Method and system for dynamically updating one-person, one-file and one-location archive for multi-location service of crowds in super-large region

Abstract

The invention belongs to the technical field of file dynamic management, and provides a method and a system for dynamically updating a one-person, one-file and one-location file for multi-location service of people in a super-large region. The method comprises the following steps: a new archive establishment step: an archive establishment mechanism checks a resident archive database by taking an identity ID of a resident as a check identifier, and judges whether the resident establishes an archive in the resident archive database or not; an archive updating step: uploading personal basic information and business data of the residents through an archive personal basic information updating interface and a basic-level business data uploading interface by a management and archive organization of the residents; an archive transfer step: sending an archive transfer application to an original archive management mechanism of the resident by the planned archive management mechanism of the resident; a non-local service step: establishing a non-local service link between the resident non-local service agency and the resident management agency; the remote service institution looks up the archives of the residents through the remote service link and uploads remote service records of the residents; new building, updating, transferring and remote service of the archives are achieved, and uniqueness and integrity of the archives in operation are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic archive management, and in particular to a method and system for dynamically updating archives of one person, one file, and one location for multi-location services for a large population area. Background Art

[0002] In the traditional resident archive management method, each district and county (or service agency) independently establishes resident archives, and each district and county (or service agency) has an independent resident archive database. With the flow of population, the same person goes to different regions for school, life, work, travel, etc., which leads to the same person receiving services in different districts and counties (or agencies) and establishing a separate archive. In this way, from the perspective of prefecture-level cities, provinces, and even the national level, there are problems of duplication of historical archives of people in super-large areas and repeated establishment of archives in multiple places for a single resident, resulting in duplicate archives, fragmented service content records, incompleteness, and discontinuity, thus losing the value of the archives. In addition, since the same person has established archives in different districts and counties, there is also a problem of chaotic archive ownership management. In order to ensure the continuity and integrity of resident archives in super-large areas and realize the recording of the entire life cycle, it is necessary to ensure the uniqueness and integrity of archives during the service process. It is necessary to innovate methods such as archive creation, updating, transfer, and off-site services to ensure the dynamic management of one person, one file, and one local archive for people in super-large areas and multiple places. Summary of the invention

[0003] The present application aims to at least solve the technical problems existing in the prior art and to provide a method and system for dynamically updating one file per person and one location for multi-location services for a large area of ​​people.

[0004] On the first aspect, the present application provides a method for dynamically updating one file for one person and one local file for multi-location services in a super-large area, including: a new file creation step: the filing agency uses the resident's identity ID as a verification mark to check the resident's file database to determine whether the resident has been filed in the resident file database. If the resident has been filed in the resident file database, it will be prompted that the file has been created; if the resident has not been filed in the resident file database, a file will be created for the resident; an update file step: the resident's file management agency uploads the updated personal basic information to the file management platform through the file personal basic information update interface, and the resident's file management agency uploads the resident's business data to the file through the grassroots business data upload interface Management platform, update file records; steps for transferring files: the proposed file management agency of the resident sends a file transfer application to the original file management agency of the resident; after the proposed file management agency receives the reply of consent to file transfer sent by the original file management agency, the file management platform allocates the file management authority of the resident to the proposed file management agency, and revokes the file management authority of the original file management agency over the resident, and records the file transfer event in the resident file database; off-site service steps: the off-site service agency of the resident establishes an off-site service link with the resident's file management agency; the off-site service agency accesses the resident's file through the off-site service link and uploads the resident's off-site service record; the file management agency updates the resident's file based on the resident's off-site service record.

[0005] In a second aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, performs the steps of the method for dynamically updating one person, one file, and one location archive for multi-location services for a large-area population provided by the present invention.

[0006] In the third aspect, the present application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned method for dynamically updating one-person-one-file-one-territory archives for multi-location services for a large-area population provided by the present invention.

[0007] In the fourth aspect, the present application provides a health record data management system, including: a data collection module, which is used to collect health data from different data sources of different residents; a data storage module, which stores the collected health data in the electronic health record unit corresponding to the resident; a slice time analysis and processing module, which responds to health event notifications throughout the life cycle of the resident, and executes the steps of slicing and saving the health record data in the dynamic update method of one person, one file, and one territorial archive for multi-location services in a super-large area provided by the present invention, obtains the slice data of the resident's health events and saves them in the resident's electronic health record unit; a service provision module, which is used to provide users with a query interface for querying the slice data of the resident's health events.

[0008] The present patent application provides a method, program product and electronic device for dynamically updating archives of one person, one file and one location for multi-location services for a large area population. Through the steps of creating new archives, updating archives, migrating archives and providing services in a different location, the method realizes the creation, updating, migrating and providing services in a different location during the operation of archives, thereby ensuring the uniqueness of archive ownership, as well as the uniqueness and integrity of archive content.

