medication prompt list includes: Define the drug display area, medication instructions area, precautions area, and supplementary tips area in the preset list template; Extract the drug name text, usage and dosage text, precautions text, and supplementary prompt text from the target prompt data; Fill the corresponding drug display area, medication instructions area, precautions area, and supplementary tips area with the drug name text, usage and dosage text, precautions text, and supplementary tips text. The pre-set list template, after being filled in, is processed to a uniform format to generate the target medication reminder list.

[0013] A drug suggestion list generation device, the device comprising: The first determination module is used to determine the target patient and the corresponding target medication information; The first matching module is used to match target drug knowledge entries from a preset structured drug knowledge base based on the target drug information. The first filtering module is used to perform text filtering on the target drug knowledge entries based on preset prompt extraction rules to obtain target prompt data; The first processing module is used to perform layout and combination processing on the target prompt data according to the preset list template to generate a target medication prompt list.

[0014] A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor, when executing the computer-readable instructions, implements the above-described method for generating a drug suggestion list.

[0015] A readable storage medium having stored computer-readable instructions thereon, which, when executed by a processor, implement the above-described method for generating a drug alert list.

[0016] The above-described method for generating a drug suggestion list involves: identifying the target patient and their corresponding target medication information; matching target drug knowledge entries from a pre-defined structured drug knowledge base based on the target medication information; performing text filtering on the target drug knowledge entries based on pre-defined suggestion extraction rules to obtain target suggestion data; and formatting and combining the target suggestion data according to a pre-defined list template to generate a target medication suggestion list. Through these steps, the method can automatically match corresponding drug knowledge content based on the target patient's medication information and organize suggestions related to medication instructions, precautions, etc., into a standardized list. This reduces the workload of manually searching and organizing drug suggestion content, improves the efficiency of drug suggestion list generation, and reduces the risk of omissions or inconsistencies in drug suggestion content. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for generating a drug alert list in one embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a drug alert list generation device in one embodiment of the present invention; Figure 3 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In one embodiment, such as Figure 1As shown, a method for generating a drug alert list is provided, including the following steps: 101. Identify the target patients and their corresponding target medication information.

[0021] In this embodiment of the invention, the above-mentioned drug alert list generation method can be applied to a drug alert list generation platform. The drug alert list generation platform has functions such as drug alert list generation data processing, drug alert list generation data sending and receiving, and drug alert list generation data memory storage. It can be built based on a server or server cluster. The server or server cluster can be an electronic device with drug alert list generation data processing capabilities.

[0022] The aforementioned target patient refers to the specific patient for whom a medication reminder list needs to be generated. This information can be entered by nursing staff in the patient information input area of ​​the medication reminder list generation platform, or selected by nursing staff in the patient list or list generation interface. The medication reminder list generation platform can then associate the subsequently selected medications, matched medication knowledge entries, and the generated target medication reminder list all with this target patient.

[0023] The aforementioned target medication information indicates the medications that require medication reminders for the target patient in this instance. This information is generally determined based on the patient's prescribed medication orders. Nursing staff can first verify the patient's medication orders, and then select the medications that match the orders in the medication selection interface of the aforementioned medication reminder list generation platform. The platform will then designate the selected medications as target medications and generate target medication information accordingly.

[0024] In practical applications, the aforementioned target medication information may include, but is not limited to, the target drug name, quantity, dosage, frequency of administration, and method of administration, as well as other information corresponding to the medication order. The target drug name is primarily used for subsequent matching with the drug name field in the pre-defined structured drug knowledge base; the dosage, frequency of administration, and method of administration can be used for supplementary display or manual verification when generating the list.

[0025] In one possible embodiment, the aforementioned medication reminder list generation platform can receive patient information entered or selected by nursing staff, and determine the target patient for whom a target medication reminder list needs to be generated based on the patient information. For example, if the target patient's medication order includes enteric-coated aspirin tablets and rosuvastatin calcium tablets, after the nursing staff selects the corresponding medications in the medication selection interface of the aforementioned medication reminder list generation platform, the aforementioned medication reminder list generation platform can identify enteric-coated aspirin tablets and rosuvastatin calcium tablets as target medications, and thereby form the target medication information corresponding to the target patient.

[0026] Through the above methods, the target medication information is determined jointly by the medication order verification and the medication selection operation, which can reduce the risk of omissions or incorrect selections when nursing staff manually compile the medication reminder list.

