Method and system for calculating and evaluating medical level of medical group applied to hospital

Abstract

The invention provides a medical group medical level calculation and evaluation method and system applied to a hospital, and relates to the field of data processing. Comprising the following steps: acquiring cure rate data Vi of each medical group within examination time T, calculating a mean value and a standard deviation, determining an intermediate value set A, inputting the intermediate value set A into a data model to obtain a total score Si, calculating a centesimal score Fi in combination with a cure rate weight sum W, and acquiring operation actions of workers by using a medical indoor camera, and inputting a violation operation identification model to determine violation operation, training and constructing the model based on standard and violation operation video data, and finally determining an evaluation result according to the violation operation, evaluation data and a violation score deduction rule so as to comprehensively evaluate the medical level of the medical group.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and system for calculating and evaluating the medical level of a medical team in a hospital. Background Art

[0002] In the process of the continuous development of the modern medical system, the performance evaluation of the medical group, as the basic unit of the hospital to provide medical services, plays a vital role in the efficient management of the hospital and the guarantee of high-quality medical services. Effective performance evaluation can promote the rational allocation of medical resources, motivate medical personnel to improve their professional skills and service quality, and thus enhance the overall competitiveness of the hospital.

[0003] The traditional performance evaluation method is that the hospital staff will extract data such as the number of surgeries, nursing time, and diagnostic accuracy from the daily work records of each medical group. Then, the evaluation team organized by the hospital will determine the performance of each medical group through collective discussion and subjective judgment based on these data and the established performance evaluation standards within the hospital.

[0004] However, the traditional method has the problem of cumbersome manual statistical data process, prone to data errors and omissions, low efficiency, and most of them only focus on surface data such as business volume, making it difficult to deeply consider the operational norms of medical staff in actual work. Therefore, it is difficult for traditional methods to efficiently determine the evaluation situation while considering the actual performance of medical staff. Summary of the invention

[0005] The present application provides a method and system for calculating and evaluating the medical level of a medical team in a hospital, which is used to efficiently determine the evaluation performance while reflecting the actual working conditions of the medical team.

[0006] In a first aspect, the present application provides a method for calculating and evaluating the medical level of a medical group applied to a hospital, the method comprising: obtaining data Vi of the cure rate corresponding to the i-th medical group from n medical groups within an assessment time T, determining a mean AVERAGE and a standard deviation STDEVP, wherein i ranges from 1 to n, and the cure rate is the ratio of the number of discharged patients to the number of received patients of the target medical group within the assessment time T; determining an intermediate value set A of the n medical groups according to the data Vi; inputting the intermediate value set A into a trained data model to obtain a total score Si of the i-th medical group; after determining the weighted sum W of the cure rate corresponding to each medical group, using F i=Si / W to obtain the assessment data, where Fi is the percentage score of the i-th medical group within the assessment time T; when the corresponding cure rate of the medical group is lower than the preset threshold, the operating actions of the staff in the target medical group within the assessment time T are obtained through the camera in each medical room, and the operating actions are input into the preset illegal operation recognition model to determine the illegal operations of the target medical group. The illegal operation recognition model is constructed by extracting and marking features in advance based on multiple known standard operation video data and corresponding illegal operation video data, and is trained and constructed using a deep learning algorithm; the assessment result is determined based on the illegal operation and the assessment data in combination with the preset violation deduction rules.

[0007] By adopting the above-mentioned technical solution, which integrates data statistical analysis and artificial intelligence technology, we can achieve an objective and accurate evaluation of the cure rate performance of the medical group, eliminate the dimensional influence of the data of medical groups of different sizes, and compare and measure the cure rate levels of the medical groups. At the same time, we can comprehensively consider the identification of illegal operations, so that the medical level assessment can more comprehensively reflect the actual work situation of the medical group, and thus more efficiently obtain the evaluation results of medical personnel.

[0008] In combination with some embodiments of the first aspect, in some embodiments, after inputting the intermediate value set A into the trained data model, the step of obtaining the total score Si of the i-th medical group includes: determining the maximum value Amax and the minimum value Amin in the intermediate value set A, and the intermediate value set A includes the intermediate value Ai of the i-th medical group; according to the formula Bi=(Ai-Amin) / (Amax-Amin), obtaining the intermediate value Bi of the i-th medical group; using the formula Ski=Bi*Wk, obtaining the i-th medical group score Ski corresponding to the cure rate, and Wk is the weight of the cure rate in the medical group corresponding to the i-th medical group; and determining the data model according to the score of Ski.

