CN112489743B - Medical data view realization method and system - Google Patents
Medical data view realization method and system Download PDFInfo
- Publication number
- CN112489743B CN112489743B CN202011341329.6A CN202011341329A CN112489743B CN 112489743 B CN112489743 B CN 112489743B CN 202011341329 A CN202011341329 A CN 202011341329A CN 112489743 B CN112489743 B CN 112489743B
- Authority
- CN
- China
- Prior art keywords
- data
- patient
- patient health
- medical data
- index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a medical data view realization method and a system, wherein a specific implementation mode of the method comprises the steps of establishing a main index model of patient health, storing index data generated by the main index model of patient health into a blockchain according to a preset main index uploading contract, and further acquiring corresponding medical data based on the main index model of patient health according to the index data; identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis node and outputting the patient health data view. Therefore, the embodiment of the invention can solve the problems of high cost and great difficulty of medical data sharing among the existing multiple institutions.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and a system for implementing a medical data view.
Background
Currently, the view of the patient 360 is generally implemented by a medical institution, for example, a hospital, the patient is serially connected in the home visit through the view of the patient 360, the line is associated through the main index of the time axis, and the detailed information of each visit is associated with a corresponding system, such as LIS, PAC, etc., so as to provide panoramic and multidimensional patient information for doctors and provide references for subsequent treatment. The view of the patient 360 of the general system is realized by adopting a centralized system and a database, and the patient treatment history and the diagnosis and treatment result are summarized by taking a time axis as a dimension, so that the change trend of the physical condition of the patient can be reflected.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
currently, traditional medical data construction of a patient 360 view is based on a single medical institution due to problems of databases, data structures, structural normative of different medical institutions. Therefore, there are many challenges for sharing medical information among multiple institutions, such as inconsistent HIS system data structure among different hospitals, high early development cost, large policy risk, and high operation cost.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a medical data view realization method and a system, which can solve the problems of high cost and great difficulty in medical data sharing among multiple institutions in the prior art.
To achieve the above object, according to an aspect of the embodiments of the present invention, there is provided a medical data view implementation method, including establishing a patient health main index model to store index data generated by the patient health main index model into a blockchain according to a preset main index uploading contract, and further acquiring corresponding medical data based on the patient health model according to the index data; identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis node and outputting the patient health data view.
Optionally, after the index data generated by the patient health master index model, the index data comprises:
acquiring preset patient identity information, and calling a dynamic update model to generate a unique identifier of the patient; and obtaining an encrypted first value through homomorphic encryption pailler algorithm according to the unique identification and the preset password of the patient, further processing the first value through sha256hash160 algorithm to obtain a second value, and using the second value as an address value to search index data stored on a blockchain.
Optionally, performing linear fitting on the medical data based on a time dimension to obtain a time axis node, including:
and performing linear fitting on the medical data through a cubic spline interpolation fitting algorithm to obtain a time axis node, generating a patient health time linear model, and further performing health data prediction through the patient health time linear model.
Optionally, the timeline data structure includes:
calculating a corresponding second numerical value serving as an index address according to index data generated by the main index model of patient health; each index address sequentially stores medical data according to the time dimension, and the medical data of each time point is stored into one data instance; a unique address is generated for each attribute of medical data and stored in a corresponding data instance.
Optionally, the method further comprises:
receiving a view update instruction, determining update authority based on an identity in the view update instruction according to a preset view update intelligent contract, and acquiring corresponding medical data from a blockchain to execute update; encrypting the updated medical data through the platform public key, and further storing the encrypted medical data into the corresponding blockchain address.
Optionally, the method further comprises:
starting a monitoring program, acquiring a circulation log and a circulation request of medical data of a patient, and recording the circulation log and the circulation request in a preset distributed account book.
Optionally, the method further comprises:
displaying a time axis through a front-end page, and calling a query interface to further acquire a corresponding patient health data view on the blockchain; if a detailed page information instruction for presenting patient health data is received, the ip address of the corresponding front-end processor is acquired, an external medical system is accessed, and detailed page information is called.
In addition, the invention also provides a medical data view realization system, which comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for establishing a main index model of patient health so as to store index data generated by the main index model of patient health into a blockchain according to a preset main index uploading contract, and further acquire corresponding medical data based on the patient health model according to the index data; the processing module is used for identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis node and outputting the patient health data view.
