Anesthesia quality evaluation method and system, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides an anesthesia quality evaluation method and system, electronic equipment, a storage medium and a program product. According to the technical scheme provided by the embodiment of the invention, the method comprises the steps: obtaining data sets corresponding to a plurality of anesthesia quality evaluation index items associated with a medical institution, and determining an anesthesia quality evaluation value of the medical institution corresponding to a first client according to the data set under each index item in the plurality of index items; wherein the index items can include but are not limited to a body temperature quality control index filling and reporting index item, an unexpected adverse event filling and reporting index item, a daily quality control data filling and reporting index item and a medical institution related information filling and reporting index item; the data set corresponding to the index item can be obtained through automatic collection and / or manual filling from related equipment. Therefore, according to the scheme provided by the embodiment of the invention, the anesthesia quality of the medical institution is evaluated more comprehensively and more efficiently, and the method has an assisting effect on improving the quality management of the medical institution.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to an anesthesia quality assessment method, system, electronic device, storage medium, and program product. Background Art

[0002] The anesthesia quality control data required for the current medical institution review process still faces difficulties in collection. Although many medical institutions have implemented medical institution-related information platforms and big data platforms, faced with new review standards and quality control requirements, many medical institutions still fail to meet the review data monitoring requirements and are unable to achieve normalized management and continuous improvement. At present, the main problems include missing data, inaccurate data, and untimely data statistics. Although some areas have achieved electronic quality control forms, quality control data cannot be automatically analyzed and summarized. There are still problems such as insufficient completeness, accuracy, and authenticity of reported data, poor data traceability, and a lack of communication mechanisms between superiors and subordinates. Especially under the premise of a relatively small number of personnel in the quality control center, how to more accurately summarize the true level of anesthesia quality control in the entire region and how to make timely improvements and adjustments to problems that arise in the anesthesia quality control process will face huge challenges.

[0003] It can be seen that establishing a detection and analysis system for anesthesia quality control indicators to better improve the work efficiency of medical staff and improve the anesthesia quality control management level of medical institutions is a problem that needs to be solved at present. Summary of the Invention

[0004] Various aspects of the present application provide an anesthesia quality assessment method, system, electronic device, storage medium, and program product to provide an assessment solution to assist in improving quality management.

[0005] A first embodiment of the present application provides a method for evaluating anesthesia quality, the method comprising: Acquire a data set corresponding to a plurality of anesthesia quality assessment indicator items associated with the first client; determining, based on a data set under each of the multiple indicator items, an anesthesia quality assessment value of the medical institution corresponding to the first client; Among them, the indicators include: temperature quality control index reporting items, unexpected adverse events reporting indicators, daily quality control data reporting indicators and medical institution related information reporting indicators; The data sets corresponding to the indicator items are obtained by automatic collection from relevant equipment and / or manual reporting.

[0006] The second embodiment of the present application provides a medical quality management system. The system includes: A first client is associated with a medical institution and is used to provide at least one data filling interface and / or collect at least one data generated by at least one device in communication with the first client; in response to data corresponding to a first indicator item filled in by a user through a data filling interface and / or data of the first indicator item collected from a device, detect the data of the first indicator item; when it is detected that the data of the first indicator item meets the preset requirements, send the data of the first indicator item to the server; when it is detected that the data of the first indicator item does not meet the preset requirements, display a prompt message indicating that the data of the first indicator item is abnormal; The server is configured to obtain a data set corresponding to a plurality of quality assessment index items associated with the first client; and determine an anesthesia quality assessment value of the medical institution corresponding to the first client based on the data set under each of the plurality of index items; Among them, the indicators include: body temperature quality control index reporting items, unexpected adverse event reporting indicators, daily quality control data reporting indicators and medical institution related information reporting indicators.

[0007] The third embodiment of the present application provides a medical quality management method. The method is applicable to a first client and includes: Providing at least one data reporting interface and / or collecting at least one data generated by at least one device in communication with the first client; In response to data corresponding to a first indicator item filled in by a user through a data filling interface and / or data of the first indicator item collected from a device, detecting the data of the first indicator item; When detecting that the data of the first indicator item meets the preset requirements, sending the data of the first indicator item to the server; When it is detected that the data of the first indicator item does not meet the preset requirements, a prompt message indicating that the data of the first indicator item is abnormal is displayed.

[0008] A fourth embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory is used to store executable instructions, and the processor executes the executable instructions to implement the steps in the above-mentioned method embodiments.

[0009] A fifth embodiment of the present application provides a computer storage medium having computer instructions stored thereon. When the instructions are executed by a processor, the steps in the anesthesia quality assessment method provided in the above embodiments can be implemented.

[0010] The sixth embodiment of the present application provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, the processor executes the steps in the anesthesia quality assessment method provided in the above embodiments.

[0011] The technical solution provided by the embodiment of the present application determines the anesthesia quality assessment value of the medical institution corresponding to the first client according to the data set under each indicator item in the multiple indicator items by obtaining the data set corresponding to the multiple anesthesia quality assessment index items associated with the medical institution; wherein the indicator items may include but are not limited to: body temperature quality control index reporting items, unexpected adverse event reporting index items, daily quality control data reporting index items and medical institution related information reporting index items; the data set corresponding to the indicator items can be obtained by automatic collection from relevant equipment and / or manual reporting. It can be seen that the solution provided by the embodiment of the present application can more comprehensively and efficiently evaluate the anesthesia quality of medical institutions, which is helpful in improving the quality management of medical institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flowchart of an anesthesia quality assessment method provided by an exemplary embodiment of the present application; Figure 2 A schematic diagram of the structure of a medical quality management system provided by an exemplary embodiment of the present application; Figure 3 、 4 5 is a schematic diagram of providing multiple interfaces on the first client in an exemplary embodiment of the present application; Figure 6 A flowchart of a medical quality management method provided as an exemplary embodiment of the present application; Figure 7 A flowchart of another medical quality management method provided as yet another exemplary embodiment of the present application; Figure 8 A schematic structural diagram of an electronic device provided as another exemplary embodiment of the present application. DETAILED DESCRIPTION

[0013] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] It should be noted that when the embodiments of this application involve user information, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation portals for users to choose to authorize or refuse. In addition, the various models involved in this application (including but not limited to language models or large models) are in compliance with relevant laws and standards.

[0015] In addition, it should be noted that when the embodiments of the present application involve user interaction operations or triggering operations, the user interaction operations or triggering operations involved in the embodiments of the present application include but are not limited to: touch operations, gesture operations, voice operations, head movement operations, eye movement operations and other interactive operations in various ways; among which, touch operations include but are not limited to: click operations, double-click operations, long press operations, sliding operations, pinch operations or mouse hover operations, etc. Sliding operations include but are not limited to: straight sliding, curved sliding, etc.

[0016] Furthermore, it should be noted that, in the case where the embodiment of the present application involves a jump between a first interface and a second interface, the jump method involved in the embodiment of the present application includes but is not limited to: jumping directly from the first interface to the second interface, jumping from the first interface to the task interface first and jumping to the second interface after completing the corresponding task operation on the task interface.

[0017] The National Anesthesia Quality Control Center was officially established at the end of 2011. Its purpose is to strengthen the management and control of medical quality in anesthesia. The center established an organizational structure, developed management standards and workflows, and began preliminary anesthesia quality control work.

