CN112487494B - Health data management system based on blockchain technology - Google Patents
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Abstract
The invention discloses a health data management system based on a blockchain technology, which comprises an intelligent contract, wherein the intelligent contract is configured with a public key database and a disease inquiry database, the disease inquiry database stores a plurality of disease names and chain numbers corresponding to the disease names, and the health data management system also comprises a data acquisition module, a data verification module and a data carding module. The medical data are input by a plurality of doctor nodes, the input medical data are signed and authenticated, the information barriers of different medical institutions can be broken to realize data sharing, the medical data of the same disease can be stored in the same information storage library, the medical data diagnosed by different doctors are compared, the medical accident can be reduced due to timely finding of the wrong diagnosis result, the obviously repeated information is deleted, and the information storage load is reduced, so that the data quality is greatly improved.
Description
Technical Field
The invention relates to the technical field of computer systems, in particular to a health data management system based on a blockchain technology.
Background
Medical record information of the same patient is stored in different medical institutions, and different disease information of the same disease is stored in different medical institutions, so that the difficulty in data storage and scattered calling is always a technical problem to be solved by the medical institutions. The prior art applies blockchain technology to medical information management to realize sharing of information of different medical institutions.
The nature of blockchains is a distributed ledger. It is increasingly known as the underlying technology of bitcoin, and by connecting numerous individual nodes, a blockchain can build an underlying architecture facility without any central node involvement. The blockchain can be used for recording important operation records and has the characteristics of decentralization, openness, autonomy, non-falsification of information and anonymity. The intelligent contracts enable users to publish custom service interfaces on the blockchain, providing business logic support.
Medical data of different medical institutions are stored in different blocks, and the blocks are all uplink to realize information sharing. However, the recording modes of the medical data by different medical institutions are different, and when the medical data of different medical institutions are summarized, the quality of the medical data is not controlled, so that the quality of the data obtained by final searching is poor.
Disclosure of Invention
The invention aims to provide a health data management system based on a blockchain technology, which is used for sharing medical data of different medical institutions and uniformly carrying out data sorting.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: the health data management system based on the blockchain technology comprises an intelligent contract, a data acquisition module, a data verification module and a data carding module, wherein the intelligent contract is configured with a public key database and a disease inquiry database, the public key database stores IDs of a plurality of doctor nodes and doctor public keys corresponding to the IDs of the doctor nodes, the disease inquiry database stores a plurality of disease names and chain numbers corresponding to the disease names;
the data acquisition module comprises a plurality of doctor nodes, the doctor nodes input patient medical information, the patient medical information comprises patient identity information, diagnosis time, disease names, disease information, treatment schemes, predicted disease information and re-diagnosis time, the treatment schemes comprise a plurality of treatment times and treatment operation information required to be carried out corresponding to the treatment times, the disease information reflects disease conditions of patients, and the patient medical information and IDs of the doctor nodes are sent to the data verification module;
the data verification module is used for calculating the patient medical information to obtain a first medical hash value by adopting a hash algorithm, signing the patient medical information and the first medical hash value by using a doctor private key, calling a doctor public key from the public key database by using an intelligent contract according to the ID of the doctor node, verifying the validity of the signature by using the doctor public key, calling the intelligent contract to verify the validity of the patient medical information, if the signature and the patient medical information are valid, sending the patient medical information and doctor input records to the data carding module, and if the signature or the patient medical information is invalid, deleting the patient medical information and giving a warning;
the data carding module comprises a plurality of information storage libraries, each disease name in the disease inquiry database corresponds to one information storage library, and the data carding module further comprises an information preliminary carding unit, a diagnosis information carding unit and a data packing unit;
the information preliminary carding unit is used for obtaining the patient medical information, storing the patient medical information corresponding to the disease in a corresponding information storage library if the disease name in the patient medical information can be matched in the disease inquiry database, inputting the disease name into the disease inquiry database if the disease name in the patient medical information can not be matched in the disease inquiry database, establishing a corresponding information storage library and storing the patient medical information corresponding to the disease in the corresponding information storage library;
the diagnosis information carding unit is preset with a time difference reference value, patient medical information of the same patient in the same information storage library is sequenced according to diagnosis time, time difference of adjacent diagnosis time is calculated to obtain diagnosis time difference, if the diagnosis time difference is smaller than the time difference reference value, similarity of corresponding disease information and similarity of corresponding treatment schemes are calculated, if the similarity of the disease information is smaller than 85%, diagnosis alarm is sent, if the similarity of the treatment schemes is smaller than 70%, treatment alarm is sent, and if the similarity of the disease information is larger than or equal to 85% and the similarity of the treatment schemes is larger than or equal to 70%, patient medical information is deleted;
and the data packaging unit is preset with an storage capacity threshold, when the storage capacity in the information storage library reaches the storage capacity threshold, the information storage library is compressed to obtain information blocks, each information block is matched with a chain number, and the information blocks are uplink to obtain a blockchain.
