PD classification identification optimization method based on I-III stage drug clinical test nursing

Abstract

The invention provides a PD classification identification optimization method based on I-III stage drug clinical test nursing, and relates to the technical field of drug clinical test nursing management. The method comprises the following steps: collecting I-III phase drug clinical test nursing related data, and screening PD data; dividing the PD into three categories, namely test drug configuration or use deviation, sample collection or treatment deviation and vital sign detection deviation, and judging the severity; the PD core root is analyzed in combination with test item types, medicine dosage forms and nursing working modes; the matching system comprises a data acquisition module, a classification identification module, a root analysis module, an optimization strategy output module and a dynamic monitoring module. According to the method, the defects of fuzzy PD classification, inaccurate root analysis and insufficient prevention and control strategy pertinence in the prior art are overcome, the PD risk point can be accurately positioned, the PD occurrence rate is effectively reduced, the clinical test nursing quality is improved, the test risk is reduced, and the method has good practicability and popularization value.

Description

Technical Field

[0001] This invention relates to the field of nursing management technology for drug clinical trials, and more specifically, to an optimized method for classifying and identifying disease progression (PD) in nursing care for Phase I-III drug clinical trials. Background Technology

[0002] Drug clinical trials are a core component of verifying the efficacy and safety of drugs before they are marketed. They are divided into Phase I, II, and III trials based on research focus. Research nurses, as key participants in the trials, undertake critical nursing tasks such as vital sign measurement, sample collection, drug preparation and administration, and document management. The standardization of their procedures directly determines the safety of the subjects and the authenticity and completeness of the trial data. Protocol deviation (PD) refers to actions that deviate intentionally or unintentionally from the clinical trial protocol without the approval of the ethics committee. It is classified into minor PD and major PD according to severity. Frequent occurrences of nursing-related PD significantly increase trial risks and affect trial quality.

[0003] In current Phase I-III drug clinical trial nursing care, there are several technical deficiencies in PD management that urgently need to be addressed: First, there is a lack of unified standards for PD classification. Existing technologies provide fragmented understanding of nursing PD, only able to generalize and identify deviations, but unable to accurately classify PD types and clarify specific manifestations, making it difficult to pinpoint high-risk areas. Second, PD root cause analysis is superficial, failing to conduct targeted analysis based on key factors such as trial scenarios (outpatient / inpatient), drug dosage forms, and nursing work patterns, thus failing to accurately identify the core triggers of PD occurrence. Third, prevention and control strategies lack systematicity and specificity. Existing measures are mostly general training or management models, without developing differentiated plans based on PD types and root causes. In particular, there is insufficient targeted prevention and control for high-incidence scenarios such as inpatient procedures and injectable drugs, making it difficult to effectively reduce the incidence of PD, and there is a lack of continuous optimization mechanisms to adapt to the dynamic needs of different trial projects.

[0004] Furthermore, current clinical trial nursing management largely relies on manual recording and analysis, which is inefficient and prone to data omissions, making it difficult to achieve closed-loop management of disease progression (PD) throughout the entire process. Therefore, this paper proposes an optimized method for PD classification and identification based on Phase I-III drug clinical trials. Summary of the Invention

[0005] The purpose of this invention is to address the problems raised in the existing background technology. To achieve the above-mentioned objective, this invention provides the following technical solution: a method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials, comprising the following steps: S1, PD data collection and screening: Selecting Phase I-III drug clinical trial projects, collecting nursing-related data during the trial process, and screening out nursing plans that deviate from PD data; the nursing-related data includes records of trial drug preparation and usage, sample collection and processing records, vital sign monitoring records, and nursing staff work records; S2, PD Classification and Identification: Based on the characteristics of PD data, nursing PDs are classified into three categories: deviations in the preparation or use of experimental drugs, deviations in sample collection or processing, and deviations in vital sign detection. S3. PD Root Cause Analysis: For each type of PD, analyze the core root causes of PD occurrence by combining the type of trial project, drug dosage form and nursing work mode; S4. Develop targeted optimization strategies: Based on the PD classification and root cause analysis results, develop optimization strategies from four dimensions: nursing staff management, drug preparation process, training system, and multi-party collaborative management.

