Method and system for evaluating quality of adverse drug reaction monitoring work

Abstract

The invention discloses an adverse drug reaction monitoring work quality evaluation method and system, and relates to the technical field of quality supervision, and the method comprises the steps: obtaining report data sent by a to-be-supervised medical institution, and calculating a secondary index used for evaluating the quality of the report data according to the report data and a target proportion coefficient of a target region where the to-be-supervised medical institution is located; inputting each secondary index into a comprehensive quality evaluation model corresponding to the target region to obtain a quality grade; and determining the working quality level of the to-be-supervised medical institution according to the quality grade, comparing the working quality level of the to-be-supervised medical institution with all medical institutions in the region where the to-be-supervised medical institution is located, and supervising the improvement of the to-be-supervised medical institution on the adverse drug reaction monitoring work. The average level of a certain area is established to quickly quantify the quality level evaluation of the adverse drug reaction monitoring work of the medical institution, so that a drug manager is helped to master the drug use risk management level of the medical institution, and collection, early warning and the like of subsequent drug use risk information are promoted.

Description

A method and system for evaluating the quality of adverse drug reaction monitoring. Technical Field

[0001] This invention relates to the field of quality supervision technology, specifically to a method and system for evaluating the quality of adverse drug reaction monitoring. Background Technology

[0002] In pharmacovigilance, adverse drug reaction (event) reports are an important vehicle for pharmacovigilance work and research data. As an inherent attribute of drugs, the probability of adverse drug reactions occurring is relatively stable under normal circumstances. In the real world, the clinical presentation of adverse drug reactions is closely related to the size of the user population. That is, the number of drug users * the probability of adverse drug reactions = the number of adverse drug reactions.

[0003] Existing systems have established network-based spontaneous reporting and collection systems for adverse drug reactions (events) to monitor them. Pharmacovigilance activities can effectively compensate for deficiencies in pre-market drug research, promote rational drug use in clinical practice, provide a basis for drug selection, regulation, and phase-out, offer technical support for post-market risk management, promote new drug research and development, promptly detect major adverse drug events, prevent the spread and escalation of adverse drug events, and safeguard public health and social stability.

[0004] However, adverse drug reaction (event) reporting is conducted through a self-reporting model. Medical institutions, as the legally mandated reporting bodies for adverse drug reactions (events), are subject to multiple factors affecting the quantity and quality of their reports, including both subjective and objective influences. Under the self-reporting system, the resources allocated to drug use risk control by medical institutions are limited due to regional medical resource allocation and medical (pharmaceutical) policies. Consequently, the number of reports actually submitted by medical institutions is inevitably less than the actual number of occurrences. A major drawback of the self-reporting system is underreporting, leading to incomplete data and data bias. Underreporting can conceal drug use risks, increasing the likelihood of clinical drug-related adverse events occurring and spreading. Medical institutions, as the legally mandated reporting bodies, are also the primary channel for reporting adverse drug reactions (events). The quality of adverse drug reaction monitoring work by medical institutions directly affects the quality of monitoring reports. For example, some medical institutions engage in false or perfunctory reporting. Currently, there are no tools to monitor the publicly available quality of adverse drug reaction monitoring work by medical institutions. Therefore, to evaluate the efficiency of medical institutions' input-output in drug risk control, it is essential to construct an evaluation model for the quality of adverse drug reaction monitoring work based on the reporting situation of medical institutions. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for evaluating the quality of adverse drug reaction monitoring. By establishing a system, the quality level of adverse drug reaction monitoring in medical institutions can be quickly quantified to assess the average level in a certain region. This helps drug managers understand the level of drug use risk management in medical institutions and promotes the collection and early warning of subsequent drug use risk information.