[0027] 102. Based on the target drug information, the target drug knowledge entries are matched from the preset structured drug knowledge base.

[0028] In this embodiment of the invention, the aforementioned preset structured drug knowledge base can be a drug information database pre-established by the aforementioned drug reminder list generation platform, used to store drug reminder content according to fixed fields. It should be noted that each drug record can correspond to one drug and includes one or more of the following fields: drug name field, drug code field, usage and dosage field, precautions field, helpful tips field, patient language field, and drug identification auxiliary field.

[0029] The aforementioned drug suggestion list generation platform can search for corresponding drug records in a pre-defined structured drug knowledge base based on the target drug information. Specifically, it can do so through drug name matching and drug code matching. Drug name matching is used to find the corresponding record based on the target drug name, while drug code matching is used to quickly locate the corresponding record based on the drug code.

[0030] The aforementioned target drug knowledge entries refer to drug records that successfully match the target drug information. These entries may include usage and dosage information, precautions, helpful tips, patient language information, and drug identification assistance information corresponding to the target drug, which serve as data sources for the subsequent generation of target prompt data.

[0031] In one possible embodiment, the aforementioned drug suggestion list generation platform parses the target medication information to obtain at least one target drug name, and matches the target drug name with the drug name field in a preset structured drug knowledge base. When a match is successful, the drug suggestion list generation platform identifies the corresponding drug record as the target drug knowledge entry. If the preset structured drug knowledge base includes a drug code field, the drug suggestion list generation platform can also determine the corresponding target drug code based on the target drug name and use the target drug code for auxiliary matching.

[0032] Through the above methods and steps, the medication reminder list generation platform can quickly locate the corresponding medication content based on the target patient's medication information, reducing the workload of nursing staff in manually searching and organizing medication reminder information, and ensuring that the extracted usage, dosage, precautions and patient-friendly content are consistent with the source, thereby reducing the risk of omissions and inconsistencies in the reminder content.

[0033] 103. Based on the preset prompt extraction rules, the target drug knowledge items are processed by text filtering to obtain target prompt data.

[0034] 104. Based on the preset list template, the target prompt data is formatted and combined to generate a target medication prompt list.

[0036] In this embodiment of the invention, the aforementioned preset prompt extraction rules can be field extraction rules pre-configured by the drug prompt list generation platform, used to determine which content to extract from the target drug knowledge entry and according to what priority. For example, the usage and dosage field is used as the core extraction field, the precautions field is used as the key extraction field, the helpful tips field is extracted when it exists, and the adverse reaction field is filtered and extracted according to its commonness or severity.

[0037] In this embodiment, the aforementioned drug reminder list generation platform can extract text content related to medication reminders from the target drug knowledge entry and organize multiple fields. For example, the platform can extract the dosage and administration field from the target drug knowledge entry and identify it as the core medication instructions text; it can also extract one or more precautions fields and merge them into key reminder text. If the target drug knowledge entry contains helpful tips fields, the platform can further extract supplementary reminder text.

[0038] The aforementioned target prompt data represents the drug prompt content that, after screening and organization, can be added to the target drug prompt list. This may include one or more of the following: core drug instructions, key prompts, supplementary prompts, and adverse reaction prompts. It is important to understand that the target prompt data is not a simple copy of the original drug knowledge entries, but rather data generated by the drug prompt list generation platform based on preset prompt extraction rules.

[0039] In one possible embodiment, after obtaining the target drug knowledge entry, the aforementioned drug information list generation platform can read the usage and dosage field, precautions field, helpful tips field, and adverse reaction field from the target drug knowledge entry. The platform uses the usage and dosage field as the basic medication instructions for the target drug, merges multiple precautions fields into the same key information, and adds the corresponding content to the target information data when a helpful tips field or an adverse reaction field that meets the extraction criteria exists.

[0040] By following the above steps, the target prompt data can cover the main medication information that patients need to pay attention to, while avoiding excessive and cluttered prompts, reducing the workload of nursing staff in manually compiling prompt texts, and reducing the risk of missing important prompts.

