A method, system, terminal and storage medium for intravenous thrombolysis analysis based on patient data

Abstract

The application relates to the field of medical information technology and discloses a venous thrombolysis analysis method and system based on patient data, a terminal and a storage medium. The method comprises the following steps: receiving and verifying a patient emergency data packet sent by a clinical terminal, calling a rule engine to analyze the patient emergency data packet, and generating a thrombolysis treatment scheme data object; assembling the thrombolysis treatment scheme data object into a decision support data packet, and pushing the decision support data packet to the clinical terminal; receiving decision participant identity data returned by the clinical terminal, calling historical preference data corresponding to the decision participant identity data, generating a personalized decision suggestion data object according to the thrombolysis treatment scheme data object and the historical preference data; pushing the personalized decision suggestion data object to the clinical terminal, receiving decision confirmation data of the clinical terminal, generating an execution instruction and sending the execution instruction to a medical order system. The application shortens in-hospital decision delay, improves doctor-patient trust and satisfaction.

Description

Technical Field

[0001] This invention relates to the field of medical information technology, and in particular to a method, system, terminal, and computer-readable storage medium for intravenous thrombolysis analysis based on patient data. Background Technology

[0002] In the current emergency decision-making process for intravenous thrombolysis in acute ischemic stroke, data interaction issues are particularly prominent, severely restricting treatment efficiency and decision-making quality. Based on clinical practice and research, existing technologies mainly suffer from the following key deficiencies: First, medical data is scattered across different systems (such as electronic health records, imaging databases, and laboratory information systems), lacking an efficient integration mechanism. This results in the inability to quickly obtain complete patient information within the thrombolysis time window, thus delaying treatment.

[0003] Second, decision support resources lack unified management and personalized delivery. Existing systems often present information using generic templates, failing to dynamically adjust content based on individual patient circumstances or clinical scenarios. This makes it difficult for patients and their families to understand the advantages and disadvantages of treatment options in a short time, prolonging the consultation process.

[0004] Third, in the current doctor-patient decision-making process, information such as patients' and their families' values ​​and past treatment preferences is often fragmented and not systematically collected in the decision-making process. This exacerbates the information asymmetry between doctors and patients, and ignoring the preferences of key decision-makers (such as family members) leads to repeated negotiations and decision delays, reducing decision acceptance.

[0005] These deficiencies have led to problems such as prolonged door-to-needle time (DNT), poor doctor-patient communication, and difficulty in reaching a consensus on decision-making.

[0006] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0007] The main objective of this invention is to provide a method, system, terminal, and computer-readable storage medium for intravenous thrombolysis analysis based on patient data, aiming to solve the problems of prolonged in-hospital delays, poor doctor-patient communication, and difficulty in reaching a consensus on decision-making in intravenous thrombolysis for acute ischemic stroke in the prior art.

[0008] To achieve the above objectives, the present invention provides a method for analyzing intravenous thrombolysis based on patient data, the method comprising the following steps: Receive and verify patient emergency data packets sent by the clinical terminal, call the rule engine to analyze the patient emergency data packets, and generate thrombolytic therapy plan data objects based on the analysis results; The decision support data component is invoked to assemble the thrombolytic therapy plan data object into a decision support data package, and the decision support data package is pushed to the clinical terminal. The system receives decision-making participant identity data returned by the clinical terminal, retrieves historical preference data corresponding to the decision-making participant identity data from the user profile database, and generates a personalized decision suggestion data object based on the thrombolytic therapy plan data object and the historical preference data. The personalized decision suggestion data object is pushed to the clinical terminal for decision interaction, the decision confirmation data of the clinical terminal is received, an execution instruction is generated, and the execution instruction is sent to the medical order system.

[0009] Furthermore, the process of receiving and verifying the patient emergency data packet sent by the clinical terminal includes: Receive patient emergency data packets sent by the clinical terminal, scan the required fields of the patient emergency data packets, and output a verification pass signal if the required fields are complete; Based on the verification pass signal, the rule engine is invoked to compare data from different sources in the patient emergency data package to detect whether there is a contradiction. If there is no contradiction, a consistency confirmation signal is output. Based on the consistency confirmation signal, the emergency level is calculated according to the patient emergency data package and time window data to obtain the emergency classification result; Based on the emergency classification results, the patient emergency data package undergoes a final review. If the final review is passed, a verification pass mark is generated for the patient emergency data package.