[0006] To achieve the above objectives, this application adopts the following approach: On one hand, this invention provides a method for evaluating the quality of adverse drug reaction monitoring, specifically including the following steps: S1, acquiring reported data sent by the medical institution to be regulated, and calculating secondary indicators for evaluating the quality of the reported data based on the target proportion coefficient of the target region where the medical institution to be regulated is located; S2, inputting each secondary indicator into the comprehensive quality evaluation model corresponding to the target region to obtain the quality level; S3, comparing the quality level of the medical institution to be regulated with all medical institutions in its region to determine the work quality level of the medical institution to be regulated, and supervising the improvement of the adverse drug reaction monitoring work of the medical institution to be regulated based on the work quality level.

[0007] In some specific implementation plans, the reported data includes total reports (a), number of reports of severity types (b), number of new and severity type reports (c), and number of hospitalizations (D).

[0008] In some specific implementation plans, the secondary indicators include the completion rate of total reports (A), the completion rate of severity type reports (B), the completion rate of newly added severity type reports (C), and the target number of reports (X).

[0009] In some specific implementation schemes, the target proportion coefficients include the target variable level λ_A corresponding to secondary indicator A, the target variable level λ_B corresponding to secondary indicator B, and the target variable level λ_C corresponding to secondary indicator C. Orthogonal experiments are used to determine the target proportion coefficients for the target region.

[0010] In some specific implementation plans, the target number of reports X = D × λ_A is calculated based on the number of hospitalizations D and the target variable level λ_A; the total number of reports completion rate A = a / X is calculated based on the total number of reports a and the target number of reports; B = b / (target number of reports × λ_B) is calculated based on the number of reports of severity types b and the target variable level λ_B; and C = c / (target number of reports × λ_C) is calculated based on the number of new and severity type reports and the target variable level λ_C.

[0011] In some specific implementation plans, the process of determining the target proportion coefficient for the target region where the medical institution to be regulated is located is as follows: Initialize each target proportion coefficient; set multiple target variable values ​​for each target variable level based on the overall situation of the target region where the medical institution to be regulated is located; orthogonally combine the target variable values ​​of each target variable level to obtain multiple target variable level combinations; collect the reported data of all medical institutions in the target region that meet the target number of inpatients; calculate the reported data of the medical institutions with each target variable level combination and input it into the comprehensive quality evaluation model corresponding to the target region to obtain the quality score corresponding to each target variable level combination; determine the optimal level combination based on the quality score of each target variable level combination, and use the optimal level combination as the target proportion coefficient for the target region where the medical institution to be regulated is located.

[0012] In some specific implementation plans, the comprehensive quality evaluation model uses a weighted normalized average method combining subjective and objective weighting to determine the final weight for each secondary indicator, and calculates the quality level Q = α*A + β*B + γ*C, where α, β, and γ are the final weights corresponding to secondary indicators A, B, and C, respectively.

[0013] In some specific implementation schemes, the subjective weighting method uses the AHP hierarchical analysis method, while the objective weighting method uses the entropy weighting method.

[0014] In some specific implementation plans, when inputting the secondary indicators corresponding to the medical institutions to be regulated into the comprehensive quality evaluation model, it is also necessary to determine whether to adjust the weights of each secondary indicator in the comprehensive quality evaluation model based on the type of medical institution to be regulated.

[0015] Secondly, this application provides a quality evaluation system for adverse drug reaction monitoring, used to implement the quality evaluation method for adverse drug reaction monitoring described in the first aspect, comprising: a data entry module for acquiring reported data sent by medical institutions under supervision; an indicator calculation module for calculating secondary indicators for evaluating the quality of reported data based on the reported data and the target proportion coefficient of the target region where the medical institution under supervision is located; a quality evaluation module for inputting each secondary indicator into the comprehensive quality evaluation model corresponding to the target region to obtain a quality level; and a quality evaluation model construction module for determining the work quality level of the medical institution under supervision based on the quality level, comparing the work quality level of the medical institution under supervision with all medical institutions in its region, and supervising the improvement of the adverse drug reaction monitoring work of the medical institution under supervision.