[0041] In this embodiment of the invention, the target patient and corresponding target medication information are determined; based on the target medication information, target drug knowledge entries are matched from a preset structured drug knowledge base; based on preset prompt extraction rules, text filtering processing is performed on the target drug knowledge entries to obtain target prompt data; according to a preset list template, the target prompt data is formatted and combined to generate a target medication prompt list. Through the above method steps, the corresponding drug knowledge content can be automatically matched according to the target patient's medication information, and prompts related to medication instructions, precautions, etc., can be organized into a standardized list, reducing the workload of manually searching and organizing drug prompt content, improving the efficiency of drug prompt list generation, and reducing the risk of omissions or inconsistencies in drug prompt content.

[0042] It is understood that in the specific embodiments of this application, data related to detection images, initial detection images, alarm signals, etc. are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0043] Optionally, in the step of determining the target patient and the corresponding target medication information, the patient information of the target patient and the medication order corresponding to the target patient can also be obtained; based on the medication order, the selected target drug is determined in the drug selection interface; and based on the selected target drug, the target medication information corresponding to the target patient is determined.

[0044] In this embodiment of the invention, the aforementioned patient information refers to information used to distinguish the current list generation object, including one or more of the following: patient name, bed number, hospital number, department, and discharge date. After obtaining patient information, the aforementioned medication suggestion list generation platform can establish a correspondence between the subsequently selected target drugs and the generated target medication suggestion list and the patient, thereby ensuring that the generated list accurately corresponds to the current patient.

[0045] The aforementioned medication orders refer to the medication usage guidelines established when the target patient is discharged or transferred to the subsequent medication phase, including details such as the medication name, quantity, dosage, frequency of administration, and method of administration. Nursing staff can verify the medication orders upon receipt and use them as the basis for selecting medications in the medication selection interface, ensuring consistency between the selected medications and the actual medication schedule.

[0046] The aforementioned target medications refer to the medications selected by nursing staff in the medication selection interface based on the medication order. The medication selection interface can display medication names from a pre-defined structured medication knowledge base, or they can be arranged by medication category or drug code. After the nursing staff selects the corresponding medication according to the medication order, the aforementioned medication suggestion list generation platform identifies the selected medication as the target medication. For example, if the medication order includes enteric-coated aspirin tablets and rosuvastatin calcium tablets, the nursing staff will select these medications in the medication selection interface, and the aforementioned medication suggestion list generation platform will then identify these medications as the target medications.

[0047] In practice, the aforementioned medication suggestion list generation platform can receive patient information in the patient information entry area and receive medication selection operations from nursing staff on the medication selection interface. Based on the selected target medication, the platform generates target medication information, which may include at least the target medication name, and may also include the dosage, frequency, or method of administration corresponding to the prescribed medication order.

[0048] Through the above methods, the drug suggestion list generation platform can link patient information, medication orders, and drug selection results, providing an accurate basis for subsequent matching of target drug knowledge items.

[0049] Optionally, in the step of matching target drug knowledge entries from a preset structured drug knowledge base based on target drug information, the target drug information can be parsed to obtain at least one target drug name; the target drug name is then matched with the drug name field in the preset structured drug knowledge base to obtain the target drug knowledge entry corresponding to the target drug name. The target drug knowledge entry includes usage and dosage information, precautions information, and drug identification auxiliary information associated with the target drug name.

[0050] In this embodiment of the invention, the aforementioned drug suggestion list generation platform can extract content usable for drug retrieval from target medication information, which may include multiple selected drugs. After parsing, one or more target drug names can be obtained. The platform can also compare the target drug names with the drug name field in a preset structured drug knowledge base and search for drug records with the same name or a preset corresponding relationship.

[0051] The aforementioned target drug knowledge entries refer to drug records that successfully match the name of the target drug. Target drug knowledge entries may include usage and dosage information, precautions information, and drug identification aids. Usage and dosage information primarily indicates the frequency, dosage, and method of administration; precautions information primarily indicates safety precautions that patients need to pay close attention to; drug identification aids may include a description of the drug's appearance, image base name, or drug image path, used to assist in subsequently generating a target drug reminder list with drug identification information.

[0052] In one possible embodiment, after obtaining the target medication information, the aforementioned drug suggestion list generation platform can read the list of drug names in the target medication information and match each target drug name in the list with the drug name field in a preset structured drug knowledge base. If a match is successful, the drug suggestion list generation platform identifies the drug record containing the drug name field as the target drug knowledge entry and reads the usage and dosage information, precautions information, and drug identification auxiliary information associated with the target drug name.