[0010] Furthermore, the step of using a rule engine to analyze the patient's emergency data package and generating a thrombolytic therapy plan data object based on the analysis results includes: Based on the validated patient emergency data package, the rule engine applies clinical guidelines to calculate the individualized benefit and risk probability for each patient. Based on the individualized benefits to the patients and the risk probabilities, multiple thrombolytic therapy regimens are generated, along with comparative data. The multiple thrombolytic therapy protocols are converted into thrombolytic therapy protocol data objects and associated with question-and-answer manuals or visualization charts.

[0011] Furthermore, the step of invoking the decision support data component, assembling the thrombolytic therapy protocol data object into a decision support data package, and pushing the decision support data package to the clinical terminal includes: Based on the thrombolytic therapy protocol data object, the decision support data component is invoked, multimedia content is integrated, and a first decision support data package is obtained. A set of family decision-making guidance questions is embedded in the first decision support data package to obtain a second decision support data package; The second decision support data package is pushed to the clinical terminal.

[0012] Furthermore, the step of receiving the decision-making participant identity data returned by the clinical terminal and retrieving historical preference data corresponding to the decision-making participant identity data from the user profile database includes: The identities of decision-making participants are confirmed by inputting information through a clinical terminal or by biometric identification, and role weights are assigned based on these identities to obtain the identity data of the decision-making participants. Based on the decision-making participant identity data, query the user profile database for historical preference data corresponding to the decision-making participant identity; If the user profile is stored in the historical preference data, then localization elements are integrated into the historical preference data to obtain the target preference data.

[0013] Furthermore, the step of generating a personalized decision suggestion data object based on the thrombolytic therapy plan data object and the historical preference data includes: Align the thrombolytic therapy plans in the thrombolytic therapy plan data object with the patient values ​​in the historical preference data to generate suggested statements; Based on the suggested statements, a family discussion process is simulated, and the simulation results are output, wherein the simulation results are potential consensus points or conflict warnings; Based on the suggested statement, a suggested data object is generated, wherein the suggested data object includes text suggestions and voice suggestions. When the simulation result is a conflict warning, a visual explanation is added to the suggested data object. Individualized risk data is highlighted in the suggested data object and linked to a detailed explanation to obtain a personalized decision-making suggestion data object.

[0014] Furthermore, the step of pushing the personalized decision suggestion data object to the clinical terminal for decision interaction, receiving decision confirmation data from the clinical terminal, generating an execution instruction, and sending the execution instruction to the medical order system includes: The personalized decision suggestion data object is pushed to the clinical terminal, which controls the display of the decision flowchart to guide step-by-step confirmation. The interaction time of the step-by-step confirmation is monitored in real time. If the interaction time exceeds the threshold, a reminder is triggered. After receiving the confirmation result of the step-by-step confirmation, the medical compliance of the verification process is verified, and an execution instruction is generated after the verification is passed, and the execution instruction is sent to the medical order system.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a patient-data-based intravenous thrombolysis analysis system, which is used to implement the patient-data-based intravenous thrombolysis analysis method described above, wherein the patient-data-based intravenous thrombolysis analysis system includes: The treatment plan generation module is used to receive and verify the patient emergency data packets sent by the clinical terminal, call the rule engine to analyze the patient emergency data packets, and generate thrombolytic therapy plan data objects based on the analysis results. The data packet assembly module is used to call the decision support data component, assemble the thrombolytic therapy plan data object into a decision support data packet, and push the decision support data packet to the clinical terminal; The decision suggestion generation module is used to receive the decision participant identity data returned by the clinical terminal, call the historical preference data corresponding to the decision participant identity data from the user profile database, and generate a personalized decision suggestion data object based on the thrombolytic therapy plan data object and the historical preference data. The decision confirmation module is used to push the personalized decision suggestion data object to the clinical terminal for decision interaction, receive the decision confirmation data from the clinical terminal, generate execution instructions, and send the execution instructions to the medical order system.

[0016] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a patient data-based intravenous thrombolysis analysis program stored in the memory and executable on the processor, wherein when the patient data-based intravenous thrombolysis analysis program is executed by the processor, it implements the steps of the patient data-based intravenous thrombolysis analysis method as described above.

[0017] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a patient-data-based intravenous thrombolysis analysis program, which, when executed by a processor, implements the steps of the patient-data-based intravenous thrombolysis analysis method as described above.