[0016] The beneficial effects of this invention are as follows: This invention establishes a corresponding comprehensive quality evaluation model by statistically analyzing the average level of the region where medical institutions are located. This comprehensive quality evaluation model uses a combined subjective and objective weighting method to evaluate the work quality level of the medical institution under supervision in its region. This allows relevant medical institutions and drug risk management personnel of drug regulatory authorities to quickly assess the current adverse drug reaction (event) monitoring work of a specific medical institution within a short time (within minutes). It can assess the overall work level of the medical institution in its region, year-on-year (month-on-month) changes in work level, and comparisons of relevant work levels among different medical institutions of the same type. This solves the problem in existing technologies where, when a medical institution voluntarily reports adverse drug reactions and there are instances of underreporting within its current scale of care, it is impossible to evaluate the institution's early warning capability regarding drug use risks.

[0017] This invention constructs a comprehensive quality evaluation model for the region where the medical institution to be regulated is located. Given the current number of discharges (or hospitalizations), total number of adverse drug reaction (event) reports, number of serious incident reports, and number of new serious incident reports for the current year, the monitoring score of the medical institution to be regulated can be calculated. This score indicates the quality level of the institution's current monitoring work. This score also allows for comparison of the relevant work levels of various medical institutions within the target region. For medical institutions with poor scores, further supervision and follow-up can be conducted, and the effectiveness of the supervision can be evaluated based on changes in the evaluation score. Attached Figure Description

[0018] Figure 1 is a flowchart of the method for constructing a quality evaluation model for adverse drug reaction monitoring provided in an embodiment of the present invention; Figure 2 is a schematic diagram of the national adverse drug reaction reporting situation in the past five years provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0021] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0022] Furthermore, for clarity and brevity, descriptions of well-known structures, functions, and configurations may have been omitted. Those skilled in the art will recognize that various changes and modifications can be made to the examples described herein without departing from the spirit and scope of this disclosure.

[0023] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0024] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0025] Example 1, as shown in Figure 1, provides a method for evaluating the quality of adverse drug reaction monitoring, specifically including the following steps: S1, acquiring reported data from the medical institutions to be monitored, and calculating secondary indicators for evaluating the quality of the reported data based on the target proportion coefficient of the target region where the medical institutions to be monitored are located; the reported data includes the total number of reports a, the number of reports of serious types b, the number of newly added and serious type reports c, and the number of hospitalizations D. The secondary indicators include the completion rate of the total number of reports A, the completion rate of reports of serious types B, the completion rate of newly added serious type reports C, and the target number of reports X. The target proportion coefficient includes the target variable level λ_A corresponding to secondary indicator A, the target variable level λ_B corresponding to secondary indicator B, and the target variable level λ_C corresponding to secondary indicator C. An orthogonal experiment is used to determine the target proportion coefficient of the target region.

[0026] The specific calculation process for each secondary indicator is as follows: S11, Calculate the target number of reports X = D × λ_A based on the number of hospitalizations D and the target variable level λ_A; S12, Calculate the total report completion rate A = a / X based on the total number of reports a and the target number of reports; S13, Calculate B = b / (target number of reports × λ_B) based on the number of reports of severe types b and the target variable level λ_B; S14, Calculate C = c / (target number of reports × λ_C) based on the number of newly added and severe type reports and the target variable level λ_C.

[0027] Because medical resources and levels vary from region to region, and even adverse drug reactions differ due to environmental variations, it is necessary to select appropriate variable levels for each region's specific circumstances and determine the target proportion coefficient. The process for determining the target proportion coefficient for the target region where the regulated medical institution is located is as follows: S01. Initialize each target proportion coefficient. Based on the overall situation of the target region where the regulated medical institution is located, set multiple target variable values ​​for each target variable level. Orthogonally combine the target variable values ​​of each target variable level to obtain multiple target variable level combinations; S02. Collect data reported by all medical institutions in the target region that meet the target number of hospitalizations; S03. Calculate the data reported by the medical institutions with each target variable level combination and input it into the comprehensive quality evaluation model corresponding to the target region to obtain the quality score corresponding to each target variable level combination; S04. Determine the optimal level combination based on the quality score of each target variable level combination, and use the optimal level combination as the target proportion coefficient for the target region where the regulated medical institution is located.

