WO2024043878A1 - Systèmes et procédés d'utilisation d'une chaîne de blocs pour sécuriser des données acquises dans des intervention chirurgicales et dans d'autres procédures médicales - Google Patents

Systèmes et procédés d'utilisation d'une chaîne de blocs pour sécuriser des données acquises dans des intervention chirurgicales et dans d'autres procédures médicales Download PDF

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Publication number
WO2024043878A1
WO2024043878A1 PCT/US2022/041266 US2022041266W WO2024043878A1 WO 2024043878 A1 WO2024043878 A1 WO 2024043878A1 US 2022041266 W US2022041266 W US 2022041266W WO 2024043878 A1 WO2024043878 A1 WO 2024043878A1
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Prior art keywords
data
procedure
records
hash
management
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PCT/US2022/041266
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English (en)
Inventor
Mike Horia Mihail TEODORESCU
Atif Mohammad RAKIN
Gelu Comanescu
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Surgibox Inc.
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Priority to PCT/US2022/041266 priority Critical patent/WO2024043878A1/fr
Publication of WO2024043878A1 publication Critical patent/WO2024043878A1/fr

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    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • 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
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • Exemplary embodiments of the present invention relate to systems and methods for securing data acquired during surgical and other medical procedures.
  • Surgical procedures and other medical procedures involve the monitoring of many physiological functions of the patient and the monitoring of various parameters of the environment in which the procedure is performed.
  • the very fast pace of technological advances in areas such as sensing and imaging technologies is likely to bring a significant increase in the monitored functions, number of parameters, precision of the measurements, and the amount of data generated and recorded by patient monitoring during surgical and other medical procedures.
  • the types of sensors, equipment and monitoring required vary with the procedure being undertaken and the patient.
  • the sensors and equipment used to monitor a patient throughout a surgical operation may be made of one single system that can be set to accurately and continuously measure a set of patient parameters that reflect the functioning of the body systems. Alternatively, multiple monitoring systems mat be employed.
  • Various sensors, electrodes, and imaging systems may be attached to the patient or positioned such as to acquire the desired information.
  • Example of measurements and parameters monitored in surgical procedures include: heart's electrical activity (via electrocardiograms); respiratory rate; blood pressure; body temperature (using temperature probes or thermometers); the cardiac output; the arterial blood oxygen level measured by a pulse oximeter (e.g., employing a photoelectric sensor attached to a finger); venous oxygenation; pulmonary functions such as end-tidal carbon dioxide; arterial pH; neurophysiological monitoring, etc.
  • intracranial pressure may be monitored in patients suffering from head trauma, patients having a high intracranial pressure because of brain tumors, patients suffering intracranial hemorrhage, or patients with edema.
  • the intracranial pressure may be measured via a sensor inserted through a hole made in the skull with the purpose of detecting rises in the pressure inside the head and with the purpose of recording the evolution in time of the measured parameters (see e.g., Intraoperative Monitoring; by Dr. Liji Thomas, MD, News Medical Net (Feb. 27, 2019)).
  • Intraoperative Monitoring by Dr. Liji Thomas, MD, News Medical Net (Feb. 27, 2019)
  • Thermistors, thermocouples, and infrared thermometers are the generally used to measure near-core temperature within 0.5 degrees.
  • a large amount of valuable patient monitoring data is acquired during medical procedures from many types of sensors and imaging equipment. Such data can be highly valuable because data analytics on it can provide important medical insights which are likely to lead to medical discoveries and significant improvements of medical procedures. More data acquired from more patients during more procedures is likely to lead to more discoveries and more improvements in safety and effectiveness of current medical procedures as well as designing novel medical procedures.
  • medical- procedure-data or "procedure-data”.
  • the operators are not always saving / storing for future use such medical procedure data. Even when stored, medical procedure data is not properly protected and is vulnerable to breaches which may lead to patient's loss of privacy and to liabilities for the healthcare provider.
  • medical procedure data acquired by one party is most often not shared with others, is not accessible by researchers from other institutions, and is generally not made accessible to the public for use. The reasons for this are primarily related to the need to protect "patient privacy" and to legal liability issues.