[0018] The beneficial effects of this invention are as follows: This invention eliminates decision-making errors caused by incorrect patient emergency data by automatically verifying patient emergency data packets, reducing manual review time. By calling a rule engine to analyze the patient emergency data packets and generating thrombolytic therapy plan data objects, it helps to quickly provide scientific evidence within a time window, avoiding decision delays caused by insufficient physician experience or information overload. Through automated verification and rule engine analysis, the system reduces manual operations by medical staff and optimizes resource allocation. The decision support data component integrates multimedia content, transforming the thrombolytic therapy plan into an easily understandable format, ensuring the integrity and intuitiveness of information transmission and improving communication efficiency. By generating personalized decision suggestion data objects based on the thrombolytic therapy plan data objects and historical preference data, decisions are more aligned with patients' actual needs, improving decision acceptance and satisfaction. By receiving decision confirmation data from clinical terminals and generating execution instructions, which are then sent to the medical order system, the final confirmation step is simplified, reducing hesitation before signing and directly shortening in-hospital delays. Attached Figure Description

[0019] Figure 1 This is a flowchart of a preferred embodiment of the intravenous thrombolysis analysis method based on patient data of the present invention; Figure 2 This is a schematic diagram of the decision flowchart in some specific embodiments of the intravenous thrombolysis analysis method based on patient data of the present invention; Figure 3 This is a structural diagram of a preferred embodiment of the intravenous thrombolysis analysis system based on patient data of the present invention; Figure 4 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0020] This application provides a method, system, terminal, and storage medium for intravenous thrombolysis analysis based on patient data. To make the objectives, technical solutions, and effects of this application clearer and more explicit, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit this application.

[0021] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0022] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0023] The preferred embodiment of the intravenous thrombolysis analysis method based on patient data described in this invention, such as... Figure 1 As shown, the intravenous thrombolysis analysis method based on patient data includes the following steps: S10. Receive and verify the patient emergency data packet sent by the clinical terminal, call the rule engine to analyze the patient emergency data packet, and generate a thrombolytic therapy plan data object based on the analysis results.

[0024] The patient emergency data package includes vital signs data, imaging reports, and laboratory test results, while the thrombolytic therapy data includes treatment plan descriptions, time window data, and risk-benefit indicators.

[0025] Further, in step S10, receiving and verifying the patient emergency data packet sent by the clinical terminal includes: S111. Receive the patient emergency data packet sent by the clinical terminal, scan the required fields of the patient emergency data packet (e.g., vital signs timestamp, imaging report completeness), and output a verification pass signal if the required fields are complete.

[0026] It should be noted that if any of the required fields are missing, a completion request will be generated and sent back to the clinical terminal, and the process will be paused until the data is completed, ensuring that S112 will not be executed based on incomplete data.

[0027] S112. Based on the verification pass signal, call the rule engine to compare data from different sources in the patient's emergency data package (e.g., laboratory test results and imaging reports) to detect whether there are any contradictions (e.g., bleeding risk indicators and thrombolysis indications). If there are no contradictions, output a consistency confirmation signal.

[0028] It should be noted that if there is a contradiction, a verification report will be generated and pushed to the clinical terminal for manual review. After review, a decision will be made on whether to continue with S113 based on the results.

[0029] S113. Based on the consistency confirmation signal, calculate the emergency level according to the patient emergency data package and time window data (e.g., onset time) to obtain the emergency classification result (e.g., red indicates extremely high emergency).

[0030] It should be noted that the classification results are directly input into the priority queue. High-urgent data packets trigger S114 first, while low-urgent data packets can be temporarily buffered to ensure resource optimization.

[0031] S114. Based on the emergency classification results, conduct a final review of the patient emergency data package (the final review includes medical privacy compliance review and audit trajectory record review). If the final review is passed, generate a verification pass mark for the patient emergency data package.

[0032] It should be noted that if the final review fails, the process will be terminated and an alert will be issued.

[0033] Further, in step S10, the analysis of the patient's emergency data package using a rule engine, and the generation of a thrombolytic therapy plan data object based on the analysis results, includes: S121. Based on the validated patient emergency data package, the rule engine applies clinical guidelines (such as AHA / ASA standards) to calculate individualized patient benefit and risk probabilities (such as based on NIHSS score and age).