[0028] S2. Input each secondary indicator into the comprehensive quality evaluation model corresponding to the target region to obtain the quality level; specifically, calculate the quality level Q=α*A+β*B+γ*C, where α, β, and γ are the final weights corresponding to the secondary indicators A, B, and C, respectively.

[0029] The comprehensive quality evaluation model uses a weighted normalized average method combining subjective and objective weighting to determine the final weight for each secondary indicator. Specifically, the subjective weighting method uses the AHP hierarchical analysis method, while the objective weighting method uses the entropy weight method.

[0030] When inputting the secondary indicators corresponding to the medical institutions under supervision into the comprehensive quality evaluation model, it is also necessary to determine whether to adjust the weights of each secondary indicator in the comprehensive quality evaluation model based on the type of medical institution under supervision. The weight ratios of different types of medical institutions can be adjusted accordingly. For example, when evaluating specialized hospitals such as maternal and child health care and mental health care, as well as township hospitals below the secondary level, the objective situation of single medication use should be considered, and data from similar medical institutions should be selected for weight analysis when assigning objective weights. The indicator weights in the comprehensive quality evaluation model of each region are not static. With the advancement of medical technology, rational drug use, costs, pharmacovigilance inspections (evaluations), hospital accreditation, and even changes in patient needs, the weights α, β, and γ corresponding to the secondary indicators A, B, and C in different regions have dynamic development characteristics (adjustable and optimized). For example, when evaluating specialized hospitals such as maternal and child health care and mental health care, as well as township hospitals below the secondary level, the objective situation of single medication use should be considered, and data from similar medical institutions should be selected for weight analysis when assigning objective weights; when using a region as the evaluation object, the target reported number indicator (D) should be constructed based on the number of hospitalizations in that region; in medically developed regions, based on resource input, a higher variable level should be considered.

[0031] S3. Compare the quality rating of the medical institution to be regulated with all medical institutions in its region to determine the work quality level of the institution. Based on the work quality level, supervise the improvement of the institution's adverse drug reaction monitoring work. Sort all medical institutions in the region of the institution to be regulated according to their work quality rating and total number of reports, obtaining a rating ranking table and a report ranking table respectively. Compare the rating ranking table and the report ranking table to determine the work quality level of the institution to be regulated in its region. For example, if the institution ranks high in the report ranking table but low in the rating ranking table, it indicates a low work quality level.

[0032] By comparing the work quality level of a medical institution under supervision with all medical institutions in its region, it is possible to assess the institution's overall work level within the region, changes in work level year-on-year (month-on-month), and comparisons of relevant work levels among different medical institutions of the same type. For example, suppose an inspection is conducted on the post-marketing risk management of drugs in 10 large and medium-sized general hospitals within a certain region. In the adverse drug reaction (event) reporting section, all relevant units voluntarily submitted adverse drug reaction (event) reports to the National Adverse Drug Reaction Monitoring System. The number of reports varied, as did the severity and number of new types of reports. While the reports could be traced after random checks by inspectors, it was impossible to assess whether the hospitals had missed any reports given their current scale of care during the voluntary reporting process, and thus, the hospitals' early warning capabilities regarding drug use risks. Using an evaluation model, given the hospital's current number of discharges, total number of adverse drug reaction (event) reports, number of severe reports, and number of new general reports, a score for the hospital's current monitoring work can be calculated. This score indicates the hospital's current monitoring work level as "good, excellent, average, or poor." This score can then be used to compare the performance of the 10 institutions. For units with poor scores, further work supervision and follow-up can be carried out, and the effectiveness of the supervision can be evaluated based on the changes in the evaluation scores.