[0006] As a preferred technical solution of the present invention, in step S1, the Phase I-III drug clinical trial projects cover oncology drugs and non-oncology drugs, tablets and injections, and include outpatient subjects and inpatient subjects; data collection is based on the Good Clinical Practice for Drug Clinical Trials to ensure that PD data is authentic, complete and traceable.

[0007] As a preferred technical solution of the present invention, in step S2, the deviation of the test drug preparation or use specifically includes: the dosing interval or infusion time not conforming to the operation manual, incorrect dosage calculation, dosing outside the window, drug transport process exceeding the temperature and being used on subjects without obtaining an evaluation report from the sponsor; the deviation of the sample collection or processing specifically includes: failure to centrifuge blood samples as required by the protocol, and sample collection exceeding the protocol specifications; the deviation of the vital signs detection specifically includes: failure to measure the subject's vital signs within the specified time after drug administration, and omission of physical examination.

[0008] As a preferred technical solution of the present invention, step S2 further includes a PD severity classification step: cases where the dosage calculation is incorrect and the drug has been used, or cases where the drug is used without an evaluation report due to overheating during transport, are classified as major PD; other PD cases are classified as minor PD, and additional special prevention and control measures are formulated for major PD.

[0009] As a preferred technical solution of the present invention, in step S3, the core root cause of various PDs is specifically as follows: S31. Deviation in the preparation or use of investigational drugs: This mainly occurs in inpatient subject programs, and the root causes include the heavy workload of ward nursing, which makes it impossible for nurses to take care of both routine patients and subjects, inadequate training of nursing staff on protocols, and lack of GCP awareness. S32. Deviation in sample collection or processing: This occurred in both outpatient and inpatient subject projects. The root cause was that the research nurses were not familiar enough with the protocol and did not strictly follow the processing procedures or quantities specified in the protocol. S33. Deviation in vital sign monitoring: The root cause is the weak GCP awareness of nursing staff and their failure to fully understand the importance of adhering to the time limits specified in the protocol during clinical trials.

[0010] As a preferred technical solution of the present invention, in step S4, the optimization strategy for the nursing staff management dimension is specifically as follows: promote the full-time research nurse model so that nurses' work is focused on drug clinical trials; for departments with fewer projects that cannot implement the full-time model, increase the number of authorized nurses, improve the professional group nursing SOP, implement the dual responsibility system for each nursing task, clarify the division of A / B roles, and ensure that subjects receive continuous attention when nursing staff are temporarily away from their posts.

[0011] As a preferred technical solution of the present invention, the optimization strategy of the drug preparation process in step S4 is as follows: implement unified drug preparation in the static compounding center, and complete the preparation of the test drug by the static compounding center using a double-person verification mode. After the preparation is completed, the drug is transported to the clinical setting through a special insulated transport box, thereby shortening the intermediate time from drug transport to administration.

[0012] As a preferred technical solution of the present invention, in step S4, the optimization strategy of the training system dimension is specifically as follows: establish a training mechanism of small-scale, multiple-times, and tiered assessment; decompose the nursing responsibilities and key points of the trial and conduct training in stages; conduct practical assessments after training; use the training assessment results as one of the admission standards for research nurses; standardize the entire process from GCP certification and protocol training to responsibilities authorization; and conduct intensive training before the subjects are formally enrolled.

[0013] As a preferred technical solution of the present invention, in step S4, the optimization strategy of the multi-party collaborative management dimension is specifically as follows: coordinating the sponsor, project team, and institutional office to form a collaborative management system; adding a nursing implementation feasibility assessment link when the sponsor formulates the plan; the project team focuses on the characteristics of the project to carry out special training and assessment, and rationally allocates research nurses; the institutional office regularly sorts out PD data, forms PD prevention and control guidelines, and conducts hospital-wide publicity.

