Monitoring method and system based on pharmaceutical clinical early warning

Abstract

The invention relates to the technical field of medical data monitoring, and provides a monitoring method and system based on pharmaceutical clinical early warning, and the method comprises the steps: setting a pharmaceutical monitoring data entry, and constructing a patient data set; collecting the patient data set in real time according to a preset frequency, and judging whether a risk exists or not according to a preset early warning condition; performing pharmacist verification on the data set of the patient with the risk for early warning and prompting, and establishing a monitoring task for the patient with the risk; and based on the process record of the monitoring task, dynamically adjusting the monitoring task, and generating a pharmaceutical monitoring record table. According to the invention, a mode of combining system presetting and actual individual setting of a clinical pharmacist according to work is adopted, a multi-dimensional multi-factor multi-logic early warning condition is set, an intelligent monitoring plan is configured for a patient brought into monitoring, an exclusive monitoring concerned event is automatically tracked and actively early warned, the result tracking time of the clinical pharmacist is shortened, and the monitoring efficiency of the clinical pharmacist is improved. Real-time early warning and intervention on the abnormity of the patient can be realized, and the working efficiency of pharmaceutical monitoring is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data monitoring, and in particular to a monitoring method and system based on pharmaceutical clinical early warning. Background Art

[0002] Pharmaceutical care refers to the use of pharmaceutical expertise by pharmacists to provide direct, drug-related pharmaceutical services to inpatients to improve the safety, effectiveness, and economy of drug therapy. In 2021, the General Office of the National Health Commission of my country issued the "Pharmacy Care Service Specifications for Medical Institutions" (hereinafter referred to as the "Specifications"), which clarified the basic requirements, service objects, and work content of pharmaceutical care.

[0003] The research on pharmaceutical care started early abroad. In 1990, Professor Hepler and Strand of the School of Pharmacy at the University of Minnesota in the United States first defined the concept, which refers to a kind of professional behavior and process, which is not completely applicable to domestic work. Based on the "Specifications", the Chinese Hospital Association has formulated a group standard - "Pharmacy Management and Pharmaceutical Services in Medical Institutions Part 2-7: Pharmaceutical Care for Clinical Pharmaceutical Services". Liao Qianwen and others constructed a scoring standard for pharmaceutical care skills through literature research and expert consultation and explored its application in teaching. At present, most relevant materials tend to be theoretical guidance, lacking a complete and popularizable standardized pharmaceutical care work model and a pharmaceutical care system that meets clinical needs.

[0004] The clinical practice of pharmaceutical care in China is still in its infancy and inefficient, mainly due to the fact that the existing pharmaceutical care system cannot fully meet clinical needs. The main deficiencies are as follows: (1) There is a lack of high-representation key warning condition settings, which cannot meet the multi-dimensional and multi-condition screening needs of high-risk drug users in actual clinical practice.

[0005] (2) Lack of secure integration capabilities for cross-hospital patient information. The pharmaceutical monitoring system early warning must integrate patient information from multiple heterogeneous information systems across different hospital systems, which places requirements on the compatibility of pharmaceutical monitoring system data storage and management.

[0006] (3) The real-time processing capability is weak. The current pharmaceutical monitoring system uses asynchronous message processing to avoid affecting the hospital's main business system by delaying early warning. However, this causes delays in pharmaceutical monitoring, seriously affecting timeliness and threatening patients' medication safety.

[0007] (4) The patients included in the monitoring lack the system's automatic monitoring tracking function. Abnormal situations in the key points of monitoring services need to be searched by clinical pharmacists one by one, which affects the efficiency of pharmaceutical monitoring work, delays the timing of intervention, and affects the safety of patients' medication.

[0008] (5) The service objects, work content, and monitoring documents of pharmaceutical monitoring are not unified, which does not comply with the "Standards for Pharmaceutical Monitoring Services in Medical Institutions" issued by the National Health Commission in 2021, and is not conducive to the standardized implementation of pharmaceutical monitoring services by clinical pharmacists.

[0009] At present, both the pharmacy departments of medical institutions and clinical medical staff and patients have a high demand for the implementation of pharmaceutical supervision. Summary of the invention

[0010] In view of this, the present invention provides a monitoring method and system based on pharmaceutical clinical early warning to solve the above problems.