  • Health information such as medical- procedure-data can be de-identified (see for example "Guidance Regarding Methods for Deidentification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule" published by the U.S. Department of Health and Human Services).
  • healthcare institutions e.g. hospitals, private practices
  • Exemplary embodiments of the present invention provide a data-management-system for managing data acquired during medical procedures.
  • the data-management-system may include a centralized-database storing a plurality of procedure-records. Each of the procedurerecords comprises data acquired by a data-acquisition-system during a medical procedure performed on a specific patient.
  • the data-management-system may further include a user- access-module configured to enable users to access data in the centralized-database and a search-module configured to enable users to perform searches on data in the centralized- database.
  • the data-management-system may further include a data-analytics-module configured to enables users to perform data-analytics and scientific studies on the data in the centralized-database.
  • the centralized-database is connected with a plurality of data-acquisition-systems.
  • Each of the data-acquisition systems may include sensors attached to a patient and monitoring physiological parameters of the patient.
  • the data-acquisition systems may further include an operator-input-module configured to receive information from an operator regarding the medical procedure to be performed and information about the patient.
  • the data-acquisition systems may further include a data-acquisition-module configured to receive data from the sensors and to form a procedure-data-structure comprising the data from the sensors.
  • the data-acquisition systems may further include a de-identification-module configured to form a de-identified-procedure-data-structure.
  • the data-management-system may further include a record-generating-module configured to receive de-identified-procedure-data-structures and to form corresponding procedure-records comprising the de-identified-procedure-data- structure.
  • the data-management-system may further include a blockchain system configured to render immutable the data in the procedure-records and the centralized-database.
  • the blockchain system may store datasets, or copies of datasets, of the centralized-database.
  • the blockchain system may include a hash-module configured to calculate hash values for one or more datasets comprised by the procedure-records and to store the hash values on the blockchain as block-hashes.
  • the data-management-system may further include a data- verification-module configured to verify, for each of the datasets of the procedure-records, the integrity of the dataset by calculating a hash of the dataset and comparing the calculated hash with the corresponding block-hash previously stored on the blockchain.
  • the data-management-system may further include a classification-module configured to classify the procedure-records in the centralized-database function of one or more classification-parameters and to form a plurality of data-classes corresponding to a classification-parameter.
  • a classification-module configured to classify the procedure-records in the centralized-database function of one or more classification-parameters and to form a plurality of data-classes corresponding to a classification-parameter.
  • the data-management-system may include a centralized-data-access-system.
  • the centralized-data-access-system may include a database- access-module configured to connect the centralized-data-access-system with a plurality of databases.
  • Each of the databases may include a plurality of procedure-records.
  • Each of the databases is connected to a plurality of data-acquisition-systems such as the ones described above.
  • Each of the procedure-records comprises de-identified information about a medical procedure performed on a patient.
  • the data-management-system may further include an user- interface-module configured to enable users to access data-files in the databases and a searchmodule configured to enable the users to search data in the databases.
  • the data-management- system may further include a data-analytics-module configured to enable users to perform data-analytics and scientific studies on data in the databases.
  • Each of the databases may be owned by a different healthcare service-provider (e.g. hospital, private practice).
  • the data- management-system may further include a classification-module and a blockchain system configured to render immutable the data in the databases.
  • a data-acquisition-system is part of a portable-surgical- system configured to be used for performing surgical procedures in one or more of the following environments: in the field, outdoors, tents, cottages, residential rooms, and environments other than operating rooms.
  • FIG. 1 shows an exemplary embodiment of a data-acquisition-system configured to acquire data generated during a surgical or other medical procedure performed on a patient by one or more medical-operators.
  • FIG. 2 shows a data-management-system connected with a data-acquisition-system and configured to process and manage data generated during surgical and other medical procedures.
  • FIG. 3 shows an exemplary embodiment of the data processing performed by the data- management-system in which the blockchain system stores datasets on the blockchain, each of the datasets having a specific address on the blockchain.