[0009] By adopting the above technical solution, we can reasonably allocate scores according to the importance of cure rate in the medical group, highlighting the impact of key indicators on the score. The data model is determined based on the Ski score, so that the model construction fully considers the characteristics and weight distribution of cure rate data, and improves the accuracy and pertinence of the model in evaluating the medical level of the medical group.

[0010] In combination with some embodiments of the first aspect, in some embodiments, before the step of determining the maximum value Amax and the minimum value Amin in the intermediate value set A, where the intermediate value set A includes the intermediate value Ai of the i-th medical group, it also includes: determining the intermediate value Ai of the i-th medical group based on the cure rate, where Ai=(Vi-AVERAGE) / STDEVP.

[0011] By adopting the above technical solution and using the mean AVERAGE and standard deviation STDEVP as the benchmark, the dimensional influence between the cure rate data of medical groups of different sizes can be eliminated, and the cure rate indicators of different medical groups can be converted to the same coordinate axis for expression, making the data in these intermediate value sets comparable and measurable.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the data model based on the Ski score specifically includes: calculating the score Sgi of other indicators G of the i-th medical group, where the other indicator G is an indicator other than the cure rate in the corresponding i-th medical group; and obtaining the data model according to Si=Ski+Sgi.

[0013] By adopting the above technical solutions and combining the scores of different types of indicators, we can comprehensively and systematically evaluate the overall performance of the medical team, avoiding ignoring other key factors by focusing only on the cure rate. This multi-indicator comprehensive data model can more completely reflect the work effectiveness and service quality of the medical team, making the performance evaluation results more in line with the actual situation.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the evaluation result based on the illegal operation and the evaluation data combined with the preset violation penalty rules, it also includes: ranking the medical group according to the evaluation result; and determining the performance bonus based on the ranking.

[0015] By adopting the above technical solutions, the performance differences between medical groups can be intuitively displayed. The ranking incentive mechanism can promote a competitive atmosphere among medical groups, prompting them to improve the quality and efficiency of medical services.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the performance bonus based on the ranking, it also includes: obtaining the average percentage score of the medical group in one year based on the percentage scores of the medical group in each month; and determining the year-end bonus based on the average percentage score.

[0017] By adopting the above technical solutions, the average score can reflect the overall performance of the medical team throughout the year, avoiding evaluation bias due to special circumstances in individual months. The year-end bonus is linked to the average score, which encourages the medical team to maintain stable work quality, focus on long-term development and continuous improvement, and is conducive to the stable improvement and long-term planning of the hospital's overall medical services.

[0018] In combination with some embodiments of the first aspect, in some embodiments, before the step of calculating the score Sgi of other indicators G of the i-th medical group, it also includes: obtaining the weight Wg of other indicators G; obtaining the total weight W according to W=Wk+Wg.

[0019] By adopting the above technical solutions, the relative importance of each indicator in performance evaluation can be clarified, and the reasonable allocation of weights can ensure that the contribution of different indicators to the total score is consistent with their actual value, making the performance evaluation system more scientific and rigorous.

[0020] In a second aspect, the present application provides an evaluation system, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the evaluation system to perform the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on an evaluation system, causes the evaluation system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Since an evaluation scheme combining statistical analysis and artificial intelligence technology is adopted, namely, a set of comparable intermediate values ​​is determined by calculating the mean and standard deviation of the cure rate of the medical group and other data processing methods, and a preset illegal operation identification model is used, the problems of the existing technology of medical level evaluation of the medical group, such as difficulty in quantitative comparison of medical groups of different sizes in terms of cure rate indicators and inability to consider the actual operation norms, are effectively solved. Thus, the technical effect of objectively evaluating the cure rate level of the medical group and comprehensively considering the actual operation of medical staff is achieved.

[0023] 2. Since a technical method based on the mean and standard deviation is adopted to calculate the median value of the cure rate of the medical group, and the median value Ai of the i-th medical group is determined based on the mean AVERAGE and the standard deviation STDEVP, the technical problem that the cure rate data of medical groups of different sizes are difficult to compare and measure under the same standard in the existing technology is effectively solved, thereby eliminating the influence of the data dimension, providing an accurate basis for reasonably evaluating the level of the cure rate of the medical group in the overall level, and improving the scientificity and fairness of performance evaluation.