[0009] The health record data management system provided by this patent application also has the following beneficial technical effects: on the one hand, it is convenient for doctors to accurately and quickly obtain patients' key health data and obtain information about disease development trends, which helps to make more accurate diagnoses; on the other hand, residents can view their own slice data and detailed information over the years through electronic health records, which can better understand the changing trends of their own health, help to detect problems early and take preventive measures, and reduce repeated examinations; on the third hand, this system can support the health data management of residents throughout their life cycle, and can generate and save slice data for health events throughout the life cycle of residents. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the steps included in a method for dynamically updating one-person-one-file-one-locality archives for serving a large-area population in multiple locations in a preferred embodiment of the present invention;

[0011] Figure 2 It is a schematic diagram of the process of data slice storage in a preferred embodiment of the present invention;

[0012] Figure 3 is a schematic diagram of the distribution of mapping time points in an example of the present invention;

[0013] Figure 4 is a schematic diagram of a kernel density curve in an example of the present invention;

[0014] Figure 5 It is a framework diagram of a health record data management system in a preferred embodiment of the present invention;

[0015] Figure 6 It is a schematic structural diagram of an electronic device in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0016] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0017] In the description of the present invention, it is necessary to understand that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0018] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0019] The execution subject of the method for dynamically updating the archives of one person, one file, and one territory for a large-scale population in multiple locations provided by the present application includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for dynamically updating the archives of one person, one file, and one territory for a large-scale population in multiple locations can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content Distribution Network, CDN), and big data and artificial intelligence platforms.

[0020] The present invention discloses a method for dynamically updating one person, one file, and one location file for multi-location services in a large area. In a preferred implementation, see Figure 1 , the method comprising:

[0021] New file creation steps: The filing agency uses the resident's ID as the verification mark to check the resident file database to determine whether the resident has been filed in the resident file database. If the resident has been filed in the resident file database, it will prompt that the file has been created. If the resident has not been filed in the resident file database, a file will be created for the resident. A super-large area is more than one prefecture-level city or more than one provincial region or national region.

[0022] In this embodiment, the filing agency is not limited to the district and county health agency, which is deployed with a district and county archive business system, and the district and county archive business system checks the resident archive database through the Internet. The resident's identity ID is preferably but not limited to the ID card number or the birth medical certificate number of the newborn. When it is prompted that the file has been created, the filing agency cannot create a file for the resident.

[0023] In this implementation, if it is prompted that no file has been created, the filing agency can create a file for the resident. Through the city's unified filing cloud service backend function, the population and family basic resource library is automatically queried. If the resident's personal basic information is available, the personal basic information is matched and downloaded, and the filing personnel verify and complete the remaining vacancies in the personal basic information according to the specifications; if the population and family basic resource library does not have the resident's personal basic information, the filing personnel need to ask the resident to fill in the personal basic information in accordance with the specifications. After completing the personal basic information, submit the data to the resident file database in real time, and update it to the city's population and family basic resource library synchronously. After the file is successfully created, the city's resident file database will return the file master index to the district and county archives business system of the filing agency through the unified filing cloud service. It is prompted that the file has been created. It is prompted that files have been created in other institutions and cannot be created repeatedly.

[0024] Steps to update archives: The resident’s archive management agency uploads the updated personal basic information to the archive management platform through the archive’s personal basic information update interface. The resident’s archive management agency uploads the resident’s business data to the archive management platform through the grassroots business data upload interface to update the archive records.

[0025] In this embodiment, the archive update process refers to uploading the basic personal information and basic business data of the archive to the archive management platform. The archive management platform provides a basic personal information update interface and a basic business data upload interface, which are used for uploading business data such as basic personal information and health examination, follow-up information of key populations, and basic medical information. When the archive is a health archive, the business data update includes:

[0026] (1) Update of basic public health services. After providing basic public health services to residents, primary medical and health institutions must, in principle, enter the relevant service data and information into the health records of the corresponding residents within five days of the end of the service.

[0027] (2) Medical update. When residents visit primary medical and health institutions, the primary doctor must inquire about and verify the basic personal information in the resident's health record. If there are any changes in work unit, current address, telephone number, etc., the relevant medical service information must be updated and entered in a timely manner.

[0028] (3) Contract renewal. The family doctor (team) is responsible for maintaining and updating the basic personal information and health and medical information of the contracted residents in a timely manner. For contracted residents who do not receive face-to-face services, they should be contacted at least once every six months to update their basic personal information and improve their personal health records and service information.

[0029] (4) Information retrieval and integration. Relying on the National Health Intelligent Management Service Platform, the archives open application retrieves and integrates the health care service information of residents throughout their life cycle, including basic medical care, immunization, maternal and child services, occupational health, disease prevention and control, basic public health and mutual recognition of medical examinations, etc., to form a data set with resident archives as the carrier and residents' personal health care information as the core, which is connected to the Municipal Urban Operation and Governance Center (IRS platform) and provides data factor services to administrative departments, scientific research institutions and third-party service companies in accordance with laws and regulations.

[0030] The steps for transferring files: the proposed file management institution of the resident sends a file transfer application to the original file management institution of the resident; after the proposed file management institution receives the reply from the original file management institution agreeing to transfer files, the file management platform allocates the file management authority of the resident to the proposed file management institution, and revokes the file management authority of the original file management institution over the resident, and records the file transfer event in the resident file database. The proposed file management institution is not limited to the district and county medical and health institutions where the resident currently lives.