  • FIG. 4 shows an exemplary embodiment of a procedure-record for a medical procedure performed on a patient.
  • FIG. 5 shows an exemplary embodiment of the data processing performed by the data- management-system including a hash-module using a hash-function to calculate hash values of datasets, wherein the hash values are stored on a blockchain system.
  • FIG. 6 shows an exemplary embodiment of a data-management-system connected with a plurality of data-acquisition-systems, each of the data-acquisition-systems being configured to acquire sensor and imaging data generated during a specific medical procedure performed on a certain patient.
  • FIG. 7 shows an exemplary embodiment of a centralized-database in which the procedure-records are classified, via a classification-module, and grouped in data-classes by the type of medical procedure.
  • FIG. 8 shows an exemplary embodiment of a data-management-system including a centralized-database, connected with a data-analytics-module, a search interface, and a data- verification-module.
  • FIG. 9 shows an exemplary embodiment of a method for securing the integrity of the data in the databases, wherein the data in the data-classes is grouped and/or organized in a plurality of datasets and a hash is calculated for each of the datasets.
  • FIG. 10 shows an exemplary embodiment of a data-management-system comprising a centralized-data-access-system connected with a plurality of databases, wherein each of the databases is owned by a different service provider and comprises procedure-records for medical procedures performed by a different service provider.
  • FIG. 11 shows an exemplary embodiment of an interface of the centralized-data-access- system showing several data-classes, wherein each of the data-classes may include pointers to data files and procedure-records stored in various databases.
  • FIG. 12 shows an exemplary embodiment of a data-acquisition-system connected with a data-management-system, wherein the data-acquisition-system is part of a portable surgical system configured to be used in environments other than operating rooms, such as: in the field, outdoors, tents, cottages, residential rooms, etc.
  • FIG. 1 shows an exemplary embodiment of a data-acquisition-system 1.
  • the data- acquisition-system is configured to acquire data generated during surgical and/or other medical procedures performed on a patient 7 by one or more medical-operators 8 (e.g., surgeons, anesthesiologists, nurses).
  • a medical service-provider may own and control the data generated during the medical-procedure and acquired by the data-acquisition-system.
  • a medical service provider will not create identifiable data, or a de-identified copy of the data will be generated, in which case the data will be transmitted directly by the data- acquisition-system to a data-management system 10.
  • the data-acquisition-system may include a plurality of sensors 2, one or more imaging devices 3, a data acquisition module 4, a controller module 5, a display module 6, an operator- input-module 9, and/or a de-identification-module.
  • data may be acquired by various sensors, electrodes, and imaging systems.
  • the sensors, electrodes, and imaging systems may be attached to the patient or positioned such as to acquire the desired information.
  • the results of these measurements may be displayed on computer monitors and displays 6.
  • the data may be saved as data files on various on-site computers or on remote data storage facilities.
  • the one or more sensors may include one or more of the following: sensors configured to measure one or more temperatures at various places on or inside the body of the patient; sensors for acquiring information about the heart's electrical activity (e.g., electrocardiograms); sensors measuring a respiratory rate; blood pressure sensors; sensors measuring cardiac output; sensors measuring arterial blood oxygen level such as pulse oximeters; sensors measuring venous oxygenation; sensors measuring pulmonary functions such as end-tidal carbon dioxide; sensors measuring arterial pH; sensors for neurophysiological monitoring; sensors for monitoring intracranial pressure in patients suffering from head trauma, or patients having a high intracranial pressure because of brain tumors, or patients having intracranial hemorrhage; sensors disposed on catheters inserted in the circulatory system (e.g., position sensors); sensors monitoring airflow, airborne oxygen content, and air temperature (e.g., ventilator systems and anesthesia delivery machines); etc.
  • sensors configured to measure one or more temperatures at various places on or inside the body of the patient
  • sensors for acquiring information about the heart's electrical activity e
  • the imaging devices may include one or more visible camera configured to monitor the surgical field; cameras configured to perform imaging inside the body, such as endoscopic cameras; and IR cameras.