[0024] 3. Since the technical means of determining performance bonuses based on rankings based on quantitative evaluation results is adopted, and the medical teams are ranked and linked to bonuses through the results obtained through a rigorous evaluation process, the technical problems of insufficient incentive mechanisms for medical teams and difficulty in intuitively distinguishing performance differences in existing technologies are effectively solved, thereby achieving the technical effect of stimulating the medical team's sense of competition, improving the enthusiasm and initiative of medical staff, and improving the hospital's medical service level and management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a method for calculating and evaluating the medical level of a medical team in a hospital applied in an embodiment of the present application; Figure 2 It is another flowchart of the method for calculating and evaluating the medical level of a medical team in a hospital applied in an embodiment of the present application; Figure 3 It is a schematic diagram of a physical device structure of the evaluation system in the embodiment of the present application. DETAILED DESCRIPTION

[0026] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0027] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0028] For ease of understanding, the following describes the process of the method provided by this implementation. Figure 1, which is a flow chart of a method for calculating and evaluating the medical level of a medical team in a hospital applied in an embodiment of the present application.

[0029] S101. After obtaining the data Vi of the cure rate of the i-th medical group from n medical groups within the assessment time T, determine the mean AVERAGE and the standard deviation STDEVP, where i ranges from 1 to n. The cure rate is the ratio of the number of discharged patients to the number of received patients of the target medical group within the assessment time T; The evaluation system first connects to the hospital's medical record system database, and queries the medical record system to obtain the patient admission and discharge records corresponding to all wards or departments within the assessment time T (for example, the past month). The system counts the total number of discharged patients and the total number of patients received by each medical group during the assessment time, based on the medical group (ward or department). Then the system calculates the cure rate Vi = total number of discharged patients / total number of patients received for each medical group. Next, the system summarizes the cure rate data Vi of each medical group, and uses statistical methods to calculate the mean AVERAGE and standard deviation STDEVP of the cure rate data set of all n medical groups. The mean AVERAGE represents the average level of cure rates of n medical groups during the assessment time, and the standard deviation STDEVP represents the degree of dispersion of the cure rate distribution. After obtaining the mean and standard deviation, the system records and saves the data to provide a reference for the subsequent determination of the medical level of the medical group.

[0030] S102, determining the intermediate value set A of the n medical groups according to the data Vi; The evaluation system reads the cure rate data Vi of all n medical groups obtained within the assessment time T. For each set of data Vi, the system uses the formula Ai=(Vi-AVERAGE) / STDEVP to calculate the median value Ai of the medical group. Among them, Ai represents the median value of the cure rate with the mean and standard deviation as the coordinate origin.

[0031] After the above calculations, the evaluation system obtains the intermediate values ​​Ai corresponding to all n medical groups, and combines these Ai into an intermediate value set A={A1, A2, ...Ai, ...An}. This set eliminates the dimensional impact caused by the size and business volume of different medical groups, standardizes the coordinates, and provides a scientific and reasonable input data basis for the subsequent construction of the scoring model.

[0032] S103, after inputting the intermediate value set A into the trained data model, the total score Si of the i-th medical group is obtained; The evaluation system first analyzes and determines the maximum value Amax and the minimum value Amin in the intermediate value set A. Among them, Amax is the highest value among the intermediate values ​​of the cure rate of all medical groups in set A, and Amin is the lowest value. Then, for each intermediate value Ai in set A, the evaluation system uses the formula Bi= (Ai-Amin) / (Amax-Amin) to calculate the normalized intermediate value Bi. Amax and Amin in this formula are fixed extreme values ​​in the same coordinate system. Therefore, by calculating the relative position ratio of each Ai to the extreme value, the dimensional effect caused by the size and business volume of different medical groups can be eliminated, and all Ai are mapped to a standardized space between 0 and 1 to express, so that the intermediate values ​​Bi of different medical groups are comparable. Next, the evaluation system reads the preset cure rate weight Wk, which is determined by the hospital management based on the importance of the cure rate in the medical quality assessment. Then, for the i-th medical group, the system uses the formula Ski=Bi*Wk to calculate its score Ski on the cure rate indicator. During the calculation process, Bi reflects the standardized relative level of the cure rate of the medical group, and Wk reflects the important weight of the cure rate. The product of these two factors gives the actual score of the cure rate indicator.