[0031] Off-site service steps: The resident’s off-site service agency establishes an off-site service link with the resident’s filing agency; the off-site service agency accesses the resident’s file through the off-site service link and uploads the resident’s off-site service record; the filing agency updates the resident’s file based on the resident’s off-site service record.

[0032] In this implementation, residents in the off-site service step mainly include key groups such as elderly people who move across districts and counties and have lived in the local area for less than 6 months, pregnant women, children aged 0-6, hypertensive patients, and diabetic patients. The district and county health record management system of the record management agency should automatically download the off-site service records from time to time, and associate them with the health records of the residents, and promptly remind the record management agency personnel to check them.

[0033] When the resident's file is a health file, the resident's health file data is a data stream formed based on time series, and its sources include hospitals, community health service centers, family doctor follow-up, personal health equipment and medical laboratories, etc., including birth records, vaccinations, follow-up records, physical examination reports, medical records, chronic disease management, elderly care and other aspects, specifically involving medication, diagnostic information, treatment, physical examination and other dimensional information and the generation time of each dimensional information. The resident's health file data is sparse and irregular, and its data points are unevenly distributed in time, sparse data in a long period of time, and dense data in a specific period of time.

[0034] Traditional health record data management systems mainly focus on the storage and query of static data. They are unable to conduct real-time retrospective viewing, dynamic management, data comparison, and data continuity analysis of health record data for specific life periods and specific disease onset and cure periods. They lack the ability to save slices of global health data associated with residents' health events, that is, snapshot retention. In addition, traditional health record management systems usually do not fully store residents' health data throughout their life cycle, do not support health data throughout all life stages of residents, such as "birth, growth, illness, rehabilitation, aging", and cannot form a time-space-related, traceable, and predictable health data chain.

[0035] In a preferred embodiment, Figure 2 As shown, the resident's file is a health file;

[0036] The above-mentioned method for dynamically updating one file per person and one local file for multi-location services for a large population in a large area also includes the step of slicing and saving the health file data of the residents by the file management agency, including:

[0037] Step S1, obtaining N health data time pairs within a preset time period corresponding to the health events of the residents, wherein each health data time pair includes a piece of health data and the generation time of the piece of health data, and N is a positive integer.

[0038] In this embodiment, a resident's health event refers to an event that occurs during the resident's medical care process and is closely related to the resident's health, such as vaccination, disease diagnosis, abnormal physical indicators (high blood pressure, high blood lipids), surgical treatment, rehabilitation, etc. A resident's health event has an event occurrence time, and the length of the preset time period is preferably but not limited to half a year or 1 year. The preset time period corresponding to the resident's health event refers to the time interval that moves the health event occurrence time forward by the length of the preset time period. Extract the health and medical data within the preset time period corresponding to the resident's health event from the health record management system, and extract N health data time pairs from the health and medical data.

[0039] In this embodiment, a health data time pair includes a piece of health data and the generation time of the health data, and the health data is not limited to medication information, diagnosis information, treatment information, vaccination information, physical examination index information, birth record, follow-up record or self-provided medical equipment detection information. The corresponding generation time is not limited to the time of prescription issuance, diagnosis issuance time, operation time, vaccination time, index test time, birth time, follow-up time or self-examination time. Physical examination indicators are not limited to blood pressure, blood lipids, blood sugar, and medical images.

[0040] Step S2, mapping the generation time of N health data time pairs to the time axis respectively, obtaining M mapping time points, where M is a positive integer and M is less than or equal to N.

[0041] In this embodiment, a time axis is first established, and the scale unit of the time axis is preferably but not limited to 12 hours or 24 hours. After the generation time of N health data time pairs are mapped to the time axis, M mapping time points are obtained, forming a one-dimensional time dot matrix. Figure 3 Shows a one-dimensional time lattice plot formed in an example.

[0042] In fact, there may be a situation where the generation time of more than two health data time pairs is the same, and by merging the mapping time points, the number of mapping time points finally obtained is less than or equal to N. It is also possible to first count the health data time pairs with the same generation time among the N health data time pairs before mapping, first merge the same generation time, and then map them to the timeline to form mapping time points.

[0043] Step S3, calculating the kernel density of each mapping time point.

[0044] In this embodiment, the kernel density of each mapping time point can be calculated by a kernel density estimation algorithm:

[0045]

[0046] in, represents the jth mapping time point t j The kernel density of m represents the mth mapping time point; h represents the bandwidth parameter of the kernel density estimation, which can be preset according to the data distribution characteristics. Exemplarily, it can be selected as 1 time axis scale unit; exp(·) represents the exponential function; m and j are both positive integers, and satisfy 1≤m≤M, 1≤j≤M.

[0047] Step S4, generating a kernel density curve based on the kernel densities of multiple mapping time points.

[0048] In this embodiment, illustratively, a two-dimensional coordinate system is established with the time axis as the horizontal axis and the kernel density as the vertical axis. Each mapping time point corresponds to a two-dimensional coordinate. The two-dimensional coordinate has the time value of the mapping time point as the horizontal axis and the kernel density value of the mapping time point as the vertical axis. In this way, multiple coordinate points corresponding to multiple mapping time points are formed on the two-dimensional coordinate system. The kernel density curve is obtained based on the multiple coordinate points without limitation through interpolation or spline curve fitting. Figure 4 Shown Figure 3 Kernel density curves generated for the mapped time points.