[0033] To better illustrate the implementation plan of this application, the following uses a specific region as an example to explain in detail the construction of a comprehensive quality evaluation model for that region and the determination of the target proportion coefficient: Considering the application environment and operability of the evaluation model, this application proposes to construct a general, simple, and practical evaluation model. The selection of evaluation model indicators considers the degree of public access to the indicator variables, the ease of obtaining them, and the credibility of the evaluation results. The comprehensive quality evaluation model selects the following indicators: Primary indicators (objective indicators reported by medical institutions): Total number of reports (a), Number of reports of severe types (b), Number of new and severe type reports (c). Based on the primary indicators and the target proportion coefficient of the primary region, secondary indicators are constructed: Total report completion rate (A), Severe type report completion rate (B), and New and Severe type report completion rate (C). A parent indicator is introduced: Target number of reports, i.e., number of hospitalizations during the period (D) * appropriate proportion.

[0034] In the comprehensive quality evaluation model, a combination of subjective (AHP) and objective (entropy weighting) weighting methods is used to determine the weights α, β, and γ corresponding to indicators A, B, and C. The comprehensive quality evaluation model calculates the quality level of drug monitoring work in medical institutions as Q = α*A + β*B + γ*C; 1. Determine the weights α, β, and γ based on the work objectives of the target region. The specific process is as follows: 1) Combining the annual reports of the National Adverse Drug Reaction Monitoring and referring to the opinions of industry experts, the subjective weighting method is selected using the Analytic Hierarchy Process (AHP) to construct a judgment matrix. Among the three indicators, the completion rate of total reports (A), the completion rate of reports of serious types (B), and the completion rate of reports of new and serious types (C), the scale of A is 1, and the scales of B and C are 5, as shown in Table 1. The judgment matrix is ​​as follows: Table 1 Judgment Matrix According to the weight calculation results of the Analytic Hierarchy Process (AHP), the weight of indicator A is 9.091%, the weight of indicator B is 45.455%, and the weight of indicator C is 45.455%. (Each element in each layer is compared pairwise, and a scale of 1-9 is used to represent relative importance to construct a judgment matrix. According to psychological research, human perception of differences is usually divided into 9 levels. The specific scales are as follows: 1: Factors i and j are equally important; 3: i is slightly more important than j; 5: i is significantly more important than j; 7: i is strongly more important than j; 9: i is extremely more important than j; 2 / 4 / 6 / 8 are intermediate values) 2) Objective weighting selection of entropy weight method. For example, the reference target number in this region is 1% of the number of hospitalizations during the period, and the total report should include at least 14% of serious cases and at least 40% of new and serious cases. Considering the differences in drug categories used by medical institutions of different types and sizes (different autonomy in drug purchase, such as smaller township hospitals mainly using essential drugs with relatively lower safety risks), and in conjunction with the purpose of model evaluation, data from 17 secondary and above medical institutions in the region with more than 10,000 inpatients per year were included. The weight calculation results show that the weight of indicator A is 35.355%, the weight of indicator B is 34.389%, and the weight of indicator C is 30.255%.

[0035] Table 2 Entropy Weight Method Based on actual work experience, the total number of reports is relatively easier to achieve, while high-value, serious, and new reports require more resources. 3) Combined subjective and objective weighting. Combining the results of subjective and objective weighting, an additive combined weighting normalized average is adopted, i.e., (AHP weight + entropy method weight) / 2. The final weights obtained are 22.223% for indicator A, 39.922% for indicator B, and 37.855% for indicator C.