[0011] In order to solve the above technical problems, the present invention provides a monitoring method based on pharmaceutical clinical early warning, comprising: Establish data associations between hospitals, users, and nursing; Set up a pharmaceutical care data portal and build a patient data set based on relevant medical advice and pathological characteristics; After the patient data set is synchronized and updated to the pharmaceutical care storage service, the patient data set is collected in real time according to the preset frequency, and based on the collection results, it is determined whether there is a risk according to the preset warning conditions. If there is a risk, an early warning prompt is issued; Pharmacists verify the patient data set that provides early warning of risks, and establish monitoring tasks for patients at risk; and dynamically adjust the monitoring tasks based on the process records of the monitoring tasks, and generate a pharmaceutical monitoring record form after the pharmacist determines that the monitoring tasks are completed.

[0012] As an optional method, access to the hospital information system includes: configuring the registration system, electronic medical record system, doctor workstation, surgical anesthesia system, nursing system, Pacs system, nursing system, LIS system and billing system according to the patient's treatment scenario.

[0013] As an optional method, the pharmaceutical care data portal is used to connect to the hospital information system to conduct multi-type heterogeneous data fusion query; patient data sets include: age, gender, weight, height, BMI, diagnosis, disease status, allergies, liver and kidney function, drug orders, non-drug orders, surgery information, test reports, examination reports, medical records, nursing records and electronic medical records; Among them, after completing the construction of the patient data set, the warning conditions are configured for any patient based on the feature knowledge base.

[0014] As an optional method, based on the collected results, determine whether there is a risk according to the preset warning conditions, including: According to the configuration of the warning conditions, it is determined whether any parameter in the patient data set exceeds the upper threshold and / or lower threshold of the preset warning modulation. If it exceeds the preset range, a warning prompt is issued.

[0015] As an optional method, when performing threshold judgment on the patient's data set, different threshold judgment strategies are set according to different data types. The judgment strategies include: The first strategy is to issue an immediate warning when the data type reaches the threshold of the preset warning condition; The second strategy is that when the data type reaches the threshold of the preset warning condition, the first time frame is immediately calibrated and the data type is highlighted; if in the subsequent data collection process, the highlighted data type still reaches the threshold of the preset warning condition, the second time frame is immediately calibrated; if the distance between the first time frame and the second time frame is less than the preset distance, an early warning is immediately issued; otherwise, continue to wait for the next judgment of the data type collection result.

[0016] As an optional manner, in the above second strategy, if the type of data marked with emphasis does not reach the threshold of the preset warning condition in the next collection, the emphasis marking is cancelled and the event is reported.

[0017] As an optional method, when collecting patient data sets in real time, it also includes: correcting the data quality of the patient data sets, including: removing duplicate data in the collection results, filling in missing data, and correcting errors in abnormal data; Among them, the missing data is filled, including the following strategies: After setting the feature data group that needs to be filled with data, set the corresponding data compensation method and compensation validity period; determine whether there is a temporary long-term medication that takes effect on the same day in the feature data group, and compensate for the missing data based on the determination result and compensation method; The data compensation method and compensation validity period include: if the feature data group is missing and the data compensation method is not set, an early warning will be issued directly; if the feature data group is missing and a data compensation method is set, compensation will be made directly according to the setting; the compensation includes: filling in the latest data within the preset number of days or all data within the preset number of days that meet the current conditions.

[0018] As an option, the feature data set includes: Drug usage group, test / inspection / treatment / pathology report group, and vital signs group.

[0019] As an optional method, after the patient's monitoring task is established, the patient's data set is marked according to the type and purpose of the monitoring task, and the numerical changes of the marked data types are continuously observed; and based on the changes, the pharmacist intervenes to adjust the marked data types in the monitoring task until the monitoring is completed.

[0020] On the other hand, the present invention also provides a monitoring system based on pharmaceutical clinical early warning, as an optional method, including: Hospital information system building module, used to access the hospital information system and establish data associations between hospitals, users and nursing; The data collection module is used to set up the pharmaceutical monitoring data entry and build the patient data set according to the relevant medical advice and pathological characteristics; The early warning prompt module is used to collect the patient data set in real time according to the preset frequency after synchronously updating the patient data set to the pharmaceutical care storage service, and judge whether there is a risk according to the collection results and the preset early warning conditions. If there is a risk, an early warning prompt is issued; The patient monitoring module is used to verify the patient data set that has been warned to be at risk by the pharmacist, and to establish the monitoring task for the patient; and based on the process record of the monitoring task, dynamically adjust the monitoring task, and generate a pharmaceutical monitoring record form after the pharmacist determines that the monitoring task is completed.