[0033] Finally, the evaluation system summarizes the Ski of all medical groups and combines it with the scores of other medical quality indicators of each medical group to construct a data model reflecting multiple indicators to generate the total score Si of the medical group. The model can be a linear weighted model or a machine learning model, which is trained by system planners using historical data. Its structure and algorithm comprehensively consider the scores and weight control of various indicators, and scientifically and rationally determine the overall score level of each medical group. Through the above technical solution, the evaluation system eliminates the impact of dimensions, assigns weights according to the importance of cure rate, and can objectively and accurately evaluate the comprehensive cure rate performance of each medical group.

[0034] In some embodiments, the determination of the data model may also take into account the influence of other indicators G, the specific content of which will be described in detail later and will not be repeated here.

[0035] S104, after determining the weighted sum W of the cure rate corresponding to each medical group, obtain the assessment data through Fi=Si / W, where Fi is the percentage score of the i-th medical group in the assessment time T; The evaluation system first determines the relative weight Wk of the cure rate in the comprehensive score of the medical group according to the business characteristics of the hospital and the focus of medical evaluation. At the same time, the weights Wg of all other non-cure rate medical indicators (such as patient satisfaction, average length of stay, etc.) are obtained. Then the sum of the weights of each indicator W=Wk+Wg is calculated to obtain the total weight W of the comprehensive score of the medical group. On this basis, for the i-th medical group, the evaluation system reads the cure rate score Ski and other indicator scores Sgi of the medical group. Substitute them into the formula Si=Ski+Sgi to calculate the total score Si of the medical group. Then, based on the preset weight sum W, the system calculates the percentage score Fi of the medical group through the formula Fi=Si / W. Among them, the denominator W is a fixed value, the numerator Si integrates and reflects the scores of each indicator, and its ratio Fi intuitively represents the comprehensive evaluation score of the medical group. For example, Fi can be set to represent poor medical level if it is less than 60 points, average medical level if it is 60-80 points, and excellent medical level if it is more than 80 points. This percentage score makes it easy for the system to generate evaluation results intuitively.

[0036] S105. When the cure rate of the medical group is lower than a preset threshold, the operation actions of the staff in the target medical group within the assessment time T are obtained through the camera in each medical room, and the operation actions are input into a preset illegal operation recognition model to determine the illegal operation of the target medical group. The illegal operation recognition model is constructed by extracting and marking features based on a plurality of known standard operation video data and corresponding illegal operation video data in advance, and is trained and constructed using a deep learning algorithm; When the cure rate of the medical group is lower than the preset threshold, it is determined that the medical group may have some operational problems, resulting in a low cure rate. The evaluation system connects the camera equipment in each ward of the hospital to collect the video data of the medical staff's operation during the assessment period. Then, the system uses the behavior recognition algorithm in computer vision technology to process the video images and identify the specific action flow of the medical staff's operations such as surgery, patient diagnosis, and drug configuration. Then, the system inputs the identified operation action data into the pre-trained illegal operation recognition model. This model is a deep learning model based on convolutional neural networks, which can analyze the time sequence characteristics and content characteristics of the operation action to determine whether there is an illegal operation. The model training data set contains a large number of labeled standard operation sequences and illegal operation sequences, so that the model can learn the difference between the two. After multiple rounds of iterative training, the model can accurately identify whether the newly input operation action flow contains illegal behavior.

[0037] Finally, the evaluation system counts the number of illegal operations performed by the medical team during the assessment period, and refers to the preset illegal operation deduction rules to calculate the illegal operation deduction value. The deduction rules are formulated by the hospital management to clearly define the deduction values ​​or deduction ratios corresponding to illegal operations of different types and severity. The identification and deduction of illegal operations provide an important basis for the final performance assessment.

[0038] S106: Determine an evaluation result based on the illegal operation and the evaluation data in combination with a preset illegal point deduction rule.