[0053] For example, when the target medication information includes enteric-coated aspirin tablets, the aforementioned drug suggestion list generation platform can search for the corresponding drug record for enteric-coated aspirin tablets in a preset structured drug knowledge base and use that drug record as a target drug knowledge entry.

[0054] Through the above processing, the drug alert list generation platform can quickly locate the corresponding drug record based on the target drug information, enabling subsequent text filtering processing to directly call the already structured and stored usage, dosage, precautions, and drug identification auxiliary information, reducing the workload of manually searching and organizing drug content, and reducing the risk of missing or mismatched drug alert content.

[0055] Optionally, in the step of matching the target drug name with the drug name field in the preset structured drug knowledge base to obtain the target drug knowledge entry corresponding to the target drug name, the corresponding target drug code can also be determined based on the target drug name; the target drug code can then be matched with the drug code field in the preset structured drug knowledge base to obtain the target drug knowledge entry.

[0056] In this embodiment of the invention, the aforementioned target drug code can refer to a search identifier generated or pre-configured based on the target drug name, primarily used to assist the aforementioned drug suggestion list generation platform in quickly locating drug records. It should be noted that the target drug code can be formed using a set of capitalized first letters of the drug's Chinese name, or it can be formed using a preset drug abbreviation, internal number, etc. For example, when the drug name is amoxicillin, the target drug code could be AMXL. Searching using the target drug code can reduce search errors caused by identical names, similar names, or incomplete name input.

[0057] In this embodiment, the drug suggestion list generation platform can first determine the target drug code based on the target drug name, and then compare the target drug code with the drug code field in the preset structured drug knowledge base; when the target drug code matches the drug code field in a certain drug record, the drug suggestion list generation platform determines the drug record as the target drug knowledge item.

[0058] In one possible embodiment, after a caregiver selects a target drug in the drug selection interface, the aforementioned drug suggestion list generation platform can read the target drug name and generate a corresponding target drug code based on the drug name. Subsequently, the drug suggestion list generation platform searches for a drug code field that matches the target drug code in a preset structured drug knowledge base, and reads the usage and dosage information, precautions information, and drug identification auxiliary information from the record containing that drug code field, as the content of the target drug knowledge entry.

[0059] In this way, the drug suggestion list generation platform not only relies on drug names for searching, but can also use target drug codes for auxiliary positioning, thereby improving the retrieval efficiency and matching accuracy of drug knowledge entries and reducing the risk of matching errors caused by long drug names, similar names, or inconsistent displayed names.

[0060] Optionally, in the step of performing text filtering on the target drug knowledge entries based on preset prompt extraction rules to obtain target prompt data, the following steps can be taken: extract the usage and dosage field and at least one precaution field from the target drug knowledge entries; determine the usage and dosage field as the core drug instruction text, and merge at least one precaution field into key prompt text; if a warm reminder field exists in the target drug knowledge entries, extract the warm reminder field to obtain supplementary prompt text; if an adverse reaction field exists in the target drug knowledge entries, filter adverse reaction prompt text from the adverse reaction field based on preset prompt extraction rules; and generate target prompt data based on the core drug instruction text, key prompt text, and the extracted supplementary prompt text and / or adverse reaction prompt text.

[0061] In this embodiment of the invention, the above-mentioned usage and dosage field can refer to the field in the target drug knowledge entry used to record the method of drug administration, which may specifically include the frequency of administration, single dose, time of administration, and method of administration. For example, once a day, before meals, swallowing whole tablets, etc., can all be information in the usage and dosage field.

[0062] The aforementioned precautions field indicates key points that patients should pay attention to during the use of the target drug. Since the same drug may have multiple precautions, the pre-defined structured drug knowledge base can store different precautions in different fields.

[0063] The aforementioned helpful tips fields can be supplementary information fields in the target drug knowledge entry, mainly used to record missed doses, precautions for daily life during medication, or other auxiliary tips.

[0064] The aforementioned adverse reaction field can be a field in the target drug's knowledge entry used to record potential adverse reactions caused by the drug. It should be noted that, due to the potentially large amount of adverse reaction information, the aforementioned drug alert list generation platform can, based on preset alert extraction rules, filter only the adverse reaction content that needs to be alerted to the patient from the adverse reaction field to obtain the adverse reaction alert text. For example, it can prioritize extracting more common or timely adverse reaction content to avoid including too much highly technical content with low relevance to the patient's daily routine in the target drug alert list.