[0034] S122. Based on the individualized benefits to the patient and the risk probability, generate multiple thrombolytic treatment plans (such as standard thrombolysis and enhanced monitoring thrombolysis), and attach comparative data (such as the possibility of prolonged time window).

[0035] S123. Convert the multiple thrombolytic treatment plans into thrombolytic treatment plan data objects and associate them with a question-and-answer manual or a visualization chart.

[0036] As can be seen from the above scheme, the purpose of step S10 is to ensure the integrity and consistency of the patient's emergency data package, provide a reliable data foundation for subsequent thrombolysis decisions, and at the same time, to quickly analyze the data through the rule engine and generate a preliminary treatment plan to address the time urgency of acute ischemic stroke.

[0037] In this embodiment, doctors need to use standardized script templates to convey the urgency of the patient's condition and basic information about the thrombolysis protocol to the patient and their family within a very short time (1 to 2 minutes). Simultaneously, after receiving the data packet, the system performs data verification and priority processing through mandatory field scanning, contradiction detection, and urgency classification (as in steps S111 to S114). The rule engine then applies clinical guidelines (such as AHA or ASA standards) to calculate individualized benefit-risk probabilities, generates multiple thrombolysis protocol options (such as standard thrombolysis or enhanced monitoring thrombolysis), and associates them with question-and-answer manuals or visual charts.

[0038] It should be noted that this step emphasizes the efficiency and accuracy of data integration. Any missing or contradictory data will trigger a supplementation or review process to avoid making decisions based on incomplete information. At the same time, emergency triage ensures that high-priority cases are treated first, in accordance with the requirements of the stroke treatment time window (≤4.5 hours).

[0039] S20. Invoke the decision support data component, assemble the thrombolytic therapy plan data object into a decision support data package, and push the decision support data package to the clinical terminal.

[0040] Further, step S20 includes: S21. Based on the thrombolytic therapy protocol data object, invoke the decision support data component to integrate multimedia content and obtain a first decision support data package. The multimedia content includes video, animation, or text / image content. It should be noted that the assembly format of the multimedia content is pre-configured according to the clinical terminal type (e.g., tablet or mobile phone) to provide materials for the family role integration in step S22.

[0041] S22. Embed a set of family decision-making guidance questions into the first decision support data package to obtain a second decision support data package to facilitate collective decision-making.

[0042] S23. Push the second decision support data package to the clinical terminal.

[0043] The second decision support data package includes a feedback button. Users can use the feedback button on the clinical terminal to evaluate the clarity of the content of the second decision support data package.

[0044] As can be seen from the above scheme, the purpose of step S20 is to transform the generated thrombolytic therapy plan data object into an easy-to-understand decision support data package, and promote efficient communication between doctors and patients and shorten decision delays through multimedia content and family decision guidance.

[0045] In this embodiment, the system invokes a decision support data component to integrate multimedia content such as videos, animations, or text and images (e.g., a decision support manual) and assembles it into a first decision support data package. Subsequently, a set of family decision-making guidance questions is embedded to form a second decision support data package, incorporating the decision-making roles of Chinese families. After the data package is pushed to the clinical terminal, users can evaluate the content clarity using the feedback button.

[0046] It should be noted that the format of multimedia content needs to be adaptively configured according to the type of terminal (such as tablet or mobile phone) to ensure that information is delivered intuitively; the family guidance problem aims to solve the delay caused by the absence of decision-makers or differing opinions, and improve decision-making efficiency.

[0047] S30. Receive the decision participant identity data returned by the clinical terminal, retrieve the historical preference data corresponding to the decision participant identity data from the user profile database, and generate a personalized decision suggestion data object based on the thrombolytic therapy plan data object and the historical preference data.

[0048] Further, in step S30, receiving the decision-making participant identity data returned by the clinical terminal and retrieving historical preference data corresponding to the decision-making participant identity data from the user profile database includes: S311. Confirm the identity of the decision-making participants (patients, family members, doctors) through clinical terminal input or biometric identification, and assign role weights according to the identity of the decision-making participants to obtain the identity data of the decision-making participants.

[0049] S312. Based on the decision-making participant identity data, query the user profile database for historical preference data (e.g., past treatment choices) corresponding to the decision-making participant identity.

[0050] It should be noted that if the historical preference data is found, input S313 directly; if the historical preference data is not found (i.e., preference data is missing), proceed to S314 for real-time data collection.

[0051] S313. Integrate localized elements (such as family-centered culture) into the historical preference data to obtain target preference data.