  • the imaging devices may further include X-ray imaging machines; Computer Tomography (CT) imaging machines; MRI machines; PET scanners; etc.
  • CT Computer Tomography
  • the data-acquisition-system may further include a sound- recording-device configured to record sound (e.g. discussions between medical operators) data during medical procedure.
  • the display-module may be a computer monitor and may be configured to show some of the data acquired by the sensors in real time.
  • the control-module may be configured to control the sensors and imaging devices.
  • the data acquisition module may be connected with the sensors and the imaging devices so as to receive information from the sensors and imaging devices via cable connections, Wi-Fi, Bluetooth or any other suitable connection means known by those skilled in the art.
  • the data acquisition module may store the data on-site (e.g., computer hard drives, flash memory modules, optical memory, etc.) or on remote data storage facilities such as in the cloud or in data centers.
  • the operator-input-module 9 may enable the operator to enter information about the patient and about the medical procedure to be performed. This data entry may be via a touchscreen where the user may be presented a set of questions and answers or categories to select from, text input via a keyboard or touchscreen, speech, or other means known by those skilled in the art.
  • FIG. 2 shows a data-management-system 10 connected with a data-acquisition-system 1 and configured to process and manage data generated during surgical and other medical procedures.
  • the data-management-system 10 may include one or more of the following: a data-storage-system 11, one or more blockchain-systems 12, one or more data-analytics-units 13, one or more user-access-modules 14, one or more user-analytics-modules 15, and a record- generator-module 16.
  • the data-storage-system may include one or more records-databases for storing data received from the data-acquisition-system 1 and from other data-acquisition- systems where other medical-procedures are performed.
  • the blockchain-systems may be configured to render immutable the data in the records-database.
  • the data-analytics-unit may be connected with the records-database and may be configured to process and perform analytics on the data.
  • the user-access-module may allow users (e.g., researchers, hospital administrators, government, etc.) to access the data in the database and to perform studies on the data associated with medical procedures.
  • the users may perform data analytics on the data in the records-database by using the data-analytics-unit connected with records-database or by using user-data-analytics-units 15 (e.g., modules located on user's computer).
  • FIG. 3 shows an exemplary embodiment of the data flow in a data-management-system connected with a data-acquisition-system.
  • the sensors generate sensor-data 21 which is transmitted, intermittently or continuously, to the data-acquisition- module.
  • the medical-operator generates operator-data 22 associated with procedure.
  • the operator-data may include one or more of the following: information about patient identity and medical history, current status of the patient, information regarding the procedure to be performed including steps, purpose, monitoring parameters, patient risk factors, end results, etc.
  • the data-acquisition-system may form a procedure-data-structure 23 which may include sensors-data (e.g., all or part of the data acquired from the sensors during the procedure, which may be processed using standard techniques known in the art such as noise removal, feature extraction, etc.) and the operator-data.
  • the data-acquisition-system may include a de- identification-module configured to remove from the procedure-data-structure all information which may identify the patient thereby forming the de-identified procedure-data-structure 24.
  • the de-identified procedure-data-structure 24 may be sent to the data-management-system 10 where it may be stored (i.e., the entire or part of the procedure-data-structure) in a procedure- record 25 of the records-database 11.
  • the de-identified procedure-data-structure 24 may be sent to the data-management-system 10 via an internet connection, a wireless intranet connection, wired connection, or other data transmission methods known in the art.
  • the de-identification of the health information is not performed, and patient identity is linked to the data in procedure-data-structure 23.
  • FIG. 4 shows an exemplary embodiment of a procedure-record 25 for a medical procedure performed on a patient.
  • the procedure-record may include a description of the surgical procedure, patient de-identified information (e.g., age, medical conditions, medical history), sensor data (e.g., intracranial pressure data, blood pressure data), and imaging data (e.g., X-ray images, MRI scans, surgery videos).
  • patient de-identified information e.g., age, medical conditions, medical history
  • sensor data e.g., intracranial pressure data, blood pressure data
  • imaging data e.g., X-ray images, MRI scans, surgery videos.
  • the procedure-record 25 may be rendered immutable by a blockchain system.