Preferably, the data carding module further comprises a review information carding unit, and the patient medical information further comprises patient execution information reflecting actual actions taken by the patient according to the treatment scheme;
the review information combing unit obtains patient medical information of different diagnosis times of the same patient in the same information storage library, obtains patient medical information recorded as primary diagnosis medical information, obtains patient medical information recorded as review medical information, the diagnosis time of which is closest to the review time of the patient medical information, calculates similarity of a treatment scheme in the primary diagnosis medical information and patient execution information in the review medical information to obtain compliance medical advice, calculates similarity of predicted disease information in the primary diagnosis medical information and disease information in the review medical information to obtain recovery degree, and stores the compliance medical advice and the recovery degree in the information storage library.
Preferably, the review information combing unit obtains the order compliance degree and the recovery degree, obtains treatment correction information according to the order compliance degree and the recovery degree, wherein the treatment correction information comprises a new treatment scheme, new predicted disease information and new review time, and stores the treatment correction information in the information storage library.
Preferably, the data carding module further comprises a complication carding unit, wherein the complication carding unit is pre-provided with a coincidence benchmark value, and the patient identity information comprises a patient number;
and the complications combing unit is used for searching a plurality of corresponding disease names in the information storage library by taking the patient number as an index to obtain a plurality of disease names as disease name groups, selecting any disease name group as a name group to be matched, selecting any disease name in the name group to be matched as a name group data pool, selecting all disease name groups containing the name to be matched as a name group data pool, adopting a disease association strategy to screen a plurality of associated disease names from the name group data pool, and simultaneously, the number of the disease name groups containing the associated disease names accounts for the disease name groups in the name group data pool, wherein the percentage of the disease name groups in the disease name group data pool is larger than the coincidence reference value, and associating the disease names corresponding to the associated disease names in the disease inquiry database.
Preferably, the condition association policy selects any condition name except the to-be-matched name from the to-be-matched name groups as a new to-be-matched name, selects all condition name groups containing the new to-be-matched name from a name group data pool as an association name data pool, calculates the percentage of the number of the condition name groups in the association name data pool to the number of the condition name groups in the name group data pool to obtain an overlap value, and if the overlap value is larger than the overlap reference value, the to-be-matched name and the new to-be-matched name are associated condition names, and repeats the condition association policy until no new to-be-matched name exists in the to-be-matched name groups.
Preferably, the reference value for overlapping is 30 to 50%.
Preferably, the reference value for overlap is 43%.
Preferably, the condition names include academic names and aliases.
Preferably, the information preliminary carding unit is configured with a network link address, and if the input disease name cannot be matched in the disease inquiry database, the information preliminary carding unit jumps to the network link address and uses the input disease name as a keyword to search.
Preferably, the storage threshold is 100m to 1g.
Compared with the prior art, the invention has the beneficial effects that: the medical data is input by a plurality of doctor nodes, and the input medical data is signed and authenticated, so that the information barriers of different medical institutions can be broken, the sharing of the data can be realized, the input medical data is ensured not to be changed, and the reliability of the data source is improved. The invention also carries out carding on medical information: medical data of the same disease is stored in the same information storage library, and medical data diagnosed by different doctors are compared, so that the incorrect diagnosis result can be found in time, medical accidents are reduced, obviously repeated information is deleted, information storage load is reduced, and data quality is greatly improved.
Drawings
FIG. 1 is a schematic diagram of a health data management system of the present invention;
FIG. 2 is a schematic diagram of a data comb module.
The reference numerals are explained as follows: 010. a public key database; 020. a condition query database; 030. a data acquisition module; 031. an information repository; 032. an information preliminary carding unit; 033. a diagnostic information grooming unit; 034. the review information carding unit; 035. a complications grooming unit; 036. a data packing unit; 040. a data verification module; 050. and a data carding module.