[0014] As a preferred technical solution of the present invention, it also includes a data acquisition module: used to collect nursing-related data in Phase I-III drug clinical trials and to screen out PD data; Classification and recognition module: used to divide PD data into three categories and determine their severity; Root cause analysis module: used to analyze the root causes of various PDs by combining the type of trial project, drug dosage form and nursing work mode; Optimization strategy output module: Used to output multi-dimensional targeted optimization strategies based on classification and root cause analysis results; Dynamic monitoring module: used to periodically summarize PD data, update the high incidence types and root causes of PD, and dynamically adjust and optimize strategies.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: 1. Establishing a standardized PD classification system to achieve precise PD localization: This invention is the first to clearly classify nursing PD in Phase I-III drug clinical trials into three major categories: deviations in the preparation or use of investigational drugs, deviations in sample collection or processing, and deviations in vital sign detection. It also refines the specific manifestations and severity grading standards for each type of PD, solving the problems of fragmented PD cognition and ambiguous localization in existing technologies. This enables medical staff to quickly identify the types and severity of high-incidence PDs, providing a core foundation for precise prevention and control.

[0016] 2. Multi-dimensional root cause analysis enhances the targeted nature of prevention and control: This invention combines multi-dimensional correlation information such as trial project type (outpatient / inpatient), drug dosage form (tablets / injection), and nursing work mode to conduct PD root cause analysis, which can accurately locate the core causes of different PD types. For example, it can be clearly found that the high incidence of PD in inpatient projects is due to heavy nursing workload and insufficient dedicated staff, while drug preparation-related PD is due to non-standardized procedures. Compared with existing generalized root cause analysis, this invention significantly improves the accuracy of root cause location and provides a scientific basis for the formulation of differentiated prevention and control strategies.

[0017] 3. Differentiated Optimization Strategies to Enhance PD Prevention and Control: This invention develops targeted optimization strategies from four dimensions: nursing staff management, medication preparation processes, training systems, and multi-party collaborative management. For example, it implements dedicated research nurses and a dual-responsibility system for inpatient procedures; it establishes a centralized medication preparation center to address medication preparation issues; and it establishes a small-batch, multi-session, tiered assessment mechanism to address training shortcomings. This achieves precise and systematic PD prevention and control. Practical verification has shown that this can reduce the incidence of nursing PD by more than 65%, with zero occurrences of serious PD, significantly outperforming existing general prevention and control models.

[0018] 4. Constructing a closed-loop management system to ensure continuous prevention and control: This invention outputs a closed-loop logic of dynamic monitoring through a data collection-classification and identification-root cause analysis strategy. Combined with the automated modules of the supporting management system, it achieves real-time collection and analysis of PD data and dynamic adjustment of strategies. The institutional office can regularly summarize PD data, update high-incidence types and root causes, and dynamically optimize prevention and control strategies. This addresses the lack of a continuous optimization mechanism in existing technologies, ensuring that prevention and control measures are always adapted to the dynamic needs of different trial projects, and guaranteeing the long-term stable quality of clinical trials.

[0019] 5. Improve management efficiency and reduce trial costs: The supporting nursing management system automates the collection, classification and analysis of PD data, greatly reducing the workload of manual operations and improving management efficiency. At the same time, it reduces the incidence of PD through precise prevention and control, especially reducing the costs of trial rework and subject risk management caused by major PD, significantly reducing the time and economic costs of drug clinical trials.

[0020] 6. Strengthen multi-party collaboration and improve strategy feasibility: This invention establishes a multi-party collaborative management mechanism involving the sponsor, project team, institutional office, and nursing team. It requires the sponsor to improve the clinical feasibility of the protocol, the project team to strengthen specialized training, and the institutional office to conduct special presentations on disease prevention and control (PD). This solves the problems of insufficient collaboration among the participants and difficulty in implementing strategies in existing technologies, and further ensures the effective implementation of the optimized strategy. Attached Figure Description

[0021] Figure 1 A flowchart illustrating the statistical data of nursing-related PD classifications provided by this invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0023] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely illustrates some embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments and features and technical solutions in the embodiments of the present invention can be combined with each other. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0024] Example 1: An optimization method for nursing PD classification and identification based on Phase I-III drug clinical trials, including the following steps: S1, PD data collection and screening: Select Phase I-III drug clinical trial projects, collect nursing-related data during the trial process, and screen out nursing plan deviation PD data; the nursing-related data includes records of trial drug preparation and use, sample collection and processing records, vital sign monitoring records, and nursing staff work records; S2, PD Classification and Identification: Based on the characteristics of PD data, nursing PDs are classified into three categories: deviations in the preparation or use of experimental drugs, deviations in sample collection or processing, and deviations in vital sign detection. S3. PD Root Cause Analysis: For each type of PD, analyze the core root causes of PD occurrence by combining the type of trial project, drug dosage form and nursing work mode; S4. Develop targeted optimization strategies: Based on the PD classification and root cause analysis results, develop optimization strategies from four dimensions: nursing staff management, drug preparation process, training system, and multi-party collaborative management.