[0065] The aforementioned target alert data refers to the list-generated data formed after field extraction, text merging, and filtering. The aforementioned drug alert list generation platform can generate target alert data based on core drug instructions text, key alert text, and extracted supplementary alert text and / or adverse reaction alert text.

[0066] Through this process, the aforementioned medication reminder list generation platform can extract reminder content suitable for patients to read and verify from the target drug knowledge entries, reducing the workload of nursing staff in manually screening and organizing texts, and lowering the risk of missing important medication reminders.

[0067] Optionally, in the step of generating target prompt data based on the core medication instructions text, key reminder text, supplementary reminder text, and adverse reaction reminder text, the patient language field corresponding to the core medication instructions text and / or key reminder text in the target drug knowledge entry can also be obtained; if the patient language field exists, the patient-understandable text corresponding to the patient language field is determined as the target display text; if the patient language field does not exist, the corresponding core medication instructions text and / or key reminder text is determined as the target display text; and target prompt data is generated based on the target display text.

[0068] In this embodiment of the invention, when generating target prompt data, the drug prompt list generation platform may not directly fill in the core drug instruction text and key prompt text as is, but instead first obtain the corresponding patient language field in the target drug knowledge item, and determine the target display text that is more suitable for the patient to read based on the patient language field.

[0069] The aforementioned patient language fields can be simplified expressions pre-entered and verified by nursing staff, used to convert professional medication descriptions into easily understandable prompts for patients. These patient language fields, along with the core medication instructions and / or key prompts, are stored in the same target drug knowledge entry. This allows the drug prompt list generation platform to directly access pre-confirmed, easily understood expressions when retrieving drug prompt content, rather than temporarily rewriting technical terms during list generation.

[0070] The aforementioned patient-understandable text refers to the displayed text recorded in the patient's language field, primarily used to reduce the difficulty for patients to read and understand medication instructions. For example, when the core medication instruction text is "Take 30 minutes before meals," the corresponding patient-understandable text could be "Take half an hour before meals"; when the key reminder text is "Avoid taking with alcohol," the corresponding patient-understandable text could be "Do not drink alcohol while taking this medication."

[0071] The aforementioned target display text refers to the content ultimately used to generate the target medication reminder list. When the target drug knowledge entry contains a patient language field, the medication reminder list generation platform determines the patient-friendly text corresponding to the patient language field as the target display text; when the target drug knowledge entry does not contain a patient language field, the platform determines the corresponding core medication instructions and / or key reminder text as the target display text to avoid affecting list generation due to the lack of a patient language field.

[0072] Through the above methods, the medication alert list generation platform can convert professional terminology into expressions that are easier for patients to understand, while retaining the source of standard medication alert content. This ensures that the target medication alert list is both accurate and readable. Simultaneously, the patient language field is pre-stored in the target medication knowledge entries, avoiding repeated manual recitation by nursing staff each time a list is generated. This reduces the risk of inconsistencies in expression among different nursing staff and improves the standardization and patient acceptance of the medication alert content.

[0073] Optionally, in the step of generating a target medication reminder list by formatting and combining the target reminder data according to a preset list template, the following steps can be taken: First, determine the drug display area, medication instructions area, precautions area, and supplementary reminder area in the preset list template. Second, extract the drug name text, dosage and administration text, precautions text, and supplementary reminder text from the target reminder data. Third, fill the corresponding drug display area, medication instructions area, precautions area, and supplementary reminder area with the drug name text, dosage and administration text, precautions text, and supplementary reminder text. Fourth, perform uniform formatting on the filled preset list template to generate the target medication reminder list.

[0074] In this embodiment of the invention, the aforementioned drug reminder list generation platform can perform layout and combination processing on the target reminder data according to a preset list template, so that the scattered drug reminder texts are formed into a target medication reminder list according to a fixed format. Generally, the aforementioned preset list template can be a pre-configured list format used to define the display position of drug names, usage and dosage, precautions, and supplementary reminders in the list.

[0075] The aforementioned drug display area can be a pre-defined list template area used to display basic drug information, primarily showing the drug name text, and can also display drug appearance images or image prompts in conjunction with drug identification auxiliary information. The aforementioned medication instructions area displays usage and dosage text, such as dosage, frequency of administration, and method of administration. The aforementioned precautions area displays precaution text, allowing patients to easily view medication tips that require special attention. The aforementioned supplementary tips area displays helpful reminders or other supplementary information, such as handling missed doses and lifestyle precautions.

