Orthopedic patient hospital infection prevention and control system based on diagnosis-related grouping

Abstract

The embodiment of the invention discloses an orthopedic patient hospital infection prevention and control system based on diagnosis-related groups. The system comprises a medical record database configured to store orthopedic medical record information; the prevention and control server is configured to execute the following processing: acquiring each piece of orthopaedic case information, and performing diagnosis-related grouping on each piece of orthopaedic case information; case combination index correction is carried out on the infection morbidity corresponding to each piece of orthopedic diagnosis grouping information, and a unit infection rate set is obtained; generating a high infection grouping information set and a risk grouping information set; generating a high disease burden grouping information set according to the disease burden corresponding to each piece of orthopedic diagnosis grouping information; generating a key orthopedic diagnosis grouping information set; based on the infection prevention and control decision tree and the key orthopedic diagnosis grouping information set, generating an orthopedic patient hospital infection prevention and control map; and the display is configured to display the hospital infection prevention and control map of the orthopedic patient. According to the embodiment, waste of medical resources can be reduced.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a hospital infection prevention and control system for orthopedic patients based on diagnosis-related grouping. Background Art

[0002] Diagnosis-related groups (DRGs), a case classification system based on disease type, treatment modality, and individual patient characteristics, provide a more scientific management approach and new insights for hospital infection prevention and control. Currently, the most common approach to hospital infection prevention and control in orthopedic patients is to quantify potential risk factors using logistic regression models.

[0003] However, when using the above-mentioned method to prevent and control hospital infections in orthopedic patients, a technical problem that often occurs is that traditional logistic regression models are difficult to effectively handle nonlinear correlations between variables and high-dimensional feature interactions, and are difficult to reveal the potential relationship between different factors and hospital infections, thus failing to provide accurate decision-making support. This leads to a lack of targeted graded warning standards for hospital infection prevention and control, which in turn leads to a waste of medical resources.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the Invention

[0005] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] Some embodiments of the present disclosure propose a hospital infection prevention and control system for orthopedic patients based on diagnosis-related grouping to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide a hospital infection prevention and control system for orthopedic patients based on diagnosis-related grouping, the hospital infection prevention and control system for orthopedic patients based on diagnosis-related grouping comprises: a medical record database configured to store information of various orthopedic cases in a hospital management information system; a prevention and control server configured to perform the following processing: obtaining information of various orthopedic cases from the medical record database, and performing diagnosis-related grouping on the information of various orthopedic cases to obtain an orthopedic diagnosis grouping information set; performing case combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set to generate a unit infection rate to obtain a unit infection rate set; and performing case combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set to generate a unit infection rate set. The infection rate set and the above-mentioned orthopedic diagnosis grouping information set are used to generate a high infection grouping information set and a risk grouping information set; according to the disease burden corresponding to each orthopedic diagnosis grouping information in the above-mentioned orthopedic diagnosis grouping information set, a high disease burden grouping information set is generated; according to the above-mentioned high infection grouping information set, the above-mentioned risk grouping information set and the above-mentioned high disease burden grouping information set, a key orthopedic diagnosis grouping information set is generated; based on the infection prevention and control decision tree and the above-mentioned key orthopedic diagnosis grouping information set, a hospital infection prevention and control map for orthopedic patients is generated; the display is configured to display the above-mentioned hospital infection prevention and control map for orthopedic patients, so as to allocate and set up hospital infection prevention and control facilities according to the displayed hospital infection prevention and control map for orthopedic patients.

[0008] In a second aspect, some embodiments of the present disclosure provide a method for preventing and controlling hospital infections in orthopedic patients based on diagnosis-related grouping, the method comprising: storing information on each orthopedic case in a hospital management information system; obtaining information on each orthopedic case from a medical record database, and performing diagnosis-related grouping on the above-mentioned information on each orthopedic case to obtain an orthopedic diagnosis grouping information set; performing case combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the above-mentioned orthopedic diagnosis grouping information set to generate a unit infection rate to obtain a unit infection rate set; and performing case combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the above-mentioned orthopedic diagnosis grouping information set to generate ... according to the above-mentioned unit infection rate set and the above-mentioned orthopedic diagnosis grouping information. The orthopedic diagnosis grouping information set is used to generate a high infection grouping information set and a risk grouping information set; a high disease burden grouping information set is generated according to the disease burden corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set; a key orthopedic diagnosis grouping information set is generated according to the high infection grouping information set, the risk grouping information set and the high disease burden grouping information set; a hospital infection prevention and control map for orthopedic patients is generated based on the infection prevention and control decision tree and the key orthopedic diagnosis grouping information set; the hospital infection prevention and control map for orthopedic patients is displayed so that hospital infection prevention and control facilities can be allocated and set according to the displayed hospital infection prevention and control map for orthopedic patients.

[0009] In a third aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation of the second aspect is implemented.