[0021] The beneficial effects of the present invention are: The present invention adopts a combination of system presets and individual settings based on actual work by clinical pharmacists to set multi-dimensional, multi-factor, and multi-logic early warning conditions. The system timely and accurately selects key patients with special disease states, specific diseases, special medication conditions, special treatments, etc. from a large number of patients for monitoring; configures intelligent monitoring plans for patients included in monitoring, and the system automatically monitors and tracks abnormal monitoring service points and issues early warnings; clinical pharmacists monitor patients, and after monitoring of similar patients, a pharmaceutical monitoring plan table that meets the requirements of the "Specifications" is automatically generated for archiving. In the created monitoring tasks, an innovative intelligent monitoring plan is set up, and the system automatically tracks and actively warns of exclusive monitoring events, shortening the time for clinical pharmacists to track results, and can achieve real-time early warning and real-time intervention of abnormal results for patients, greatly improving the efficiency of pharmaceutical monitoring work. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a monitoring method flow chart provided by an embodiment of the present invention; Figure 2 A schematic diagram of the business process of the monitoring method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with specific implementation methods.

[0024] In response to the needs of medical data collection and management, the purpose of this embodiment is to study a widely covered medical data collection method, combined with the research of cross-system compatible medical data storage architecture and multi-type heterogeneous data fusion query methods that take both streaming and batch into consideration, to achieve simultaneous support for online streaming retrieval and offline batch analysis of medical data. In this way, in response to the special monitoring needs of key patients, according to the pharmaceutical monitoring service objects required by the "Specifications", a feature knowledge base can be constructed based on the research on the key patient feature extraction method, and the key patient warning conditions can be configured to meet the needs of real-time and accurate warning of pharmaceutical monitoring service objects.

[0025] To do this, see Figure 1 and Figure 2 The present embodiment provides a monitoring method based on pharmaceutical clinical early warning, including: establishing a data association relationship between a hospital, a user, and nursing; setting a pharmaceutical monitoring data entry, and constructing a patient data set according to relevant medical advice and pathological characteristics; after synchronously updating the patient data set to the pharmaceutical monitoring storage service, collecting the patient data set in real time according to a preset frequency, and judging whether there is a risk according to the collection result and the preset early warning condition, and if there is a risk, issuing an early warning prompt; verifying the patient data set for which an early warning prompt is issued by a pharmacist, and establishing a monitoring task for the patient; and dynamically adjusting the monitoring task based on the process record of the monitoring task, and generating a pharmaceutical monitoring record table after the pharmacist judges that the monitoring task is completed.

[0026] For implementation in this embodiment, please refer to Figure 2 In a certain scenario, this embodiment is implemented as follows: First, access the hospital information system, form a data channel in each information system, and then update it synchronously to the pharmaceutical monitoring hospital business data storage center. After that, collect the updated data and set intelligent early warning conditions according to different departments or patient types. At this time, the pharmacist determines whether the conditions for establishing the monitoring task are met. If not, the rule false positive identification feedback is given; if it is met, the pharmaceutical monitoring task is established. During the monitoring process, first establish an intelligent monitoring plan, and then provide parameter prompts for monitoring events based on the data from the pharmaceutical monitoring hospital business data storage center. After that, the pharmacist intervenes in the treatment to determine whether the patient is out of the warning conditions. If not, the monitoring plan is adjusted dynamically. In addition, in addition to warning and monitoring patients who may be at risk in the pharmaceutical monitoring hospital business data storage center, pharmacists can also manually screen patients to join the monitoring plan. For such patients, rule false negative identification feedback can be given to optimize the intelligent early warning.

[0027] In this embodiment, it is first necessary to connect to various information systems in the hospital, which may include: configuring the registration system, electronic medical record system, doctor workstation, surgical anesthesia system, nursing system, Pacs system, nursing system, LIS system and billing system according to the patient's medical treatment scenario. The patient data set includes: age, gender, weight, height, BMI, diagnosis, disease status, allergy, liver and kidney function, drug prescription, non-drug prescription, surgical information, test report, examination report, medical record report, nursing record and electronic medical record. After that, this embodiment synchronously updates the patient's data set from the hospital information system as the basic parameter for early warning judgment by setting the program docking mode of the pharmaceutical monitoring data entry. After completing the construction of the patient data set, the early warning condition is configured for any patient based on the feature knowledge base. Among them, the logic of the early warning condition should meet the multi-dimensional and multi-factor complex logic requirements of clinical screening of high-risk patients, including and, or, including / not including, M selects N, and hierarchical logic, etc. within or between data of different business data systems. For example: patients who use drugs that impair renal function and whose glomerular filtration rate is lower than the reference value, patients who use two or more antibacterial and antifungal drugs in combination, patients who use antibiotics for more than 7 days and lack etiological evidence or have not had their infection indicators rechecked, etc.