One embodiment of the above invention has the following advantages or benefits: the invention utilizes the block chain technology to establish the technical means of the patient view time axis, thereby realizing the trusted and tamper-proof data processing and sharing; automatically processing and generating by using the intelligent contract, and controlling the access right of the view through the authority; the block chain distributed ID generation based on cryptography, namely, a homomorphic encryption-based algorithm is adopted, a user editable password is introduced into a generation process of a unique ID value, and the privacy and the system safety of user data are ensured; real-time supervision and real-time intervention provide reliable supervision functions based on blockchain for supervision departments; medical data sharing among medical institutions reduces repeated examination and data risk; the distributed storage avoids single-point faults, and the localization nodes improve the view efficiency of patients; and (5) medical data of the latitude of the time axis of the patient are subjected to summarized analysis, the risk is predicted, and the patient is protected for healthy driving.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main flow of a medical data view implementation method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a timeline data structure, in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of a timeline presentation according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the main flow of a medical data view implementation method according to a second embodiment of the present invention;
FIG. 5 is a schematic diagram of the main flow of a medical data view implementation method according to a third embodiment of the present invention;
FIG. 6 is a schematic diagram of the major modules of a medical data view implementation system according to an embodiment of the present invention;
FIG. 7 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
fig. 8 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of main flow of a medical data view implementation method according to a first embodiment of the present invention, the medical data view implementation method including:
step S101, a patient health main index model is established, index data generated through the patient health main index model is stored into a blockchain according to a preset main index uploading contract, and corresponding medical data based on the patient health model is obtained according to the index data.
In some embodiments, after the index data is generated by the patient health main index model, preset patient identity information can be acquired, and a dynamic update model is invoked to generate the unique identifier of the patient. And obtaining an encrypted first value through homomorphic encryption pailler algorithm according to the unique identification and the preset password of the patient, further processing the first value through sha256hash160 algorithm to obtain a second value, and using the second value as an address value to search index data stored on a blockchain.
Specific examples: a is set to the patient identification number, where "X" is replaced with "0" and b is set to the patient provided password, such as an 8 digit number. When a patient main index is established, a dynamic update model is called to generate a unique identifier a ' of the patient, and an encrypted first numerical value is obtained through a homomorphic encryption pailler algorithm according to the unique identifier and the patient identity information, namely P (a ')+P (b) =P (a ' +b) is calculated. The patient address value is then calculated by the sha256hash160 algorithm: sha256hash160 (P (a ') +p (b')).
When the patient applies for the history data, the password b is input, and the method calculates the sha256hash160 (P (b) +P (sha 1 (identification card number+suffix code)))) to obtain the id of the main index of the patient, so that the history treatment data can be searched. If the patient password is incorrectly entered, the sha256hash160 (P (b') +p (sha 1 (identification number+suffix)) will not be able to obtain the corresponding medical data. If the patient password is modified from b to c, the P (c) value is only needed to be recalculated, the value of the original main index key is calculated by using sha256hash160 (P (b) +p (sha 1 (identification card number+suffix))), the value of the original main index key is assigned to the key value of sha256hash160 (P (c) +p (sha 1 (identification card number+suffix))), and the value of sha256hash160 (P (b) +p (sha 1 (identification card number+suffix))) is emptied.
According to the invention, the health information of the patient can not be directly obtained through the identification card number, so that a malicious user is prevented from crawling the information of all patients in the treatment, the data privacy is ensured, and the control right and the data are separated, so that the method is applicable to a plurality of scenes.
Preferably, establishing the patient health master index model includes: the diagnosis feature of a patient is marked from the following table dimensions, and is of a character string type. Preferably, the generating method of the partial is as follows: sha1 (sha 1 (identification card number+suffix code)), the suffix code is systematic code, and the suffix code between different mechanisms is different, so that repetition can be avoided. Wherein sha1 is a secure hash algorithm.