[0010] The above-described embodiments of the present disclosure have the following beneficial effects: The orthopedic patient hospital infection prevention and control system based on diagnosis-related grouping in some embodiments of the present disclosure can reduce the waste of medical resources. Specifically, the reason for this waste of medical resources is that traditional logistic regression models have difficulty effectively handling nonlinear correlations between variables and high-dimensional feature interactions, making it difficult to reveal the potential relationships between different factors and hospital infections. Consequently, they cannot provide accurate decision support, resulting in a lack of targeted, graded early warning standards for hospital infection prevention and control. Based on this, the orthopedic patient hospital infection prevention and control system based on diagnosis-related grouping in some embodiments of the present disclosure first generates an orthopedic diagnosis grouping information set based on individual orthopedic case information. This allows for the establishment of a structured orthopedic case system. Next, the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set is corrected for the case-combination index to generate a unit infection rate, resulting in a unit infection rate set. This removes the coupled effects of different treatment modalities and the patient's physical condition on the statistical results, resulting in more accurate infection rate results. Then, based on the unit infection rate set, a high-infection grouping information set and a risk grouping information set are generated. Different levels of subdivided diagnosis groups are selected based on the unit infection rate. Next, based on the disease burden corresponding to each orthopedic diagnosis group information in the orthopedic diagnosis group information set, a high disease burden group information set is generated. This allows the identification of latent risk groups with low technical difficulty but high infection risk. Subsequently, a key orthopedic diagnosis group information set is generated based on the high infection group information set, the risk group information set, and the high disease burden group information set. A comprehensive analysis of the previously generated orthopedic diagnosis group information sets is performed to generate a key orthopedic diagnosis group information set. This helps hospitals identify high-risk patients and focus resources on priority intervention, thereby achieving optimal allocation of prevention and control resources. Finally, based on the infection prevention and control decision tree and the key orthopedic diagnosis group information set, a hospital infection prevention and control pathway for orthopedic patients is generated. This precise prevention and control pathway avoids the waste of medical resources caused by generalized processing and makes hospital infection prevention and control measures more targeted. This allows for tiered early warning and prevention measures based on the infection risk of different patients, avoiding resource waste due to misjudgment or excessive prevention and control of low-risk patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0012] Figure 1 is a schematic structural diagram of some embodiments of a hospital infection prevention and control system for orthopedic patients based on diagnosis-related groups according to the present disclosure; Figure 2 is a flow chart of some embodiments of a method for preventing and controlling hospital infection in orthopedic patients based on diagnosis-related groups according to the present disclosure; Figure 3 1 is an example diagram of hospital infection prevention and control for orthopedic patients according to some embodiments of the hospital infection prevention and control system for orthopedic patients based on diagnosis-related groups of the present disclosure. DETAILED DESCRIPTION

[0013] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0014] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0015] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0016] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0017] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0018] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0019] Figure 1 A schematic structural diagram of some embodiments of a hospital infection prevention and control system for orthopedic patients based on diagnosis-related groups according to the present disclosure is shown. Figure 1 It includes a case database 1, a prevention and control server 2 and a display 3.

[0020] In some embodiments, the case database 1 can be configured to store information on various orthopedic cases in a hospital management information system. Each of these orthopedic case information can be information on an orthopedic patient. This orthopedic case information can include patient identification number, department visited, whether the patient contracted the disease, visit time, visit fee, patient age, underlying medical conditions, whether surgery was performed, and surgical information. In practice, the case database 1 can store information on various orthopedic cases stored in a hospital management information system.

[0021] In some embodiments, the control server 2 may be configured to perform the following processing: First, the orthopedic case information is obtained from the medical record database, and the orthopedic case information is grouped into diagnosis-related groups to obtain an orthopedic diagnosis grouping information set.

[0022] In some embodiments, the control server 2 may obtain individual orthopedic case information from the medical record database and perform diagnosis-related grouping on each of the orthopedic case information to obtain an orthopedic diagnosis grouping information set. Each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set may include individual nosocomial infection grouping information and individual non-nosocomial infection grouping information. Each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set may correspond to various variables. These variables may include, but are not limited to, the department visited, whether a nosocomial infection was present, the time of visit, the cost of the visit, the patient's age, the patient's underlying medical conditions, whether surgery was performed, etc. In practice, the orthopedic case information may be grouped according to diagnosis using a preset diagnosis-related grouping technique to obtain an orthopedic diagnosis grouping information set. Here, each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set may include at least one orthopedic case information. Next, it may be determined whether each orthopedic case information in the at least one orthopedic case information is a nosocomial infection. Each orthopedic case information with a nosocomial infection is used as a nosocomial infection grouping information, and each orthopedic case information without a nosocomial infection is used as a non-nosocomial infection grouping information.

[0023] Second, the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set was corrected by case-mix index to generate a unit infection rate, thereby obtaining a unit infection rate set.

[0024] In some embodiments, the prevention and control server 2 may perform case combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set to generate a unit infection rate, thereby obtaining a unit infection rate set.

[0025] In some optional implementations of some embodiments, the prevention and control server 2 may perform case-combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set to generate a unit infection rate, thereby obtaining a unit infection rate set: In the first step, for each orthopedic diagnosis group information in the above orthopedic diagnosis group information set, perform the following steps to obtain a unit infection rate set: In a first sub-step, the ratio of the average cost per case corresponding to the orthopedic diagnosis grouping information to the average cost per case for all cases is determined as the group cost weight corresponding to the orthopedic diagnosis grouping information. The average cost per case corresponding to the orthopedic diagnosis grouping information may be the average of the costs of each orthopedic case information in the at least one orthopedic case information included in the orthopedic diagnosis grouping information. The average cost per case for all cases may be the average of the costs of all orthopedic case information.

[0026] The second sub-step is to generate a case combination index corresponding to the orthopedic diagnosis grouping information based on the total number of cases, the above-mentioned grouping cost weights, and the number of cases corresponding to the above-mentioned orthopedic diagnosis grouping information. The total number of cases is the number of all the above-mentioned orthopedic case information. In practice, the case combination index corresponding to the above-mentioned orthopedic diagnosis grouping information can be generated using the following formula: .

[0027] in, Represents the grouping cost weight corresponding to the i-th group of orthopedic diagnosis grouping information. Indicates the number of orthopedic case information corresponding to the i-th group of orthopedic diagnosis grouping information. =CMI represents the case mix index corresponding to the orthopedic diagnosis grouping information of group i.

[0028] The third sub-step is to determine the unit infection rate by the ratio of the infection morbidity rate corresponding to the orthopedic diagnosis grouping information to the case-combination index corresponding to the orthopedic diagnosis grouping information. In practice, first, for each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set, the infection morbidity rate can be determined based on the proportion of orthopedic case information corresponding to nosocomial infections among the individual orthopedic case information contained in the orthopedic diagnosis grouping information. Subsequently, the unit infection rate can be determined by the ratio of the infection morbidity rate corresponding to the orthopedic diagnosis grouping information to the case-combination index corresponding to the orthopedic diagnosis grouping information.