[0039] The evaluation system integrates the percentage medical level score Fi of the i-th medical group and the penalty value for illegal operations, and generates the final evaluation result according to the preset comprehensive calculation rules. The rule can set different calculation weights, for example, 80% of Fi plus 20% of the penalty value is used to calculate the final score. Fi can also be used as the base score, and the penalty points for illegal operations are directly subtracted from Fi. The system configures the above calculation rules in the program code to realize the automatic generation of evaluation results. The evaluation results directly reflect the business capabilities and standardized operations of the medical group, and can more comprehensively and accurately evaluate the actual work performance of the medical group. The results are recorded in the system database and actively pushed to hospital managers through the system interface in the form of a summary table.

[0040] In the embodiment of the present application, an evaluation scheme combining data statistical analysis and artificial intelligence technology is adopted. By calculating the mean and standard deviation of the cure rate of the medical group and other data processing means, a comparable set of intermediate values ​​is determined, and a preset illegal operation identification model is used. Therefore, the problems of the medical level evaluation of the medical group in the prior art, such as the difficulty in quantitatively comparing medical groups of different sizes in terms of cure rate indicators and the inability to consider the actual operation norms, are effectively solved. Thus, the technical effect of being able to objectively evaluate the cure rate level of the medical group and comprehensively consider the actual operation of medical staff is achieved.

[0041] In some embodiments, after obtaining the final evaluation scores of each medical group, the evaluation system calls the ranking module to sort the evaluation results of each medical group. Specifically, the ranking module reads the evaluation scores of all medical groups stored in the evaluation result database, and ranks each medical group from high to low according to the score value. Among them, the medical group with the highest score ranks first, and the medical group with the lowest score ranks last. The ranking module generates a ranking list based on the ranking results, and saves and updates it to the evaluation result database. After that, the bonus allocation submodule of the evaluation system reads the ranking list, and calculates and determines the performance bonus amount of each medical group according to the bonus allocation rules preset by the hospital, that is, the higher the ranking, the more bonus the medical group gets. The bonus allocation submodule generates a bonus allocation list and sends it to the hospital financial system to complete the bonus distribution. At the same time, the evaluation system will also summarize and count the monthly evaluation result reports of each medical group. The report contains the comprehensive evaluation score of each medical group in that month and the corresponding percentage score. The evaluation system calls the statistics module, reads the monthly percentage score of each medical group, and calculates the average monthly percentage score of each medical group within one year, that is, the annual average percentage score of the medical group. Finally, the year-end bonus distribution submodule of the evaluation system will obtain the annual average percentage score of each medical group. According to the hospital's preset year-end bonus distribution rules, that is, the higher the annual average percentage score, the more year-end bonus will be obtained, the year-end bonus amount of each medical group is calculated and determined, and the year-end bonus distribution submodule generates a year-end bonus distribution plan and sends it to the hospital's financial system to complete the year-end bonus distribution work.

[0042] By linking bonus distribution with ranking, we can actively motivate the work performance of the medical team. The year-end bonus takes into account the performance throughout the year, which can more comprehensively reflect the long-term development quality of the medical team and avoid distortion due to abnormalities in individual months.

[0043] After combining the above content, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the method for calculating and evaluating the medical level of a medical team in a hospital applied in an embodiment of the present application.

[0044] S201, determining the median value Ai of the i-th medical group according to the cure rate, where Ai=(Vi-AVERAGE) / STDEVP; After obtaining the cure rate data Vi of each medical group, the evaluation system calls the statistical module for each medical group, inputs the cure rate data Vi of the corresponding medical group and the pre-calculated mean AVERAGE and standard deviation STDEVP of the cure rate data of all medical groups. The statistical module calculates the median cure rate Ai of the medical group based on the input Vi, AVERAGE and STDEVP using the formula Ai=(Vi-AVERAGE) / STDEVP. Among them, Ai represents the median cure rate of the medical group with the mean and standard deviation as the coordinate origin. Using the mean and standard deviation to standardize the coordinates can eliminate the dimensional impact caused by the uneven business scale of different medical groups, express the cure rates of different medical groups in the same coordinate system, and lay the foundation for subsequent scoring processing.