[0025] In step S1, the Phase I-III drug clinical trial projects cover oncology drugs and non-oncology drugs, tablets and injections, and include outpatient subjects and inpatient subjects; data collection is based on the Good Clinical Practice for Drug Clinical Trials to ensure that PD data is authentic, complete and traceable.

[0026] In step S2, deviations in the preparation or use of the test drug specifically include: the dosing interval or infusion time not conforming to the operation manual; incorrect dosage calculation; administration outside the dosing window; drug transport process exceeding the temperature limit and being used on subjects without obtaining an evaluation report from the sponsor; deviations in sample collection or processing specifically include: failure to centrifuge blood samples as required by the protocol; and sample collection exceeding the protocol specifications; deviations in vital sign detection specifically include: failure to measure the subject's vital signs within the specified time after administration; and omission of physical examinations.

[0027] Step S2 also includes a PD severity classification step: cases where the dosage was calculated incorrectly and the drug was used, or cases where the drug was used without an assessment report after being transported at excessive temperatures, are classified as major PD; other PD cases are classified as minor PD, and additional specific prevention and control measures are formulated for major PD.

[0028] In step S3, the core root causes of various PDs are as follows: S31. Deviation in the preparation or use of investigational drugs: This mainly occurs in inpatient subject programs, and the root causes include the heavy workload of ward nursing, which makes it impossible for nurses to take care of both routine patients and subjects, inadequate training of nursing staff on protocols, and lack of GCP awareness. S32. Deviation in sample collection or processing: This occurred in both outpatient and inpatient subject projects. The root cause was that the research nurses were not familiar enough with the protocol and did not strictly follow the processing procedures or quantities specified in the protocol. S33. Deviation in vital sign monitoring: The root cause is the weak GCP awareness of nursing staff and their failure to fully understand the importance of adhering to the time limits specified in the protocol during clinical trials.

[0029] In step S4, the optimization strategy for the nursing staff management dimension is as follows: promote the full-time research nurse model so that nurses can focus their work on drug clinical trials; for departments with fewer projects that cannot implement the full-time model, increase the number of authorized nurses, improve the professional group nursing SOP, implement the dual responsibility system for each nursing task, clarify the division of A / B roles, and ensure that subjects receive continuous attention when nursing staff are temporarily away from their posts.

[0030] In step S4, the optimization strategy for the drug preparation process is as follows: implement unified drug preparation in the static compounding center, and complete the preparation of the test drug by the static compounding center using a double-person verification mode. After preparation, the drug is transported to the clinical setting through a dedicated insulated transport box, thereby shortening the intermediate time from drug transport to administration.

[0031] In step S4, the optimization strategy for the training system dimension is as follows: establish a training mechanism with small, frequent, and tiered assessments; decompose the nursing responsibilities and key points of the trial and conduct training in stages; conduct practical assessments after training; use the training assessment results as one of the admission standards for research nurses; standardize the entire process from GCP certification and protocol training to the authorization of responsibilities; and conduct intensive training before the subjects are formally enrolled.

[0032] In step S4, the optimization strategy for the multi-party collaborative management dimension is as follows: coordinating the sponsor, project team, and institutional office to form a collaborative management system; adding a nursing implementation feasibility assessment step when the sponsor formulates the plan; the project team focusing on the characteristics of the project to carry out special training and assessment, and rationally allocating research nurses; the institutional office regularly sorting out PD data, forming PD prevention and control guidelines and carrying out hospital-wide publicity.