[0029] In practice, considering that the types of medical staff, diagnosis and treatment technology levels, number of patients treated, disease types, and severity and complexity of diseases in each DRGs group of each subspecialty of orthopedics are different, there are many confounding factors in directly comparing and analyzing the incidence of hospital infections. Therefore, the case mix index is used to adjust the hospital infection rate based on the DRGs grouping, and the hospital infection incidence rate of different DRGs groups in different subspecialties of orthopedics is corrected. The "unit infection rate" of each DRGs group is calculated, so that the hospital infection rate between DRGs groups with different disease severity and different diagnosis and treatment difficulties can be compared horizontally.

[0030] Third, based on the unit infection rate set and the orthopedic diagnosis grouping information set, a high infection grouping information set and a risk grouping information set are generated.

[0031] In some embodiments, the prevention and control server 2 may generate a high infection group information set and a risk group information set based on the unit infection rate set and the orthopedic diagnosis group information set.

[0032] In some optional implementations of some embodiments, the prevention and control server 2 may generate a high infection group information set and a risk group information set according to the unit infection rate set and the orthopedic diagnosis group information set through the following steps: In the first step, the orthopedic diagnosis grouping information corresponding to each unit infection rate in the unit infection rate set that meets a preset first infection rate condition is determined as high infection grouping information, thereby obtaining a high infection grouping information set. The first infection rate condition may be the top k unit infection rates with the highest values ​​in the unit infection rate set. The first infection rate condition may also be each unit infection rate whose unit infection rate value exceeds a preset unit infection rate threshold. The k may be a positive integer. The preset unit infection rate threshold may be a percentage. This is not specifically limited herein.

[0033] In the second step, based on the above-mentioned unit infection rate set and the case combination index corresponding to each orthopedic diagnosis grouping information in the above-mentioned orthopedic diagnosis grouping information set, the orthopedic diagnosis grouping information corresponding to each unit infection rate in the above-mentioned unit infection rate set that meets a preset second infection rate condition is determined as risk grouping information, thereby obtaining a risk grouping information set. The second infection rate condition can be the top n orthopedic diagnosis grouping information with the highest unit infection rate among the top m orthopedic diagnosis grouping information with the lowest case combination index. Both m and n are positive integers, and m is greater than n. The specific values ​​of m and n are not specifically limited.

[0034] Fourth, a high disease burden grouping information set is generated according to the disease burden corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set.

[0035] In some embodiments, the prevention and control server 2 may generate a high disease burden group information set based on the disease burden corresponding to each orthopedic diagnosis group information in the orthopedic diagnosis group information set. Each orthopedic diagnosis group information in the orthopedic diagnosis group information set has a corresponding disease burden. The disease burden may represent the consultation time and consultation fee of the orthopedic diagnosis group information.

[0036] In some optional implementations of some embodiments, the prevention and control server 2 may generate a high disease burden group information set according to the disease burden corresponding to each orthopedic diagnosis group information in the orthopedic diagnosis group information set through the following steps: In the first step, for each orthopedic diagnosis group information in the orthopedic diagnosis group information set, perform the following steps: In a first sub-step, a set of hospital infection visit times and a set of hospital infection visit costs are generated based on the visit times and costs corresponding to each orthopedic case information in the hospital infection grouping information included in the orthopedic diagnosis grouping information. Furthermore, a set of non-hospital infection visit times and a set of non-hospital infection visit costs are generated based on the visit times and costs corresponding to each orthopedic case information in the non-hospital infection grouping information included in the orthopedic diagnosis grouping information. The orthopedic diagnosis grouping information may include hospital infection grouping information and non-hospital infection grouping information.

[0037] The second sub-step is to determine the significance level of the difference in the visiting time corresponding to the above-mentioned orthopedic diagnosis grouping information based on the non-parametric rank sum test algorithm, the above-mentioned hospital infection visiting time set and the above-mentioned non-hospital infection visiting time set. Among them, the above-mentioned non-parametric rank sum test algorithm can be the Wilcoxon algorithm in SPSS software. The above-mentioned significance level of the difference in visiting time can be the significance level that characterizes the difference between hospital infection and no hospital infection. In practice, the above-mentioned non-parametric rank sum test algorithm can be used to perform a non-parametric rank sum test on the above-mentioned hospital infection visiting time set and the above-mentioned non-hospital infection visiting time set to obtain the significance level of the difference in visiting time corresponding to the above-mentioned orthopedic diagnosis grouping information.

[0038] The third sub-step is to determine the significance level of the difference in medical expenses corresponding to the above-mentioned orthopedic diagnosis grouping information based on the non-parametric rank sum test algorithm, the above-mentioned hospital infection medical expense set, and the above-mentioned non-hospital infection medical expense set. Among them, the significance level of the above-mentioned medical expense difference can be a significance level that characterizes the difference between hospital infection and no hospital infection. In practice, the above-mentioned second sub-step can be used to determine the significance level of the difference in medical expenses corresponding to the above-mentioned orthopedic diagnosis grouping information based on the non-parametric rank sum test algorithm, the above-mentioned hospital infection medical expense set, and the above-mentioned non-hospital infection medical expense set. I will not go into details here.

[0039] The fourth sub-step is to determine the difference in consultation time and consultation fee between the above-mentioned hospital infection grouping information and the above-mentioned non-hospital infection grouping information based on the resampling method. The above-mentioned consultation time difference can represent the difference between the consultation time corresponding to the above-mentioned hospital infection grouping information and the consultation time corresponding to the above-mentioned non-hospital infection grouping information. The consultation time corresponding to the above-mentioned hospital infection grouping information can be the median of each hospital infection consultation time in the above-mentioned hospital infection consultation time set. The consultation time corresponding to the above-mentioned non-hospital infection grouping information can be the median of each non-hospital infection consultation time in the above-mentioned non-hospital infection consultation time set. The above-mentioned consultation fee difference can represent the difference between the consultation fee corresponding to the above-mentioned hospital infection grouping information and the consultation fee corresponding to the above-mentioned hospital infection grouping information. The consultation fee corresponding to the above-mentioned hospital infection grouping information can be the median of each hospital infection consultation fee in the above-mentioned hospital infection consultation fee set. The consultation fee corresponding to the above-mentioned non-hospital infection grouping information can be the median of each non-hospital infection consultation fee in the above-mentioned non-hospital infection consultation fee set.