TABLE 1
| Sequence number | Data item | Description of the |
| 1 | patientid | |
| 2 | hospitalCode | Hospital of seeing a |
| 3 | diseaseCode | Disease coding |
| 4 | cost | Amount of |
| 5 | diagnosis | Diagnosis of |
| 6 | doctorAdvise | Doctor's advice |
| 7 | enable | Sign bit |
In addition, the invention adopts the solubility (the solubility is the set of codes and data located at specific addresses of the Ethernet blockchain) language to realize the acquisition of the corresponding medical data based on the patient health model:
struct MainIndexDetail{
address patientid；
string hospitalCode；
string diseaseCode；
string cost；
string diagnosis；
string doctorAdvise；
bytes32 media_info; data structure address of index of/medical information
boolean enable；
}
Wherein the data structure can calculate the bytes32 value of the structure body instance through the sha256 algorithm, and the value is used as a link address value and is subsequently used as a retrieval key. Meanwhile, defining global storage mapping, storing all uploaded data instances, wherein keys are the byte 32 values, and the mapping is in a solubility format as follows:
mapping (bytes 32= > mainindixdetail) main_index_detail_mapping. The mapping relation between the main index and the address is saved, so that the numerical value of a specific field of the main index can be obtained only by obtaining the address value, and model information of patient treatment can be obtained.
It should be further noted that the present invention further defines a main index upload contract to generate a event_time_services core data structure:
function upload_index(MainIndexDetail mainindex,string date)returns(uint8 code){
if (upload authority does not exist) {
Returning no permission;
}
calculating byte 32 values by using sha256 according to the main index detail data structure;
main_index_detail_mapping[bytes32]＝mainindex；
record/save the details of the present visit
if (bytes 32 are not in the bytes32 array) {
Patient_time_series[mainindex.patientid][date].push(bytes32)；
This step saves the link address into the timeline visit array
}
}
Step S102, identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node.
In some embodiments, the medical data is linearly fitted based on a time dimension to obtain a time axis node, the medical data is linearly fitted through a cubic spline interpolation fitting algorithm to obtain the time axis node, a patient health time linear model is generated, and then health data prediction is performed through the patient health time linear model. That is, a cubic spline interpolation fitting is performed in a time dimension with a certain dimension of the patient health model as a basic unit, a time axis generation algorithm is calculated by using the characteristic of automatic execution of the smart contract, and the time axis nodes are connected in series through a time axis data structure. Preferably, the patient view main index data structure is a mapping of a main index health data structure array with patient core health model data as a mapping structure, patient addresses as keys, and dates (yyyyMMdd format) as keys. mapping (patient address= > mapping (date= > main index detail array)). The specific codes and data structures are as follows:
mapping(address＝>mapping(string＝>bytes32[]))Patient_time_series；
the aforementioned map is used as a standard structure not only for the storage and logic of the diagnosis time axis in the patient view, but also for the diagnosis time axis, the medication time axis, the examination time axis, etc.
Furthermore, based on the basic structure of the medical data of the patient health model, the characteristic values of the patient present visit data can be obtained, a great number of data items can be obtained according to the examination of specific items, and the following patient health characteristic values designed for the invention are arrays in byte 32 format and can be linked to blocks and details of detailed data contents. The linkage is that the sha256 algorithm calculates the bytes32 value of the structure instance and gets it from the mapping of the detailed information.
struct Medical_Info{
bytes32[ ] diagnostics; diagnostic method
bytes32[ ] exam; check/check test
bytes32 cis_main; first page of case
bytes32[ ] descriptions; formula/prescription
bytes32[ ] doctorOrder; instructions of the doctor/doctor
bytes32[ ] temp; temperature// body temperature
bytes32[ ] blood; record with blood
}
The memory map mapping (id= > medical_info data structure instance) of this data structure is defined, the solubility format mapping is as follows:
mapping(bytes32＝>Medical_Info)medical_detail_mapping；
and the mapping relation between the content and the address of the medical data detail is stored, so that the numerical value of a specific field corresponding to the medical data can be obtained only by obtaining the address value of the data structure, and the specific type information of the patient visit can be obtained.