[0045] S202, determining a maximum value Amax and a minimum value Amin in the intermediate value set A, wherein the intermediate value set A includes the intermediate value Ai of the i-th medical group; The evaluation system calls the data analysis module and reads the stored set A of intermediate values ​​of the cure rates of the medical groups into the analysis module. The analysis module traverses each element of the input set A, that is, the intermediate value Ai of each medical group, compares the value of each element according to the size relationship, and determines the maximum value Amax and the minimum value Amin. Among them, Amax is the highest value among the intermediate values ​​of the cure rates of all medical groups in set A, reflecting the highest cure rate level; Amin is the lowest value, reflecting the lowest cure rate level. The analysis module outputs the confirmed maximum and minimum values ​​and stores and updates them in the intermediate value set storage unit. The maximum and minimum extreme values ​​corresponding to the standardized coordinate system are obtained to provide the upper and lower bounds of quantitative judgment for the subsequent normalization of the data of different medical groups on the cure rate indicators.

[0046] S203, according to the formula Bi=(Ai-Amin) / (Amax-Amin), obtain the intermediate value Bi of the i-th medical group; The scoring module of the evaluation system calls the stored intermediate cure rate Ai of the ith medical group, as well as the highest value Amax and the lowest value Amin of the set, and substitutes them into the formula Bi=(Ai-Amin) / (Amax-Amin) for calculation to obtain the normalized intermediate value Bi of the medical group. Among them, Amax and Amin are fixed extreme values ​​in the same coordinate system. The formula compares the relative position ratio of Ai and the extreme value, eliminates the dimensional influence of medical groups of different sizes on quantitative indicators, maps Ai to a standardized space between 0 and 1, and makes the Bi values ​​of different medical groups comparable and measurable. The scoring module summarizes the calculated Bi of each medical group to form a normalized intermediate value set, and stores and updates it to the normalized intermediate value set storage unit, providing standardized parameter input for subsequent calculation of the comprehensive score.

[0047] S204, using the formula Ski=Bi*Wk, to obtain the score Ski of the i-th medical group corresponding to the cure rate, where Wk is the weight of the cure rate in the i-th medical group; The scoring module of the evaluation system calls the cure rate weight parameter Wk pre-set by the hospital, which is set by the hospital management according to the importance of cure rate in medical quality evaluation. The scoring module reads the normalized median value Bi of the i-th medical group, substitutes Bi and Wk into the formula Ski=Bi*Wk for calculation, and obtains the quantitative score Ski of the medical group on the cure rate indicator. Among them, Bi reflects the standardized relative level of the cure rate of the medical group, and Wk reflects the importance weight of the cure rate indicator. The multiplication of the two can eliminate the dimensional impact brought by different medical group sizes and judge the actual score of the medical group on the cure rate indicator.

[0048] S205, calculating the score Sgi of the other index G of the i-th medical group, where the other index G is an index other than the cure rate in the corresponding i-th medical group; The scoring module of the evaluation system is connected to the various medical management information systems of the hospital to obtain the original data of each medical group on other medical quality indicators G such as nursing quality, patient satisfaction, and safety accident rate, which are not the cure rate. For each medical group, the scoring module reads its original data on other indicators G, calls the corresponding scoring analysis algorithm to perform quantitative conversion on each G, and obtains the score Sgi of the medical group on various other indicators. The evaluation system summarizes the Sgi of each medical group to form a score set of other indicators, and stores and updates it in the scoring data set storage unit to obtain the scoring data of other indicators, providing a comprehensive evaluation basis for this evaluation from other medical quality perspectives in addition to the cure rate.

[0049] S206. According to Si=Ski+Sgi, obtain a data model.

[0050] The scoring module of the evaluation system calls the stored cure rate score Ski and other indicator scores Sgi of the i-th medical group. The scoring module sums Ski and Sgi according to the formula Si=Ski+Sgi, and comprehensively considers the specific scores of each medical group in cure rate and other medical quality indicators to calculate the total score Si of the medical group. Among them, Ski reflects the score of the main evaluation indicator of cure rate, and Sgi reflects the scores of other indicators. The sum Si of the two can comprehensively consider the key medical level indicators of the medical group as the final overall score of the medical group. Based on Si of each medical group, the evaluation system constructs a data model that comprehensively reflects multi-dimensional indicator data.

[0051] In the embodiments of the present application, due to the use of a systematic and scientific data processing process and model construction method, including a series of technical means such as determining the intermediate value based on the mean and standard deviation, normalization processing, weight allocation, and comprehensive consideration of multiple indicators, the problems of inconsistent data and one-sided evaluation in the traditional medical level evaluation method of the medical group are effectively solved, thereby achieving the technical effect of accurately and objectively evaluating the medical level of the medical group and effectively motivating the medical group to improve the quality of medical services and the standardization of work.