[0049] Step S5, determining one or more target time intervals according to the size and / or change trend of the kernel density on the kernel density curve.

[0050] In this embodiment, when a resident's health event (such as abdominal surgery) occurs, before the health event occurs, the resident will conduct a large number of preoperative tests, medical records, outpatient examinations, physical examinations and other activities. Compared with the period without health events, there are more health data time pairs, that is, the mapping time points are more dense, the kernel density is larger, and the change trend of the kernel density (which can be characterized by the slope, there are positive and negative) is also larger. A continuous time interval with a kernel density greater than a preset kernel density threshold can be used as a target time interval; or, a continuous time interval with an absolute value of the curve slope greater than a preset slope threshold can be used as a target time interval; or, a continuous time interval with a kernel density greater than a preset kernel density threshold and an absolute value of the curve slope greater than a preset slope threshold can be used as a target time interval. It can be seen that the target time interval is a time interval in which residents frequently generate health data closely related to health events, which excludes the time points other than the target time interval in the preset time period corresponding to the health event, reduces data redundancy, and makes the slice data more accurate. The kernel density threshold can be obtained by historical data statistics, and can be taken as 0.3 to 1.5 times the average or median of the kernel density curve.

[0051] Step S6, save the health data in each target time interval as slice data of the resident's health event, store the slice data, realize snapshot storage, and facilitate backtracking. The health data in the target time interval is more closely associated with the health event, ensuring the accuracy and utilization value of the slice data.

[0052] The method and system for dynamically updating one file for one person and one territorial file for multi-location services for a large population in a large area provided by this patent application maps the generation time of N health data time pairs within a preset time period corresponding to the health events of residents to the time axis, obtains M mapping time points, and calculates the kernel density of each mapping time point. The kernel density of each mapping time point can reflect the density of health data occurring near the mapping time point. The larger the kernel density, the more frequent the medical examinations, treatments, etc. occurring near the mapping time point, and the corresponding health data are more critical and important for the health event. Therefore, this method generates a kernel density curve based on the kernel density of multiple mapping time points, automatically and accurately locates the target time interval closely related to the health event based on the size and / or change trend of the kernel density on the kernel density curve, and finally, saves the health data in each target time interval as slice data of the resident's health event. It can be seen that this application not only saves global snapshots of health data related to health events, helping residents and medical institutions to trace, manage and assist in diagnosis of health data; it also accurately locates the target time interval closely related to the health event, and only saves the health data within the target time interval in slices, which can reduce data redundancy in the sliced ​​data and improve storage and query efficiency. The sliced ​​data can reflect the global difference changes at key time points.

[0053] In a preferred embodiment, in step S3, the step of calculating the kernel density of each mapping time point includes:

[0054] Step S31, setting the kernel density weight of each mapping time point according to the mapped health data of each mapping time point, wherein the mapped health data of each mapping time point includes the health data in the health data time pair to which all generation times mapped to the mapping time point belong. j The kernel density weight is denoted as w j .

[0055] Step S32, using a kernel density estimation algorithm to calculate the original kernel density of each mapping time point. j According to the formula Calculated As the jth mapping time point t j The original kernel density.

[0056] Step S33, weighting the original kernel density of each mapping time point by the kernel density weight of each mapping time point to obtain the kernel density of each mapping time point. j The kernel density is:

[0057] In this embodiment, in a preferred solution, in step S31, the step of setting the kernel density weight of each mapping time point according to the mapped health data of each mapping time point includes:

[0058] Step A1, calculating the detection item weight factor or indicator quantity weight factor or abnormal indicator weight factor or data source weight factor at each mapping time point based on the mapped health data at each mapping time point.

[0059] Step B1, using the detection item weight factor or the indicator quantity weight factor or the abnormal indicator weight factor or the data source weight factor at each mapping time point as the kernel density weight at each mapping time point.

[0060] In this embodiment, preferably, when calculating the detection item weight factor of each mapping time point, it includes:

[0061] Step a1, identifying all test items included in the mapped health data at each mapped time point;

[0062] Step a2, determining the highest project importance level among all detection projects at each mapping time point according to the project-importance level mapping relationship, and normalizing the highest project importance level to obtain the detection project weight factor at each mapping time point.

[0063] In this embodiment, the test items include blood routine test, ultrasound test, contrast test, biopsy, BMI test, hepatitis B core antibody test, urine vitamin C test, low-density lipoprotein cholesterol test, etc. The project-importance level mapping relationship includes the corresponding relationship between the test items and the project importance level, and the unique code value corresponding to each project importance level, and the higher the project importance level, the greater the code value. There is a possibility that more than one test item corresponds to the same project importance level. Each test item may include multiple test indicators, such as blood routine test items including red blood cell indicators, white blood cell indicators, neutrophil indicators, etc. The project importance level of each test item can be set according to the close relationship between each test item and human health. Test items such as blood pressure detection, blood sugar detection, BMI detection, hepatitis B core antibody detection, and urine vitamin C detection are given a higher project importance level, and the larger the corresponding code value, because these indicators are closely related to health status. The low-density lipoprotein cholesterol index is set to a lower project importance level, and the corresponding code value is lower, which helps to more accurately assess individual health status.