  • the blockchain system may include a private or a public blockchain.
  • the procedure-record 25 my include one or more datasets 26 (see for example dataset-1, dataset-2, and dataset-n in FIG. 3).
  • the datasets may have substantially the same size (e.g., lOOkB, 1MB, 10MB, etc.), the size being predetermined so as to be suitable for storing on the blockchain.
  • the datasets are stored on the blockchain 20, each of the datasets having a specific address on the blockchain 20 (i.e., address-1, address-2, address-n).
  • a counterpart-dataset (i.e., a copy of the dataset) may be stored on the records-database.
  • the blockchain-addresses may be stored in the databases.
  • the datasets stored on blockchain may be rendered immutable and their integrity may be ensured by the blockchain technology.
  • the datasets may be accessed (for example, via a download) by using their address but the datasets cannot be altered.
  • the data-management-system may further store links to the blockchain-addresses and/or the datasets on a website associated with the database so as to allow others (e.g., healthcare administrators, researchers, physicians, surgeons, etc.) to view and access specific datasets and procedure-records.
  • the data integrity of the counterpart-datasets may be periodically evaluated by comparing the counterpart-datasets (stored on the records-database, not on blockchain) with the corresponding blockchain stored dataset and the dataset hash value.
  • FIG. 5 shows an exemplary embodiment of the data-management-system in which the hashes of the datasets are stored on the blockchain.
  • the data- management-system may include a hash-module.
  • the hash-module is configured to employ a hash-function to calculate hash values 28 for the datasets 26 of the procedure-record (see dataset-1, dataset-2, dataset-n).
  • the data-management-system may store the dataset hash values on the blockchain as block-hash-values 29, wherein each of the block-hash-values 29 has a specific address on the blockchain 20.
  • the blockchain-system may further store the blockchain addresses of the dataset-hash-values in the database and as associated with their corresponding procedure-record 25. For example, consider dataset-1 and block-hash-1 of the procedure-record-1 stored on the blockchain at time-1.
  • the blockchain address-1 (where block- hash-1 is located on the blockchain) is stored on the database and is associated with dataset-1 of the procedure-record.
  • the blockchain-system may be configured to verify the integrity of any one of the datasets by calculating a hash of the dataset and comparing the calculated hash with the corresponding block-hash stored on the blockchain. For example, once the dataset-1 is stored in the database and its corresponding block-hash-1 is stored on the blockchain, the blockchainsystem may verify the integrity of dataset-1 (at a verification-time which is after time-1) by calculating the hash value of dataset-1 (as stored in the database at the verification-time) and comparing the calculated hash with the block-hash-1 stored on the blockchain. If the calculated hash is different from block-hash-1, then dataset-1 has been corrupted or altered. If the calculated hash is identical with block-hash-1, then dataset-1 has not been altered. This way the blockchain-system is able to periodically verify the integrity of the datasets in the procedurerecords and to keep the procedure-records immutable.
  • FIG. 6 shows an exemplary embodiment of a data-management-system connected with a plurality of data-acquisition-systems 31.
  • Each of the data-acquisition-systems 31 is configured to acquire sensor and imaging data generated during a specific medical procedure performed on a certain patient.
  • data-acquisition-system-1 may be used to acquire data during a medical-procedure-1 performed on patient-1 by operator-1 of a service-provider-1;
  • data-acquisition-system-2 may be used to acquire data during a medical-procedure-2 performed on patient-2 (which is different from patient-1) by operator-2 (different from operator-1) of a service-provider-2 (which may be different form service-provider-1).
  • Each of the data-acquisition-systems 31 may perform the operations described above with reference to FIG.
  • data-acquisition-system-1 may form a de identified procedure-data-structure-1
  • data-acquisition-system-2 may form deidentified procedure-data-structure-2.
  • the data-management-system may include a centralized-database 32 configured to receive the procedure-data-structure generated by various data-acquisition-systems and to form procedure-records (see e.g., Record-1, Record-2, to Record-n in FIG. 6) corresponding to each of the procedure-data-structure.