[0076] The aforementioned drug name text, usage and dosage text, precautions text, and supplementary reminder text can be the content to be filled extracted from the target reminder data by the aforementioned drug reminder list generation platform. The aforementioned drug reminder list generation platform can, according to text type, fill the drug name text into the drug display area, the usage and dosage text into the medication instructions area, the precautions text into the precautions area, and, if supplementary reminder text exists, fill the supplementary reminder text into the supplementary reminder area.

[0077] In this embodiment, the aforementioned medication reminder list generation platform can perform a formatting process on the pre-filled list template. This formatting process can include one or more of the following: font adjustment, border settings, area alignment, line spacing adjustment, bolding of key content, highlighting of precautions, and page pagination. Through this formatting process, the reminder content for different medications can be output in the same format, facilitating verification by nursing staff and making it easier for patients to read and save the information.

[0078] In one possible embodiment, the aforementioned medication reminder list generation platform can also reserve editable supplementary space in the medication instructions area, allowing caregivers to supplement individualized dosages or dosing frequencies based on the target patient's medication orders. The platform can also fill the medication display area with images of the medication's appearance and add prompts such as "The appearance of the medication is for reference only; the actual medication shall prevail" to the target medication reminder list.

[0079] Through the above-mentioned layout and processing, the target prompt data can be transformed into a target medication prompt list with a clear structure and uniform format, reducing the workload of nursing staff in manual layout and repetitive sorting, and improving the generation efficiency and readability of the medication prompt list.

[0080] In another possible embodiment, the aforementioned drug suggestion list generation platform can also be deployed using a combination of mobile app and cloud processing. It should be noted that, compared to the implementation using local form files, this embodiment differs only in the front-end interaction method and data deployment location.

[0081] The aforementioned mobile mini-programs can be interactive front-ends used by nursing staff or patients, such as the "My Medication Assistant" mini-program.

[0082] The aforementioned cloud processing terminal can be used to deploy a preset structured drug knowledge base, drug name matching rules, drug code matching rules, preset prompt extraction rules, and preset list templates. It is understood that although the front-end interface in this embodiment changes from a local table interface to a mini-program interface, the field structure of drug knowledge entries, the matching logic of target drug knowledge entries, the calling method of patient language fields, and the generation rules of target prompt data can remain consistent.

[0083] In practical applications, nurses or patients can access the "My Medication Assistant" mini-program via mobile devices, enter or select target patient information on the mini-program, and generate target medication information by scanning drug barcodes, entering drug names, or selecting drug names.

[0084] After receiving the target patient information and target medication information, the mini-program of the aforementioned drug suggestion list generation platform can send the information to the cloud processing terminal.

[0085] After receiving the target medication information, the cloud processing terminal can identify the target drug name or target drug code from the target medication information, and match the corresponding target drug knowledge entry in the preset structured drug knowledge base based on the target drug name or target drug code.

[0086] After a successful match with the target drug knowledge entry, the cloud-based processing terminal can extract information such as usage and dosage fields, precautions fields, helpful tips fields, adverse reaction fields, and patient language fields from the target drug knowledge entry based on preset prompt extraction rules, and generate target prompt data.

[0087] After generating the target medication reminder data, the aforementioned cloud processing terminal can return the data to the mini-program terminal. The mini-program terminal of the aforementioned medication reminder list generation platform can render the target reminder data according to a preset list template, displaying the drug name, usage and dosage, precautions, supplementary information, and patient-friendly text in the corresponding areas, generating a target medication reminder list and displaying it on the mobile terminal screen. The target medication reminder list can also be saved, forwarded, screenshotted, or distributed electronically as needed.

[0088] Through this embodiment, the aforementioned drug alert list generation platform can extend the front-end interaction method to the mini-program terminal without changing the preset structured drug knowledge base and rule engine. This allows caregivers or patients to access the drug alert list generation service without installing dedicated local software. Simultaneously, the preset structured drug knowledge base, deployed on the cloud processing terminal, facilitates centralized maintenance and synchronous updates of drug alert content, improving the ease of use and data consistency of the drug alert list generation platform across different terminals.