[0064] In one example, assuming that the health record management system only includes four test items, namely, blood routine test, ultrasound test, radiography test and biopsy, the code value of the item importance level corresponding to blood routine test is 1, the code value of the item importance level corresponding to ultrasound test is 2, the code value of the item importance level corresponding to radiography test is 3, and the code value of the item importance level corresponding to biopsy is 4.

[0065] In this embodiment, for the jth mapping time point t j The weight factor w of the detection item project (j) is:

[0066]

[0067] Among them, CODE project (j) represents the jth mapping time point t j The coding value of the project importance level of any detection item in the mapping health data, max[CODE project (j)] indicates to find the jth mapping time point t j The maximum code value among the code values ​​of the item importance levels of all detection items existing in the mapping health data. ∑CODE project The sum of the code values ​​of the project importance levels of all test items in the health record management system. For example, in the above example, ∑CODE project =1+2+3+4=10, if the jth mapping time point t j The mapping health data includes two detection items: index detection and contrast detection. Then max[CODE project (j)] is the maximum value 3 between 1 and 3, then

[0068]

[0069] In this embodiment, by assigning different item importance levels to different detection items, the degree of their influence on the overall result can be better reflected, so that the obtained slice data is more accurate and reliable.

[0070] In this embodiment, preferably, when calculating the data source weight factor of each mapping time point, it includes:

[0071] Step b1, identifying all data sources included in the mapped health data at each mapped time point;

[0072] Step b2, determining the highest source importance level among all data sources at each mapping time point according to the source-importance level mapping relationship, and normalizing the highest source importance level to obtain the data source weight factor at each mapping time point.

[0073] In this embodiment, the data sources are not limited to hospitals, community health service centers, family doctor follow-up, personal health equipment and medical laboratories. The source importance level of each data source increases in the order of personal health equipment, family doctor follow-up, community health service center, hospital and medical laboratory. The source-importance level mapping relationship includes the corresponding relationship between the data source and the source importance level, and the unique coding value of each source importance level, and the higher the source importance level, the larger the coding value. There is a possibility that more than one data source corresponds to the same source importance level.

[0074] In one example, the source importance level corresponding to personal health equipment is coded as 1, the source importance level corresponding to family doctor follow-up is coded as 2, the source importance level corresponding to community health service centers is coded as 3, the source importance level corresponding to hospitals is coded as 4, and the source importance level corresponding to medical laboratories is coded as 5. The source importance level of hospitals and laboratories is set higher because the data from hospitals and laboratories are usually more reliable and have higher reference value. This allocation method helps to make more effective use of high-quality data in comprehensive analysis.

[0075] In this embodiment, the jth mapping time point t obtained by normalization processing is j The data source weight factor w S (j) is:

[0076]

[0077] Among them, CODE S (j) represents the jth mapping time point t j The source importance level of any data source in the mapped health data is coded, max[CODE S (j)] indicates to find the jth mapping time point t j The maximum coded value among the coded values ​​of the source importance level of all data sources present in the mapped health data. MAX CODES The maximum code value among the code values ​​of the source importance level of all data sources in the health record management system. For example, in the above example, MAX CODES =5, if the jth mapping time point t j The mapped health data includes two data sources: personal health devices and hospitals. The max[CODE S (j)] is the maximum value 4 between 1 and 4, then

[0078] In this implementation, preferably, when calculating the indicator quantity weight factor at each mapping time point, it includes:

[0079] Step c1, counting the number of detection indicators included in the mapped health data at each mapping time point; specifically, counting the number of all detection indicators in the mapped health data at each mapping time point.

[0080] Step c2, normalizing the number of detection indicators to obtain the indicator quantity weight factor at each mapping time point.

[0081] For example, the jth mapping time point t j The mapped health data includes three detection indicators: blood pressure detection, blood routine detection, and abdominal color Doppler ultrasound detection. Then the jth mapping time point t j The number of detection indicators num j is 3. Then the jth mapping time point t obtained by normalization is j The weight factor w of the number of indicators num (j) is:

[0082]

[0083] Among them, num max Indicates the maximum number of detection indicators among the M numbers of detection indicators possessed by the mapped health data at M mapped time points.

[0084] In this embodiment, the mapped health data at different mapping time points may have different importance. The more detection indicators a mapping time point has, the larger the indicator weight factor should be. This method of dynamically adjusting weights can better reflect health trends and risks that change over time.

[0085] In this embodiment, preferably, when calculating the abnormality index weight factor of each mapping time point, it includes:

[0086] Step d1, counting the number of anomaly detection indicators included in the mapping health data at each mapping time point.

[0087] Step d2, normalize the number of anomaly detection indicators to obtain the anomaly indicator weight factor for each mapping time point.