Detailed Description
The invention will now be described in further detail with reference to the drawings and examples.
Example 1:
as shown in fig. 1, a health data management system based on a blockchain technology comprises an intelligent contract, wherein the intelligent contract is configured with a public key database 010 and a disease inquiry database 020, the public key database 010 stores IDs of a plurality of doctor nodes and doctor public keys corresponding to the IDs of the doctor nodes, the disease inquiry database 020 stores a plurality of disease names and chain numbers corresponding to the disease names, and the health data management system further comprises a data acquisition module 030, a data verification module 040 and a data carding module 050;
the data acquisition module 030 includes a plurality of doctor nodes, the doctor nodes input patient medical information, the patient medical information includes patient identity information, diagnosis time, disease name, disease information, treatment plan, predicted disease information and re-diagnosis time, the treatment plan includes a plurality of treatment times and treatment operation information required to be performed corresponding to the treatment times, the disease information reflects disease conditions of patients, and the patient medical information and the ID of the doctor node are sent to the data verification module 040;
the data verification module 040 calculates the patient medical information by adopting a hash algorithm to obtain a first medical hash value, signs the patient medical information and the first medical hash value by using a doctor private key, the intelligent contract retrieves a doctor public key from the public key database 010 according to the ID of the doctor node, verifies the validity of the signature by using the doctor public key and invokes the intelligent contract to verify the validity of the patient medical information, if the signature and the patient medical information are valid, the patient medical information and doctor input records are sent to the data carding module 050, and if the signature or the patient medical information is invalid, the patient medical information is deleted and a warning is sent;
as shown in fig. 2, the data carding module 050 includes a plurality of information storages 031, each disease name in the disease inquiry database 020 corresponds to one information storages 031, and the data carding module 050 further includes an information preliminary carding unit 032, a diagnostic information carding unit 033, and a data packaging unit 036;
the preliminary information carding unit 032 obtains the patient medical information, if the disease name in the patient medical information can be matched in the disease inquiry database 020, stores the patient medical information corresponding to the disease in the corresponding information repository 031, if the disease name in the patient medical information can not be matched in the disease inquiry database 020, inputs the disease name into the disease inquiry database 020, establishes the corresponding information repository 031 and stores the patient medical information corresponding to the disease in the corresponding information repository 031. The information preliminary carding unit 032 is configured with a network link address, and if the input disease name cannot be matched in the disease inquiry database 020, the information preliminary carding unit jumps to the network link address and uses the input disease name as a keyword to search;
the diagnostic information sorting unit 033 presets a time difference reference value, sorts the patient medical information of the same patient in the same information storage library 031 according to the diagnostic time, calculates the time difference between adjacent diagnostic time to obtain the diagnostic time difference, if the diagnostic time difference is smaller than the time difference reference value, calculates the similarity of the corresponding disease information and the similarity of the corresponding treatment scheme, if the similarity of the disease information is smaller than 85%, sends out a diagnostic alarm, if the similarity of the treatment scheme is smaller than 70%, sends out a treatment alarm, if the similarity of the disease information is greater than or equal to 85% and the similarity of the treatment scheme is greater than or equal to 70%, deletes one patient medical information;
the data packing unit 036 is preset with a storage capacity threshold (the storage capacity threshold is 100M), when the storage capacity in the information storage library 031 reaches the storage capacity threshold, the information storage library 031 is compressed to obtain information blocks, each information block is matched with a chain number, and the information blocks are uplink to obtain a block chain.
The medical data is input by a plurality of doctor nodes, and the input medical data is signed and authenticated, so that the information barriers of different medical institutions can be broken, the sharing of the data can be realized, the input medical data is ensured not to be changed, and the reliability of the data source is improved. The information preliminary carding unit 032 and the diagnostic information carding unit 033 also card medical information: medical data of the same disease is stored in the same information storage library 031, and medical data diagnosed by different doctors are compared, so that the medical accident can be reduced due to timely finding out an incorrect diagnosis result, and obviously repeated information is deleted to reduce information storage load, so that the data quality is greatly improved.