In addition, the invention defines medical data uploading contracts, and the medical data with different attributes are respectively processed, taking a prescription as an example:
the data structure on the blockchain of the recipe is struct:
struct Prescription{
string PrescriptionId; number of// prescription
string Prescription _content; content of/(prescription), encrypted using public key
string timestamp; time of opening/closing
bootable; flag for activation/not
}
Uploading prescription information into a prescription map in a blockchain, and returning byte 32 address values;
saving the prescription information address value into a description array of medical information index data, and judging repetition before adding;
function add(string patientid,string date,string jzlsh,Prescription prescriptions)returns(uint8){
calculating the address a of the patient by using the partial id;
calculate bytes32 b using jzlsh and medical facility code;
storing the prescriptions in a prescription map of the blockchain to obtain pres_bytes32;
saving the prescribed pres_bytes32 to the media_description_mapping [ b ]
if(Patient_time_series[a].enable＝＝false){
Patient_time_series[a][date]＝b；
}
return 200；
}
Step S103, generating and outputting a patient health data view based on a preset time axis data structure according to the time axis node.
In some embodiments, the corresponding second value, i.e., index address, is calculated based on a preset timeline data structure, as shown in fig. 2, from index data generated by a patient health master index model. And each index address sequentially stores medical data according to the time dimension, for example, the stored medical data comprises diagnosis results, medical orders, disease types, amounts, treatment hospitals and the like, and the medical data at each time point can be stored into one data instance. And, a unique address is generated for each attribute of medical data, and is correspondingly stored in a data instance. Preferably, the bytes32 value of the structure instance is calculated by the sha256 algorithm as the link address value.
In still other embodiments, the present invention provides a query interface, i.e., the query interface is invoked via the front-end page presentation timeline (as shown in FIG. 3), to thereby obtain a corresponding view of patient health data on the blockchain. If a detailed page information instruction for presenting patient health data is received, the ip address of the corresponding front-end processor is acquired, an external medical system is accessed, and detailed page information is called. The invention establishes a query smart contract to automatically execute a timeline patient health data view. Specifically:
function query360(string patientid,string date)returns(bytes32[]){
calculating the address of the patient partial id;
return Patient_time_series[address(patientid)][date]；
}
function query360detail(bytes32 detail_id)returns(MainIndexDetail mainindexdetail){
return main_index_detail_mapping[detail_id]；
}
function query360medical_detail(bytes32 medical_detail_id)returns(Medical_Info medical_info){
return medical_detail_mapping[medical_detail_id]；
}
as other embodiments, the present invention may further receive a view update instruction, determine update authority based on an identity in the view update instruction according to a preset view update smart contract, so as to obtain corresponding medical data from a blockchain to perform update; encrypting the updated medical data through the platform public key, and further storing the encrypted medical data into the corresponding blockchain address. That is, the present invention establishes a patient update smart contract, since the special case requires updating the view, or the medical data requires perfecting, is modified by updating the smart contract, the modification of the rights is guaranteed, and the behavior is recorded.
It is also worth to describe that the invention starts the monitoring program, obtains the circulation log and circulation request of the patient medical data, and records the circulation log and circulation request in the preset distributed account book. Thereby, the real-time monitoring and recording of the medical data processing is realized.
In summary, the invention realizes the patient view time axis based on the intelligent contract of the blockchain, improves the data reliability and the time dimension of the main index of the patient, can be popularized to the view angles of different dimensions, integrates hashed patient data into a continuous patient health tracking structure, and greatly improves the range data query function in the blockchain medical information sharing system. And the hierarchical design of the system based on the block chain ensures the data security, improves the expandability of the system and ensures the seamless connection of the data during upgrading. In addition, in medical information sharing, the patient view is built through decentralization of intelligent contracts, the flexibility of the system is improved, the front end is combined for direct display, and the adaptability of the system is improved. Also, the present invention is based on a shared ledger-stored patient view, which can avoid data loss or errors.