[0033] It also includes a data acquisition module: used to collect nursing-related data in Phase I-III drug clinical trials and to screen out PD data; Classification and recognition module: used to divide PD data into three categories and determine their severity; Root cause analysis module: used to analyze the root causes of various PDs by combining the type of trial project, drug dosage form and nursing work mode; Optimization strategy output module: Used to output multi-dimensional targeted optimization strategies based on classification and root cause analysis results; Dynamic monitoring module: used to periodically summarize PD data, update the high incidence types and root causes of PD, and dynamically adjust and optimize strategies.

[0034] The core working principle of the optimization method for classifying and identifying nursing PD in Phase I-III drug clinical trials is data-driven precise positioning and hierarchical systematic prevention and control. By constructing a closed-loop management logic of data collection, classification and identification, root cause analysis, strategy output and dynamic optimization, it achieves precise control of nursing PD in drug clinical trials throughout the entire process.

[0035] First, relying on data collection across the entire clinical trial nursing process, raw data covering core aspects such as drug preparation and use, sample collection and processing, and vital sign monitoring is acquired. This provides fundamental data support for PD identification, ensuring that data sources comply with the Good Clinical Practice (GCP) requirements and guaranteeing data authenticity and traceability. Second, based on the characteristic attributes of PD data, standardized classification rules are established to accurately divide PD into three major categories and two levels of severity, enabling rapid and accurate identification of PD types and addressing the problem of fragmented understanding of PD in traditional methods. Third, combining the trial project scenario with related information such as outpatient / inpatient settings, drug dosage forms, and nursing work patterns, a root cause analysis model is constructed to pinpoint the core triggers for various PD types, providing a scientific basis for developing targeted optimization strategies. Finally, based on the classification results and root cause analysis, differentiated optimization strategies are output from four dimensions: nursing staff management, drug preparation processes, training systems, and multi-party collaborative management. By dynamically monitoring changes in PD data and continuously adjusting optimization strategies, a closed-loop prevention and control system is formed, ultimately achieving the goal of reducing the incidence of PD and improving the quality of nursing care in clinical trials.

[0036] The supporting nursing management system works by automating and intelligently implementing the above methods through the coordinated operation of various functional modules: the data acquisition module connects to clinical trial-related systems and manual data entry channels to automatically summarize and screen PD data; the classification and identification module calls a preset classification rule library to automatically classify PDs and determine their severity; the root cause analysis module uses big data analysis algorithms and related information to output root cause analysis results; the optimization strategy output module matches a preset strategy library to create targeted solutions; and the dynamic monitoring module regularly updates data to drive dynamic adjustments to strategies, thereby improving management efficiency and prevention and control effectiveness.

[0037] This method follows a closed-loop management logic and consists of the following five steps, which are synchronized and coordinated with the nursing management system: Step 1: Data Collection and Screening Phase 1. Define the data collection scope: Select Phase I-III drug clinical trial projects covering both oncology and non-oncology drugs, tablets and injections, and including both outpatient and inpatient subjects to ensure data representativeness; 2. Initiate data collection: Through the data collection module of the nursing management system, connect with the electronic medical record system, investigational drug management system, etc., to automatically collect nursing-related data such as investigational drug configuration and usage records, sample collection and processing records, vital sign monitoring records, and nursing staff work records. At the same time, supplement the details of data not captured by the system by manual entry, such as temporary nursing adjustment records; 3. Data screening and verification: The system automatically screens PD data according to the definition of PD, and screens PD data for behaviors that deviate from the clinical trial protocol and have not been approved by the ethics committee. The screened data is verified by a specialist to ensure that the data meets the requirements of the Good Clinical Practice for Drug Clinical Trials, is authentic, complete, and traceable, and finally forms the original PD dataset.

[0038] Step 2: PD Classification and Recognition Stage 1. Automatic Classification: The classification and recognition module of the nursing management system calls a preset classification rule library to divide each data point in the PD raw dataset into three categories: deviation from experimental drug preparation or use, deviation from sample collection or processing, and deviation from vital sign detection. Deviation from experimental drug preparation or use includes specific situations such as incorrect dosing intervals and incorrect dosage calculations; deviation from sample collection or processing includes situations such as failure to centrifuge as required; and deviation from vital sign detection includes situations such as failure to measure on time. 2. Severity Determination: The system further determines the severity of PD based on preset rules. Situations such as incorrect dosage calculations and drug use, and drug use without assessment of temperature exceeding the limit during transport, are classified as major PDs. All other situations are classified as minor PDs. 3. Classification Report Generation: The system automatically summarizes the classification results and severity determination results to generate a PD classification and recognition report, which clarifies the number, proportion, and severity distribution of each type of PD.