[0018] We will promote the concepts and methods of anesthesia quality control nationwide, strengthen connections and cooperation with provincial and municipal clinical anesthesia quality control centers, and jointly improve the quality of anesthesia care. We are committed to promoting the refinement and scientific nature of quality control work, and continuously improve the effectiveness and quality of quality control work through data support and goal orientation.

[0019] The National Center for Quality Control has released three versions of anesthesia quality control indicator documents. These documents are designed to standardize the anesthesia process, improve anesthesia quality, and ensure patient safety. By continuously updating and optimizing anesthesia quality control indicators, we ensure that anesthesia care services remain relevant and provide patients with safer, more effective, and more comfortable medical care. These indicators primarily cover the anesthesia department's patient-doctor ratio, the proportion of anesthesia patients receiving each American Society of Anesthesiologists (ASA) classification, and the proportion of emergency non-elective anesthesia. These indicators primarily focus on the basic configuration and operation of the anesthesia department, as well as the quality of anesthesia care for patients with varying degrees of critical illness.

[0020] The current state of domestic anesthesia quality control management: Limited staff resources lead to the majority of organizations engaging in traditional manual accounting or no accounting at all, relying on manual data collection and making calculations difficult. Data collection issues: Data authenticity is difficult to verify. A common problem across provinces and cities is that reported data is manually submitted, not authentically captured, and the authenticity of the data cannot be confirmed. There are discrepancies in understanding indicator definitions, with reporting personnel varying from year to year. Information is sketchy and detailed, and reporting progress varies, leading to a lack of accuracy in verification. Quality control centers lack comprehensive data visibility, making horizontal and vertical comparisons impossible. The timeliness and completeness of data reporting vary across provinces and cities.

[0021] The technical solution provided in the embodiment of the present application can be applied to the evaluation and management of anesthesia quality in medical institutions. Figure 1 As shown, an embodiment of the present application further provides an anesthesia quality assessment method, the method comprising: S31, obtaining a data set corresponding to a plurality of anesthesia quality assessment index items associated with the first client; S32. Determine, based on the data set under each indicator item in the multiple indicator items, an anesthesia quality assessment value of the medical institution corresponding to the first client; Among them, the indicators include: temperature quality control reporting indicators, unexpected adverse event reporting indicators, daily quality control data reporting indicators and medical institution related information reporting indicators; The data sets corresponding to the indicator items are obtained by automatic collection from relevant equipment and / or manual reporting.

[0022] The technical solution provided by this embodiment obtains data sets corresponding to multiple anesthesia quality assessment index items associated with the medical institution, and determines the anesthesia quality assessment value of the medical institution corresponding to the first client according to the data sets under each index item in the multiple index items; wherein, the index items may include but are not limited to: body temperature quality control index reporting items, unexpected adverse event reporting index items, daily quality control data reporting index items and medical institution related information reporting index items; the data sets corresponding to the index items can be obtained by automatic collection from relevant equipment and / or manual reporting. It can be seen that the solution provided by the embodiment of the present application can more comprehensively and efficiently evaluate the anesthesia quality of medical institutions, which is helpful in improving the quality management of medical institutions.

[0023] Furthermore, the method of this embodiment may further include: S33. Analyze the data set under each of the multiple indicators using the analysis model, output analysis results and optimization suggestions, and display them on the first client; The analysis model is obtained by training and learning using a training sample set.

[0024] Furthermore, the method of this embodiment may further include: S34. Receive data of a first indicator item sent by the first client; wherein the first client is associated with a medical institution; S35. Storing the data of the first indicator item in a data set corresponding to the first indicator item; S36. Perform statistical analysis on multiple data in the data set under the first indicator item to obtain statistical information corresponding to the first indicator item of a single medical institution; and / or perform statistical analysis on multiple data in the data set under the first indicator item sent by the first client corresponding to different medical institutions belonging to the same area to obtain statistical information corresponding to the first indicator item in the area.

[0025] Before storing the data of the first indicator item in the data set corresponding to the first indicator item, the method further includes: Checking whether the data of the first indicator item meets the preset requirements; If the requirements are met, a step of storing the data of the first indicator item in a data set corresponding to the first indicator item is triggered; If the requirements are not met, a message is sent to the first client so that the first client displays a prompt message indicating that the data of the first indicator item is abnormal.

[0026] Furthermore, in this embodiment, the data set corresponding to the temperature quality control index reporting item may include data uploaded through the temperature data reporting interface and / or temperature data generated by at least one temperature management device obtained through collection. The data set corresponding to the unexpected adverse event reporting module may include: data uploaded through the unexpected adverse event reporting interface. The data set corresponding to the daily quality control data reporting module may include: data uploaded through the daily quality control data reporting interface; wherein the daily quality control data includes at least one of the following: the number of patients undergoing anesthesia for each type of surgery, the number of patients receiving blood transfusion during anesthesia, the number of cases where pre-anesthesia visits were completed before surgery, the number of general anesthesia cases with temperature testing during surgical anesthesia, the total number of patients receiving autologous blood transfusion during surgical anesthesia, the number of patients with postoperative analgesia follow-up VAS less than the set value in the anesthesia department, the total number of patients receiving postoperative analgesia in the anesthesia department, the total number of patients admitted to the PACU, the number of patients with hypothermia during surgical anesthesia, and the number of patients with body temperature admitted to the PACU per unit time. The data set corresponding to the medical institution related information reporting indicator item may include data uploaded through the medical institution related information reporting interface.

[0027] Furthermore, in the method provided in this embodiment, step S32 of "determining the anesthesia quality assessment value of the medical institution corresponding to the first client based on the data set under each indicator item in the multiple indicator items" may include: S321. Determine a data quality index corresponding to each indicator item according to a data set under each indicator item in the multiple indicator items; S322. Determine an anesthesia quality assessment value of the medical institution corresponding to the first client based on a data quality index corresponding to each indicator item in the multiple indicator items.

[0028] The multiple indicator items include a second indicator item, and the data set under the second indicator item includes: multiple body temperature data and the reported first probability value. Accordingly, in one achievable embodiment, the above step S321 "determining the data quality index corresponding to the second indicator item based on the data set under the second indicator item" may include the following steps: S3211. Analyze, using a preset model, whether the plurality of body temperature data conform to a set change pattern; S3212: If yes, calculate a second probability value based on the plurality of body temperature data, and compare the first probability value with the second probability value; S3213: If the deviation between the first probability value and the second probability value is within a set range, determine that the data quality index corresponding to the second indicator item is a first set value; S3214: If the deviation between the first probability value and the second probability value is not within a set range, a comparison with medical institutions of the same level in the same region is triggered, and a data quality index corresponding to the second indicator item is determined based on the comparison result; S3215: If not, the data quality index corresponding to the second indicator item is reduced from the first set value to the second set value. In the above step S3214, "triggering a comparison with medical institutions of the same level in the same region, and determining the data quality index corresponding to the second indicator item based on the comparison result" may include: S01. Obtain the average probability value of medical institutions of the same level in the same area; S02. If the second probability value of the medical institution corresponding to the first client is lower than the average probability value, rank multiple medical institutions in the same area based on the medical institution-related information of each medical institution; S03. If the ranking of the medical institution corresponding to the first client in the ranking result is at or before the set ranking, determining the data quality index corresponding to the second indicator item to be a first set value; S04. If the ranking of the medical institution corresponding to the first client in the ranking result is after the set ranking, obtaining a medical institution corresponding to a second client in the same area that has a second probability value similar to that of the medical institution corresponding to the first client; S05. Analyze the medical disparity between the two medical institutions based on the medical-related information of the medical institution corresponding to the first client and the medical-institution-related information of the medical institution corresponding to the second client; S06. If the medical gap between the medical institution corresponding to the first client and the medical institution corresponding to the second client is greater than a threshold, the data quality index corresponding to the second indicator item is reduced from the first set value to a third set value; S07. If the medical gap between the medical institution corresponding to the first client and the medical institution corresponding to the second client is less than or equal to a threshold, the data quality index corresponding to the second indicator item is reduced from the first set value to a fourth set value.