Example 2:
as shown in fig. 2, the data grooming module 050 further includes a review information grooming unit 034, and the patient medical information further includes patient execution information reflecting an operation actually taken by the patient according to the treatment scheme, which is different from embodiment 1;
the review information sorting unit 034 obtains patient medical information of different diagnosis times of the same patient in the same information repository 031, obtains a patient medical information recorded as first-order medical information, obtains patient medical information of which the diagnosis time is closest to the review time of the patient medical information recorded as second-order medical information, calculates the similarity between the treatment plan in the first-order medical information and the patient execution information in the second-order medical information, calculates the similarity between the predicted condition information in the first-order medical information and the condition information in the second-order medical information, obtains a recovery degree, and stores the compliance with the order degree and the recovery degree in the information repository 031. The doctor can intuitively know the actual recovery condition of the patient according to the doctor's advice degree and recovery degree, and provides a real data basis for the adjustment of the treatment scheme.
Obtaining the order compliance and the recovery, obtaining treatment revision information according to the order compliance and the recovery, the treatment revision information including a new treatment plan, new predicted condition information, and a new review time, and storing the treatment revision information in the information repository 031.
The data carding module 050 further comprises a complication carding unit 035, wherein the complication carding unit 035 is preset with a coincidence reference value, the patient identity information comprises a patient number, and the coincidence reference value is 30%;
the complication carding unit 035 uses the patient number as an index to retrieve a plurality of corresponding disorder names (the disorder names comprise academic names and aliases) in the information repository 031 as disorder name groups, selects any disorder name group as a to-be-matched name group, selects any disorder name in the to-be-matched name group as a to-be-matched name, selects all disorder name groups containing the to-be-matched name as a name group data pool, adopts a disorder association policy to screen a plurality of associated disorder names from the name group data pool, and simultaneously, the percentage of the number of the disorder name groups containing the associated disorder names to the disorder name groups in the name group data pool is larger than the coincidence reference value, and associates the disorder names corresponding to the associated disorder names in the disorder inquiry database 020.
And selecting any disease name except the to-be-matched name from the to-be-matched name groups to serve as a new to-be-matched name, selecting all disease name groups containing the new to-be-matched name from a name group data pool to serve as an associated name data pool, calculating the percentage of the number of the disease name groups in the associated name data pool to the number of the disease name groups in the name group data pool to obtain a coincidence value, and if the coincidence value is larger than the coincidence reference value, repeating the disease association strategy until the new to-be-matched name is not available in the to-be-matched name groups.
Example 3:
the difference from example 2 is that the overlap reference value is 43%.
Example 4:
the difference from example 2 is that the coincidence reference value is 50% and the memory threshold is 1G.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (8)
1. The health data management system based on the blockchain technology is characterized by comprising an intelligent contract, wherein the intelligent contract is configured with a public key database (010) and a disease inquiry database (020), the public key database (010) stores IDs of a plurality of doctor nodes and doctor public keys corresponding to the IDs of the doctor nodes, the disease inquiry database (020) stores a plurality of disease names and chain numbers corresponding to the disease names, and the health data management system further comprises a data acquisition module (030), a data verification module (040) and a data carding module (050);
the data acquisition module (030) comprises a plurality of doctor nodes, the doctor nodes input patient medical information, the patient medical information comprises patient identity information, diagnosis time, disease name, disease information, treatment scheme, predicted disease information and review time, the treatment scheme comprises a plurality of treatment times and treatment operation information required to be carried out corresponding to the treatment times, the disease information reflects disease conditions of patients, and the patient medical information and IDs of the doctor nodes are sent to the data verification module (040);
the data verification module (040) is used for calculating the first medical hash value of the patient medical information by adopting a hash algorithm, signing the patient medical information and the first medical hash value by using a doctor private key, calling a doctor public key from the public key database (010) by using an intelligent contract according to the ID of the doctor node, verifying the validity of the signature by using the doctor public key and calling the intelligent contract to verify the validity of the patient medical information, and if the signature and the patient medical information are valid, sending the patient medical information and doctor input records to the data carding module (050), and if the signature or the patient medical information are invalid, deleting the patient medical information and giving a warning;
the data carding module (050) comprises a plurality of information storage libraries (031), each disease name in the disease inquiry database (020) corresponds to one information storage library (031), and the data carding module (050) further comprises an information preliminary carding unit (032), a diagnostic information carding unit (033) and a data packaging unit (036);