Fig. 4 is a schematic diagram of a main flow of a medical data view implementation method according to a second embodiment of the present invention, in which a medical institution is incorporated into a alliance chain by means of a blockchain feature, medical data is circulated by a P2P network protocol of the blockchain, and data dispersed in each information island is integrated by a unified model by means of characteristics of a distributed ledger book, a consensus mechanism, privacy protection, security traceability, etc., so as to finally generate a trusted resident electronic health record. Preferably using a intelligence contract developed based on solubility, deployed in a fsco BCOS (financial version blockchain underlying platform) alliance chain, provides a mapping structure type based on a time dimension of a patient, stores patient view data, and intuitively operates and exposes in a Dapp (decentralization application, i.e., decentralised application) manner. Specifically:
and storing index data generated through the patient health main index model into a blockchain according to a preset main index uploading contract, and further acquiring corresponding medical data based on the patient health model according to the index data. And identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node. And finally, generating a patient health data view based on a preset time axis data structure according to the time axis node and outputting the patient health data view. And calculating the corresponding second numerical value, namely the index address, according to the index data generated by the main index model of the patient health. And each index address sequentially stores medical data according to the time dimension, for example, the stored medical data comprises diagnosis results, medical orders, disease types, amounts, treatment hospitals and the like, and the medical data at each time point can be stored into one data instance. And, a unique address is generated for each attribute of medical data, and is correspondingly stored in a data instance. Preferably, the bytes32 value of the structure instance is calculated by the sha256 algorithm as the link address value. Preferably, preset patient identity information is acquired, and a dynamic update model is called to generate a unique identifier of the patient. And obtaining an encrypted first numerical value through a homomorphic encryption pailler algorithm according to the unique identifier and the preset password of the patient, and further processing the first numerical value through a sha256hash160 algorithm to obtain a second numerical value.
Therefore, in the government medical insurance business scene, the regional medical data sharing system acquires the treatment data from each hospital, generates the patient view intelligently through the intelligent contracts, can directly display the patient view stored on the blockchain through the front-end page, can also inquire the patient information through the intelligent contracts of other dimensions, such as time period, diseases, hospitals, fees and the like, can also provide a front-end processor access path and parameters for the detail data in the patient view, and forwards the detail data content after permission verification and request log record are carried out by the platform. In the prescription circulation business scene, a patient view-prescription time axis is established, and when a patient visits a hospital, a doctor is authorized to check historical data of the doctor as a reference by inputting a password. After the prescription flows out, the prescription is taken by a designated pharmacy, and a password is input when a pharmacist examines the prescription, so that the pharmacist can be authorized to check the historical prescription information of the patient as a reference of the current examination party. The platform side can analyze, group and predict according to the patient view time axis, and provide health management and insurance class services for the platform side.
Fig. 5 is a schematic diagram of main flow of a medical data view implementation method according to a third embodiment of the present invention, the medical data view implementation method including:
step S501, a patient health main index model is established, and index data is generated through the patient health main index model according to a preset main index uploading contract.
Step S502, obtaining preset patient identity information, and calling a dynamic update model to generate a unique identifier of the patient.
Step S503, according to the unique identification and the preset password of the patient, the encrypted first value is obtained through a homomorphic encryption pailler algorithm, and then the first value is processed through a sha256hash160 algorithm to obtain a second value, and the second value and index data are stored in a blockchain.
Step S504, corresponding medical data based on the patient health model is obtained according to the index data.
Step S505, identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node.
Step S506, generating a patient health data view based on a preset time axis data structure according to the time axis node.
Step S507, displaying a time axis through the front-end page, and calling a query interface to further acquire a corresponding patient health data view on the blockchain.
Step S508, receiving the detailed page information instruction for presenting the patient health data, acquiring the ip address of the corresponding front-end processor, accessing the external medical system, and calling the detailed page information.
The patient view time axis is shared by multiple medical institutions, so that the circulation and usability of data are effectively improved, and data analysis can be performed on big data. According to the health model of the patient, the method acquires and screens part of medical information through the blockchain, calculates the number of indexes, forms the health model of the patient in a certain time period, continuously updates the model result of the patient through intelligent contracts and the like, and improves the accuracy of the health view model of the patient.
Fig. 6 is a schematic diagram of main modules of a medical data view implementation system according to an embodiment of the present invention, and as shown in fig. 6, the medical data view implementation system 600 includes an acquisition module 601 and a processing module 602. The obtaining module 601 establishes a patient health main index model to store index data generated by the patient health main index model into a blockchain according to a preset main index uploading contract, and further obtains corresponding medical data based on the patient health model according to the index data. The processing module 602 identifies a target dimension in medical data based on a patient health model, invokes a preset view intelligent contract, and linearly fits the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis node and outputting the patient health data view.