[0088] For example, if the jth mapping time point t j The mapped health data includes three test indicators: blood pressure test, blood routine test, and abdominal color Doppler ultrasound test. Among them, the blood pressure test and blood routine test are abnormal. The jth mapping time point t j The number of anomaly detection indicators A j If is 2, the jth mapping time point t obtained by normalization is j The abnormal index weight factor w A (j) is:

[0089]

[0090] In this embodiment, the time point with a larger abnormal indicator ratio also needs to increase the weight value, so the abnormal indicator weight factor is larger. This method of dynamically adjusting the weight can better reflect the health trends and risks that change over time.

[0091] In a preferred embodiment, the step of setting the kernel density weight of each mapping time point according to the mapped health data of each mapping time point comprises:

[0092] Step B1, calculating at least two weight factors of the four weight factors of the detection item weight factor, the indicator quantity weight factor, the abnormal indicator weight factor and the data source weight factor at each mapping time point based on the mapped health data at each mapping time point;

[0093] Step B2: weighting the at least two weight factors calculated for each mapping time point, and using the weighted processing result as the kernel density weight of each mapping time point.

[0094] For example, each mapping time point is a weighted sum of four weight factors, and the jth mapping time point t j The kernel density weight is:

[0095] w j =α·w project (j)+β·w S (j)+γ·w num (j)+δ·w A (j)

[0096] Among them, α, β, γ, and δ represent the first weighting coefficient, the second weighting coefficient, the third weighting coefficient, and the fourth weighting coefficient respectively. The four weighting coefficients can be set according to experience to satisfy the value interval of (0,1) and α+β+γ+δ=1.

[0097] In this embodiment, the kernel density weight of each mapping time point is obtained by weighted processing of more than two weight factors, and the kernel density weight can be set from multiple dimensions so that the kernel density weight more accurately reflects the importance of the mapped health data at each mapping time point.

[0098] In the above two preferred embodiments, by setting the kernel density weight of each mapping time point, the importance of the data at each mapping time point can be more accurately reflected, thereby improving the reliability of the analysis results and the effectiveness of the decision. It is also possible to improve data processing efficiency, as shown in the following: when faced with a large amount of data, weight allocation can reduce the impact of irrelevant data, thereby improving data processing efficiency. For example, in data analysis based on in vitro diagnosis, by setting indicator weights, outliers with a greater impact on health can be quickly identified, and corresponding intervention measures can be taken. It is also possible to enhance robustness: weight allocation can better cope with noise or outliers in the data.

[0099] In a preferred embodiment, in step S5, the step of determining one or more target time intervals according to the size and / or change trend of the kernel density on the kernel density curve includes:

[0100] Traverse along the time axis from the starting time point to the ending time point of the kernel density curve according to the preset step size, and execute each traversal:

[0101] If the kernel density of the current traversal time point is greater than or equal to the kernel density threshold, the kernel density of the previous traversal time point of the current traversal time point is less than the kernel density threshold, and the slope of the current traversal time point on the kernel density curve is greater than the positive slope threshold, indicating that the number of healthy data items has increased suddenly between the current traversal time point and the previous traversal time point, then the current traversal time point is taken as a starting time point;

[0102] If the kernel density of the current traversal time point is less than or equal to the kernel density threshold, the kernel density of the previous traversal time point of the current traversal time point is greater than the kernel density threshold, and the slope of the current traversal time point on the kernel density curve is less than the negative slope threshold, indicating that the number of healthy data items has suddenly decreased between the current traversal time point and the previous traversal time point, then the current traversal time point is taken as an end time point;

[0103] After the traversal is completed, the time interval between a pair of adjacent start time points and the end time point of the target time interval, which satisfies the start time point being earlier and the end time point being later, is taken as a target time interval.

[0104] In this embodiment, the preset step size preferably ranges from 0.1 to 1 times the unit scale of the time axis, but is not limited thereto. The absolute values ​​of the positive slope threshold and the negative slope threshold preferably range from 0 to 2, but are not limited thereto, and can be adjusted experimentally.

[0105] Figure 4 In an example of this embodiment, two target time intervals are obtained. This embodiment takes into account the size of the kernel density and the size of the slope, and can more accurately capture the start time point and the end time point of each target time interval, thereby improving the accuracy of the slice data.

[0106] The present application also discloses a health record data management system. In a preferred embodiment, Figure 5 The framework structure of the health record data management system is shown, including:

[0107] The data collection module is used to collect health data from different data sources of different residents. The data collection module is preferably, but not limited to, an Internet interface module, which is responsible for collecting raw health data from multiple sources, such as hospital information systems (HIS), laboratory information systems (LIS), picture archiving and communication systems (PACS), and other external medical equipment and service providers. It also needs to have the ability to automatically identify and extract structured and unstructured data from raw health data, such as text descriptions, image files, etc.

[0108] The data storage module stores the collected health data in the electronic health record unit corresponding to the resident. The data storage module is preferably, but not limited to, a distributed, high-efficiency database that accelerates the query speed by establishing a detailed index structure. This module meets the needs of rapid retrieval of large amounts of data, and can also support complex multi-condition combination queries, such as screening the health data of a specific population by age group, population label, gender, region and other factors, and providing it to doctors and residents for review. An electronic health record unit is allocated for each resident in the data storage module to store each resident's health record data and slice data, and to associate the slice data with health time.