[0040] In the second step, based on the determined differences in visit time and visit cost, each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set that meets a preset difference condition is determined as initial high disease burden grouping information, thereby obtaining an initial high disease burden grouping information set. The difference condition may be the top p orthopedic diagnosis grouping information with the highest visit time difference and the top p orthopedic diagnosis grouping information with the highest visit cost difference in the orthopedic diagnosis grouping information set. The above p can be a positive integer and is not specifically limited herein. In practice, first, the top p orthopedic diagnosis grouping information with the highest visit time difference in the orthopedic diagnosis grouping information set can be determined as the initial high-time grouping information, thereby obtaining an initial high-time grouping information set. Second, the top p orthopedic diagnosis grouping information with the highest visit cost difference in the orthopedic diagnosis grouping information set can be determined as the initial high-cost grouping information, thereby obtaining an initial high-cost grouping information set. Thereafter, the intersection of the initial high-time grouping information set and the initial high-cost grouping information set is determined as the initial high disease burden grouping information set.

[0041] The third step is to determine, based on the determined significance levels of each difference in consultation time and each difference in consultation cost, each initial high disease burden grouping information that meets the preset significance level conditions in the above-mentioned initial high disease burden grouping information set as high disease burden grouping information, thereby obtaining a high disease burden grouping information set. In practice, first, each initial high time grouping information in the above-mentioned initial high time grouping information set whose corresponding significance level of consultation time difference is lower than the preset significance level threshold can be determined as high disease burden grouping information. Secondly, each initial high cost grouping information in the above-mentioned initial high cost grouping information set whose corresponding significance level of consultation cost difference is lower than the preset significance level threshold can be determined as high disease burden grouping information. Then, based on the determined individual high disease burden grouping information, a high disease burden grouping information set can be obtained.

[0042] In practice, when using the above technical solution to determine high disease burden grouping information, a common technical problem is that the consultation time and cost of orthopedic patients are easily affected by the patient's physical condition, and the data often contain many outliers. Directly using the original sample median difference makes it difficult to accurately identify orthopedic diagnosis groups with increased disease burden due to nosocomial infection. This leads to biased judgment of nosocomial infection-related risk factors, affecting the rational allocation of subsequent prevention and control facilities, and resulting in a waste of medical resources. Therefore, the following solution can be decided.

[0043] Optionally, the prevention and control server 2 may determine the difference in consultation time and consultation fee between the hospital infection group information and the non-hospital infection group information based on a resampling method by the following steps: The first step is to perform outlier detection on the hospital infection visit time set corresponding to the above-mentioned hospital infection grouping information and the non-hospital infection visit time set corresponding to the above-mentioned non-hospital infection grouping information, respectively, to obtain the outlier results of the hospital infection visit time and the outlier results of the non-hospital infection visit time. Among them, the outlier results of the hospital infection visit time can be with outliers and without outliers. The outlier results of the non-hospital infection visit time can be with outliers and without outliers. In practice, the hospital infection visit time set corresponding to the above-mentioned hospital infection grouping information and the non-hospital infection visit time set corresponding to the above-mentioned non-hospital infection grouping information can be respectively performed outlier detection by a preset outlier detection algorithm, to obtain the outlier results of the hospital infection visit time and the outlier results of the non-hospital infection visit time.

[0044] As an example, the outlier detection algorithm may include but is not limited to at least one of the following: a DBScan clustering algorithm, a box plot method.

[0045] In the second step, based on the preset number of sampling times, the following median difference detection steps are performed: In the first sub-step, the hospital infection visit time set corresponding to the hospital infection grouping information is sampled to obtain a hospital infection visit time sample set. In practice, the hospital infection visit time set corresponding to the hospital infection grouping information can be sampled according to a preset sampling rate to obtain a hospital infection visit time sample set. The sampling rate can be a percentage and is not specifically limited here.

[0046] In the second sub-step, the set of visit times without hospital infection corresponding to the above-mentioned grouping information without hospital infection is sampled to obtain a sample set of visit times without hospital infection. In practice, the set of visit times without hospital infection corresponding to the above-mentioned grouping information without hospital infection can be sampled according to the above-mentioned sampling rate to obtain a sample set of visit times without hospital infection.

[0047] In a third sub-step, in response to determining that the outlier result of the hospital infection visit time or the outlier result of the non-hospital infection visit time satisfies a preset outlier condition, the median of the hospital infection visit time corresponding to the sample set of hospital infection visit time and the median of the non-hospital infection visit time corresponding to the sample set of non-hospital infection visit time are determined, and the difference between the median of the hospital infection visit time and the median of the non-hospital infection visit time is determined. The outlier condition may be that the outlier result of the hospital infection visit time is no outlier and the outlier result of the non-hospital infection visit time is no outlier.

[0048] The fourth sub-step, in response to determining that the above-mentioned outlier results of the hospital infection visit time and the above-mentioned outlier results of the non-hospital infection visit time do not meet the above-mentioned outlier conditions, determines the truncated median of the hospital infection visit time corresponding to the above-mentioned hospital infection visit time sample set and the truncated median of the non-hospital infection visit time corresponding to the above-mentioned non-hospital infection visit time sample set, and determines the difference between the truncated median of the hospital infection visit time and the truncated median of the non-hospital infection visit time.

[0049] The third step is to generate a median of the differences based on the determined differences, wherein the median of the differences is the median of the corresponding differences.

[0050] The fourth step is to determine the median of the above differences as the difference in consultation time.