[0052] The following describes the evaluation system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of an evaluation system in an embodiment of the present application.

[0053] It should be noted that Figure 3 The structure of the evaluation system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0054] like Figure 3 As shown, the evaluation system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0055] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0056] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0057] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0058] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0059] Specifically, the evaluation system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the medical level calculation and evaluation method for a medical group in a hospital provided in the above embodiment is implemented.

[0060] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the evaluation system described in the above embodiment; or may exist independently without being assembled into the evaluation system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the evaluation system, the evaluation system implements the medical level calculation evaluation method for a medical group in a hospital provided in the above embodiment.

[0061] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0062] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0063] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for calculating and evaluating the medical level of a medical team in a hospital, characterized in that: The method comprises: After obtaining the data Vi of the cure rate of the i-th medical group from n medical groups within the assessment time T, determine the mean AVERAGE and the standard deviation STDEVP, where i ranges from 1 to n, and the cure rate is the ratio of the number of discharged patients to the number of received patients of the target medical group within the assessment time T; Determine the intermediate value set A of the n medical groups according to the data Vi; After inputting the intermediate value set A into the trained data model, the total score Si of the i-th medical group is obtained; After determining the weighted sum W of the cure rates corresponding to each medical group, the assessment data is obtained by Fi=Si / W, where Fi is the percentage score of the i-th medical group within the assessment time T; When the cure rate of the medical group is lower than the preset threshold, the operation actions of the staff in the target medical group within the assessment time T are obtained through the camera in each medical room, and the operation actions are input into the preset illegal operation recognition model to determine the illegal operation of the target medical group. The illegal operation recognition model is constructed by extracting and marking features based on a plurality of known standard operation video data and corresponding illegal operation video data in advance, and is trained and constructed using a deep learning algorithm; The evaluation result is determined according to the number of illegal operations and the evaluation data combined with the preset illegal point deduction rules.

2. The method according to claim 1, characterized in that After inputting the intermediate value set A into the trained data model, the step of obtaining the total score Si of the i-th medical group includes: Determine a maximum value Amax and a minimum value Amin in the intermediate value set A, wherein the intermediate value set A includes the intermediate value Ai of the i-th medical group; According to the formula Bi=(Ai-Amin) / (Amax-Amin), the intermediate value Bi of the i-th medical group is obtained; Use the formula Ski=Bi*Wk to obtain the score Ski of the i-th medical group corresponding to the cure rate, where Wk is the weight of the cure rate in the medical group corresponding to the i-th medical group; The data model is determined according to the Ski score.

3. The method according to claim 2, characterized in that Before the step of determining a maximum value Amax and a minimum value Amin in the intermediate value set A, wherein the intermediate value set A includes the intermediate value Ai of the i-th medical group, the step further includes: The median value Ai of the i-th medical group is determined according to the cure rate, wherein Ai=(Vi-AVERAGE) / STDEVP.

4. The method according to claim 2, characterized in that: The step of determining the data model according to the Ski score specifically includes: Calculating the score Sgi of the other index G of the i-th medical group, where the other index G is an index other than the cure rate in the corresponding i-th medical group; According to Si=Ski+Sgi, the data model is obtained.

5. The method according to claim 1, characterized in that: After the step of determining the evaluation result according to the illegal operation and the evaluation data combined with the preset illegal deduction rules, the method further includes: Ranking the medical group according to the evaluation results; Performance bonuses are determined based on said rankings.

6. The method according to claim 5, characterized in that After the step of determining the performance bonus according to the ranking, the method further includes: According to the percentage score of each month of the medical group, the average percentage score of the medical group in one year is obtained; The year-end bonus is determined based on the average percentile score.

7. The method according to claim 4, characterized in that Before the step of calculating the score Sgi of the other indicator G of the i-th medical group, the step further includes: Get the weight Wg of other indicators G; The weight sum W is obtained according to W=Wk+Wg.

8. An evaluation system, characterized in that: The evaluation system comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the evaluation system executes the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an evaluation system, the evaluation system is caused to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an evaluation system, the evaluation system is caused to execute the method according to any one of claims 1 to 7.

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