In some embodiments, after obtaining the index data generated by the module 601 through the patient health master index model, it includes:
and acquiring preset patient identity information, and calling a dynamic update model to generate a unique identifier of the patient. And obtaining an encrypted first value through homomorphic encryption pailler algorithm according to the unique identification and the preset password of the patient, further processing the first value through sha256hash160 algorithm to obtain a second value, and using the second value as an address value to search index data stored on a blockchain.
In some embodiments, the processing module 602 performs a linear fit on the medical data based on a time dimension to obtain a time axis node, including:
and performing linear fitting on the medical data through a cubic spline interpolation fitting algorithm to obtain a time axis node, generating a patient health time linear model, and further performing health data prediction through the patient health time linear model.
In some embodiments, the processing module 602 the timeline data structure includes:
calculating a corresponding second numerical value serving as an index address according to index data generated by the main index model of patient health; each index address sequentially stores medical data according to the time dimension, and the medical data of each time point is stored into one data instance; a unique address is generated for each attribute of medical data and stored in a corresponding data instance.
In some embodiments, the processing module 602 is further configured to:
receiving a view update instruction, determining update authority based on an identity in the view update instruction according to a preset view update intelligent contract, and acquiring corresponding medical data from a blockchain to execute update; encrypting the updated medical data through the platform public key, and further storing the encrypted medical data into the corresponding blockchain address.
In some embodiments, the processing module 602 is further configured to: starting a monitoring program, acquiring a circulation log and a circulation request of medical data of a patient, and recording the circulation log and the circulation request in a preset distributed account book.
In some embodiments, the processing module 602 is further configured to: displaying a time axis through a front-end page, and calling a query interface to further acquire a corresponding patient health data view on the blockchain; if a detailed page information instruction for presenting patient health data is received, the ip address of the corresponding front-end processor is acquired, an external medical system is accessed, and detailed page information is called.
It should be noted that, in the medical data view implementation method and the medical data view implementation system of the present invention, there is a corresponding relationship between implementation contents, so repeated contents will not be described.
Fig. 7 illustrates an exemplary system architecture 700 to which the medical data view implementation method or medical data view implementation system of embodiments of the present invention may be applied.
As shown in fig. 7, a system architecture 700 may include terminal devices 701, 702, 703, a network 704, and a server 705. The network 704 is the medium used to provide communication links between the terminal devices 701, 702, 703 and the server 705. The network 704 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 705 via the network 704 using the terminal devices 701, 702, 703 to receive or send messages or the like. Various communication client applications such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 701, 702, 703.
The terminal devices 701, 702, 703 may be various electronic devices having a medical data view implementation screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 705 may be a server providing various services, such as a background management server (by way of example only) providing support for shopping-type websites browsed by users using the terminal devices 701, 702, 703. The background management server may analyze and process the received data such as the product information query request, and feedback the processing result (e.g., the target push information, the product information—only an example) to the terminal device.
It should be noted that, the method for implementing the medical data view according to the embodiment of the present invention is generally performed by the server 705, and accordingly, a computing system is generally disposed in the server 705.
It should be understood that the number of terminal devices, networks and servers in fig. 7 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 8, there is illustrated a schematic diagram of a computer system 800 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 8 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 8, the computer system 800 includes a Central Processing Unit (CPU) 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the computer system 800 are also stored. The CPU801, ROM802, and RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, mouse, etc.; an output section 807 including a display such as a Cathode Ray Tube (CRT), a liquid crystal medical data view finder (LCD), and a speaker; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809, and/or installed from the removable media 811. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 801.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes an acquisition module and a processing module. The names of these modules do not constitute a limitation on the module itself in some cases.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs, which when executed by one of the devices, cause the device to include establishing a patient health primary index model to store index data generated by the patient health primary index model into a blockchain according to a preset primary index upload contract, and further obtain corresponding patient health model-based medical data according to the index data; identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis node and outputting the patient health data view.