[0051] In the fifth step, a median difference test is performed on the set of hospital infection treatment costs corresponding to the hospital infection grouping information and the set of non-hospital infection treatment costs corresponding to the non-hospital infection grouping information to obtain the treatment cost difference. In practice, the median difference test can be performed on the set of hospital infection treatment costs corresponding to the hospital infection grouping information and the set of non-hospital infection treatment costs corresponding to the non-hospital infection grouping information to obtain the treatment cost difference through the above-mentioned steps 1 to 4. This will not be repeated here.

[0052] The optional steps 1 through 5 and their related content, as an inventive feature of an embodiment of the present disclosure, address the aforementioned technical issue of "waste of medical resources." Factors contributing to this technical issue are often as follows: the duration and cost of orthopedic patients' visits are easily affected by their physical condition, and the data often contains a large number of outliers. Directly using the median difference of the original sample makes it difficult to accurately identify orthopedic diagnosis groups with increased disease burden due to nosocomial infection, leading to biased judgment of nosocomial infection-related risk factors and affecting the rational deployment of subsequent prevention and control facilities. Addressing these factors can reduce the waste of medical resources. To achieve this, first, determine whether the visit times corresponding to the nosocomial infection grouping information and the nosocomial infection grouping information contain outliers. Subsequently, multiple sampling can be performed on the nosocomial infection visit time set and the nosocomial infection visit time set. Each sampling step determines the median and its difference corresponding to the sampled samples. By performing multiple sampling to determine the median, errors caused by individual differences can be reduced. This improves the ability to identify nosocomial infection factors. Secondly, to mitigate bias caused by outliers, the median is replaced by the truncated median for both the nosocomial infection visit time sets with and without outliers. This reduces the impact of extreme data on the analysis results, improving their reliability and accuracy. Finally, the median of the determined differences can be used as the visit time difference. The same principle applies to the visit cost difference. Thus, after obtaining the visit time and visit cost differences, these differences can be used to more accurately identify orthopedic diagnostic groups with increased disease burden due to nosocomial infections. Based on these orthopedic diagnostic groups, nosocomial infection prevention and control facilities (such as intraoperative medical warming equipment and cleaning and disinfection equipment) can be more targeted and deployed. This ensures that high-risk departments truly in need of strengthened prevention and control measures receive adequate resources while avoiding unnecessary and excessive interventions in low-risk departments, thereby preventing waste of medical resources.

[0053] Fifth, based on the high infection group information set, risk group information set and high disease burden group information set, a key orthopedic diagnosis group information set is generated.

[0054] In some embodiments, the prevention and control server 2 may generate a key orthopedic diagnosis group information set based on the high infection group information set, the risk group information set, and the high disease burden group information set. In practice, the union of the high infection group information set, the risk group information set, and the high disease burden group information set may be determined as the key orthopedic diagnosis group information set.

[0055] Sixth, based on the infection prevention and control decision tree and the above-mentioned key orthopedic diagnosis grouping information set, a hospital infection prevention and control map for orthopedic patients was generated.

[0056] In some embodiments, the control server 2 can generate a hospital infection control map for orthopedic patients based on the infection control decision tree and the key orthopedic diagnosis grouping information set. In practice, the orthopedic patient hospital infection control decision tree corresponding to the key orthopedic diagnosis grouping information set can be determined using a preset infection control decision tree, and the orthopedic patient hospital infection control decision tree can be visualized to obtain the orthopedic patient hospital infection control map. The infection control decision tree can be a CHAID decision tree.

[0057] In practice, it has been found that the distribution of orthopedic patients varies significantly across departments, and some departments have a relatively small caseload. Using the aforementioned technical solution to identify key risk information presents technical challenges. The traditional CHAID decision tree can introduce model bias due to insufficient sample size or uneven data distribution. This bias reduces the reliability of risk identification results, impacting the accuracy of medical resource allocation and resulting in waste. Therefore, the following solution was chosen.

[0058] In some optional implementations of some embodiments, the prevention and control server 2 may generate a hospital infection prevention and control map for orthopedic patients based on the infection prevention and control decision tree and the key orthopedic diagnosis grouping information set through the following steps: The first step is to perform a chi-square test on each independent variable based on the above-mentioned key orthopedic diagnosis grouping information set to obtain the chi-square value and statistical significance level of each independent variable relative to the above-mentioned dependent variable. The above-mentioned independent variables can include "patient identification serial number," "consultation department," "consultation time," "consultation fee," "patient age," "patient underlying disease information," "whether surgery was performed," "surgery information," and so on, contained in the above-mentioned orthopedic case information. The above-mentioned dependent variable can be "whether hospital infection occurred." In practice, a chi-square test can be performed on each independent variable based on the above-mentioned key orthopedic diagnosis grouping information set to obtain the chi-square value and statistical significance level of each independent variable relative to the above-mentioned dependent variable.

[0059] The second step is to determine the number of independent variable categories corresponding to each of the above-mentioned independent variables, obtain the independent variable category number set, and determine the number of dependent variable categories corresponding to the above-mentioned dependent variable. Among them, the number of dependent variable categories can be 2, representing hospital infection and no hospital infection respectively. The number of independent variable categories of each independent variable in the above-mentioned independent variable category number set can be related to the corresponding independent variable. For example, the number of independent variable categories corresponding to the independent variable "whether surgery" can be 2, representing surgery and no surgery respectively. The number of independent variable categories corresponding to the independent variable "consultation time" can be 3, representing time less than 7 days, time between 8 and 16 days, and time more than 16 days respectively. Here, the division method of each independent variable can be determined according to the above-mentioned diagnosis-related grouping technology.

[0060] The third step is to perform bias correction on each independent variable category in the above independent variable category set to obtain the corrected independent variable category set. In practice, the following formula can be used to perform bias correction on each independent variable category in the above independent variable category set to obtain the corrected independent variable category set.

[0061] .

[0062] in, Indicates the number of independent variable categories after adjustment. Indicates the sample size. Indicates the number of categories of the independent variable.