[0052] S314. Collect preference data in real time to obtain target preference data, and update the target preference data to the user profile database.

[0053] This embodiment forms a dynamic learning loop through conditional jumps and real-time updates, enabling preference management to flexibly adapt to emergency scenarios.

[0054] Further, in step S30, generating a personalized decision suggestion data object based on the thrombolytic therapy plan data object and the historical preference data includes: S321. Align the thrombolytic treatment plan of the thrombolytic treatment plan data object with the patient value in the historical preference data to generate a suggestion statement (such as "Given your emphasis on quality of life, thrombolysis is recommended").

[0055] S322. Based on the suggested statement, simulate the family discussion process and output the simulation results, wherein the simulation results are potential consensus points or conflict warnings.

[0056] S323. Generate a suggestion data object based on the suggestion statement, wherein the suggestion data object includes text suggestions and voice suggestions, and when the simulation result is a conflict warning, add a visual explanation to the suggestion data object.

[0057] S324. Highlight individualized risk data (such as the probability of bleeding) in the suggested data object and link it to a detailed explanation to obtain a personalized decision-making suggested data object.

[0058] As can be seen from the above scheme, the purpose of step S30 is to generate personalized decision-making suggestions based on the identity and historical preference data of decision-making participants (patients, family members, and doctors), so as to align the treatment plan with the patient's values ​​and enhance the acceptance of the decision.

[0059] In this embodiment, after receiving the decision-making participant identity data returned by the clinical terminal, the system queries the user profile database for historical preferences, and collects them in real time if they are missing. Then, it combines medical evidence with preference data to generate suggested statements and simulates a family discussion process to output consensus points or conflict warnings. Finally, the suggested data is presented in text, voice, or visualization form, highlighting individualized risk data.

[0060] It should be noted that preference management uses a dynamic learning cycle to update user profiles in real time; simulated discussions can anticipate decision-making conflicts and adjust the suggestion format accordingly to ensure smooth communication.

[0061] S40. Push the personalized decision suggestion data object to the clinical terminal for decision interaction, receive the decision confirmation data from the clinical terminal, generate an execution instruction, and send the execution instruction to the medical order system.

[0062] Further, step S40 includes: S41. Push the personalized decision suggestion data object to the clinical terminal, control the clinical terminal to display the decision flowchart, guide the step-by-step confirmation, monitor the interaction time of the step-by-step confirmation in real time, and if the interaction time exceeds the threshold (e.g., 5 minutes), trigger a reminder (or upgrade the program).

[0063] S42. After receiving the confirmation result of the step-by-step confirmation, verify the medical compliance of the process (such as contraindication examination), and generate an execution instruction after the verification is passed, and send the execution instruction to the medical order system. In some embodiments, the execution instruction connects to the medical order system via API, and the medical order system automatically allocates resources (such as drug inventory).

[0064] As can be seen from the above scheme, the purpose of step S40 is to guide both doctors and patients to quickly confirm decisions through a decision-making interaction interface, generate execution instructions, and ensure process compliance, ultimately reducing in-hospital delays (DNT). In this embodiment, the system pushes personalized decision suggestion data objects to the clinical terminal, displays a decision flowchart to guide step-by-step confirmation, and monitors the interaction time in real time, triggering a reminder if a threshold (e.g., 5 minutes) is exceeded. After confirmation, the system verifies medical compliance, generates execution instructions, and sends them to the medical order system via API. Compliance verification avoids legal risks, while API integration enables automatic resource allocation (e.g., drug inventory), improving execution efficiency.

[0065] It should be noted that in some specific embodiments, such as Figure 2 As shown, the decision-making flowchart is based on a structured collaborative decision-making model, which decomposes the process into stages such as information synchronization and relationship establishment, scheme evaluation and preference exploration, consensus reaching and decision execution.

[0066] In summary, the beneficial effects of this invention are as follows: This invention eliminates decision-making errors caused by incorrect patient emergency data by automatically verifying patient emergency data packets, reducing manual review time. By calling a rule engine to analyze the patient emergency data packets and generating thrombolytic therapy plan data objects, it helps to quickly provide scientific evidence within a time window, avoiding decision delays caused by insufficient physician experience or information overload. Through automated verification and rule engine analysis, the system reduces manual operations by medical staff and optimizes resource allocation. The decision support data component integrates multimedia content, transforming the thrombolytic therapy plan into an easily understandable format, ensuring the integrity and intuitiveness of information transmission and improving communication efficiency. By generating personalized decision suggestion data objects based on the thrombolytic therapy plan data objects and historical preference data, decisions are more aligned with patients' actual needs, improving decision acceptance and satisfaction. By receiving decision confirmation data from clinical terminals and generating execution instructions, which are then sent to the medical order system, the final confirmation step is simplified, reducing hesitation before signing and directly shortening in-hospital delays.