the information preliminary carding unit (032) is used for obtaining the patient medical information, storing the patient medical information corresponding to the disease in a corresponding information storage library (031) if the disease name in the patient medical information can be matched in the disease inquiry database (020), inputting the disease name into the disease inquiry database (020) if the disease name in the patient medical information can not be matched in the disease inquiry database (020), establishing a corresponding information storage library (031) and storing the patient medical information corresponding to the disease in the corresponding information storage library (031);
the diagnosis information combing unit (033) presets a time difference reference value, patient medical information of the same patient in the same information storage library (031) is sequenced according to diagnosis time, time difference of adjacent diagnosis time is calculated to obtain diagnosis time difference, if the diagnosis time difference is smaller than the time difference reference value, similarity of corresponding disease information and similarity of corresponding treatment schemes are calculated, if the similarity of the disease information is smaller than 85%, diagnosis alarm is sent, if the similarity of the treatment schemes is smaller than 70%, treatment alarm is sent, and if the similarity of the disease information is larger than or equal to 85% and the similarity of the treatment schemes is larger than or equal to 70%, patient medical information is deleted;
the data packaging unit (036) is preset with a storage capacity threshold, when the storage capacity in the information storage library (031) reaches the storage capacity threshold, the information storage library (031) is compressed to obtain information blocks, each information block is matched with a chain number, and the information blocks are uplink to obtain a block chain;
the data grooming module (050) further comprises a review information grooming unit (034), the patient medical information further comprising patient performance information reflecting the actual actions taken by the patient according to the treatment regimen;
the review information combing unit (034) obtains patient medical information of the same patient in the same information storage library (031) at different diagnosis times, obtains a patient medical information which is marked as initial diagnosis medical information, obtains patient medical information which has the most similar diagnosis time to the review time of the patient medical information and is marked as review medical information, calculates the similarity between a treatment scheme in the initial diagnosis medical information and patient execution information in the review medical information to obtain an adherence doctor's advice, calculates the similarity between predicted disease information in the initial diagnosis medical information and disease information in the review medical information to obtain a recovery degree, and stores the adherence doctor's advice degree and the recovery degree in the information storage library (031);
the review information grooming unit (034) obtains the order compliance and the recovery, obtains treatment correction information according to the order compliance and the recovery, the treatment correction information including a new treatment plan, new predicted condition information, and a new review time, and stores the treatment correction information in the information repository (031).
2. The blockchain technology-based health data management system of claim 1, wherein the data grooming module (050) further comprises a complications grooming unit (035), the complications grooming unit (035) being preset with a coincidence reference value, the patient identity information including a patient number;
the complications carding unit (035) uses the patient number as an index to retrieve a plurality of corresponding disease names in the information storage library (031) as disease name groups, selects any disease name group as a name group to be matched, selects any disease name in the name group to be matched as a name group to be matched, selects all disease name groups containing the name to be matched as a name group data pool, adopts a disease association strategy to screen a plurality of associated disease names from the name group data pool, and simultaneously, the percentage of the number of the disease name groups containing the associated disease names to the disease name groups in the name group data pool is larger than the coincidence reference value, and associates the disease names corresponding to the associated disease names in the disease inquiry database (020).
3. The blockchain technology-based health data management system according to claim 2, wherein the condition association policy selects any condition name except the to-be-matched name from the to-be-matched name groups as a new to-be-matched name, selects all condition name groups containing the new to-be-matched name from a name group data pool as an associated name data pool, calculates the percentage of the number of condition name groups in the associated name data pool to the number of condition name groups in the name group data pool to obtain an overlap value, if the overlap value is greater than the overlap reference value, the to-be-matched name and the new to-be-matched name are associated condition names, and repeats the condition association policy until no new to-be-matched name exists in the to-be-matched name groups.
4. A health data management system based on a blockchain technique as in claim 2 or 3, wherein the coincidence reference value is 30-50%.
5. A blockchain technology based health data management system as in claim 2 or 3, wherein the coincidence reference value is 43%.
6. The blockchain technology based health data management system of claim 1, wherein the condition names include academic names and aliases.
7. The blockchain technology-based health data management system according to claim 1, wherein a network link address is configured in the information preliminary carding unit (032), and if an input disorder name cannot be matched in the disorder query database (020), the network link address is skipped and the input disorder name is used as a keyword for searching.
8. The blockchain technology-based health data management system of claim 1, wherein the storage threshold is 100 m-1 g.
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| CN113130067B (en) * | 2021-04-01 | 2022-11-08 | 上海市第一人民医院 | Intelligent reminding method for ultrasonic examination based on artificial intelligence |
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