[0109] The slice time analysis and processing module responds to notifications of health events throughout the life cycle of residents, executes the steps of slicing and saving the health record data in the above-mentioned method of dynamically updating one person, one file, and one local file for multi-location services for a large area population, obtains slice data of residents' health events and saves it in the residents' electronic health record unit to facilitate subsequent in-depth analysis.

[0110] The service provision module is used to provide users with a query interface for querying slice data of residents' health events.

[0111] In this embodiment, preferably, Figure 5 As shown, the archive management system also includes:

[0112] Data cleaning and preprocessing module, because data from different channels may have inconsistent formats or uneven quality, this module is dedicated to data cleaning and preprocessing module to standardize all input data. This step includes removing duplicates, filling missing values, correcting erroneous information, and converting to a unified standard format.

[0113] The index module is used to establish the electronic health record unit index of residents and the slice data index within the electronic health record unit to improve the query efficiency.

[0114] The analysis and visualization module performs advanced functions such as statistical analysis, predictive modeling, and machine learning algorithms, while providing an intuitive and easy-to-understand data visualization interface, allowing users to easily view electronic health record historical slice data, statistical charts, and other analytical aggregation results.

[0115] The present invention also discloses a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps of the method for dynamically updating one-person-one-file-one-territorial archive for multi-location services for a large-area population provided by the present invention. The computer program product should be understood as a software product that mainly implements its solution through a computer program, such as a program product integrated in the cloud or a software library.

[0116] The present invention analyzes the data flow formed based on the time series of health examination medical data, which has the characteristics of different institutional sources, different detection indicators, non-stationarity, different importance and huge data volume. These different data are sliced ​​snapshot data and analysis data at the key time stage of the whole life cycle of the medical field for associated storage, and personal snapshot data of key time nodes are extracted from the massive medical data and provided to residents and doctors for viewing and comparison, which can provide data validity, reduce redundancy, and improve storage and query efficiency. It can help residents and medical units to trace back, manage and assist in diagnosis of health data. The present invention uses a kernel density estimation algorithm to extract the data and analysis results of the corresponding time of each event for aggregation, analysis and storage, accurately judge whether there are sudden factors, thereby causing changes in global data, extract the key time node, and form a global data snapshot to restore and trace historical events.

[0117] The present invention also discloses an electronic device. In one embodiment, the electronic device includes at least one processor; and a memory connected to the at least one processor in communication; wherein:

[0118] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for dynamically updating one-person-one-file-one-locality archives for multi-location services for a large-area population provided by the present invention.

[0119] like Figure 6 , is a schematic diagram of the structure of an electronic device for a method for dynamically updating a one-file-one-locality file for a large-area crowd with multiple locations provided by an embodiment of the present invention. The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for dynamically updating a one-file-one-locality file for a large-area crowd with multiple locations.

[0120] Among them, the processor 10 in some embodiments may be composed of an integrated circuit, for example, it may be composed of a single packaged integrated circuit, or it may be composed of multiple integrated circuits packaged with the same function or different functions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing a dynamic update method for one person, one file, and one place file for multi-location services for a large area of ​​people, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device and process data.

[0121] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the electronic device. Further, the memory 11 can also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the program of the dynamic update method of one person, one file, and one place archive for multi-location services in a large area, etc., but can also be used to temporarily store data that has been output or is to be output.

[0122] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industrial Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0123] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0124] Figure 6 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 6 The structure shown does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0125] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0126] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited by this structure.

[0127] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0128] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", "an implementation", "a preferred implementation" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0129] Although the 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for dynamically updating one person, one file, and one location file for a large-scale population in multiple locations, characterized in that: include: New file creation steps: The file creation agency uses the resident's identity ID as the verification mark to check the resident file database to determine whether the resident has been filed in the resident file database. If the resident has been filed in the resident file database, it will prompt that the file has been created. If the resident has not been filed in the resident file database, a file will be created for the resident; Update file steps: The resident's file management agency uploads the updated personal basic information to the file management platform through the file personal basic information update interface, and the resident's file management agency uploads the resident's business data to the file management platform through the grassroots business data upload interface to update the file records; Steps for transferring archives: The proposed archive management institution of the resident sends an application for transfer to the original archive management institution of the resident; after the proposed archive management institution receives the reply of consent for transfer from the original archive management institution, the archive management platform allocates the archive management authority of the resident to the proposed archive management institution, and revokes the archive management authority of the resident from the original archive management institution, and records the archive transfer event in the resident archive database; Off-site service steps: The off-site service agency of the resident establishes an off-site service link with the resident's file management agency; the off-site service agency accesses the resident's file through the off-site service link and uploads the resident's off-site service record; The filing agency updates the residents’ files based on their off-site service records.