[0028] After completing the setting of the early warning conditions, pharmaceutical monitoring is performed on the patient data set at a preset frequency (in days or hours, etc.). In addition to basic patient vital sign detection, when conducting early warnings, it is possible to refer to the service objects listed in the "Specifications", determine key patients in combination with the actual types of diseases admitted by the hospital, and connect with medical data to realize the configuration of early warning conditions. For example, key patients include patients with special pathological and physiological conditions such as organ dysfunction, children, and the elderly; patients with disease characteristics such as severe infection, hypertensive crisis, and a history of drug allergy; patients who use drugs with narrow therapeutic windows, anti-infective drugs, and patients with serious adverse drug reactions; patients who receive special treatments such as hemodialysis, hemofiltration, and plasma exchange. By monitoring the data of key patients, the purpose of this embodiment can be accurately achieved.

[0029] In the specific implementation, it is determined whether there is a risk according to the preset warning conditions. This embodiment is implemented according to the following method: According to the configuration of the warning conditions, it is determined whether any parameter in the patient data set exceeds the upper threshold and / or lower threshold of the preset warning modulation. If the pharmacist determines that there is no risk, it is ignored and the false positive is identified according to the rules. If it exceeds the preset interval, an early warning prompt is issued. In addition, considering that different patients need to monitor different data parameters, when this embodiment performs threshold judgment on the patient's data set, different threshold judgment strategies are set according to different data types. The judgment strategy includes: The first strategy is to immediately issue an early warning when the data type reaches the threshold of the preset early warning condition. The first strategy is used to monitor indicators such as heart rate, blood pressure, and blood oxygen.

[0030] The second strategy is that when the data type reaches the threshold of the preset warning condition, the first time frame is immediately calibrated and the data type is highlighted; if in the subsequent data collection process, the highlighted data type still reaches the threshold of the preset warning condition, the second time frame is immediately calibrated; if the distance between the first time frame and the second time frame is less than the preset distance, an early warning is immediately issued; otherwise, continue to wait for the judgment of the next collection result of the data type. For example: some examples of physiological indicators can be added. In addition, in the second strategy, if the highlighted data type does not reach the threshold of the preset warning condition in the next collection, its highlighting is cancelled and the event is reported. For this type of normal fluctuation in values, pharmacists or medical staff only need to keep records.

[0031] This embodiment takes into account that in the actual monitoring process, errors in different links may lead to omissions in data collection. Therefore, when collecting patient data sets in real time, this embodiment will also correct the data quality of the patient data sets, for example, remove duplicate data in the collection results, fill in missing data, and correct errors in abnormal data. In particular, this embodiment additionally provides a data compensation mechanism, including the following strategies: After setting the feature data group that needs to be filled with data (for example, long-term observation data such as drug usage group, test / inspection / treatment / pathology report group and vital signs group), set the corresponding data compensation method and compensation validity period; determine whether there is a temporary long-term medication that takes effect on the same day in the feature data group, and compensate for the missing data based on the judgment result and compensation method. Among them, the data compensation method and compensation validity period include: if the data feature group is missing and the data compensation method is not set, then a warning is directly issued; if the feature data group is missing and a data compensation method is set, then compensation is directly performed according to the setting; the compensation includes: filling in the latest data within the preset number of days or all data that meets the current conditions within the preset number of days.

[0032] Optionally, when compensating the above-mentioned characteristic data group, judging whether the characteristic data group contains temporary long-term medication effective on the same day includes judging whether the patient is taking the medicine for the first time, judging the category or number of medicines, judging the combination relationship of the medicines taken by the patient, and the reminder of medication use. In addition, in addition to supplementing the medication taking, this embodiment can also be performed in the following ways: Set up compensation rules for the test / examination / treatment / pathology report group, make time compensation for the validity period of the patient's test results, and replace the last test results within the specified time; compensate for the tendency of the test results; compensate for the patient's special treatment conditions such as some non-drug medical orders; and compensate for the patient's medical record results.