[0029] Furthermore, step S321 “determining the data quality index corresponding to the second indicator item based on the data set under the second indicator item” may further include the following steps: Analyze the change trend of the first probability value corresponding to multiple consecutive time periods based on the data set under the second indicator; If the upward trend fluctuation of the first probability value exceeds the fluctuation threshold within a time period, and the medical-related information does not change or the change does not exceed the preset amount within the time period, the data quality index corresponding to the second indicator item is reduced from the first set value to the fifth set value.

[0030] Furthermore, step S321 “determining the data quality index corresponding to the second indicator item based on the data set under the second indicator item” may further include the following steps: If the data set under the second indicator item has not changed for multiple consecutive cycles, or the patient to whom the temperature data in the data set belongs has not changed, or the active insulation mark in the temperature data is missing or the set duration has not changed, then the data quality index corresponding to the second indicator item is reduced from the first set value to the sixth set value.

[0031] It should be noted that the first probability value can be the incidence rate of hypothermia or the active heat preservation rate. The first, second, third, fourth, fifth, and sixth set values ​​are not limited in this embodiment and can be set based on actual implementation.

[0032] To facilitate understanding of the above scheme, the incidence of hypothermia during surgical anesthesia is used as an example to illustrate.

[0033] A Class A tertiary comprehensive hospital reported quality control reporting and related quality control construction data from January to December 2024.

[0034] In the existing reporting system, only the number of hypothermia cases and the number of patients undergoing general anesthesia undergoing temperature measurement are required. However, this system will require more detailed and accurate data reporting, such as detailed temperature data for the sampled population, the number of medical staff, their configuration, temperature measurement equipment, regulations, and improvement measures for the current year. For example, the hypothermia rate in April 2024 is 30%.

[0035] First, the system performs AI-powered analysis based on the reported temperature data of a sample of patients, examining whether the changes in the data conform to the patterns of temperature changes observed during surgery. If so, the system then compares the hypothermia rate calculated from the consistent data with the actual reported hypothermia rate. If the deviation is within a certain range, such as less than 5%, the data quality index is initially assigned a value of 100.

[0036] Next, the system analyzes the average hypothermia rate among hospitals of the same rank in the region. For example, if the average hypothermia rate among hospitals of the same rank in the region is 35%, the system will intelligently analyze and rank the hospital based on its medical staff numbers, staffing, temperature measurement equipment, and regulations. If the hospital's ranking is higher than the regional median, the system maintains the original quality index of 100. If the ranking lags behind the regional median, the system will compare it to other top-ranked hospitals with a hypothermia rate of 30%. If the gap is significant, the system will lower the quality index to 80.

[0037] Furthermore, the system will automatically compare the hospital's historical data for the past 12 months. If it is found that the upward trend of the incidence of hypothermia fluctuates greatly within a certain period of time, and at the same time, there have been no major changes in the number of medical staff, configuration, temperature measurement equipment, rules and regulations of the hospital during this period of time, the quality index will be reduced to 60.

[0038] At the same time, the system will also analyze the hospital's zombie data. For example, if the incidence of hypothermia remains constant for several consecutive months, the quality index will be automatically defined as 0, including the detailed temperature data of the sampled proportion of people submitted. If the temperature data of each patient is the same or has been the same for several consecutive months, the quality index can be defined as 0.

[0039] Let’s take the incidence of hypothermia during surgical anesthesia as an example.

[0040] A Class A tertiary general hospital submitted quality control reporting and related quality control construction data from January to December 2024; Compared to previous information reporting systems, this system no longer satisfies simple statistics on the number of surgeries under general anesthesia and the number of active warming procedures implemented during surgery. Instead, it requires the integration of more detailed and authentic data elements. This includes, but is not limited to, sampling and analyzing patient temperature data to identify any signs of active warming and assessing whether the patient data conforms to the expected warming trend. The system also delves into multiple aspects, including medical staff ratios, the use of active warming equipment, equipment qualification rates, relevant regulations and systems, and annual improvement measures.

[0041] For example, in June 2024, the reported active temperature retention rate was 50%. First, the system performs an in-depth AI analysis of the reported temperature data to verify whether its changes comply with temperature management standards during surgery. If the data changes as expected, the system further compares the active temperature retention rate calculated based on the data with the actual reported active temperature retention rate. If the deviation between the two remains within a reasonable range, for example, within 5%, the data quality index is initially determined to be 100.

[0042] The system also analyzes patient data to determine whether active warming measures were implemented using active warming devices or passive methods. If the data lacks a clear indication of active warming, the data quality index is adjusted to 90.

[0043] Next, the system examines the average active thermal insulation rate among hospitals of the same tier in the region, assuming it's 35%. Based on this, the system conducts a comprehensive, intelligent assessment and ranking of the target hospitals based on their medical staff ratio, resource allocation, active thermal insulation equipment utilization, equipment qualification rate, and regulations. If a hospital ranks above the regional median, its data quality index remains unchanged. If it lags behind, the system compares it to higher-ranked hospitals with similar active thermal insulation rates (e.g., 30%). If a significant gap remains, the data quality index is lowered to 80.

[0044] The system also automatically integrates the target hospital's historical data from the past 12 months for in-depth comparative analysis. If an abnormal upward trend in active thermal insulation rates is detected over a specific period, while no significant changes have occurred in the medical staff ratio, active thermal insulation equipment, equipment qualification rates, regulations, or improvement measures during the same period, the data quality index will be reduced to 60.

[0045] Equally important, the system screens hospitals for "zombie data." If the active temperature retention rate remains unchanged for multiple consecutive months, or if the active temperature retention mark in the submitted sampled temperature data is missing or remains unchanged for an extended period, the system will automatically assign a data quality index of 0 to ensure data authenticity and validity.

[0046] The multiple indicators include a third indicator, and the data set under the third indicator includes: multiple postoperative analgesia visit score data and reported score data. Accordingly, in another achievable embodiment, the above step S321 "determining the data quality index corresponding to the third indicator based on the data set under the third indicator" may include the following steps: A. Analyzing the deviation between the multiple postoperative analgesia visit score data and the reported score data using a preset model; B. If the deviation is within the set range, determining the data quality index corresponding to the third indicator item to be the first set value; C. If the deviation is not within the set range, calculating the postoperative analgesia satisfaction rate of the medical institution corresponding to the first client based on the multiple postoperative analgesia visit score data, and obtaining the average postoperative analgesia satisfaction rate of medical institutions of the same level in the same area; D. if the postoperative analgesia satisfaction rate of the medical institution corresponding to the first client is higher than the postoperative analgesia satisfaction rate, ranking multiple medical institutions in the same area based on the medical institution-related information of each medical institution; E. If the ranking of the medical institution corresponding to the first client in the ranking result is at or before the set ranking, determining the data quality index corresponding to the second indicator item to be a first set value; F. If the ranking of the medical institution corresponding to the first client is after the set ranking in the ranking results, obtain a medical institution corresponding to a second client in the same region that has a postoperative analgesia satisfaction rate similar to that of the medical institution corresponding to the first client; G. Analyzing the medical disparity between the two medical institutions based on the medical-related information of the medical institution corresponding to the first client and the medical-institution-related information of the medical institution corresponding to the second client; H. If the medical gap between the medical institution corresponding to the first client and the medical institution corresponding to the second client is greater than a threshold, the data quality index corresponding to the second indicator item is reduced from the first set value to a third set value; I. If the medical gap between the medical institution corresponding to the first client and the medical institution corresponding to the second client is less than or equal to a threshold, the data quality index corresponding to the second indicator item is reduced from a first set value to a fourth set value.