[0089] It should be noted that the application scenarios of this invention are not limited to Excel front-end or WeChat mini-program front-end, but can also be applied to other front-end forms such as web pages and mobile apps. As long as their back-ends use the same structured drug knowledge base and rule engine, they can all be used as specific implementation methods for the above-mentioned drug reminder list generation platform.

[0090] Through the above-mentioned layout and processing, the target prompt data can be transformed into a target medication prompt list with a clear structure and uniform format, reducing the workload of nursing staff in manual layout and repetitive sorting, and improving the generation efficiency and readability of the medication prompt list.

[0091] In one embodiment, a drug alert list generation device is provided, which corresponds one-to-one with the drug alert list generation method described in the above embodiments. For example... Figure 2 As shown, the drug suggestion list generation device includes: The first determining module 201 is used to determine the target patient and the corresponding target medication information; The first matching module 202 is used to match target drug knowledge entries from a preset structured drug knowledge base based on the target drug information. The first filtering module 203 is used to perform text filtering processing on the target drug knowledge entries based on preset prompt extraction rules to obtain target prompt data; The first processing module 204 is used to perform layout and combination processing on the target prompt data according to the preset list template to generate a target medication prompt list.

[0092] Optionally, the first determining module 201 is further configured to: Identify the target patients and their corresponding target medication information; Based on the target medication information, target medication knowledge entries are matched from a preset structured drug knowledge base; Based on preset prompt extraction rules, the target drug knowledge items are subjected to text filtering processing to obtain target prompt data; Based on the preset list template, the target prompt data is formatted and combined to generate a target medication prompt list.

[0093] Optionally, the first matching module 202 is further configured to: The target drug information is parsed to obtain at least one target drug name; The target drug name is matched with the drug name field in the preset structured drug knowledge base to obtain the target drug knowledge entry corresponding to the target drug name. The target drug knowledge entry includes usage and dosage information, precautions information and drug identification auxiliary information associated with the target drug name.

[0094] Optionally, the first matching module 202 is further configured to: Based on the target drug name, determine the corresponding target drug code; The target drug code is matched with the drug code field in the preset structured drug knowledge base to obtain the target drug knowledge entry.

[0095] Optionally, the first filtering module 203 is further configured to: Extract the usage and dosage field and at least one precaution field from the target drug knowledge entry; The usage and dosage field is determined as the core medication instructions text, and the at least one precaution field is merged into a key reminder text. If a "Friendly Reminder" field exists in the target drug knowledge entry, extract the "Friendly Reminder" field to obtain supplementary reminder text; If the adverse reaction field exists in the target drug knowledge entry, the adverse reaction prompt text is filtered from the adverse reaction field based on the preset prompt extraction rules; The target prompt data is generated based on the core medication instructions text, key reminder text, and extracted supplementary reminder text and / or adverse reaction reminder text.

[0096] Optionally, the first screening module 203 is further configured to: Obtain the patient language field in the target drug knowledge entry that corresponds to the core drug instructions text and / or key reminder text; If the patient language field exists, the patient-friendly text corresponding to the patient language field will be determined as the target display text; In the absence of the patient language field, the corresponding core medication instructions text and / or key reminder text will be determined as the target display text. Based on the target display text, the target prompt data is generated.

[0097] Optionally, the first processing module 204 further includes: Define the drug display area, medication instructions area, precautions area, and supplementary tips area in the preset list template; Extract the drug name text, usage and dosage text, precautions text, and supplementary prompt text from the target prompt data; Fill the corresponding drug display area, medication instructions area, precautions area, and supplementary tips area with the drug name text, usage and dosage text, precautions text, and supplementary tips text. The pre-set list template, after being filled in, is processed to a uniform format to generate the target medication reminder list.

[0098] Specific limitations regarding the drug alert list generation device can be found in the limitations of the drug alert list generation method described above, and will not be repeated here. Each module in the aforementioned drug alert list generation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0099] In one embodiment, a computer device is provided, which may be a terminal device, and its internal structure diagram may be as follows: Figure 3As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a readable storage medium storing computer-readable instructions. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer-readable instructions implement a method for generating a drug suggestion list. The readable storage medium provided in this embodiment includes both non-volatile and volatile readable storage media.

[0100] In this application embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, it implements the steps of the drug alert list generation method described above.

[0101] In one embodiment of the application, a readable storage medium is provided, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, they implement the steps of the drug alert list generation method described above.