[0067] Furthermore, such as Figure 3 As shown, based on the above-described intravenous thrombolysis analysis method based on patient data, the present invention also provides a intravenous thrombolysis analysis system based on patient data, wherein the intravenous thrombolysis analysis system based on patient data includes: The treatment plan generation module 51 is used to receive and verify the patient emergency data packets sent by the clinical terminal, call the rule engine to analyze the patient emergency data packets, and generate a thrombolytic therapy plan data object based on the analysis results. The data packet assembly module 52 is used to call the decision support data component, assemble the thrombolytic therapy plan data object into a decision support data packet, and push the decision support data packet to the clinical terminal; The decision suggestion generation module 53 is used to receive the decision participant identity data returned by the clinical terminal, call the historical preference data corresponding to the decision participant identity data from the user profile database, and generate a personalized decision suggestion data object based on the thrombolytic therapy plan data object and the historical preference data. The decision confirmation module 54 is used to push the personalized decision suggestion data object to the clinical terminal for decision interaction, receive the decision confirmation data from the clinical terminal, generate an execution instruction, and send the execution instruction to the medical order system.

[0068] Furthermore, such as Figure 4 As shown, based on the above-mentioned intravenous thrombolysis analysis method and system based on patient data, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 4 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0069] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal. Further, the memory 20 may include both internal and external storage units of the terminal. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a patient-data-based intravenous thrombolysis analysis program 40, which can be executed by the processor 10 to implement the patient-data-based intravenous thrombolysis analysis method of this application.

[0070] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the intravenous thrombolysis analysis method based on patient data.

[0071] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0072] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a patient-data-based intravenous thrombolysis analysis program, which, when executed by a processor, implements the steps of the patient-data-based intravenous thrombolysis analysis method as described above.

[0073] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0074] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various 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), etc.

[0075] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for analyzing intravenous thrombolysis based on patient data, characterized in that, The patient data-based intravenous thrombolysis analysis method includes: Receive and verify patient emergency data packets sent by the clinical terminal, call the rule engine to analyze the patient emergency data packets, and generate thrombolytic therapy plan data objects based on the analysis results; The decision support data component is invoked to assemble the thrombolytic therapy plan data object into a decision support data package, and the decision support data package is pushed to the clinical terminal. The system receives decision-making participant identity data returned by the clinical terminal, retrieves historical preference data corresponding to the decision-making participant identity data from the user profile database, and generates a personalized decision suggestion data object based on the thrombolytic therapy plan data object and the historical preference data. The personalized decision suggestion data object is pushed to the clinical terminal for decision interaction, the decision confirmation data of the clinical terminal is received, an execution instruction is generated, and the execution instruction is sent to the medical order system.

2. The intravenous thrombolysis analysis method based on patient data according to claim 1, characterized in that, The process of receiving and verifying patient emergency data packets sent by the clinical terminal includes: Receive patient emergency data packets sent by the clinical terminal, scan the required fields of the patient emergency data packets, and output a verification pass signal if the required fields are complete; Based on the verification pass signal, the rule engine is invoked to compare data from different sources in the patient emergency data package to detect whether there is a contradiction. If there is no contradiction, a consistency confirmation signal is output. Based on the consistency confirmation signal, the emergency level is calculated according to the patient emergency data package and time window data to obtain the emergency classification result; Based on the emergency classification results, the patient emergency data package undergoes a final review. If the final review is passed, a verification pass mark is generated for the patient emergency data package.

3. The intravenous thrombolysis analysis method based on patient data according to claim 1, characterized in that, The rule engine analyzes the patient's emergency data package and generates a thrombolytic therapy plan data object based on the analysis results, including: Based on the validated patient emergency data package, the rule engine applies clinical guidelines to calculate the individualized benefit and risk probability for each patient. Based on the individualized benefits to the patients and the risk probabilities, multiple thrombolytic therapy regimens are generated, along with comparative data. The multiple thrombolytic therapy protocols are converted into thrombolytic therapy protocol data objects and associated with question-and-answer manuals or visualization charts.