[0047] Furthermore, the step of “determining a data quality index corresponding to the third indicator item based on the data set under the third indicator item” may also include: J. Based on the data set under the third indicator, analyze the changing trend of postoperative analgesia satisfaction rate corresponding to multiple consecutive time periods; K. If the upward trend fluctuation of the postoperative analgesia satisfaction rate exceeds the fluctuation threshold within a period of time, and the medical-related information does not change or the change does not exceed the preset amount within the period of time, then the data quality index corresponding to the third indicator item is reduced from the first set value to the fifth set value.

[0048] Furthermore, the step of “determining a data quality index corresponding to the third indicator item based on the data set under the third indicator item” may also include: L. If the data set under the third indicator item does not change in multiple consecutive cycles, or the postoperative analgesia satisfaction rate does not change in multiple consecutive cycles, the data quality index corresponding to the third indicator item is reduced from the first set value to the sixth set value.

[0049] To facilitate understanding of the above scheme, the following explanation is given using the postoperative analgesia satisfaction rate as an example.

[0050] A Grade A general hospital submitted quality control reporting and related quality control construction data from January to December 2024; Compared to previous reporting models, this system significantly improves the detail and authenticity of data reporting. It's no longer limited to simply reporting the number of patients with analgesia visit scores below 3 compared to the total number of patients receiving postoperative analgesia to calculate a ratio. Instead, it comprehensively supports detailed data reporting from multiple dimensions, including but not limited to sampling and analyzing pain ratings of patients receiving postoperative analgesia, as well as in-depth examinations of medical staff ratios, the use of analgesia pumps, the selection of pain assessment tools, patient education, and annual improvement measures.

[0051] For example, in September 2024, the reported postoperative analgesia satisfaction rate was 20%. The system first meticulously analyzes the reported data, using artificial intelligence to sample and analyze detailed pain score data from patients, paying particular attention to follow-up analgesia scores below 3 compared to the overall score. If the overall data deviates from the actual reported data within 5%, the data quality index is initially determined to be 100.

[0052] Next, the system analyzes the postoperative analgesia satisfaction rates of similar hospitals in the region, assuming the average rate is 25%. Based on this, the system intelligently evaluates and ranks the target hospitals based on their medical staffing, staffing, use of analgesia pumps, equipment qualification rates, and regulations. If a hospital ranks above the regional median, its data quality index remains unchanged at 100. If it lags behind, the system compares it to higher-ranked hospitals with similar postoperative analgesia satisfaction rates (e.g., 30%). If a significant gap exists, the data quality index is lowered to 80.

[0053] In addition, the system automatically integrates the target hospital's historical data from the past 12 months for in-depth comparative analysis. If an abnormal upward trend in postoperative analgesia satisfaction is detected within a specific period, while no significant changes have occurred in the ratio of medical staff to nurses, analgesia pump equipment, pain assessment tools, patient education, or improvement measures during the same period, the data quality index will be reduced to 60.

[0054] Similarly, the system will also screen the hospital's "zombie data." If the postoperative analgesia satisfaction rate remains unchanged for several consecutive months, or if this indicator is not actually implemented in the submitted sample analgesia visit score data, or if the data shows high consistency without significant fluctuations, the system will automatically determine the data quality index as 0 to ensure the authenticity and validity of the data. To this end, the present application provides the following multiple embodiments to provide an anesthesia quality management system, a medical quality management method and an electronic device to solve or improve some of the above-mentioned problems.

[0055] like Figure 2 As shown, an anesthesia quality management system provided by an embodiment of the present application includes: A first client 1 is associated with a medical institution and is used to provide at least one data filling interface and / or collect at least one data generated by at least one device in communication with the first client 1; in response to data corresponding to a first indicator item filled in by a user through a data filling interface and / or data of a first indicator item collected from a device, detect the data of the first indicator item; when it is detected that the data of the first indicator item meets the preset requirements, send the data of the first indicator item to the server; when it is detected that the data of the first indicator item does not meet the preset requirements, display a prompt message indicating that the data of the first indicator item is abnormal; The server 2 is configured to obtain a data set corresponding to a plurality of quality assessment indicators associated with the first client; and determine an anesthesia quality assessment value of the medical institution corresponding to the first client based on the data set under each of the plurality of indicators; Among them, the indicators include: body temperature quality control index reporting items, unexpected adverse event reporting indicators, daily quality control data reporting indicators and medical institution related information reporting indicators.

[0056] Furthermore, the server is also used to store the data of the first indicator item sent by the first client 1, perform statistical analysis on multiple data under the first indicator item to obtain statistical information corresponding to the first indicator item of a single medical institution; and / or perform statistical analysis on multiple data under the first indicator item sent by the first client 1 corresponding to different medical institutions belonging to the same area to obtain statistical information corresponding to the first indicator item within the area.

[0057] like Figure 2 As shown, the system provided in this embodiment may further include a second client 3. Accordingly, the first client 1 and / or the second client 3 is used to display the statistical information corresponding to the first indicator item of the single medical institution and / or the statistical information corresponding to the first indicator item in the region; The second client 3 is associated with a quality control party.

[0058] The technical solution provided by this embodiment provides a first client for data reporting and / or automatic data collection. Users can report data or automatically collect data through the first client, which solves the problem of manual accounting in the prior art. In addition, the solution of automatic data collection can improve the problem of uncertainty in data authenticity. In addition, in this embodiment, the first client has pre-defined various indicator items. Users can fill in the corresponding indicator items on the first client. The data of the indicator items filled in by the user can also be tested to promptly reflect whether the indicator item data is abnormal, thereby improving the problem of deviation in the understanding of the indicator definition by the reporting personnel. The indicator item data reported by the user through the first client or automatically collected by the first client can be sent to the server. The server can evaluate the anesthesia quality of the medical institution based on the data set of multiple indicator items. The server can also perform statistical analysis on all received data. The statistical information can also be displayed on the first client and / or the second client for easy reference by the medical institution and / or quality control party. In this embodiment, the server evaluates the data sets corresponding to the temperature quality control index reporting items, the data sets corresponding to the unexpected adverse event reporting items, the data sets corresponding to the daily quality control data reporting items, and the data sets corresponding to the medical institution related information reporting items. The evaluation results are comprehensive and efficient, which can help further improve the quality management of medical institutions.

[0059] The system provided in this embodiment can be understood as an anesthesia quality control platform, which is divided into a reporting end (i.e., the first client) and a quality control center management end (i.e., the service end). The core is to promote intelligent analysis of anesthesia quality control data.

[0060] Reporting end: used by department teachers, usually by quality control secretaries to report anesthesia professional medical quality control related data.

[0061] Quality control center analysis and management terminal: used to manage the anesthesia quality control indicators and data of each medical institution in the area.

[0062] The second client is used by users in the quality control center to review and approve various data.

[0063] Based on the collected data, the anesthesia quality control system can provide decision support to management. Through data analysis, management can understand the department's operating status, anesthesia quality level, and existing problems, thereby formulating more reasonable quality control strategies and management measures.

[0064] This embodiment shows that the first client not only provides a data filling interface, but also provides Figure 3The login interface shown. This login interface can be a web interface, an application (APP) interface on a mobile device, or a third application (or mini-program) interface built on a second application on the mobile device that does not require downloading and installation, etc. This embodiment does not specifically limit this. Figure 4 The data filling interface provided by the first client is shown. Figure 5 It shows the data statistics information interface provided by the first client.

[0065] In specific implementation, the digital reporting of quality control data, unexpected adverse event reporting, and temperature quality control management can be used as evaluation pilots to establish an authenticity evaluation and analysis system for anesthesia quality control indicators, thereby better improving the work efficiency of medical staff, improving the level of anesthesia quality control management in medical institutions, strengthening patient safety, and comprehensively improving the quality of anesthesia medical services. That is, in an implementable example, the first client may include: A temperature quality control reporting module, configured to provide a temperature data reporting interface and / or collect temperature data generated by at least one temperature management device; Unexpected adverse event reporting module, used to provide an unexpected adverse event reporting interface; The daily quality control data reporting module is used to provide a daily quality control data reporting interface, wherein the daily quality control data includes at least one of the following: the number of patients undergoing anesthesia for each type of surgery, the number of patients receiving blood transfusion during anesthesia, the number of patients who completed pre-anesthesia visits before surgery, the number of patients undergoing general anesthesia who received temperature checks during surgery, the total number of patients receiving autologous blood transfusion during surgery, the number of patients whose VAS was less than the set value during postoperative analgesia follow-up in the anesthesia department, the total number of patients receiving postoperative analgesia in the anesthesia department, the total number of patients admitted to the PACU, the number of patients with hypothermia during surgery anesthesia, and the number of patients with temperature admitted to the PACU per unit time; The medical institution related information management module is used to provide an interface for filling in medical institution related information.

[0066] Information related to medical institutions may include but is not limited to: department data, equipment data, medical staff information, etc.

[0067] The daily quality control data reporting module provided by the first client also has a detection function, that is, when data is detected that does not occur at a certain time, it is prohibited to report it to avoid arbitrary reporting; it also detects whether there are any reporting errors in the data, that is, it performs intelligent error correction on the reported data. Based on the anesthesia medical and nursing information reported by the user, the corresponding quality control indicators can be calculated, such as the anesthesia doctor-nurse ratio, the annual number of anesthesia cases performed by anesthesiologists, etc. By regularly updating and verifying this information, the system can establish a reliable personnel database and provide an accurate data source for subsequent statistical analysis. By statistically analyzing medical and nursing data, the rationality of staffing can be evaluated, potential bottlenecks and problems can be discovered, and a basis for optimizing staffing can be provided. This improves the efficiency and accuracy of statistical work and provides strong support for the continuous improvement and optimization of anesthesia quality control work.

[0068] Users can report unexpected adverse events through the unexpected adverse event reporting interface. An adverse event reporting data pool is established to record detailed data for each adverse event. This supports the intelligent scoring system to score the authenticity analysis of quality control instruments and provide improvement suggestions, allowing for faster intervention on adverse events and potential safety hazards.

[0069] Body temperature data, daily quality control data, information related to medical institutions and non-adverse events, etc. can be reported in real time or periodically (such as once a week, once a month or once a quarter).

[0070] Accurately collecting reported data lays a solid foundation for AI-powered analysis. This data not only enriches analytical material but also enhances the accuracy and comprehensiveness of quality control analysis. Through in-depth analysis of this data, the system can accurately identify potential risks, optimize anesthesia processes, and improve quality control. This is crucial for quality control analysis and helps drive continuous improvement in anesthesia services and patient safety.

[0071] Medical institution department data is used to evaluate department levels, optimize processes and establish unified standards, thereby improving quality control accuracy and efficiency, helping to ensure patient safety and promote continuous improvement and development of departments.

[0072] Reporting quality control data enhances the accuracy and comprehensiveness of analysis, helping to identify potential risks and optimize anesthesia processes. This is of great significance to quality control analysis and can drive improvements in quality control levels.

[0073] Adverse event reporting enriches the case library for risk assessment and decision support. Artificial intelligence can summarize the occurrence patterns, causes and preventive measures of adverse events, and provide guidance for subsequent anesthesia quality control.

[0074] Body temperature data is reported, which helps to accurately predict patient temperature changes and identify potential risks.

[0075] Accordingly, in some specific embodiments, for example, when the data corresponding to the first indicator item is body temperature data, when the first client 1 detects the data of the first indicator item, the body temperature quality control reporting module is used to: According to a first preset cycle, periodically detecting the amount of body temperature data generated by the user through the body temperature data reporting interface and / or collected by at least one body temperature management device within a time period corresponding to each cycle; If it is detected that the number of body temperature data reported and / or collected in a cycle is less than the preset number, the difference is calculated and displayed.

[0076] The body temperature quality control reporting module is also used to: Generate and display intraoperative hypothermia risk assessment information for each patient based on the reported and / or collected temperature data; and / or Based on the reported and / or collected temperature data, analyze and display at least one of the following: hypothermia rate, PACU admission hypothermia rate, active warming rate, and delayed awakening rate within a specified time period.

[0077] The daily quality control data reporting module is further used to periodically remind the user to report daily quality control data according to a second preset period.

[0078] Among them, periodic reminders for users to fill in daily quality control data include at least one of the following: If the first client is an application on a mobile device, a pop-up window is displayed on the interface of the mobile device to remind the user to fill in daily quality control data; If the first client is a web client, obtain the mobile device bound to the first client account, and send information to the bound mobile device to remind the user to fill in daily quality control data; or obtain the email address bound to the first client account, and send information via email to remind the user to fill in daily quality control data.

[0079] When the data corresponding to the first indicator item is daily quality control data, when the first client detects the data of the first indicator item, the daily quality control data reporting module is used to: Test the daily quality control data generated during a period corresponding to a cycle reported by the user to detect target daily quality control data that does not match data among multiple daily quality control data; When target daily quality control data with data mismatch is detected, a prompt message indicating the abnormality of the target daily quality control data and the reason for the abnormality are displayed.

[0080] It should be added here that the first client 1 in this embodiment can be a web client, or a first application on a mobile device, or a third application (or mini program) built on a second application on the mobile device that does not require downloading and installation.

[0081] Furthermore, in this embodiment, when the server 2 performs statistical analysis on the multiple data under the first indicator item to obtain statistical information corresponding to the first indicator item of a single medical institution, it is used to: Statistical analysis is performed on multiple data under the first indicator item to determine the reporting quality of the first indicator item of a single medical institution.

[0082] Furthermore, the system provided by this embodiment supports the audit of anesthesia quality control, body temperature data, and adverse event data through configuration; account audit is only visible to the quality control center role. That is, the quality control party can log in to the account through the second client to enter the corresponding interface and conduct manual audit. If the manual audit is passed, the status of the data audited by the first client side can be displayed as "audit passed" and so on. If the manual audit fails, the status of the data audited by the first client side can be displayed as "rejected". The user on the first client side needs to fill in the form again. That is, in the system provided by this embodiment, The second client 3 is further configured to display the plurality of data under the first indicator in response to a user's operation of viewing the plurality of data under the first indicator; and to send a rejection message to the server in response to a user's rejection operation triggered on the displayed plurality of data; The server 2 is used to respond to the rejection information, obtain the medical institution, the first client 1 and the indicator items targeted by the rejection information; and send information that multiple data under the first indicator items need to be re-filled to the first client 1 associated with the medical institution targeted by the rejection information.

[0083] Server 2 can compile statistics on all data reported by the first client. The second client, the quality control user, can view data preview information for the quality control user's permissions on the second client's interface. For example, statistics on the total number of general anesthesia cases per month, statistics on adverse event reports per month, statistics on the proportion of four temperature-related indicators per month, and statistics on the professional titles and educational qualifications of medical staff.

[0084] Among them, the four indicators related to body temperature may include: intraoperative body temperature detection rate, incidence of hypothermia during surgical anesthesia, incidence of hypothermia upon admission to the PACU, and active warming rate during surgery.

[0085] Furthermore, the server 2 in the system of this embodiment is also used for: Storing data of multiple indicator items sent by the first client; Analyzing the plurality of data under each of the plurality of indicator items using the analysis model, outputting analysis results and optimization suggestion information for display on the first client; The analysis model is obtained by training and learning using a training sample set.

[0086] Furthermore, the server 2 is further configured to: Based on the stored data of multiple indicator items of each medical institution, a quality score is given to each medical institution according to the preset quality scoring rules.

[0087] Multi-dimensional data analysis of quality control center data and medical institutions. Through multi-institutional trend analysis, the anesthesia quality control system can gain insight into the commonalities and differences in the anesthesia process among various medical institutions, revealing the dynamic patterns of anesthesia quality changes over time, providing a scientific basis for policymakers, and promoting continuous improvement and development of the entire industry. Comparative analysis further highlights the differences in strengths and weaknesses between institutions, helping medical institutions recognize their own position and potential in the field of anesthesia. This comparison also provides a valuable opportunity for experience exchange and learning among medical institutions. Average analysis is an objective evaluation of the overall anesthesia quality, reflecting the average level of an entire region or industry, providing a reference for medical institutions to set reasonable quality goals. Medical institutions can clearly understand the gap between themselves and the average level and formulate targeted improvement measures.

[0088] In the analysis of adverse event data for anesthesia quality control, this solution leverages the power of data science. Using big data analysis techniques, it deeply explores the causes, influencing factors, and development trends of various adverse events. Leveraging advanced algorithmic models, it accurately processes and analyzes massive amounts of data, resulting in a series of guiding conclusions and recommendations. Regarding departmental interoperative and instrument data analysis, data percentages for departmental and PACU equipment are analyzed, allowing departmental performance to be measured using data from multiple institutions.

[0089] This embodiment also provides a quality control evaluation and analysis system based on AI big data, with three scoring systems: quality control score, quality score, and effectiveness score. Highly flexible and customizable, it accurately and comprehensively reflects the score of anesthesia quality, promoting the development of quality control work in medical institutions and improving the quality of medical services.

[0090] Quality control analysis is the core of data analysis. Comprehensive analysis of anesthesia quality control data allows assessment of the safety, effectiveness, and stability of anesthesia quality. Anesthesia quality can be evaluated using anesthesia quality control scores, quality scores, and effectiveness scores. Furthermore, based on the analysis results, targeted improvement measures can be proposed to enhance anesthesia quality and safety, providing a more comprehensive picture of quality management and control during the anesthesia medical process.

[0091] Through data model training and deep learning, as well as quality control indicator prediction results, quality control indicator analysis reports can be automatically generated. The analysis results and optimization suggestions are presented to management in a visual format, guiding the anesthesia team to achieve data-driven decision-making, identify potential problems in advance, optimize quality control measures, and improve anesthesia quality and patient safety. Through predictive analysis reports, managers can identify safety hazards in the clinical anesthesia workflow, thereby improving anesthesia quality and perioperative safety.

[0092] Starting with quality control and quality management, the system has established a more systematic and standardized anesthesia quality control system. Through the implementation of quality control ledger management measures, records of quality control-related activities, organization meeting information, and measures to promote quality management implementation are recorded. This can also be included in the assessment points of quality control and quality management work, helping to improve the professional quality and skills of volunteers and promote the overall medical level of medical institutions.

[0093] Leveraging big data analytics capabilities, historical data and trend analysis automatically aggregates and generates a quality control dashboard, predicting future trends in quality control indicators. This visualization platform allows managers to view the province's overall quality control performance, the rankings of municipal medical institutions, and the occurrence of unexpected adverse events. It also supports municipal quality control centers in reviewing their own anesthesia quality control status. By comparing and analyzing data, they can identify potential quality issues and facilitate timely correction and improvement.

[0094] Figure 6 The flowchart of the medical quality management method provided by an embodiment of the present application is shown. The execution subject of the method provided by this embodiment can be the first client in the above system embodiment. Specifically, the method includes: S11. Providing at least one data reporting interface and / or collecting at least one data generated by at least one device in communication with the first client; S12. In response to data corresponding to a first indicator item submitted by a user through a data submission interface and / or data of the first indicator item collected from a device, detecting the data of the first indicator item; S13. When detecting that the data of the first indicator item meets the preset requirements, sending the data of the first indicator item to the server; S14: When it is detected that the data of the first indicator item does not meet the preset requirements, a prompt message indicating that the data of the first indicator item is abnormal is displayed.

[0095] Furthermore, when the data corresponding to the first indicator item is body temperature data, detecting the data of the first indicator item includes: According to a first preset cycle, periodically detecting the amount of body temperature data generated by the user through the body temperature data reporting interface and / or collected by at least one body temperature management device within a time period corresponding to each cycle; If it is detected that the number of body temperature data reported and / or collected in a cycle is less than the preset number, the difference is calculated and displayed.

[0096] Furthermore, the method provided in this embodiment may further include the following steps: S15. Generate and display intraoperative hypothermia risk assessment information corresponding to each patient based on the reported and / or collected body temperature data; and / or S16. Based on the reported and / or collected temperature data, analyze and display at least one of the following: hypothermia rate, PACU admission hypothermia rate, active warming rate, and delayed awakening rate within a specified time period.

[0097] When the data corresponding to the first indicator item is daily quality control data, the method further includes: According to the second preset cycle, periodically remind users to fill in daily quality control data; Among them, the daily quality control data include at least one of the following: the number of patients undergoing anesthesia for each type of surgery, the number of patients receiving blood transfusion during anesthesia, the number of cases in which pre-anesthesia visits were completed before surgery, the number of general anesthesia cases in which body temperature was detected during surgical anesthesia, the total number of patients receiving autologous blood transfusion during surgical anesthesia, the number of patients whose VAS was less than the set value during postoperative analgesia follow-up in the Department of Anesthesiology, the total number of patients receiving postoperative analgesia in the Department of Anesthesiology, the total number of patients admitted to the PACU, the number of patients with hypothermia during surgical anesthesia, and the number of patients with body temperature admitted to the PACU per unit time.

[0098] When the data corresponding to the first indicator item is daily quality control data, the data of the first indicator item is tested, including: Test the daily quality control data generated during a period corresponding to a cycle reported by the user to detect target daily quality control data that does not match data among multiple daily quality control data; When target daily quality control data with data mismatch is detected, a prompt message indicating the abnormality of the target daily quality control data and the reason for the abnormality are displayed.

[0099] The detailed implementation and beneficial effects of each step in the method of this embodiment have been described in detail in the aforementioned embodiments and will not be elaborated here.

[0100] Figure 7 The flowchart of the medical quality management method provided by an embodiment of the present application is shown. The execution subject of the method provided by this embodiment can be the server in the above system embodiment. Specifically, the method includes: S21. Receive data of a first indicator item sent by a first client; wherein the first client is associated with a medical institution; S22. Storing the data of the first indicator item in a data set corresponding to the first indicator item; S23. Perform statistical analysis on multiple data in the data set under the first indicator item to obtain statistical information corresponding to the first indicator item of a single medical institution; and / or perform statistical analysis on multiple data in the data set under the first indicator item sent by the first client corresponding to different medical institutions belonging to the same area to obtain statistical information corresponding to the first indicator item in the area.

[0101] Furthermore, the method may further comprise the following steps: S24. Obtain data sets corresponding to multiple indicator items associated with the first client; S25. Analyze multiple data in the data set under each of the multiple indicator items using the analysis model, output analysis results and optimization suggestion information, and display them on the first client; The analysis model is obtained by training and learning using a training sample set.

[0102] Furthermore, the method may further comprise the following steps: S26. Based on the multiple data in the data set corresponding to the multiple indicator items associated with the first client, perform quality scoring on each medical institution according to a preset quality scoring rule.

[0103] The detailed implementation and beneficial effects of each step in the method of this embodiment have been described in detail in the aforementioned embodiments and will not be elaborated here.

[0104] Further, Figure 8 FIG2 shows an electronic device provided by an embodiment of the present application, which includes a memory 54 and a processor 55 .

[0105] The memory 54 is used to store computer programs and may be configured to store various other data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on the electronic device, data structures, contact data, phone book data, messages, images, videos, etc.

[0106] The processor 55 is coupled to the memory 54 and is configured to execute the computer program in the memory 54 to perform the steps in any of the above method embodiments.

[0107] Further, if Figure 8 As shown, the electronic device also includes: a communication component 56, a display 57, a power component 58, an audio component 59 and other components. Figure 8 Only some components are shown schematically, which does not mean that the electronic device only includes Figure 8 In addition, Figure 8 The components within the dashed box are optional, not mandatory, and depend on the product form factor of the worker node. The worker node of this embodiment can be implemented as a terminal device such as a desktop computer, laptop computer, smartphone, or IoT device, or as a server-side device such as a conventional server, cloud server, or server array.

[0108] The above-mentioned memory can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random-access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0109] The communication component is configured to facilitate wired or wireless communication between the device in which the communication component resides and other devices. The device in which the communication component resides can access a wireless network based on a communication standard, such as a 2G, 3G, 4G / LTE, 5G, or other mobile communication network, or a combination thereof. In an exemplary embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel.

[0110] The display includes a screen, which may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, it may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensors can detect not only the boundaries of a touch or slide action, but also the duration and pressure associated with the touch or slide operation.

[0111] The power supply assembly provides power to various components of the device in which the power supply assembly is located. The power supply assembly may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device in which the power supply assembly is located.

[0112] The above-mentioned audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC). When the device where the audio component is located is in an operating mode, such as call mode, recording mode, and voice recognition mode, the microphone is configured to receive external audio signals. The received audio signal can be further stored in the memory or sent via the communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals.

[0113] Accordingly, an embodiment of the present application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to implement the steps in the above method embodiment. The computer-readable storage medium includes volatile or non-volatile or a combination thereof, and may be removable or non-removable. Examples of computer-readable storage media include, but are not limited to, phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, magnetic cassette, tape disk storage or other magnetic storage device or any other non-transmission medium. Accordingly, an embodiment of the present application further provides a computer program product, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the processor is enabled to implement the steps in the above-mentioned method embodiment. It should be understood that each process or a combination of multiple processes in the above-mentioned method flow can be implemented by a computer program or instruction. In addition, these computer programs or instructions can be applied to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device, so that the processor of the general-purpose computer, the special-purpose computer, the embedded processor or other programmable data processing device can be implemented as a device for implementing the corresponding functions in the above-mentioned method embodiment.

[0114] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for evaluating anesthesia quality, characterized in that: include: Acquire a data set corresponding to a plurality of anesthesia quality assessment indicator items associated with the first client; determining, based on a data set under each of the multiple indicator items, an anesthesia quality assessment value of the medical institution corresponding to the first client; Among them, the indicators include: temperature quality control reporting indicators, unexpected adverse event reporting indicators, daily quality control data reporting indicators and medical institution related information reporting indicators; The data sets corresponding to the indicator items are obtained by automatic collection from relevant equipment and / or manual reporting.

2. The method according to claim 1, characterized in that Also includes: Analyzing the data set under each of the multiple indicators using the analysis model, outputting analysis results and optimization suggestions for display on the first client; The analysis model is obtained by training and learning using a training sample set.

3. The method according to claim 1 or 2, characterized in that Also includes: Receiving data of a first indicator item sent by the first client; wherein the first client is associated with a medical institution; Storing the data of the first indicator item in a data set corresponding to the first indicator item; Perform statistical analysis on multiple data in the data set under the first indicator item to obtain statistical information corresponding to the first indicator item of a single medical institution; and / or perform statistical analysis on multiple data in the data set under the first indicator item sent by the first client corresponding to different medical institutions belonging to the same area to obtain statistical information corresponding to the first indicator item in the area.

4. The method according to claim 3, characterized in that Before storing the data of the first indicator item in the data set corresponding to the first indicator item, the method further includes: Checking whether the data of the first indicator item meets the preset requirements; If the requirements are met, a step of storing the data of the first indicator item in a data set corresponding to the first indicator item is triggered; If the requirements are not met, a message is sent to the first client so that the first client displays a prompt message indicating that the data of the first indicator item is abnormal.

5. The method according to claim 1, characterized in that Determining, based on a data set under each indicator item in the plurality of indicator items, an anesthesia quality assessment value of the medical institution corresponding to the first client, includes: Determining a data quality index corresponding to each indicator item according to a data set under each indicator item of the multiple indicator items; Based on the data quality index corresponding to each indicator item in the multiple indicator items, an anesthesia quality assessment value of the medical institution corresponding to the first client is determined.

6. The method according to claim 5, characterized in that The plurality of indicator items include a second indicator item, and the data set under the second indicator item includes: a plurality of body temperature data and a reported first probability value; and Determining a data quality index corresponding to the second indicator item based on the data set under the second indicator item includes: Using a preset model, analyzing whether the multiple body temperature data conform to the set change rules; If yes, a second probability value is calculated based on the plurality of body temperature data, and the first probability value is compared with the second probability value; If the deviation between the first probability value and the second probability value is within a set range, then determining that the data quality index corresponding to the second indicator item is a first set value; If the deviation between the first probability value and the second probability value is not within a set range, a comparison with medical institutions of the same level in the same region is triggered, and a data quality index corresponding to the second indicator item is determined based on the comparison result; If not, the data quality index corresponding to the second indicator item is reduced from the first set value to the second set value; The first probability value is the incidence rate of hypothermia or the active heat preservation rate.