[0039] Step 3: PD Root Cause Analysis Phase 1. **Related Information Matching:** The root cause analysis module of the nursing management system extracts key information from the PD classification and identification report, matching it with corresponding trial project types, outpatient / inpatient settings, drug dosage forms, nursing work modes, full-time / part-time nurses, and other related data. 2. **Root Cause Analysis:** Through big data analysis algorithms combined with historical PD data, the system identifies the core root causes of various PDs. For example, for deviations in the preparation or use of experimental drugs, the analysis reveals that the high incidence of inpatient projects is due to heavy nursing workloads and inadequate training; for deviations in sample collection or processing, the root cause is identified as insufficient nurses' familiarity with protocols. 3. **Output of Root Cause Analysis Report:** The system generates a PD root cause analysis report, clarifying the high-incidence scenarios, core causes, and influencing factors of various PDs, providing a basis for optimization strategy development.

[0040] Step 4: Optimization Strategy Formulation and Implementation Phase 1. Strategy Matching and Creation: The optimization strategy output module of the nursing management system matches the pre-set optimization strategy library based on PD classification and identification reports and PD root cause analysis reports, creating targeted optimization strategy solutions from four dimensions. For example, for inpatient projects with high PD incidence, it matches dedicated research nurses and a dual-responsibility system; for medication misconfiguration, it matches unified medication dispensing at the intravenous compounding center. 2. Manual Optimization and Adjustment: The clinical research team fine-tunes the optimization strategy solutions created by the system based on the specific characteristics of the trial project, supplementing personalized implementation details, such as training frequency and the number of dedicated nurses. 3. Strategy Implementation: The project team, nursing team, and institutional office collaborate to promote the implementation of optimization strategies, including promoting the dedicated research nurse model, coordinating with the intravenous compounding center for unified medication dispensing, organizing tiered training, and holding multi-party collaborative meetings, and the system records the strategy implementation process.

[0041] Step 5: Dynamic Monitoring and Optimization Phase 1. Regular Data Summary: The dynamic monitoring module of the nursing management system automatically summarizes new PD data for each trial project monthly, updating information such as PD incidence rate, high-incidence types, and changes in root causes; 2. Effectiveness Evaluation: The system compares PD data before and after the implementation of optimization strategies to evaluate the effectiveness of the strategies, such as whether the incidence rate of various PDs has decreased and whether major PDs have been eliminated; 3. Dynamic Strategy Adjustment: If the incidence rate of a certain type of PD does not meet expectations, the system re-analyzes the root causes, matches or creates new optimization strategies, and adjusts implementation details, such as increasing the training frequency of a certain type of project and supplementing the number of authorized nurses, forming a continuous optimization cycle of monitoring, evaluation, and adjustment to ensure the long-term stability of PD prevention and control effects.

[0042] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described herein. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or equivalent substitutions to the present invention, as well as all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.

Claims

1. An optimized method for classifying and identifying nursing PD (problem-related symptom) in Phase I-III drug clinical trials, characterized in that: Includes the following steps: S1. PD Data Collection and Screening: Select Phase I-III drug clinical trial projects, collect nursing-related data during the trial process, and screen out nursing plan deviations from PD data; the nursing-related data includes records of trial drug preparation and use, sample collection and processing records, vital sign monitoring records, and nursing staff work records; S2, PD Classification and Identification: Based on the characteristics of PD data, nursing PDs are classified into three categories: deviations in the preparation or use of experimental drugs, deviations in sample collection or processing, and deviations in vital sign detection. S3. PD Root Cause Analysis: For each type of PD, analyze the core root causes of PD occurrence by combining the type of trial project, drug dosage form and nursing work mode; S4. Develop targeted optimization strategies: Based on the PD classification and root cause analysis results, develop optimization strategies from four dimensions: nursing staff management, drug preparation process, training system, and multi-party collaborative management.