[0033] Set compensation rules for the vital signs group to compensate for the patient's body temperature, arterial oxygen saturation, respiration, pulse, systolic and diastolic blood pressure. For example, when the current data fluctuates for a short period of time but does not reach the emergency time, or the patient's current data is lost due to poor contact, the data within the specified time in the past will be compensated to the current time, and then supervised for a period of time to determine whether data loss or crisis occurs.

[0034] In this way, through risk monitoring and judgment of patients, it is possible to preliminarily judge whether it is necessary to implement a monitoring plan to ensure the medical health and life safety of patients. Through the above risk warning, after the patient's monitoring task is established, the patient's data set can be marked according to the type and purpose of the monitoring task, and the numerical changes of the marked data types can be continuously observed; and based on the changes, the key marked data types in the monitoring task are adjusted through pharmacist intervention until the monitoring is completed. For example, for patients who have been included in pharmaceutical monitoring, the events and key nodes that clinical pharmacists focus on are listed in the intelligent monitoring plan, and the system automatically prompts. For example, whether the patient has a new symptom or sign; whether the vital signs exceed the set upper and lower thresholds; whether a certain type of drug is used or discontinued; whether the patient has or has not undergone a certain examination; whether a certain test indicator exceeds the set upper and lower thresholds, etc. The intelligent monitoring plan will reduce the workload of clinical pharmacists and facilitate efficient and accurate monitoring of patients' conditions.

[0035] Through the above scheme, this embodiment adopts a combination of system presets and clinical pharmacists' individual settings based on actual work to set multi-dimensional, multi-factor, and multi-logic warning conditions. The system timely and accurately screens out key patients with special pathological conditions, specific diseases, special medication conditions, special treatments, etc. from a large number of patients for monitoring; configures intelligent monitoring plans for patients included in the monitoring, and the system automatically monitors and tracks abnormal monitoring service points and issues warnings; clinical pharmacists monitor patients, and the monitoring work includes: evaluation of the rationality of medication regimens, monitoring of medication efficacy, monitoring of adverse drug reactions, monitoring of drug treatment processes, monitoring of patient compliance, drug gene testing, interpretation of therapeutic drug monitoring results, etc.; after the patient monitoring is completed, a pharmaceutical monitoring plan that meets the requirements of the "Specifications" is automatically generated for archiving; clinical pharmacists regularly provide feedback on the use of efficient and intelligent pharmaceutical monitoring and early warning systems to achieve continuous optimization of the monitoring system.

[0036] In addition, this embodiment also provides a monitoring system based on pharmaceutical clinical early warning, as an optional method, including: a hospital information system construction module, which is used to establish a data association relationship between hospitals, users and nursing; a data acquisition module, which is used to set a pharmaceutical monitoring data entry and build a patient data set according to relevant medical orders and pathological characteristics; an early warning prompt module, which is used to synchronize and update the patient data set to the pharmaceutical monitoring storage service, and then collect the patient data set in real time at a preset frequency, and determine whether there is a risk according to the collection results and preset early warning conditions. If there is a risk, an early warning prompt is issued; a patient monitoring module, which is used to verify the patient data set that has been warned to be at risk by a pharmacist, and establish a monitoring task for the patient; and based on the process record of the monitoring task, dynamically adjust the monitoring task, and generate a pharmaceutical monitoring record sheet after the pharmacist determines that the monitoring task is completed.

[0037] The above are only preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be regarded as limiting the present invention, and the protection scope of the present invention should be based on the scope defined by the claims. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the spirit and scope of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A monitoring method based on pharmaceutical clinical early warning, characterized in that: include: Access the hospital information system to establish data associations between hospitals, users, and nursing; Set up a pharmaceutical care data portal and build a patient data set based on relevant medical advice and pathological characteristics; After the patient data set is synchronously updated to the pharmaceutical care storage service, the patient data set is collected in real time according to a preset frequency, and based on the collected results, it is determined whether there is a risk according to the preset warning conditions, and if there is a risk, a warning prompt is issued; Pharmacists verify the patient data set that provides early warning of risks, and establish monitoring tasks for patients at risk; and based on the process records of the monitoring tasks, dynamically adjust the monitoring tasks, and generate a pharmaceutical monitoring record form after the pharmacist determines that the monitoring tasks are completed.