According to the technical scheme provided by the embodiment of the invention, the problems of high cost and high difficulty in medical data sharing among multiple mechanisms in the prior art can be solved.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for implementing a medical data view, comprising:
establishing a patient health main index model, storing index data generated by the patient health main index model into a blockchain according to a preset main index uploading contract, and further acquiring corresponding medical data based on the patient health model according to the index data;
wherein after generating the index data by the patient health master index model, the method comprises: acquiring preset patient identity information, and calling a dynamic update model to generate a unique identifier of the patient; according to the unique identification and the patient preset password, an encrypted first value is obtained through a homomorphic encryption pailler algorithm, the first value is further processed through a sha256hash160 algorithm to obtain a second value, and the second value is used as an address value to search index data stored on a blockchain;
identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node, wherein the method comprises the following steps of: performing cubic spline interpolation fitting on the medical data in a time dimension through a cubic spline interpolation fitting algorithm to obtain a time axis node, generating a patient health time linear model, and further performing health data prediction through the patient health time linear model;
and generating a patient health data view based on a preset time axis data structure according to the time axis node and outputting the patient health data view.
2. The method of claim 1, wherein the timeline data structure comprises:
calculating a corresponding second numerical value serving as an index address according to index data generated by the main index model of patient health;
each index address sequentially stores medical data according to the time dimension, and the medical data of each time point is stored into one data instance; a unique address is generated for each attribute of medical data and stored in a corresponding data instance.
3. The method as recited in claim 1, further comprising:
receiving a view update instruction, determining update authority based on an identity in the view update instruction according to a preset view update intelligent contract, and acquiring corresponding medical data from a blockchain to execute update;
encrypting the updated medical data through the platform public key, and further storing the encrypted medical data into the corresponding blockchain address.
4. The method as recited in claim 1, further comprising:
and starting a monitoring program, acquiring a circulation log and a circulation request of the medical data, and recording the circulation log and the circulation request in a preset distributed account book.
5. The method of any one of claims 1-4, further comprising:
displaying a time axis through a front-end page, and calling a query interface to further acquire a corresponding patient health data view on the blockchain;
if a detailed page information instruction for presenting patient health data is received, the ip address of the corresponding front-end processor is acquired, an external medical system is accessed, and detailed page information is called.
6. A medical data view realization system, comprising:
the acquisition module is used for establishing a patient health main index model so as to store index data generated by the patient health main index model into a blockchain according to a preset main index uploading contract, and further acquire corresponding medical data based on the patient health model according to the index data;
after the acquisition module generates index data through the patient health main index model, the acquisition module comprises: acquiring preset patient identity information, and calling a dynamic update model to generate a unique identifier of the patient; according to the unique identification and the patient preset password, an encrypted first value is obtained through a homomorphic encryption pailler algorithm, the first value is further processed through a sha256hash160 algorithm to obtain a second value, and the second value is used as an address value to search index data stored on a blockchain;
the processing module is used for identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node, and comprises the following steps: performing cubic spline interpolation fitting on the medical data in a time dimension through a cubic spline interpolation fitting algorithm to obtain a time axis node, generating a patient health time linear model, and further performing health data prediction through the patient health time linear model; and generating a patient health data view based on a preset time axis data structure according to the time axis node and outputting the patient health data view.
7. An electronic device, comprising:
one or more processors;
a storage system for storing one or more programs,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-5.
8. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011341329.6A CN112489743B (en) | 2020-11-24 | 2020-11-24 | Medical data view realization method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011341329.6A CN112489743B (en) | 2020-11-24 | 2020-11-24 | Medical data view realization method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112489743A CN112489743A (en) | 2021-03-12 |
| CN112489743B true CN112489743B (en) | 2023-07-04 |
Family
ID=74934510
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011341329.6A Active CN112489743B (en) | 2020-11-24 | 2020-11-24 | Medical data view realization method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112489743B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104699715A (en) * | 2013-12-09 | 2015-06-10 | 北京大学第六医院 | Construction method for master patient index platform system |
| CN109461476A (en) * | 2018-10-22 | 2019-03-12 | 南京医科大学附属逸夫医院 | A kind of classification diagnosis and treatment supporting method and platform |
| CN111091884A (en) * | 2019-12-24 | 2020-05-01 | 无锡识凌科技有限公司 | Patient main index matching system and method of hospital information integration platform |
| CN111128325A (en) * | 2019-12-23 | 2020-05-08 | 南京医睿科技有限公司 | Medical data storage method and device, electronic equipment and storage medium |
| CN111508575A (en) * | 2019-04-19 | 2020-08-07 | 中国医学科学院阜外医院 | Medical system integrating big data |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150317435A1 (en) * | 2014-05-02 | 2015-11-05 | Practice Fusion, Inc. | Presenting a patient's disparate medical data on a unified timeline |
| US11837344B2 (en) * | 2018-06-29 | 2023-12-05 | OutcomeMD, Inc. | Systems and methods for securely storing patient information and providing access thereto |
-
2020
- 2020-11-24 CN CN202011341329.6A patent/CN112489743B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104699715A (en) * | 2013-12-09 | 2015-06-10 | 北京大学第六医院 | Construction method for master patient index platform system |
| CN109461476A (en) * | 2018-10-22 | 2019-03-12 | 南京医科大学附属逸夫医院 | A kind of classification diagnosis and treatment supporting method and platform |
| CN111508575A (en) * | 2019-04-19 | 2020-08-07 | 中国医学科学院阜外医院 | Medical system integrating big data |
| CN111128325A (en) * | 2019-12-23 | 2020-05-08 | 南京医睿科技有限公司 | Medical data storage method and device, electronic equipment and storage medium |
| CN111091884A (en) * | 2019-12-24 | 2020-05-01 | 无锡识凌科技有限公司 | Patient main index matching system and method of hospital information integration platform |
Non-Patent Citations (3)
| Title |
|---|
| 基于信息集成平台的互联网医院信息系统探索;安健 等;《中国医院管理》;第40卷(第10期);全文 * |
| 基于智能电子健康档案平台的大数据应用研究与实践;叶荔姗 等;《中国卫生信息管理杂志》;第16卷(第06期);全文 * |
| 患者主索引服务在医院信息互联互通中的实现与应用;雷保仓 等;《中国现代医生》;第58卷(第13期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112489743A (en) | 2021-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11087878B2 (en) | Methods and systems for improving connections within a healthcare ecosystem | |
| Vellela et al. | An integrated approach to improve e-healthcare system using dynamic cloud computing platform | |
| US11380426B1 (en) | Facilitating computerized interactions with EMRs | |
| CA3074266C (en) | Cloud-based image access systems and methods | |
| JP6878578B2 (en) | Systems and methods for anonymizing health data and modifying and editing health data across geographic areas for analysis | |
| US11144660B2 (en) | Secure data sharing | |
| US10846424B2 (en) | Method for multi-tiered, rule-based data sharing and ontology mapping | |
| US11562812B2 (en) | Computer implemented method for secure management of data generated in an EHR during an episode of care and a system therefor | |
| US20230350891A1 (en) | Systems and methods for bi-directional database application programming interface, extract transform and load system, and user computing device | |
| US10650478B2 (en) | Real-time aggregation and processing of healthcare records | |
| US11574075B2 (en) | Distributed machine learning technique used for data analysis and data computation in distributed environment | |
| US20170052943A1 (en) | Method, apparatus, and computer program product for generating a preview of an electronic document | |
| Haque et al. | An automated approach towards smart healthcare with blockchain and smart contracts | |
| Sarangi et al. | Healthcare 4.0: A voyage of fog computing with iot, cloud computing, big data, and machine learning | |
| Schilling et al. | Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) technology infrastructure for a distributed data network | |
| US11031112B2 (en) | Emergency department communication system | |
| Rai et al. | Patient-centric multichain healthcare record | |
| Mishra et al. | InterPlanetary file system based blockchain for internet of medical things | |
| Yongjoh et al. | Development of an internet-of-healthcare system using blockchain | |
| CN112489743B (en) | Medical data view realization method and system | |
| US20160063077A1 (en) | Data brokering system for fulfilling data requests to multiple data providers | |
| US11901075B2 (en) | Method and apparatus for generating medical information of object | |
| Maghraby et al. | Applied blockchain technology in saudi arabia electronic health records | |
| KR101524181B1 (en) | A system for exchanging clinical information based on lazy response model and the method thereof | |
| US20160357912A1 (en) | System for unitary display of patient data from mulitple care providers |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |