Critical Care Data Collaborative Sharing System

Abstract

The present invention discloses a critical care data collaborative sharing system, which belongs to the field of critical care technology. The data acquisition module is used to collect critical care data based on big data; the data processing module is used to process critical care data based on big data; and the shared management platform is used to share, analyze and remotely manage critical care data based on big data based on smart chips. The present invention solves the problem that existing critical care data cannot be effectively collaboratively shared, critical care behaviors cannot be effectively analyzed and managed, resulting in low medical work efficiency and quality. The present invention collects critical care data, processes the critical care data, determines standardized critical care data, and shares, analyzes and remotely manages the critical care data. This allows critical care data to be effectively collaboratively shared, critical care behaviors to be effectively analyzed and managed, and medical work efficiency and quality to be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of critical care nursing, and in particular to a critical care nursing data collaborative sharing system. Background Art

[0002] As an important medical field, critical care requires timely and accurate information on changes in patients' conditions, treatment effects, etc., in order to better carry out treatment and nursing work.

[0003] Chinese patent publication number CN112349394A discloses a critical care data management system and method based on an automatic calculator. The system includes: a voice input module, an image acquisition module, a verification information input module, a scanning module, a touch display module, a central processing module, a wireless communication module, a local area network module, an automatic calculation and summary module, a verification and identification module, a database update module, and a prediction and alarm module. By providing the voice input module and the automatic calculation and summary module, data can be input through voice input. During the care process of critically ill patients, a large number of operations are not required, avoiding the time-consuming writing of operation records and calculations. However, the patent has the following defects:

[0004] Existing critical care data cannot be effectively shared and coordinated, and critical care behaviors cannot be effectively analyzed and managed, resulting in low medical work efficiency and quality. Summary of the Invention

[0005] The purpose of the present invention is to provide a critical care data collaborative sharing system, which can enable critical care data to be effectively collaboratively shared, effectively analyze and manage critical care behaviors, improve medical work efficiency and quality, and solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Critical care data collaborative sharing system, including:

[0008] Data collection module, used to collect critical care data based on big data;

[0009] Data processing module, used to process critical care data based on big data;

[0010] A shared management platform for sharing, analyzing and remotely managing critical care data based on big data using smart chips.

[0011] Preferably, the data acquisition module includes:

[0012] The basic information collection unit is used to collect the basic information of critically ill patients and determine the basic information data of critically ill patients, wherein the basic information includes the name, age, gender, address, contact information, patient ID, hospitalization number, past medical history, allergy history and surgical history of critically ill patients;

[0013] Vital signs collection unit, used to collect the body temperature, respiratory rate, respiratory depth, heart rate, blood pressure and blood oxygen saturation of critically ill patients to determine their vital signs data;

[0014] Medical examination collection unit, used to examine the blood and urine of critically ill patients, and use X-ray, CT or MRI to diagnose critically ill patients and determine the medical examination data of critically ill patients;

[0015] The medication record collection unit is used to collect the name, dosage, usage, and time of medication used by critically ill patients, as well as their allergic reactions or adverse reactions to the drugs, to determine the medication record data of critically ill patients;

[0016] Nursing record collection unit, used to collect nursing measures, nursing actions and nursing plans for critically ill patients, and determine nursing record data for critically ill patients;

[0017] Among them, critical care data based on big data is determined based on the basic information data of critically ill patients, vital signs data of critically ill patients, medical examination data of critically ill patients, medication record data of critically ill patients and nursing record data of critically ill patients.

[0018] Preferably, the data processing module includes:

[0019] Data cleaning unit, used to clean critical care data based on big data, including: deleting duplicate data, processing outliers and missing values;

[0020] Deduplication: Use data tools or programming languages ​​to examine critical care data based on big data, identify duplicate data in critical care data, and delete the duplicate data;

[0021] Handling outliers: Using data tools or programming languages ​​to examine critical care data based on big data, identify outliers in critical care data, and delete or correct them;

[0022] Handling missing values: Use data tools or programming languages ​​to examine critical care data based on big data, identify missing values ​​in critical care data, and delete or fill in missing values;

[0023] Among them, after deleting duplicate data and processing outliers and missing values, the critical care data is verified to check whether there are still duplicate data, outliers and missing values ​​in the critical care data, and whether any non-duplicate, non-outlier and non-missing data is deleted.

[0024] Preferably, the data processing module further includes:

[0025] Data conversion unit, used to convert critical care data based on big data, including: data standardization, data integration and storage;

[0026] Data standardization: Use data standardization tools to convert critical care data based on big data into a unified format, eliminate the dimensional differences between critical care data based on big data, and determine standardized critical care data;

[0027] Data integration: Use data integration tools to integrate critical care data from different sources into a unified view, making critical care data easy to analyze and report;

[0028] Data storage: Store consolidated critical care data in a secure database.

[0029] Preferably, the specific steps of data integration in the data processing module are as follows:

[0030] Based on the view display features, a logical data model is established, and based on the logical data model, data virtualization is performed on the critical care data to obtain surface data;

[0031] Based on the surface features of the surface data, the data matching value of the critical care data is calculated according to the following formula;

[0032]

[0033] in, represents the data matching value of critical care data, n represents the number of characteristic attributes of the data surface features, Represents the definition value of the data surface feature of the i-th feature attribute, represents the attribute weight of the i-th feature attribute, Represents the model weight of the logical data model, represents the data matching correction coefficient;

[0034] Based on the view display feature, the view matching value displayed at each view position is calculated according to the following formula;

[0035]

[0036] in, Indicates the view matching value displayed at the view position, e represents a constant with a value of 2.72. The position feature value that represents the position of the view. Indicates the position area weight of the view position display, Indicates the display type value of the view position display. The standard display coefficient representing the display characteristics of the view, represents the standard view correction factor;

[0037] Based on a preset numerical matching relationship, the data matching value and the view matching value are matched, and the corresponding relationship between the data surface data and the view position display is determined according to the matching result. Based on the corresponding relationship, the surface data is integrated at the view position to obtain a unified display view.

[0038] Preferably, the shared management platform includes:

[0039] Collaborative sharing module, used to establish a data sharing transmission connection between the transmitter and the receiver, enabling collaborative sharing of critical care data;

[0040] A data analysis module is used to analyze critical care data and determine critical care analysis results;

[0041] The remote management module is used to automatically warn of abnormal behaviors during critical care and remotely assist nursing staff in guiding and managing the care of critically ill patients.

[0042] Preferably, the collaborative sharing module includes:

[0043] A port connection unit, used to establish a data sharing transmission connection between the transmitting end and the receiving end;

[0044] Wherein, the transmitting end transmits an instruction requesting to establish a data sharing transmission connection to the receiving end;

[0045] After the receiving end receives the instruction from the transmitting end requesting to establish a data sharing transmission connection, the receiving end checks the data transmission port of the transmitting end to determine whether the transmitting end is qualified to perform data sharing transmission with the receiving end;

[0046] When the transmitting end is qualified to perform data sharing transmission with the receiving end, the receiving end transmits an instruction to the transmitting end to agree to establish a data sharing transmission connection;

[0047] After the transmitting end receives the instruction transmitted by the receiving end for agreeing to establish the data sharing transmission connection, the transmitting end establishes the data sharing transmission connection with the receiving end according to the instruction transmitted by the receiving end for agreeing to establish the data sharing transmission connection, for shared transmission of critical care data;

[0048] Data sharing unit, used to share critical care data;

[0049] After the data sharing transmission connection is established between the transmitter and the receiver, the transmitter transmits the critical care data to the receiver, enabling the collaborative sharing of critical care data.

[0050] Preferably, the data analysis module includes:

[0051] a standard storage unit for storing pre-set critical care standards;

[0052] A data analysis unit, used for analyzing critical care data and determining critical care analysis results;

[0053] access to shared critical care data;

[0054] Analyze critical care data based on critical care standards to determine whether there are abnormal behaviors in the critical care process and confirm the results of critical care analysis;

[0055] Among them, if the critical care data is within the critical care standard range, the critical care analysis result is that there is no abnormal behavior in the critical care process;

[0056] Among them, if the critical care data is not within the scope of critical care standards, the critical care analysis result will be that there is abnormal behavior in the critical care process.

[0057] Preferably, the data analysis unit includes:

[0058] a first analyzing unit, configured to obtain a first critical care standard range for each critical care type from the critical care standard, obtain a type care value for the critical care type based on the critical care data, and determine whether the type care value is within the first critical care standard range;

[0059] If so, it is determined that the critical care analysis result is that there is no abnormal behavior of a single critical care type in the critical care process;

[0060] Otherwise, the critical care analysis result is determined to be that abnormal behavior exists in the critical care process, and based on the critical care type, a single abnormal behavior feature is determined;

[0061] a second analyzing unit configured to, when the critical care analysis result indicates that there is no abnormal behavior caused by a single critical care type in the critical care process, obtain a second critical care standard range for the combined effects of multiple critical care types from the critical care standard, obtain a type care value for the critical care type based on the critical care data, and determine whether the type care value is within the second critical care standard range;

[0062] If so, confirm that the critical care analysis result is that there is no abnormal behavior in the critical care process;

[0063] Otherwise, determining that the critical care analysis result is that abnormal behavior exists in the critical care process, and determining the number of critical care types within the second critical care standard range and the type characteristics of the critical care types, determining a first abnormal weight based on the number of types, determining a second abnormal weight based on the type characteristics of the critical care types, integrating the individual behavior characteristics of all critical care types based on the type association of the critical care types to obtain a comprehensive abnormal behavior characteristic, and weighting the comprehensive abnormal behavior characteristic based on the first abnormal weight and the second abnormal weight to obtain a target comprehensive abnormal behavior characteristic;

[0064] The result display unit is used to display the critical care analysis results and the single abnormal behavior characteristics and target comprehensive abnormal behavior characteristics when abnormalities exist.

[0065] Preferably, the remote management module includes:

[0066] The abnormal warning unit is used to automatically warn of abnormal behaviors during critical care, promptly reminding nursing staff to pay attention to critically ill patients and provide timely management of critically ill patients;

[0067] The remote collaboration unit is used to remotely assist nursing staff in guiding and managing the care of critically ill patients. It can formulate remote guidance opinions in a timely manner based on abnormal behaviors during critical care, and provide intelligent remote guidance to nursing staff to optimize the allocation and sharing of medical resources.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The present invention determines critical care data based on big data by collecting basic information data, vital signs data, medical examination data, medication record data and nursing record data of critically ill patients, determines standardized critical care data by processing the critical care data based on big data, and stores the standardized critical care data for backup. By sharing, analyzing and remotely managing the critical care data based on big data, the critical care data can be effectively collaboratively shared, critical care behaviors can be effectively analyzed and managed, and the efficiency and quality of medical work can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a structural block diagram of the critical care data collaborative sharing system of the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0072] In order to solve the problem that the existing critical care data cannot be effectively shared and analyzed, critical care behaviors cannot be effectively analyzed and managed, resulting in low medical work efficiency and quality, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0073] The critical care data collaborative sharing system includes: data acquisition module, data processing module and sharing management platform.

[0074] It should be noted that the basic information data, vital signs data, medical examination data, medication record data and nursing record data of critically ill patients are collected through the data acquisition module to determine the critical care data based on big data, and the critical care data based on big data are processed through the data processing module to determine the standardized critical care data, and the standardized critical care data are stored for backup. The critical care data based on big data is shared, analyzed and remotely managed through the shared management platform based on the smart chip, which enables the critical care data to be effectively collaboratively shared, and the critical care behavior to be effectively analyzed and managed, thereby improving the efficiency and quality of medical work.

[0075] In this embodiment, the shared management platform is equipped with a built-in smart chip, which is a miniature "computer" that can be embedded in the shared management platform. Based on the smart chip, critical care data based on big data can be shared, analyzed and remotely managed.

[0076] In this embodiment, as a preferred technical solution of the present invention, the data acquisition module includes:

[0077] The basic information collection unit is used to collect the basic information of critically ill patients and determine the basic information data of critically ill patients, wherein the basic information includes the name, age, gender, address, contact information, patient ID, hospitalization number, past medical history, allergy history and surgical history of critically ill patients;

[0078] It should be noted that the patient ID and hospitalization number are used to uniquely identify the patient.

[0079] Vital signs collection unit, used to collect the body temperature, respiratory rate, respiratory depth, heart rate, blood pressure and blood oxygen saturation of critically ill patients to determine their vital signs data;

[0080] It should be noted that body temperature reflects the body temperature condition of critically ill patients and helps to judge infection or other diseases. Respiratory rate and depth reflect the number and intensity of respirations of critically ill patients and can be used to understand the status of the respiratory system. Heart rate reflects the heart rate of critically ill patients. Blood pressure includes systolic pressure and diastolic pressure, which reflects the function of the cardiovascular system of critically ill patients. Blood oxygen saturation is the oxygen content in the blood, which reflects the oxygenation status of critically ill patients. These data can reflect the vital signs and organ function status of critically ill patients.

[0081] Medical examination collection unit, used to examine the blood and urine of critically ill patients, and use X-ray, CT or MRI to diagnose critically ill patients and determine the medical examination data of critically ill patients;

[0082] It should be noted that blood tests include routine blood tests, electrolytes, blood sugar, and liver and kidney function, which are used to assess the physiological status of critically ill patients. Urinalysis is used to analyze the components in urine to help diagnose urinary system diseases. X-rays, CT or MRI are used to diagnose the internal structure and lesions of critically ill patients. These data can help doctors understand the disease condition and drug efficacy of critically ill patients, and can help doctors determine the scope and nature of the disease, and then formulate corresponding treatment plans.

[0083] The medication record collection unit is used to collect the name, dosage, usage, and time of medication used by critically ill patients, as well as their allergic reactions or adverse reactions to the drugs, to determine the medication record data of critically ill patients;

[0084] It should be noted that by collecting medication records and drug reactions, the drug treatment status and drug efficacy of critically ill patients can be reflected.

[0085] Nursing record collection unit, used to collect nursing measures, nursing actions and nursing plans for critically ill patients, and determine nursing record data for critically ill patients;

[0086] It should be noted that nursing measures include nursing operations such as oxygen inhalation, infusion, catheterization, and turning over. Nursing actions mainly focus on whether the nurses' actions when performing nursing operations are standardized and accurate, mainly including the following aspects, such as operational standardization: check whether the nurses perform nursing in accordance with standard operating procedures, such as aseptic operation, hand hygiene, etc.; such as action accuracy: evaluate the accuracy of nurses when performing nursing actions, such as the dosage of injected drugs, control of infusion speed, etc.; such as communication and coordination: analyze whether the nurses' communication with patients, family members and other medical staff during the nursing process is effective, and whether they can coordinate nursing work; among them, nursing plans formulated according to the condition and needs of critically ill patients.

[0087] Specifically, based on the basic information data of critically ill patients, the vital signs data of critically ill patients, the medical examination data of critically ill patients, the medication record data of critically ill patients and the nursing record data of critically ill patients, the critical care data based on big data is determined.

[0088] In this embodiment, as a preferred technical solution of the present invention, the data processing module includes:

[0089] Data cleaning unit, used to clean critical care data based on big data;

[0090] Including: deleting duplicate data, handling outliers and missing values;

[0091] Among them, deleting duplicate data: using data tools or programming languages ​​to check critical care data based on big data, identifying duplicate data in critical care data, and deleting the duplicate data;

[0092] Among them, processing outliers: using data tools or programming languages ​​to check critical care data based on big data, identifying outliers in critical care data, and deleting or correcting outliers;

[0093] Among them, processing missing values: using data tools or programming languages ​​to check critical care data based on big data, identifying missing values ​​in critical care data, and deleting or filling missing values;

[0094] It should be noted that after deleting duplicate data and processing outliers and missing values, the critical care data were verified to check whether there were any duplicate data, outliers and missing values ​​in the critical care data, and whether any non-duplicate, non-outlier and non-missing data were deleted.

[0095] It should be noted that by cleaning the critical care data based on big data, duplicate data, outliers and missing values ​​in the critical care data can be removed, and the subsequent processing accuracy and efficiency of the critical care data can be improved.

[0096] In this embodiment, as a preferred technical solution of the present invention, the data processing module further includes:

[0097] Data conversion unit, used to convert critical care data based on big data;

[0098] Including: data standardization, data integration and storage;

[0099] Among them, data standardization: using data standardization tools to convert critical care data based on big data into a unified format, eliminating the dimensional differences between critical care data based on big data, and determining standardized critical care data;

[0100] Among them, data integration: using data integration tools to integrate critical care data from different sources into a unified view, making critical care data easy to analyze and report;

[0101] Among them, data storage: storing the integrated critical care data in a secure database.

[0102] In this embodiment, as a preferred technical solution of the present invention, the specific steps of data integration in the data processing module are as follows:

[0103] Based on the view display features, a logical data model is established, and based on the logical data model, data virtualization is performed on the critical care data to obtain surface data;

[0104] Based on the surface features of the surface data, the data matching value of the critical care data is calculated according to the following formula;

[0105]

[0106] in, represents the data matching value of critical care data, n represents the number of characteristic attributes of the data surface features, Represents the definition value of the data surface feature of the i-th feature attribute, represents the attribute weight of the i-th feature attribute, Represents the model weight of the logical data model, represents the data matching correction coefficient;

[0107] Based on the view display feature, the view matching value displayed at each view position is calculated according to the following formula;

[0108]

[0109] in, Indicates the view matching value displayed at the view position, e represents a constant with a value of 2.72. The position feature value that represents the position of the view. Indicates the position area weight of the view position display, Indicates the display type value of the view position display. The standard display coefficient representing the display characteristics of the view, represents the standard view correction factor;

[0110] Based on a preset numerical matching relationship, the data matching value and the view matching value are matched, and the corresponding relationship between the data surface data and the view position display is determined according to the matching result. Based on the corresponding relationship, the surface data is integrated at the view position to obtain a unified display view.

[0111] In this embodiment, the view display characteristics are determined based on the view of the data integration.

[0112] In this example, a logical data model is created to hide the complexity of the physical data. This approach allows users and applications to access data through the logical layer without having to understand the underlying physical storage details of the data, facilitating the understanding of critical care data.

[0113] In this embodiment, the preset numerical matching relationship is designed in advance based on the association between the view display characteristics and the data surface characteristics. The preset numerical matching relationship can clarify the display of the surface data at the view position, providing a basis for data integration.

[0114] In this embodiment, the parameter value in the data matching value calculated for each critical care data has a predetermined value range of (0, 1).

[0115] In this embodiment, the parameter values ​​in the view matching values ​​calculated for each view position display are in a predetermined range of (0, 1), the standard view correction coefficient is a constant and is determined based on actual conditions, the standard display coefficient of the view display feature is a constant and is determined based on actual conditions, and the position area weight of the view position display is determined based on the position information of the view position in the entire view.

[0116] The beneficial effects of the above design scheme are: after the mechanical energy data is virtualized based on the view display feature, data integration of the surface data at the view position is realized according to the relationship between the matching value of the virtualized data and the view matching value displayed at the view position, a unified display view is obtained, and data integration is realized. In the calculation process of the matching value and the view matching value displayed at the view position, the characteristic conditions of the data surface characteristics and the characteristic conditions of the view display characteristics are considered to ensure the accuracy of the matching value, realize accurate data integration, obtain accurate display view, and provide a data basis for subsequent sharing, analysis and remote management of critical care data.

[0117] It should be noted that by converting the critical care data based on big data, standardized critical care data can be determined, which facilitates subsequent analysis of the critical care data.

[0118] In this embodiment, as the preferred technical solution of the present invention, the shared management platform includes: a collaborative sharing module, a data analysis module and a remote management module.

[0119] Among them, the collaborative sharing module is used to establish a data sharing transmission connection between the transmitter and the receiver, so as to enable the collaborative sharing of critical care data;

[0120] In this embodiment, as a preferred technical solution of the present invention, the collaborative sharing module includes:

[0121] A port connection unit, used to establish a data sharing transmission connection between the transmitting end and the receiving end;

[0122] Wherein, the transmitting end transmits an instruction requesting to establish a data sharing transmission connection to the receiving end;

[0123] After the receiving end receives the instruction from the transmitting end requesting to establish a data sharing transmission connection, the receiving end checks the data transmission port of the transmitting end to determine whether the transmitting end is qualified to perform data sharing transmission with the receiving end;

[0124] When the transmitting end is qualified to perform data sharing transmission with the receiving end, the receiving end transmits an instruction to the transmitting end to agree to establish a data sharing transmission connection;

[0125] After the transmitting end receives the instruction transmitted by the receiving end for agreeing to establish the data sharing transmission connection, the transmitting end establishes the data sharing transmission connection with the receiving end according to the instruction transmitted by the receiving end for agreeing to establish the data sharing transmission connection, for shared transmission of critical care data;

[0126] Data sharing unit, used to share critical care data;

[0127] After the data sharing transmission connection is established between the transmitter and the receiver, the transmitter transmits the critical care data to the receiver, enabling the collaborative sharing of critical care data.

[0128] Among them, the data analysis module is used to analyze critical care data and determine the critical care analysis results;

[0129] In this embodiment, as a preferred technical solution of the present invention, the data analysis module includes:

[0130] a standard storage unit for storing pre-set critical care standards;

[0131] A data analysis unit, used for analyzing critical care data and determining critical care analysis results;

[0132] access to shared critical care data;

[0133] Analyze critical care data based on critical care standards to determine whether there are abnormal behaviors in the critical care process and confirm the results of critical care analysis;

[0134] Among them, if the critical care data is within the critical care standard range, the critical care analysis result is that there is no abnormal behavior in the critical care process;

[0135] Among them, if the critical care data is not within the scope of critical care standards, the critical care analysis result will be that there is abnormal behavior in the critical care process.

[0136] In this embodiment, as a preferred technical solution of the present invention, the data analysis unit includes:

[0137] a first analyzing unit, configured to obtain a first critical care standard range for each critical care type from the critical care standard, obtain a type care value for the critical care type based on the critical care data, and determine whether the type care value is within the first critical care standard range;

[0138] If so, it is determined that the critical care analysis result is that there is no abnormal behavior of a single critical care type in the critical care process;

[0139] Otherwise, the critical care analysis result is determined to be that abnormal behavior exists in the critical care process, and based on the critical care type, a single abnormal behavior feature is determined;

[0140] a second analyzing unit configured to, when the critical care analysis result indicates that there is no abnormal behavior caused by a single critical care type in the critical care process, obtain a second critical care standard range for the combined effects of multiple critical care types from the critical care standard, obtain a type care value for the critical care type based on the critical care data, and determine whether the type care value is within the second critical care standard range;

[0141] If so, confirm that the critical care analysis result is that there is no abnormal behavior in the critical care process;

[0142] Otherwise, determining that the critical care analysis result is that abnormal behavior exists in the critical care process, and determining the number of critical care types within the second critical care standard range and the type characteristics of the critical care types, determining a first abnormal weight based on the number of types, determining a second abnormal weight based on the type characteristics of the critical care types, integrating the individual behavior characteristics of all critical care types based on the type association of the critical care types to obtain a comprehensive abnormal behavior characteristic, and weighting the comprehensive abnormal behavior characteristic based on the first abnormal weight and the second abnormal weight to obtain a target comprehensive abnormal behavior characteristic;

[0143] The result display unit is used to display the critical care analysis results and the single abnormal behavior characteristics and target comprehensive abnormal behavior characteristics when abnormalities exist.

[0144] In this embodiment, the standard for a single standard value in the first critical care standard range is greater than the standard for a single standard value in the second critical care standard range. The first critical care standard range is used to judge the result of a single critical care data acting alone, and the second critical care standard range is used to judge the result of a combined effect of multiple critical care data. The combined effect judgment is performed after the single effect judgment is completed.

[0145] In this embodiment, the greater the number of types, the greater the first abnormal weight; the greater the image of the type feature of the critical care type for the patient's health, the greater the corresponding second abnormal weight.

[0146] In this embodiment, the type association of the critical care type is determined based on actual data or experience.

[0147] In this embodiment, the target comprehensive abnormal behavior feature is added with the abnormal degree of the abnormal behavior feature compared to the comprehensive abnormal behavior feature.

[0148] The beneficial effects of the above design scheme are: by analyzing the critical care data, the critical care analysis results are determined; the results of the individual critical care data and the combined effects of multiple critical care data are not considered in the analysis process, and different standard data ranges are assigned to the two. The individual effect judgment is completed first and then the comprehensive effect judgment is performed. When making the comprehensive judgment, the type characteristics of the critical care type, the type association of the critical care type, and the number of critical care types are combined for analysis to obtain the target comprehensive abnormal behavior characteristics with abnormalities, thereby achieving effective analysis and management of critical care behaviors and improving the efficiency and quality of medical work.

[0149] Among them, the remote management module is used to automatically warn of abnormal behaviors during critical care and remotely assist nursing staff in guiding and managing the care of critically ill patients.

[0150] In this embodiment, as a preferred technical solution of the present invention, the remote management module includes:

[0151] The abnormal warning unit is used to automatically warn of abnormal behaviors during critical care, promptly reminding nursing staff to pay attention to critically ill patients and provide timely management of critically ill patients;

[0152] The remote collaboration unit is used to remotely assist nursing staff in guiding and managing the care of critically ill patients. It can formulate remote guidance opinions in a timely manner based on abnormal behaviors in the critical care process, and provide intelligent remote guidance to nursing staff, so as to optimize the allocation and sharing of medical resources and improve the utilization efficiency of medical resources.

[0153] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0154] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The critical care data collaborative sharing system is characterized by: include: Data collection module, used to collect critical care data based on big data; Data processing module, used to process critical care data based on big data; A shared management platform for sharing, analyzing, and remotely managing big data-based critical care data using smart chips; The data processing module includes: Data conversion unit, used to convert critical care data based on big data, including: data standardization, data integration and storage; Data standardization: Use data standardization tools to convert critical care data based on big data into a unified format, eliminate the dimensional differences between critical care data based on big data, and determine standardized critical care data; Data integration: Use data integration tools to integrate critical care data from different sources into a unified view, making critical care data easy to analyze and report; Data storage: Store the integrated critical care data in a secure database; The specific steps of data integration in the data processing module are as follows: Based on the view display features, a logical data model is established, and based on the logical data model, data virtualization is performed on the critical care data to obtain surface data; Based on the surface features of the surface data, the data matching value of the critical care data is calculated according to the following formula; in, represents the data matching value of critical care data, n represents the number of characteristic attributes of the data surface features, Represents the definition value of the data surface feature of the i-th feature attribute, represents the attribute weight of the i-th feature attribute, Represents the model weight of the logical data model, represents the data matching correction coefficient; Based on the view display feature, the view matching value displayed at each view position is calculated according to the following formula; in, Indicates the view matching value displayed at the view position, e represents a constant with a value of 2.

72. The position feature value that represents the position of the view. Indicates the position area weight of the view position display, Indicates the display type value of the view position display. The standard display coefficient representing the display characteristics of the view, represents the standard view correction factor; Based on a preset numerical matching relationship, the data matching value and the view matching value are matched, and the corresponding relationship between the surface data and the view position display is determined according to the matching result. Based on the corresponding relationship, the surface data is integrated at the view position to obtain a unified display view.

2. The critical care data collaborative sharing system according to claim 1, characterized in that: The data acquisition module includes: The basic information collection unit is used to collect the basic information of critically ill patients and determine the basic information data of critically ill patients, wherein the basic information includes the name, age, gender, address, contact information, patient ID, hospitalization number, past medical history, allergy history and surgical history of critically ill patients; Vital signs collection unit, used to collect the body temperature, respiratory rate, respiratory depth, heart rate, blood pressure and blood oxygen saturation of critically ill patients to determine their vital signs data; Medical examination collection unit, used to examine the blood and urine of critically ill patients, and use X-ray, CT or MRI to diagnose critically ill patients and determine the medical examination data of critically ill patients; The medication record collection unit is used to collect the name, dosage, usage, and time of medication used by critically ill patients, as well as their allergic reactions or adverse reactions to the drugs, to determine the medication record data of critically ill patients; Nursing record collection unit, used to collect nursing measures, nursing actions and nursing plans for critically ill patients, and determine nursing record data for critically ill patients; Among them, critical care data based on big data is determined based on the basic information data of critically ill patients, vital signs data of critically ill patients, medical examination data of critically ill patients, medication record data of critically ill patients and nursing record data of critically ill patients.

3. The critical care data collaborative sharing system according to claim 2, characterized in that: The data processing module includes: Data cleaning unit, used to clean critical care data based on big data, including: deleting duplicate data, processing outliers and missing values; Deduplication: Use data tools or programming languages ​​to examine critical care data based on big data, identify duplicate data in critical care data, and delete the duplicate data; Handling outliers: Using data tools or programming languages ​​to examine critical care data based on big data, identify outliers in critical care data, and delete or correct them; Handling missing values: Use data tools or programming languages ​​to examine critical care data based on big data, identify missing values ​​in critical care data, and delete or fill in missing values; Among them, after deleting duplicate data and processing outliers and missing values, the critical care data is verified to check whether there are still duplicate data, outliers and missing values ​​in the critical care data, and whether any non-duplicate, non-outlier and non-missing data is deleted.

4. The critical care data collaborative sharing system according to claim 3, characterized in that: The shared management platform includes: Collaborative sharing module, used to establish a data sharing transmission connection between the transmitter and the receiver, enabling collaborative sharing of critical care data; A data analysis module is used to analyze critical care data and determine critical care analysis results; The remote management module is used to automatically warn of abnormal behaviors during critical care and remotely assist nursing staff in guiding and managing the care of critically ill patients.

5. The critical care data collaborative sharing system according to claim 4, characterized in that: The collaborative sharing module includes: A port connection unit, used to establish a data sharing transmission connection between the transmitting end and the receiving end; Wherein, the transmitting end transmits an instruction requesting to establish a data sharing transmission connection to the receiving end; After the receiving end receives the instruction from the transmitting end requesting to establish a data sharing transmission connection, the receiving end checks the data transmission port of the transmitting end to determine whether the transmitting end is qualified to perform data sharing transmission with the receiving end; When the transmitting end is qualified to perform data sharing transmission with the receiving end, the receiving end transmits an instruction to the transmitting end to agree to establish a data sharing transmission connection; After the transmitting end receives the instruction transmitted by the receiving end for agreeing to establish the data sharing transmission connection, the transmitting end establishes the data sharing transmission connection with the receiving end according to the instruction transmitted by the receiving end for agreeing to establish the data sharing transmission connection, for shared transmission of critical care data; Data sharing unit, used to share critical care data; After the data sharing transmission connection is established between the transmitter and the receiver, the transmitter transmits the critical care data to the receiver, enabling the collaborative sharing of critical care data.

6. The critical care data collaborative sharing system according to claim 5, characterized in that: The data analysis module includes: a standard storage unit for storing pre-set critical care standards; A data analysis unit, used for analyzing critical care data and determining critical care analysis results; access to shared critical care data; Analyze critical care data based on critical care standards to determine whether there are abnormal behaviors in the critical care process and confirm the results of critical care analysis; Among them, if the critical care data is within the critical care standard range, the critical care analysis result is that there is no abnormal behavior in the critical care process; Among them, if the critical care data is not within the scope of critical care standards, the critical care analysis result will be that there is abnormal behavior in the critical care process.

7. The critical care data collaborative sharing system according to claim 6, characterized in that: The data analysis unit comprises: a first analyzing unit, configured to obtain a first critical care standard range for each critical care type from the critical care standard, obtain a type care value for the critical care type based on the critical care data, and determine whether the type care value is within the first critical care standard range; If so, it is determined that the critical care analysis result is that there is no abnormal behavior of a single critical care type in the critical care process; Otherwise, the critical care analysis result is determined to be that abnormal behavior exists in the critical care process, and based on the critical care type, a single abnormal behavior feature is determined; a second analyzing unit configured to, when the critical care analysis result indicates that there is no abnormal behavior caused by a single critical care type in the critical care process, obtain a second critical care standard range for the combined effects of multiple critical care types from the critical care standard, obtain a type care value for the critical care type based on the critical care data, and determine whether the type care value is within the second critical care standard range; If so, confirm that the critical care analysis result is that there is no abnormal behavior in the critical care process; Otherwise, determining that the critical care analysis result is that abnormal behavior exists in the critical care process, and determining the number of critical care types within the second critical care standard range and the type characteristics of the critical care types, determining a first abnormal weight based on the number of types, determining a second abnormal weight based on the type characteristics of the critical care types, integrating the individual behavior characteristics of all critical care types based on the type association of the critical care types to obtain a comprehensive abnormal behavior characteristic, and weighting the comprehensive abnormal behavior characteristic based on the first abnormal weight and the second abnormal weight to obtain a target comprehensive abnormal behavior characteristic; The result display unit is used to display the critical care analysis results and the single abnormal behavior characteristics and target comprehensive abnormal behavior characteristics when abnormalities exist.

8. The critical care data collaborative sharing system according to claim 7, characterized in that: The remote management module includes: The abnormal warning unit is used to automatically warn of abnormal behaviors during critical care, promptly reminding nursing staff to pay attention to critically ill patients and provide timely management of critically ill patients; The remote collaboration unit is used to remotely assist nursing staff in guiding and managing the care of critically ill patients. It can formulate remote guidance opinions in a timely manner based on abnormal behaviors during critical care, and provide intelligent remote guidance to nursing staff to optimize the allocation and sharing of medical resources.

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