[0036] Table 3 Final Weights The final comprehensive quality evaluation model for secondary and above medical institutions in this region is Q=α*A+β*B+γ*C =22.223%*A+39.922%*B+37.855%*C; 2. Determine the target proportion coefficients λ_A, λ_B, and λ_C based on the actual situation of the target region. 1) The calculation of the three indicators A, B, and C requires the use of orthogonal experiments to screen the relevant indicator variable levels (target proportion coefficients), clarify the statistical significance behind the indicator variable levels, and provide a reference for subsequent adjustments. The level design mainly refers to publicly available data such as the "National Adverse Drug Reaction Monitoring Annual Report" and the "2023 Statistical Bulletin on the Development of my country's Health and Wellness Undertakings". In 2023, the number of inpatient admissions to medical and health institutions nationwide was 301.873 million. As shown in Figure 2, according to the adverse drug reaction reports in my country over the past five years, there were approximately 2.419 million adverse drug reaction reports nationwide, accounting for approximately 0.8% of inpatient admissions. Some publicly available literature reports that the incidence of adverse drug reactions among hospitalized patients is much higher than this level. In 2024, the national serious reporting rate was approximately 17.5%, and the new and serious reporting rate was approximately 35% (the serious rate showed an increasing trend, while the new and general reporting rate showed a decreasing trend).

[0037] 2) Taking an orthogonal design with three levels of three indicator variables as an example, considering local conditions: Variable 1: Indicator A = Actual number of reports / Target number of reports. Target number of reports = Number of hospitalizations during the period (D) * Target variable level. The target variable values ​​for the three target variable levels λ_A are designed as follows: 1 = 0.8%, 2 = 0.9%, 3 = 1%; Variable 2: Indicator B = Actual number of serious reports / (Target number of reports * Target variable level), that is, the ratio of the actual number of serious reports to the target total number of reports. The target variable values ​​for the three target variable levels λ_B are designed as follows: 1 = 14%, 2 = 16%, 3 = 18%; Variable 3: Indicator C = Actual number of new and serious reports / (Target number of reports * Target variable level), that is, the ratio of the actual number of new and serious reports to the target total number of reports. The target variable values ​​for the three target variable levels λ_C are designed as follows: 1 = 36%, 2 = 38%, 3 = 40%.

[0038] Table 4 Orthogonal Experiment Table Based on the target variable values ​​corresponding to the variable level combinations in Table 4, A, B, and C can be calculated and then input into the comprehensive quality evaluation model (Q = 22.223%*A + 39.922%*B + 37.855%*C) to calculate the quality score of medical institution drug monitoring work under each variable level combination. Combined with statistical analysis and statistical significance, in indicator A, the Q values ​​corresponding to levels 1 and 3 of the target reported number (hospitalization visits (D) * target variable level) show significant differences (considering the characteristics of the work, this suggests that lower targets have a high probability of misjudging differences in the quality of medical institution work monitoring). Taking into account the national reporting level and regulatory requirements, when the level of the confirmed variables is 3 (all indicators are selected at a higher level), the target proportion coefficients selected for evaluating the quality of medical institution monitoring work under the current level in the region are: (1) λ_A: the target report is not less than 1% of the number of hospitalizations; (2) λ_B: the proportion of serious reports is not less than 18% of the target reports (0.18% of the number of hospitalizations); (3) λ_C: the proportion of new and serious reports is not less than 40% of the target reports (0.4% of the number of hospitalizations).

[0039] Once the target proportion coefficient and secondary indicator weights for a certain region are determined, the comprehensive quality evaluation model can be modified to convert the secondary indicators into primary indicators. The modified comprehensive quality evaluation model, after incorporating the determined weights and target proportion coefficients, is: Q = (a*22.223 + b*221.789 + c*94.638) / D. For each monitored medical institution with an annual inpatient volume ≥10,000, based on the total number of reports (a), the number of severe type reports (b), the number of newly added and severe type reports (c), and the number of inpatients (D), the modified comprehensive quality evaluation model can be used to calculate the corresponding quality score. The quality level is then determined based on the quality score. For example, Q ≥ 0.9 indicates excellent work quality; [0.8, 0.9) indicates good work quality; [0.7, 0.8) indicates medium work quality; [0.6, 0.7) indicates average work quality; Q < 0.6 indicates poor work quality.