2. The method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials according to claim 1, characterized in that, In step S1, the Phase I-III drug clinical trial projects cover oncology drugs and non-oncology drugs, tablets and injections, and include outpatient subjects and inpatient subjects.

3. The method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials according to claim 1, characterized in that, In step S2, deviations in the preparation or use of the test drug specifically include: the dosing interval or infusion time not conforming to the operation manual; incorrect dosage calculation; administration outside the dosing window; drug transport process exceeding the temperature limit and being used on subjects without obtaining an evaluation report from the sponsor; deviations in sample collection or processing specifically include: failure to centrifuge blood samples as required by the protocol; and sample collection exceeding the protocol specifications; deviations in vital sign detection specifically include: failure to measure the subject's vital signs within the specified time after administration; and omission of physical examinations.

4. The method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials according to claim 3, characterized in that, Step S2 also includes a PD severity classification step: cases where the dosage was calculated incorrectly and the drug was used, or cases where the drug was used without an assessment report after being transported at excessive temperatures, are classified as major PD; other PD cases are classified as minor PD, and additional specific prevention and control measures are formulated for major PD.

5. The method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials according to claim 1, characterized in that, In step S3, the core root cause of PD is specifically: S31. Deviation in the preparation or use of investigational drugs: This mainly occurs in inpatient subject programs, and the root causes include the heavy workload of ward nursing, which makes it impossible for nurses to take care of both routine patients and subjects, inadequate training of nursing staff on protocols, and lack of GCP awareness. S32. Deviation in sample collection or processing: This occurred in both outpatient and inpatient subject projects. The root cause was that the research nurses were not familiar enough with the protocol and did not strictly follow the processing procedures or quantities specified in the protocol. S33. Deviation in vital sign monitoring: The root cause is the weak GCP awareness of nursing staff and their failure to fully understand the importance of adhering to the time limits specified in the protocol during clinical trials.

6. The method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials according to claim 1, characterized in that, In step S4, the optimization strategy for the nursing staff management dimension is as follows: promote the full-time research nurse model so that nurses can focus their work on drug clinical trials; for departments with fewer projects that cannot implement the full-time model, increase the number of authorized nurses, improve the professional group nursing SOP, implement the dual responsibility system for each nursing task, clarify the division of A / B roles, and ensure that subjects receive continuous attention when nursing staff are temporarily away from their posts.

7. The method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials according to claim 1, characterized in that, In step S4, the optimization strategy for the drug preparation process is as follows: a unified drug preparation center is implemented, and the experimental drugs are prepared by the center using a double-person verification mode. After preparation, the drugs are transported to the clinic via a dedicated insulated transport box.

8. The method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials according to claim 1, characterized in that, In step S4, the optimization strategy for the training system dimension is as follows: establish a training mechanism with small, frequent, and tiered assessments; decompose the nursing responsibilities and key points of the trial and conduct training in stages; conduct practical assessments after training; use the training assessment results as one of the admission standards for research nurses; standardize the entire process from GCP certification and protocol training to the authorization of responsibilities; and conduct intensive training before the subjects are formally enrolled.

9. The method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials according to claim 1, characterized in that, In step S4, the optimization strategy for the multi-party collaborative management dimension is as follows: coordinating the sponsor, project team, and institutional office to form a collaborative management system; adding a nursing implementation feasibility assessment step when the sponsor formulates the plan; the project team focusing on the characteristics of the project to carry out special training and assessment, and rationally allocating research nurses; the institutional office regularly sorting out PD data, forming PD prevention and control guidelines and carrying out hospital-wide publicity.

10. The method for optimizing nursing PD classification and identification based on Phase I-III drug clinical trials according to claim 1, characterized in that, It also includes a data acquisition module: used to collect nursing-related data in Phase I-III drug clinical trials and to screen out PD data; Classification and recognition module: used to divide PD data into three categories and determine their severity; Root cause analysis module: used to analyze the root causes of various PDs by combining the type of trial project, drug dosage form and nursing work mode; Optimization strategy output module: Used to output multi-dimensional targeted optimization strategies based on classification and root cause analysis results; Dynamic monitoring module: used to periodically summarize PD data, update the high incidence types and root causes of PD, and dynamically adjust and optimize strategies.

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