[0063] The fourth step is to perform bias correction on the above dependent variable categories to obtain the corrected number of dependent variable categories. In practice, the following formula can be used to perform bias correction on the above dependent variable categories to obtain the corrected number of dependent variable categories.

[0064] .

[0065] in, represents the number of categories of the dependent variable after adjustment. represents the number of categories of the dependent variable.

[0066] In the fifth step, the chi-square value with the largest corresponding value among the obtained chi-square values ​​is determined as the first chi-square value. The independent variable corresponding to the first chi-square value can be the root node of the decision tree for hospital infection prevention and control of orthopedic patients.

[0067] In step 6, in response to determining that the statistical significance level corresponding to the first chi-square value satisfies a preset significance condition, the following steps are performed: The first sub-step is to determine the mean square chi-square value corresponding to the first chi-square value based on the bias correction equation. In practice, the bias correction equation is: .

[0068] in, represents the mean square chi-square value. represents the chi-square value.

[0069] The second sub-step is to generate a variable association strength value corresponding to the first chi-square value based on the above-mentioned mean square chi-square value, the above-mentioned number of dependent variable categories after correction, and the number of independent variable categories after correction corresponding to the first chi-square value. The above-mentioned variable association strength value can be a statistic that characterizes the strength of association between the above-mentioned independent variable and the above-mentioned dependent variable. The value of the above-mentioned variable association strength value can be between 0 and 1. When the variable association strength value is close to 0, it can be indicated that the strength of association between the independent variable and the dependent variable is low; when the variable association strength value is close to 1, it can be indicated that the strength of association between the independent variable and the dependent variable is high. In practice, the variable association strength value corresponding to the first chi-square value can be generated by the following formula based on the above-mentioned mean square chi-square value, the above-mentioned number of dependent variable categories after correction, and the number of independent variable categories after correction corresponding to the first chi-square value.

[0070] .

[0071] in, Indicates the strength of association between variables.

[0072] In practice, it has been found that when data is sparse or unbalanced, direct calculation of the variable association strength values ​​can lead to bias. For example, when the independent variable is absolutely correlated or uncorrelated with the dependent variable, the variable association strength value does not approach 1 or 0, and the corresponding root mean square error (RMSE) does not approach 0. Therefore, through bias correction in steps 3 and 4, the error in the generated variable association strength can be reduced, especially when data is sparse (n is small).

[0073] In a third sub-step, in response to determining that the statistical significance level corresponding to the first chi-square value is less than the variable association strength value, the independent variable corresponding to the first chi-square value is determined as key risk information, and the chi-square value with the largest value among the obtained chi-square values ​​other than the first chi-square value is determined as the first chi-square value, and the above steps are performed again. In practice, when it is determined that the statistical significance level corresponding to the first chi-square value is greater than or equal to the variable association strength value, the chi-square value with the largest value among the obtained chi-square values ​​other than the first chi-square value can be directly determined as the first chi-square value, and the above steps are performed again.

[0074] In practice, the first to third sub-steps described above can represent the process of constructing a decision tree for hospital infection prevention and control in orthopedic patients.

[0075] In step 7, in response to determining that the statistical significance level corresponding to the first chi-square value is greater than the aforementioned variable association strength value, a key risk information set is generated based on the determined key risk information. In practice, when the statistical significance level corresponding to the first chi-square value is greater than the aforementioned variable association strength value, it indicates that the independent variable corresponding to the first chi-square value is not a significant factor affecting nosocomial infection, and thus the decision tree partitioning is stopped.

[0076] Step 8: Generate a hospital infection prevention and control pathway for orthopedic patients based on the aforementioned key risk information set. Each key risk information in the aforementioned key risk information set is associated with a risk severity value. In practice, the key risk information in the aforementioned key risk information set can be sorted in descending order of risk severity to obtain a key risk information sequence. This key risk information sequence can then be determined as the hospital infection prevention and control pathway for orthopedic patients. Here, medical resources can be allocated based on the order of the key risk information in the aforementioned hospital infection prevention and control pathway for orthopedic patients.

[0077] The ninth step is to visualize the hospital infection prevention and control path of the orthopedic patients to obtain the orthopedic patients hospital infection prevention and control map. Among them, the example of the orthopedic patients hospital infection prevention and control map is as follows: Figure 3 shown.

[0078] The optional steps 1 through 9 and their contents, as an inventive feature of an embodiment of the present disclosure, address the aforementioned technical issue of "waste of medical resources." Factors contributing to this technical issue are often as follows: Traditional CHAID decision trees may suffer from model bias due to insufficient sample size or uneven data distribution. This bias reduces the reliability of risk identification results, thereby impacting the accuracy of medical resource allocation. Addressing these factors can reduce the waste of medical resources. To achieve this, first, a chi-square test is performed on the data to obtain the chi-square value and statistical significance level between each independent variable and the dependent variable. Secondly, bias correction is performed on the number of independent and dependent variable categories, respectively, to better measure the strength of the relationship between component traversals. Then, bias correction is performed on the chi-square value with the largest corresponding value among the aforementioned chi-square values. This makes the chi-square test more reliable in situations with small sample sizes or uneven data distribution, avoiding overfitting due to sample irregularities. Next, the corresponding variable association strength value is determined based on the corrected chi-square value, the number of independent and dependent variable categories, and the number of dependent variable categories. Next, the decision to continue building child nodes is made based on the relationship between the variable association strength value and the statistical significance level. This variable association strength value takes into account bias correction, providing a more stable measure of variable relationships. This prevents unnecessary splits based on spurious or insignificant relationships, making the tree construction process more reliable and avoiding overly complex tree models. Finally, each node in the decision tree is identified as a key risk information point. This allows for the rational allocation of medical resources based on the identified key risk information, reducing waste of medical resources.