[0040] The above model can be used to evaluate and supervise the work quality of all medical institutions in the target region: 1) Compare the adverse drug reaction (event) reporting situation of medical institutions in a certain city in 2024 to compare the quality of their monitoring work. Given the adverse drug reaction (event) reporting situation of 10,000 hospital discharges in a certain city in 2024, an evaluation model is constructed based on the adverse drug reaction (event) reporting situation of medical institutions in that city in 2024, outputting the quality score of the medical institution's drug monitoring work: Q = (a*22.223 + b*221.789 + c*94.638) / D. The evaluation scores of each medical institution are shown in Table 5.

[0041] Table 5 Quality Comparison Based on Table 5, the top five units in terms of the total number of reports are Institution 1, Institution 2, Institution 3, Institution 8, and Institution 4. However, according to the evaluation model, the top five units in terms of Q-value scores are: Institution 2, Institution 8, Institution 9, Institution 4, and Institution 1. From these rankings, it is clear that, based on the evaluation model, Institution 2's work quality is significantly better than Institution 1's; and compared to Institution 3, Institution 9's work performance is more commendable given its limited resource investment.

[0042] 2) Work Supervision: A comprehensive medical institution underwent a drug inspection in early June 2025. It was known that the institution had 18,471 discharges from January to May and 109 adverse drug reaction (event) reports (34 serious reports and 58 new general and serious reports). Based on the evaluation model (adjusting the model's D value to estimate 6-month discharges), the 6-month work evaluation model score was 0.697, indicating average work quality. A follow-up inspection at the end of September 2025 revealed that the institution had 28,300 discharges from January to August and 296 adverse drug reaction (event) reports (93 serious reports and 147 new general and serious reports). Based on the evaluation model (adjusting the model's D value to estimate 9-month discharges), the 9-month work evaluation model score was 1.291, indicating excellent work quality and significant improvement.

[0043] Table 6 Comparison of Work Improvements It is understood that this application constructs a simple quality evaluation model for adverse drug reaction monitoring, clarifies the main indicator system of the evaluation model, including but not limited to indicators, corresponding weights of indicators and related parameter adjustment methods, calculation formulas, and result evaluation methods. With the help of the evaluation model, the quality level (quantitative) assessment of adverse drug reaction monitoring work in medical institutions can be quickly realized, which helps drug managers to understand the level of drug use risk management in medical institutions and promotes the collection and early warning of subsequent drug use risk information.

[0044] Example 2 This example provides a quality evaluation model system for adverse drug reaction monitoring, used to implement the quality evaluation method for adverse drug reaction monitoring in Example 1. It includes: a data entry module for acquiring reported data from medical institutions under supervision; an indicator calculation module for calculating secondary indicators for evaluating the quality of reported data based on the reported data and the target proportion coefficient of the target region where the medical institution under supervision is located; a quality evaluation module for inputting each secondary indicator into the comprehensive quality evaluation model corresponding to the target region to obtain a quality level; and a quality supervision module for determining the work quality level of the medical institution under supervision based on the quality level, comparing the work quality level of the medical institution under supervision with all medical institutions in its region, and supervising the improvement of the adverse drug reaction monitoring work by the medical institution under supervision.

[0045] Example 3 provides a computer-readable storage medium, including: one or more processors; and a storage unit for storing one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the drug adverse reaction monitoring quality evaluation method described in Example 1.

[0046] It should be noted that the processor mentioned in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0047] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0048] Computer-readable storage media can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0049] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0050] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0051] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0052] It should be understood that the system disclosed in the embodiments of the present invention can be implemented in other ways. For example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the communication connection between units may be through some interface, server, or indirect coupling or communication connection, and may be electrical or other forms.

[0053] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one processing unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0054] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0055] Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the quality of adverse drug reaction monitoring, characterized in that, Specifically, the following steps are included: S1. Obtain the reported data sent by the medical institutions to be regulated, and calculate the secondary indicators for evaluating the quality of the reported data based on the target proportion coefficient of the target region where the medical institutions to be regulated are located; S2. Input each secondary indicator into the comprehensive quality evaluation model corresponding to the target region to obtain the quality level; S3. Compare the quality level of the medical institutions to be regulated with all medical institutions in its region to determine the work quality level of the medical institutions to be regulated, and supervise the improvement of the adverse drug reaction monitoring work of the medical institutions to be regulated based on the work quality level.