4. The intravenous thrombolysis analysis method based on patient data according to claim 1, characterized in that, The step of invoking the decision support data component, assembling the thrombolytic therapy protocol data object into a decision support data package, and pushing the decision support data package to the clinical terminal includes: Based on the thrombolytic therapy protocol data object, the decision support data component is invoked, multimedia content is integrated, and a first decision support data package is obtained. A set of family decision-making guidance questions is embedded in the first decision support data package to obtain a second decision support data package; The second decision support data package is pushed to the clinical terminal.

5. The intravenous thrombolysis analysis method based on patient data according to claim 1, characterized in that, The process of receiving the decision-making participant identity data returned by the clinical terminal and retrieving historical preference data corresponding to the decision-making participant identity data from the user profile database includes: The identities of decision-making participants are confirmed by inputting information through a clinical terminal or by biometric identification, and role weights are assigned based on these identities to obtain the identity data of the decision-making participants. Based on the decision-making participant identity data, query the user profile database for historical preference data corresponding to the decision-making participant identity; If the user profile is stored in the historical preference data, then localization elements are integrated into the historical preference data to obtain the target preference data.

6. The intravenous thrombolysis analysis method based on patient data according to claim 1, characterized in that, The step of generating a personalized decision suggestion data object based on the thrombolytic therapy plan data object and the historical preference data includes: Align the thrombolytic therapy plans in the thrombolytic therapy plan data object with the patient values ​​in the historical preference data to generate suggested statements; Based on the suggested statements, a family discussion process is simulated, and the simulation results are output, wherein the simulation results are potential consensus points or conflict warnings; Based on the suggested statement, a suggested data object is generated, wherein the suggested data object includes text suggestions and voice suggestions. When the simulation result is a conflict warning, a visual explanation is added to the suggested data object. Individualized risk data is highlighted in the suggested data object and linked to a detailed explanation to obtain a personalized decision-making suggestion data object.

7. The intravenous thrombolysis analysis method based on patient data according to claim 1, characterized in that, The process of pushing the personalized decision suggestion data object to the clinical terminal for decision interaction, receiving decision confirmation data from the clinical terminal, generating execution instructions, and sending the execution instructions to the medical order system includes: The personalized decision suggestion data object is pushed to the clinical terminal, which controls the display of the decision flowchart to guide step-by-step confirmation. The interaction time of the step-by-step confirmation is monitored in real time. If the interaction time exceeds the threshold, a reminder is triggered. After receiving the confirmation result of the step-by-step confirmation, the medical compliance of the verification process is verified, and an execution instruction is generated after the verification is passed, and the execution instruction is sent to the medical order system.

8. A patient data-based intravenous thrombolysis analysis system, characterized in that, The patient data-based intravenous thrombolysis analysis system is used to implement the patient data-based intravenous thrombolysis analysis method as described in any one of claims 1-7, wherein the patient data-based intravenous thrombolysis analysis system comprises: The treatment plan generation module is used to receive and verify the patient emergency data packets sent by the clinical terminal, call the rule engine to analyze the patient emergency data packets, and generate thrombolytic therapy plan data objects based on the analysis results. The data packet assembly module is used to call the decision support data component, assemble the thrombolytic therapy plan data object into a decision support data packet, and push the decision support data packet to the clinical terminal; The decision suggestion generation module is used to receive the decision participant identity data returned by the clinical terminal, call the historical preference data corresponding to the decision participant identity data from the user profile database, and generate a personalized decision suggestion data object based on the thrombolytic therapy plan data object and the historical preference data. The decision confirmation module is used to push the personalized decision suggestion data object to the clinical terminal for decision interaction, receive the decision confirmation data from the clinical terminal, generate execution instructions, and send the execution instructions to the medical order system.

9. A terminal, characterized in that, The terminal includes: a memory, a processor, and a patient-data-based intravenous thrombolysis analysis program stored in the memory and executable on the processor. When the patient-data-based intravenous thrombolysis analysis program is executed by the processor, it implements the steps of the patient-data-based intravenous thrombolysis analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a patient-data-based intravenous thrombolysis analysis program, which, when executed by a processor, implements the steps of the patient-data-based intravenous thrombolysis analysis method as described in any one of claims 1-7.

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