7. The method according to claim 6, characterized in that Triggering a comparison with medical institutions of the same level in the same region, and determining the data quality index corresponding to the second indicator item based on the comparison result, including: Get the average probability value of medical institutions of the same level in the same area; If the second probability value of the medical institution corresponding to the first client is lower than the average probability value, ranking multiple medical institutions in the same area based on the medical institution related information of each medical institution; If the ranking of the medical institution corresponding to the first client in the ranking result is at or before the set ranking, determining the data quality index corresponding to the second indicator item to be a first set value; If the ranking of the medical institution corresponding to the first client in the ranking result is after the set ranking, obtaining a medical institution corresponding to a second client in the same area that has a second probability value similar to that of the medical institution corresponding to the first client; analyzing the medical disparity between the two medical institutions based on the medical-related information of the medical institution corresponding to the first client and the medical-institution-related information of the medical institution corresponding to the second client; If the medical gap between the medical institution corresponding to the first client and the medical institution corresponding to the second client is greater than a threshold, the data quality index corresponding to the second indicator item is reduced from the first set value to a third set value; If the medical gap between the medical institution corresponding to the first client and the medical institution corresponding to the second client is less than or equal to a threshold, the data quality index corresponding to the second indicator item is reduced from the first set value to a fourth set value.

8. The method according to claim 6, characterized in that Determining a data quality index corresponding to the second indicator item based on the data set under the second indicator item further includes: Analyze the change trend of the first probability value corresponding to multiple consecutive time periods based on the data set under the second indicator; If the upward trend fluctuation of the first probability value exceeds the fluctuation threshold within a time period, and the medical-related information does not change or the change does not exceed the preset amount within the time period, the data quality index corresponding to the second indicator item is reduced from the first set value to the fifth set value.

9. The method according to claim 6, characterized in that Determining a data quality index corresponding to the second indicator item based on the data set under the second indicator item further includes: If the data set under the second indicator item has not changed for multiple consecutive cycles, or the patient to whom the temperature data in the data set belongs has not changed, or the active insulation mark in the temperature data is missing or the set duration has not changed, then the data quality index corresponding to the second indicator item is reduced from the first set value to the sixth set value.

10. The method according to claim 6, characterized in that The multiple indicator items include a third indicator item, and the data set under the third indicator item includes: multiple postoperative analgesia visit score data and reported score data; and Determining a data quality index corresponding to the third indicator item based on the data set under the third indicator item includes: Analyzing the deviations between the multiple postoperative analgesia visit score data and the reported score data using a preset model; If the deviation is within the set range, determining the data quality index corresponding to the third indicator item to be the first set value; If the deviation is not within the set range, the postoperative analgesia satisfaction rate of the medical institution corresponding to the first client is calculated based on the multiple postoperative analgesia visit score data, and the average postoperative analgesia satisfaction rate of medical institutions of the same level in the same area is obtained; If the postoperative analgesia satisfaction rate of the medical institution corresponding to the first client is higher than the postoperative analgesia satisfaction rate, ranking multiple medical institutions in the same area based on the medical institution-related information of each medical institution; If the ranking of the medical institution corresponding to the first client in the ranking result is at or before the set ranking, determining the data quality index corresponding to the second indicator item to be a first set value; If the ranking of the medical institution corresponding to the first client is after the set ranking in the ranking results, obtain a medical institution corresponding to a second client in the same area that has a postoperative analgesia satisfaction rate similar to that of the medical institution corresponding to the first client; analyzing the medical disparity between the two medical institutions based on the medical-related information of the medical institution corresponding to the first client and the medical-institution-related information of the medical institution corresponding to the second client; If the medical gap between the medical institution corresponding to the first client and the medical institution corresponding to the second client is greater than a threshold, the data quality index corresponding to the second indicator item is reduced from the first set value to a third set value; If the medical gap between the medical institution corresponding to the first client and the medical institution corresponding to the second client is less than or equal to a threshold, the data quality index corresponding to the second indicator item is reduced from the first set value to a fourth set value.

11. The method according to claim 10, characterized in that Determining a data quality index corresponding to the third indicator item based on the data set under the third indicator item further includes: Based on the data set under the third indicator, analyzing the changing trend of the postoperative analgesia satisfaction rate corresponding to multiple consecutive time periods; If the upward trend fluctuation of the postoperative analgesia satisfaction rate exceeds the fluctuation threshold within a time period, and the medical-related information does not change or the change does not exceed the preset amount within the time period, the data quality index corresponding to the third indicator item is reduced from the first set value to the fifth set value.

12. The method according to claim 11, characterized in that Determining a data quality index corresponding to the third indicator item based on the data set under the third indicator item further includes: If the data set under the third indicator item does not change in multiple consecutive cycles, or the postoperative analgesia satisfaction rate does not change in multiple consecutive cycles, the data quality index corresponding to the third indicator item is reduced from the first set value to the sixth set value.

13. The method according to claim 1 or 2, characterized in that The data set corresponding to the temperature quality control reporting indicator item includes data uploaded through the temperature data reporting interface and / or temperature data generated by at least one temperature management device obtained through collection; The data sets corresponding to the unexpected adverse event reporting module include: data uploaded through the unexpected adverse event reporting interface; The data set corresponding to the daily quality control data reporting module includes: data uploaded through the daily quality control data reporting interface; wherein, the daily quality control data includes at least one of the following: the number of patients undergoing anesthesia for each type of surgery, the number of patients receiving blood transfusion during anesthesia, the number of patients who completed pre-anesthesia visits before surgery, the number of patients undergoing general anesthesia who received temperature checks during surgery, the total number of patients receiving autologous blood transfusion during surgery, the number of patients whose VAS was less than the set value during postoperative analgesia follow-up in the Department of Anesthesiology, the total number of patients receiving postoperative analgesia in the Department of Anesthesiology, the total number of patients admitted to the PACU, the number of patients with hypothermia during surgery and anesthesia, and the number of patients with temperature admitted to the PACU per unit time; The data set corresponding to the medical institution related information reporting indicator items includes the data uploaded through the medical institution related information reporting interface; wherein, the medical institution related information includes at least one of the following: the number of medical staff, the ratio of medical staff, medical hardware configuration information, medical hardware qualification rate, and rules and regulations.

14. A medical quality management system, characterized in that: include: A first client is associated with a medical institution and is used to provide at least one data filling interface and / or collect at least one data generated by at least one device in communication with the first client; In response to data corresponding to a first indicator item filled in by a user through a data filling interface and / or data of the first indicator item collected from a device, the data of the first indicator item is tested; when the data of the first indicator item is detected to meet preset requirements, the data of the first indicator item is sent to a server; when the data of the first indicator item is detected to not meet the preset requirements, a prompt message indicating that the data of the first indicator item is abnormal is displayed; The server is configured to obtain data sets corresponding to a plurality of quality assessment indicator items associated with the first client; determining, based on a data set under each of the multiple indicator items, an anesthesia quality assessment value of the medical institution corresponding to the first client; Among them, the indicators include: body temperature quality control index reporting items, unexpected adverse event reporting indicators, daily quality control data reporting indicators and medical institution related information reporting indicators.

15. An electronic device, characterized in that: including a memory and a processor; wherein, The memory is used to store executable instructions; The processor implements the steps in the anesthesia quality assessment method according to any one of claims 1 to 13 by running the executable instructions.

16. A computer storage medium, characterized in that The storage medium stores computer instructions, which, when executed by a processor, can implement the steps of the anesthesia quality assessment method as described in any one of claims 1 to 13 above.

17. A computer program product, characterized in that The computer program product includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the processor is caused to perform the steps of the anesthesia quality assessment method according to any one of claims 1 to 13.

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