2. The method for evaluating the quality of adverse drug reaction monitoring according to claim 1, characterized in that, The reported data includes total reports (a), number of reports of serious types (b), number of new and serious type reports (c), and number of hospitalizations (D).

3. The method for evaluating the quality of adverse drug reaction monitoring according to claim 2, characterized in that, The secondary indicators include the completion rate of total reports (A), the completion rate of severe type reports (B), the completion rate of new and severe type reports (C), and the target number of reports (X).

4. The method for evaluating the quality of adverse drug reaction monitoring according to claim 3, characterized in that, The target proportion coefficients include the target variable level λ_A corresponding to secondary indicator A, the target variable level λ_B corresponding to secondary indicator B, and the target variable level λ_C corresponding to secondary indicator C. Orthogonal experiments are used to determine the target proportion coefficients for the target region.

5. The method for evaluating the quality of adverse drug reaction monitoring according to claim 4, characterized in that, The target number of reports X = D × λ_A is calculated based on the number of hospitalizations D and the target variable level λ_A; the total number of reports A = a / X is calculated based on the total number of reports a and the target number of reports; B = b / (target number of reports × λ_B) is calculated based on the number of reports of severity types b and the target variable level λ_B; and C = c / (target number of reports × λ_C) is calculated based on the number of new and severity type reports and the target variable level λ_C.

6. The method for evaluating the quality of adverse drug reaction monitoring according to claim 4, characterized in that, The process of determining the target proportion coefficients for the target regions where the medical institutions to be regulated are located is as follows: Initialize each target proportion coefficient, set multiple target variable values ​​for each target variable level according to the overall situation of the target regions where the medical institutions to be regulated are located, and orthogonally combine the target variable values ​​of each target variable level to obtain multiple target variable level combinations. Collect data from all medical institutions in the target region that meet the target number of inpatient admissions. Combine the data reported by the medical institutions with the levels of each target variable and input the results into the comprehensive quality evaluation model corresponding to the target region to obtain the quality score for each combination of target variable levels. Determine the optimal level combination based on the quality score of each combination of target variable levels and use the optimal level combination as the target proportion coefficient for the target region where the medical institution to be supervised is located.

7. The method for evaluating the quality of adverse drug reaction monitoring according to claim 3, characterized in that, The comprehensive quality evaluation model uses a weighted normalized average method combining subjective and objective weighting to determine the final weight for each secondary indicator, and calculates the quality level Q = α*A + β*B + γ*C, where α, β, and γ are the final weights corresponding to secondary indicators A, B, and C, respectively.

8. The method for evaluating the quality of adverse drug reaction monitoring according to claim 7, characterized in that, The subjective weighting method uses the AHP hierarchical analysis method, while the objective weighting method uses the entropy weighting method.

9. The method for evaluating the quality of adverse drug reaction monitoring according to claim 1, characterized in that, When inputting the secondary indicators corresponding to the medical institutions to be regulated into the comprehensive quality evaluation model, it also includes determining whether to adjust the weights of each secondary indicator in the comprehensive quality evaluation model based on the type of medical institution to be regulated.

10. A quality evaluation system for adverse drug reaction monitoring, used to implement the quality evaluation method for adverse drug reaction monitoring as described in claim 1, characterized in that, include: The data entry module is used to acquire the reported data sent by the medical institutions to be supervised; The indicator calculation module is used to calculate secondary indicators for evaluating the quality of the reported data based on the target proportion coefficient of the target region where the medical institution under supervision is located. The quality evaluation module is used to input each secondary indicator into the comprehensive quality evaluation model corresponding to the target region to obtain the quality level; The quality supervision module is used to determine the work quality level of the medical institutions to be supervised based on the quality grade, compare the work quality level of the medical institutions to be supervised with all medical institutions in the same region, and supervise the improvement of the medical institutions to be supervised in their adverse drug reaction monitoring work.

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