[0079] In some embodiments, the display 3 can be configured to display the orthopedic patient hospital infection prevention and control diagram so that hospital infection prevention and control facilities can be allocated and set according to the displayed orthopedic patient hospital infection prevention and control diagram. The hospital infection prevention and control facilities can be devices that are communicatively connected to the control server. The hospital infection prevention and control facilities can be devices used to reduce hospital infections. For example, the hospital infection prevention and control facilities can include, but are not limited to, a dispenser of a disinfectant containing chlorhexidine, intraoperative medical warming equipment, cleaning and disinfection equipment, and the like.

[0080] Figure 2 The flowchart 200 of some embodiments of the orthopedic patient hospital infection prevention and control method based on diagnosis-related grouping according to the present disclosure, which is applied to the orthopedic patient hospital infection prevention and control system based on diagnosis-related grouping, is shown. The orthopedic patient hospital infection prevention and control method based on diagnosis-related grouping includes the following steps: Step 201: Store the information of each orthopedic case in the hospital management information system.

[0081] In some embodiments, the execution subject of the orthopedic patient hospital infection prevention and control system based on diagnosis-related groups can store information on each orthopedic case in the hospital management information system.

[0082] Step 202: Obtain information of each orthopedic case from the medical record database, and group each orthopedic case information into diagnosis-related groups to obtain an orthopedic diagnosis grouping information set.

[0083] In some embodiments, the execution entity may obtain information on various orthopedic case studies from the medical record database, and perform diagnosis-related grouping on the information on various orthopedic case studies to obtain an orthopedic diagnosis grouping information set.

[0084] Step 203 : Perform case-combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set to generate a unit infection rate, thereby obtaining a unit infection rate set.

[0085] In some embodiments, the execution entity may perform case-combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set to generate a unit infection rate, thereby obtaining a unit infection rate set.

[0086] Step 204 : generating a high infection group information set and a risk group information set based on the unit infection rate set and the orthopedic diagnosis group information set.

[0087] In some embodiments, the execution entity may generate a high infection group information set and a risk group information set based on the unit infection rate set and the orthopedic diagnosis group information set.

[0088] Step 205 : generating a high disease burden grouping information set according to the disease burden corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set.

[0089] In some embodiments, the execution entity may generate a high disease burden grouping information set based on the disease burden corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set.

[0090] Step 206: Generate a key orthopedic diagnosis group information set based on the high infection group information set, the risk group information set, and the high disease burden group information set.

[0091] In some embodiments, the execution entity may generate a key orthopedic diagnosis group information set based on the high infection group information set, the risk group information set, and the high disease burden group information set.

[0092] Step 207: Generate a hospital infection prevention and control map for orthopedic patients based on the infection prevention and control decision tree and the key orthopedic diagnosis grouping information set.

[0093] In some embodiments, the above-mentioned execution entity can generate a hospital infection prevention and control map for orthopedic patients based on the infection prevention and control decision tree and the above-mentioned key orthopedic diagnosis grouping information set.

[0094] Step 208: Display the orthopedic patient hospital infection prevention and control map so that hospital infection prevention and control facilities can be allocated and set according to the displayed orthopedic patient hospital infection prevention and control map.

[0095] In some embodiments, the execution entity may display the orthopedic patient hospital infection prevention and control map so as to allocate and set up hospital infection prevention and control facilities according to the displayed orthopedic patient hospital infection prevention and control map.

[0096] The above-described embodiments of the present disclosure have the following beneficial effects: The methods for preventing and controlling nosocomial infections in orthopedic patients based on diagnosis-related grouping, as described in some embodiments of the present disclosure, can reduce the waste of medical resources. Specifically, this waste of medical resources is caused by the difficulty of traditional logistic regression models in effectively handling nonlinear correlations between variables and high-dimensional feature interactions, making it difficult to reveal the potential relationships between different factors and nosocomial infections. Consequently, they cannot provide accurate decision support, resulting in a lack of targeted, graded early warning standards for nosocomial infection prevention and control. Based on this, the methods for preventing and controlling nosocomial infections in orthopedic patients based on diagnosis-related grouping, as described in some embodiments of the present disclosure, first generate an orthopedic diagnosis grouping information set based on individual orthopedic case information. This allows for the establishment of a structured orthopedic case system. Next, the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set is corrected for the case-combination index to generate a unit infection rate, resulting in a unit infection rate set. This removes the coupled effects of different treatment modalities and the patient's physical condition on the statistical results, resulting in more accurate infection rate results. Then, based on the unit infection rate set, a high-infection grouping information set and a risk grouping information set are generated. Different levels of subdivided diagnosis groups are selected based on the unit infection rate. Next, based on the disease burden corresponding to each orthopedic diagnosis group information in the orthopedic diagnosis group information set, a high disease burden group information set is generated. This allows the identification of latent risk groups with low technical difficulty but high infection risk. Subsequently, a key orthopedic diagnosis group information set is generated based on the high infection group information set, the risk group information set, and the high disease burden group information set. A comprehensive analysis of the previously generated orthopedic diagnosis group information sets is performed to generate a key orthopedic diagnosis group information set. This helps hospitals identify high-risk patients and focus resources on priority intervention, thereby achieving optimal allocation of prevention and control resources. Finally, based on the infection prevention and control decision tree and the key orthopedic diagnosis group information set, a hospital infection prevention and control pathway for orthopedic patients is generated. This precise prevention and control pathway avoids the waste of medical resources caused by generalized processing and makes hospital infection prevention and control measures more targeted. This allows for tiered early warning and prevention measures based on the infection risk of different patients, avoiding resource waste due to misjudgment or excessive prevention and control of low-risk patients.