2. A monitoring method based on pharmaceutical clinical early warning according to claim 1, characterized in that: The access to the hospital information system includes: configuring the registration system, electronic medical record system, doctor workstation, surgical anesthesia system, nursing system, Pacs system, nursing system, LIS system and billing system according to the patient's medical treatment scenario.

3. A monitoring method based on pharmaceutical clinical early warning according to claim 1, characterized in that: The pharmaceutical monitoring data entry is used to connect to the hospital information system to conduct multi-type heterogeneous data fusion query; the patient data set includes: age, gender, weight, height, BMI, diagnosis, disease status, allergies, liver and kidney function, drug orders, non-drug orders, surgery information, test reports, examination reports, medical records, nursing records and electronic medical records; Among them, after completing the construction of the patient data set, the warning conditions are configured for any patient based on the feature knowledge base.

4. A monitoring method based on pharmaceutical clinical early warning according to claim 1, characterized in that: According to the collected results, judging whether there is a risk according to the preset warning conditions includes: According to the configuration of the warning conditions, it is determined whether any parameter in the patient data set at risk exceeds the upper threshold and / or lower threshold of the preset warning modulation. If it exceeds the preset range, a warning prompt is issued.

5. A monitoring method based on pharmaceutical clinical early warning according to claim 4, characterized in that: When performing threshold judgment on a patient's data set, different threshold judgment strategies are set according to different data types. The judgment strategies include: The first strategy is to issue an immediate warning when the data type reaches the threshold of the preset warning condition; The second strategy is that when the data type reaches the threshold of the preset warning condition, the first time frame is immediately calibrated and the data type is highlighted; if in the subsequent data collection process, the highlighted data type still reaches the threshold of the preset warning condition, the second time frame is immediately calibrated; if the distance between the first time frame and the second time frame is less than the preset distance, an early warning is immediately issued; otherwise, continue to wait for the next judgment of the data type collection result.

6. A monitoring method based on pharmaceutical clinical early warning according to claim 5, characterized in that: In the above second strategy, if the data type of the key mark does not reach the threshold of the preset warning condition in the next collection, the key mark is cancelled and the event is reported.

7. A monitoring method based on pharmaceutical clinical early warning according to claim 1, characterized in that: When the patient data set is collected in real time, the method further includes: correcting the data quality of the patient data set, including: removing duplicate data in the collection results, filling in missing data, and correcting errors in abnormal data; Wherein, the filling of missing data includes the following strategies: After setting the feature data group that needs to be filled with data, set the corresponding data compensation method and compensation validity period; determine whether the feature data group contains temporary long-term medication that takes effect on the same day, and compensate for the missing data based on the determination result and compensation method; The data compensation method and compensation validity period include: if the feature data group is missing and the data compensation method is not set, an early warning is directly issued; if the feature data group is missing and a data compensation method is set, compensation is directly performed according to the setting; the compensation includes: filling in the latest data within a preset number of days or all data within a preset number of days that meet the current conditions.

8. A monitoring method based on pharmaceutical clinical early warning according to claim 7, characterized in that: The characteristic data set includes: Drug usage group, test / inspection / treatment / pathology report group, and vital signs group.

9. A monitoring method based on pharmaceutical clinical early warning according to claim 1, characterized in that: After the monitoring task for patients at risk is established, the patient's data set is marked according to the type and purpose of the monitoring task, and the numerical changes of the marked data types are continuously observed; and based on the changes, the pharmacist intervenes to adjust the marked data types in the monitoring task until the monitoring is completed.

10. A monitoring system based on pharmaceutical clinical early warning, used to execute the monitoring method based on pharmaceutical clinical early warning as described in any one of claims 1 to 9, characterized in that: include: Hospital information system building module, used to access the hospital information system and establish data associations between hospitals, users and nursing; The data collection module is used to set up the pharmaceutical monitoring data entry and build the patient data set according to the relevant medical advice and pathological characteristics; An early warning prompt module is used to collect the patient data set in real time according to a preset frequency after synchronously updating the patient data set to the pharmaceutical care storage service, and determine whether there is a risk according to the collection results and the preset early warning conditions, and if there is a risk, issue an early warning prompt; The patient monitoring module is used to verify the patient data set that provides early warning of risks by pharmacists, establish monitoring tasks for patients at risk, and dynamically adjust the monitoring tasks based on the process records of the monitoring tasks, and generate a pharmaceutical monitoring record form after the pharmacist determines that the monitoring tasks are completed.

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