[0102] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When executed, these computer-readable instructions can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0103] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0104] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for generating a drug alert list, characterized in that, The method includes: Identify the target patients and their corresponding target medication information; Based on the target medication information, target medication knowledge entries are matched from a preset structured drug knowledge base; Based on preset prompt extraction rules, the target drug knowledge items are subjected to text filtering processing to obtain target prompt data; Based on the preset list template, the target prompt data is formatted and combined to generate a target medication prompt list.

2. The method for generating a drug alert list as described in claim 1, characterized in that, The determination of the target patient and the corresponding target medication information includes: Obtain the patient information of the target patient and the corresponding medication prescription for the target patient; Based on the medication prescription, the target medication is selected in the medication selection interface; Based on the selected target drug, determine the target medication information corresponding to the target patient.

3. The method for generating a drug alert list as described in claim 1, characterized in that, The step of matching target drug knowledge entries from a preset structured drug knowledge base based on the target drug information includes: The target drug information is analyzed to obtain at least one target drug name; The target drug name is matched with the drug name field in the preset structured drug knowledge base to obtain the target drug knowledge entry corresponding to the target drug name. The target drug knowledge entry includes usage and dosage information, precautions information and drug identification auxiliary information associated with the target drug name.

4. The method for generating a drug alert list as described in claim 3, characterized in that, The step of matching the target drug name with the drug name field in the preset structured drug knowledge base to obtain the target drug knowledge entry corresponding to the target drug name includes: Based on the target drug name, determine the corresponding target drug code; The target drug code is matched with the drug code field in the preset structured drug knowledge base to obtain the target drug knowledge entry.

5. The method for generating a drug alert list as described in claim 1, characterized in that, The text filtering process for the target drug knowledge entries, based on preset prompt extraction rules, yields target prompt data, including: Extract the usage and dosage field and at least one precaution field from the target drug knowledge entry; The usage and dosage field is determined as the core medication instructions text, and the at least one precaution field is merged into a key reminder text. If a "Friendly Reminder" field exists in the target drug knowledge entry, extract the "Friendly Reminder" field to obtain supplementary reminder text; If the adverse reaction field exists in the target drug knowledge entry, the adverse reaction prompt text is filtered from the adverse reaction field based on the preset prompt extraction rules; The target prompt data is generated based on the core medication instructions text, key reminder text, and extracted supplementary reminder text and / or adverse reaction reminder text.

6. The method for generating a drug alert list as described in claim 5, characterized in that, The generation of the target prompt data based on the core medication instructions text, key reminder text, supplementary reminder text, and adverse reaction reminder text includes: Obtain the patient language field from the target drug knowledge entry that corresponds to the core medication instructions text and / or key reminder text; If the patient language field exists, the patient-friendly text corresponding to the patient language field will be determined as the target display text; In the absence of the patient language field, the corresponding core medication instructions text and / or key reminder text will be determined as the target display text. Based on the target display text, the target prompt data is generated.

7. The method for generating a drug alert list as described in claim 1, characterized in that, The step of formatting and combining the target prompt data according to a preset list template to generate a target medication prompt list includes: Define the drug display area, medication instructions area, precautions area, and supplementary tips area in the preset list template; Extract the drug name text, usage and dosage text, precautions text, and supplementary prompt text from the target prompt data; Fill the corresponding drug display area, medication instructions area, precautions area, and supplementary tips area with the drug name text, usage and dosage text, precautions text, and supplementary tips text. The pre-set list template, after being filled in, is processed to a uniform format to generate the target medication reminder list.

8. A drug suggestion list generation device, characterized in that, The apparatus for implementing the drug suggestion list generation method as described in claim 1 includes: The first determination module is used to determine the target patient and the corresponding target medication information; The first matching module is used to match target drug knowledge entries from a preset structured drug knowledge base based on the target drug information. The first filtering module is used to perform text filtering on the target drug knowledge entries based on preset prompt extraction rules to obtain target prompt data; The first processing module is used to perform layout and combination processing on the target prompt data according to the preset list template to generate a target medication prompt list.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and running on the processor, characterized in that, When the processor executes the computer-readable instructions, it implements the drug alert list generation method as described in any one of claims 1 to 7.

10. A readable storage medium having computer-readable instructions stored thereon, characterized in that, When the computer-readable instructions are executed by a processor, they implement the drug alert list generation method as described in any one of claims 1 to 7.

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