[0097] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), 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 some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. Furthermore, in some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0098] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0099] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: stores each orthopedic case information in the hospital management information system; obtains each orthopedic case information from the medical record database, and performs diagnosis-related grouping on the above-mentioned each orthopedic case information to obtain an orthopedic diagnosis grouping information set; performs case combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the above-mentioned orthopedic diagnosis grouping information set to generate a unit infection rate, and obtains a unit infection rate set; generates a unit infection rate set based on the above-mentioned unit infection rate set and the above-mentioned orthopedic diagnosis grouping information set. A high infection grouping information set and a risk grouping information set are generated; a high disease burden grouping information set is generated according to the disease burden corresponding to each orthopedic diagnosis grouping information in the above orthopedic diagnosis grouping information set; a key orthopedic diagnosis grouping information set is generated according to the above high infection grouping information set, the above risk grouping information set and the above high disease burden grouping information set; a hospital infection prevention and control map for orthopedic patients is generated based on the infection prevention and control decision tree and the above key orthopedic diagnosis grouping information set; the above hospital infection prevention and control map for orthopedic patients is displayed so that hospital infection prevention and control facilities can be allocated and set according to the displayed hospital infection prevention and control map for orthopedic patients.

[0100] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0101] The flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, systems and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the prescribed logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the prescribed function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0103] The above descriptions are merely some preferred embodiments of the present disclosure and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A hospital infection prevention and control system for orthopedic patients based on diagnosis-related groups, comprising: A medical record database is configured to store information on various orthopedic cases in the hospital management information system; The control server is configured to perform the following processing: Acquiring each orthopedic case information from the medical record database, and performing diagnosis-related grouping on each orthopedic case information to obtain an orthopedic diagnosis grouping information set; performing case-combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set to generate a unit infection rate, thereby obtaining a unit infection rate set; generating a high infection group information set and a risk group information set according to the unit infection rate set and the orthopedic diagnosis group information set; generating a high disease burden grouping information set according to the disease burden corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set; generating a key orthopedic diagnosis group information set according to the high infection group information set, the risk group information set, and the high disease burden group information set; Generate a hospital infection prevention and control map for orthopedic patients based on the infection prevention and control decision tree and the key orthopedic diagnosis grouping information set; The display is configured to display the hospital infection prevention and control map for orthopedic patients so as to allocate and set hospital infection prevention and control facilities according to the displayed hospital infection prevention and control map for orthopedic patients.

2. The system according to claim 1, wherein: The control server is further configured to: For each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set, the following steps are performed to obtain a unit infection rate set: Determine the ratio between the average cost per case corresponding to the orthopedic diagnosis grouping information and the average cost per case of all cases as the grouping cost weight corresponding to the orthopedic diagnosis grouping information; Generate a case mix index corresponding to the orthopedic diagnosis grouping information based on the total number of cases, the grouping cost weight, and the number of cases corresponding to the orthopedic diagnosis grouping information; The ratio of the infection incidence rate corresponding to the orthopedic diagnosis grouping information to the case combination index corresponding to the orthopedic diagnosis grouping information is determined as the unit infection rate.

3. The system according to claim 2, wherein: The control server is further configured to: Determining orthopedic diagnosis grouping information corresponding to each unit infection rate that meets a preset first infection rate condition in the unit infection rate set as high infection grouping information to obtain a high infection grouping information set; According to the unit infection rate set and the case combination index corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set, the orthopedic diagnosis grouping information corresponding to each unit infection rate in the unit infection rate set that meets the preset second infection rate condition is determined as risk grouping information to obtain a risk grouping information set.

4. The system according to claim 1, wherein: Each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set includes hospital infection grouping information and non-hospital infection grouping information.

5. The system according to claim 4, wherein: The control server is further configured to: For each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set, perform the following steps: Generate a hospital infection consultation time set and a hospital infection consultation cost set based on the consultation time and consultation cost corresponding to each orthopedic case information in the hospital infection grouping information included in the orthopedic diagnosis grouping information, and generate a non-hospital infection consultation time set and a non-hospital infection consultation cost set based on the consultation time and consultation cost corresponding to each orthopedic case information in the non-hospital infection grouping information included in the orthopedic diagnosis grouping information; Determine the significance level of the difference in visit time corresponding to the orthopedic diagnosis grouping information based on the non-parametric rank sum test algorithm, the hospital infection visit time set and the non-hospital infection visit time set; Determine the significance level of the difference in medical expenses corresponding to the orthopedic diagnosis grouping information based on the non-parametric rank sum test algorithm, the hospital infection medical expense set and the non-hospital infection medical expense set; Based on the resampling method, determining the difference in consultation time and consultation fee between the hospital infection group information and the non-hospital infection group information; According to the determined differences in each consultation time and each consultation fee, each orthopedic diagnosis grouping information that meets a preset difference condition in the orthopedic diagnosis grouping information set is determined as initial high disease burden grouping information to obtain an initial high disease burden grouping information set; According to the determined significance levels of the differences in each consultation time and each consultation cost, each initial high disease burden grouping information that meets the preset significance level conditions in the initial high disease burden grouping information set is determined as high disease burden grouping information, thereby obtaining a high disease burden grouping information set.

6. A method for preventing and controlling hospital infections in orthopedic patients based on diagnosis-related groups, applied to the system for preventing and controlling hospital infections in orthopedic patients based on diagnosis-related groups according to any one of claims 1 to 5, the method comprising: Store the information of each orthopedic case in the hospital management information system; Acquiring each orthopedic case information from the medical record database, and performing diagnosis-related grouping on each orthopedic case information to obtain an orthopedic diagnosis grouping information set; performing case-combination index correction on the infection incidence rate corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set to generate a unit infection rate, thereby obtaining a unit infection rate set; generating a high infection group information set and a risk group information set according to the unit infection rate set and the orthopedic diagnosis group information set; generating a high disease burden grouping information set according to the disease burden corresponding to each orthopedic diagnosis grouping information in the orthopedic diagnosis grouping information set; generating a key orthopedic diagnosis group information set according to the high infection group information set, the risk group information set, and the high disease burden group information set; Generate a hospital infection prevention and control map for orthopedic patients based on the infection prevention and control decision tree and the key orthopedic diagnosis grouping information set; The hospital infection prevention and control map for orthopedic patients is displayed so that hospital infection prevention and control facilities can be allocated and set according to the displayed hospital infection prevention and control map for orthopedic patients.

7. A computer-readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to claim 6 is implemented.

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