2. The method for dynamically updating one-person-one-file-one-locality archives for multi-location services for a large-area population as claimed in claim 1, characterized in that: The resident's file is a health file; The method also includes a step in which the file management agency slices and saves the health file data of the residents, including: Access to residents’ health record data; Obtaining N health data time pairs within a preset time period corresponding to the health events of the residents from the health file data, wherein each health data time pair includes a piece of health data and the generation time of the piece of health data, and N is a positive integer; Map the generation time of N health data time pairs onto the time axis respectively to obtain M mapping time points, where M is a positive integer and is less than or equal to N; Calculate the kernel density for each mapping time point; Generate a kernel density curve based on the kernel density of multiple mapped time points; Determine one or more target time intervals according to the size and / or change trend of the kernel density on the kernel density curve; The health data within each target time interval is saved as slice data of residents' health events.

3. The method for dynamically updating one person, one file, and one location file for multi-location services for a large-area population as claimed in claim 2, characterized in that: The step of calculating the kernel density of each mapping time point comprises: Setting a kernel density weight for each mapping time point according to the mapped health data at each mapping time point, wherein the mapped health data at each mapping time point includes health data in health data time pairs to which all generation times mapped to the mapping time point belong; The original kernel density of each mapping time point was calculated using a kernel density estimation algorithm; The original kernel density of each mapping time point is weighted by the kernel density weight of each mapping time point to obtain the kernel density of each mapping time point.

4. The method for dynamically updating one person, one file, and one location file for multi-location services for a large-area population as claimed in claim 2, characterized in that: The step of setting the kernel density weight of each mapping time point according to the mapped health data of each mapping time point comprises: Calculate the detection item weight factor or indicator quantity weight factor or abnormal indicator weight factor or data source weight factor at each mapping time point based on the mapped health data at each mapping time point; The detection item weight factor, indicator quantity weight factor, abnormal indicator weight factor, or data source weight factor at each mapping time point is used as the kernel density weight at each mapping time point.

5. The method for dynamically updating one person, one file, and one location file for multi-location services for a large-area population as claimed in claim 2, characterized in that: The step of setting the kernel density weight of each mapping time point according to the mapped health data of each mapping time point comprises: Calculate at least two weight factors among the four weight factors of the detection item weight factor, the indicator quantity weight factor, the abnormal indicator weight factor and the data source weight factor at each mapping time point based on the mapped health data at each mapping time point; The at least two weight factors calculated for each mapping time point are weighted, and the weighted processing result is used as the kernel density weight of each mapping time point.

6. The method for dynamically updating one person, one file, and one location file for multi-location services for a large-area population as claimed in claim 4 or 5, characterized in that: When calculating the weight factor of the detection item at each mapping time point, include: Identify all test items included in the mapped health data at each mapped time point; According to the mapping relationship between items and importance levels, the highest item importance level among all the inspection items at each mapping time point is determined, and the highest item importance level is normalized to obtain the inspection item weight factor at each mapping time point; Alternatively, when calculating the data provenance weight factor for each mapping time point, include: Identify all data sources included in the mapped health data at each mapped time point; According to the source-importance level mapping relationship, the highest source importance level among all data sources at each mapping time point is determined, and the highest source importance level is normalized to obtain the data source weight factor at each mapping time point.

7. The method for dynamically updating one person, one file, and one location file for multi-location services for a large-area population as claimed in claim 6, characterized in that: When calculating the weight factor for the number of indicators at each mapping time point, include: Count the number of detection indicators included in the mapped health data at each mapped time point; The number of detection indicators is normalized to obtain the indicator quantity weight factor at each mapping time point; Alternatively, when calculating the anomaly indicator weight factor for each mapped time point, include: Count the number of anomaly detection indicators included in the mapping health data at each mapping time point; The number of anomaly detection indicators is normalized to obtain the anomaly indicator weight factor at each mapping time point.

8. The method for dynamically updating one person, one file, and one location archives for multi-location services for a large-area population as described in claim 2, 3, 4, 5, or 7, characterized in that: The step of determining more than one target time interval according to the size and / or change trend of the kernel density on the kernel density curve includes: Traverse along the time axis from the starting time point to the ending time point of the kernel density curve according to the preset step size, and execute each traversal: If the kernel density of the current traversal time point is greater than or equal to the kernel density threshold, the kernel density of the previous traversal time point of the current traversal time point is less than the kernel density threshold, and the slope of the current traversal time point on the kernel density curve is greater than the positive slope threshold, then the current traversal time point is taken as a starting time point; If the kernel density of the current traversal time point is less than or equal to the kernel density threshold, the kernel density of the previous traversal time point of the current traversal time point is greater than the kernel density threshold, and the slope of the current traversal time point on the kernel density curve is less than the negative slope threshold, then the current traversal time point is taken as an end time point; After the traversal is completed, the time interval between a pair of adjacent start time points and the end time point of the target time interval, which satisfies the start time point being earlier and the end time point being later, is taken as a target time interval.

9. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for dynamically updating one-person-one-file-one-locality archives for multi-location services for a large-area population as described in any one of claims 1 to 8.

10. A health record data management system, characterized in that: include: Data collection module, which collects health data from different residents and different data sources; A data storage module stores the collected health data in the electronic health record unit corresponding to the resident; A slice time analysis and processing module, in response to notifications of health events throughout the life cycle of residents, executes the step of slicing and saving the health record data as described in claim 2, obtains slice data of the health events of residents and saves it in the electronic health record unit of the residents; The service provision module is used to provide users with a query interface for querying slice data of residents' health events.

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