  • the data in the centralized-database e.g., the procedure-records
  • the centralized-database 32 may be configured to enable a plurality of users 33 (e.g., user-1 to user-5 in FIG. 6) to access the data in the centralized-database.
  • the users may be researchers, scientists and other professionals which may use the procedure-records to perform various studies.
  • the centralized-database may be made accessible to the public or to a specific group of people.
  • the centralized-database may be connected with or may include an interface and search module enabling users to search, select, view, and/or download data in the database.
  • the centralized-database may be connected with a data-analytics-module 36 enabling users to perform data analytics studies on various data. Alternatively, users may be enabled to access data (e.g., download files) and to perform data analytics and other studies via their own data analytics software or other analytics resources.
  • the data-management-system may include a classification-module for categorizing and forming classes/groups of medical-procedures into one or more data-classes.
  • the procedurerecords stored by the centralized-database may be categorized / classified function of various classification-parameters of the procedure-record and/or the corresponding medical procedure, such as: the type of medical procedure; patient's age; procedure's date; type of equipment used during the medical-procedure; physiological parameters recorded by sensors during the medical procedure (patient's blood pressure, cranial pressure, body temperature, heart rate, etc.).
  • data-classes 35 e.g., data-class-1, data- class-2, data-class-3) by the type of medical procedure.
  • data-classes 35 e.g., data-class-1, data- class-2, data-class-3
  • data-class-1 may include the procedure-records for brain-surgeries
  • data-class-2 may include the procedure-records for open heart surgery
  • data-class-3 may include the procedure-records for spine surgery.
  • the grouping of procedures in classes may be performed so that the classes are the optimal "data units" for performing analytics and studies.
  • the data in a data-class may be analyzed via data analytics procedures (e.g. data-mining, machine learning, artificial Intelligence) with the purpose of finding "correlations" between various medicalparameters, patient-parameters and medical-outcomes.
  • data analytics procedures e.g. data-mining, machine learning, artificial Intelligence
  • This classification may be performed via a wide variety of techniques known to those skilled in the art.
  • the classification may be done with Natural Language Processing techniques.
  • topic modeling may be applied to procedure records and may be used to create a 'topic' frequency vector for each procedure note to compare notes across procedures and cluster procedures with similar 'topic' distributions into groups to facilitate search.
  • Term frequency vectors extracted per note may be combined with similarity calculations across the corpus of notes, vector similarity computation techniques such as cosine similarity or Jaccard similarity may be used, whereas the vectors may contain weighted term frequencies such as but not limited to Term Frequency-Inverse Document Frequency (TF-IDF) or may contain unweighted term frequencies.
  • TF-IDF Term Frequency-Inverse Document Frequency
  • Named Entity Recognition algorithms may be used to identify drugs and diseases in the notes and classify the notes based on such entities.
  • Machine translation algorithms may be used in case of notes in different languages.
  • Supervised learning classification algorithms may cause these and other features to automate assignment of the procedure into categories that are relevant for the users of the database (for example, procedure names commonly accepted in the medical profession, or ICD 10 may be used to categorize based on diagnoses or disease codes).
  • Combinations of terms such as bigrams, trigrams, or any type of n-gram may be used as features for a classifier.
  • Word embeddings or sentence embeddings may also be used as features.
  • Classification algorithms may include but are not limited to standard techniques known in the art, such as: decision trees, k-nearest-neighbors, naive-bayes, support vector machine, neural networks, random forests, or ensemble methods, to name a few.
  • the procedure data may not necessarily be in textual format.
  • the notes may be spoken to by a physician during a procedure and transformed into a textual note via a speech to text translation algorithm.
  • Features such as intonations, breathing rate, rate of speech, etc. may be used as well to identify procedures that were complex or where unexpected events occurred during a procedure.
  • the classification of the procedure may not necessarily require textual data; in another embodiment, a video of the surgical area may be used instead (or still images from a camera) in order to classify the type of procedure, complications, blood loss, or any other adverse events, as well as to identify standard steps common to most procedures within a procedure category. Such data may be used to train a surgical robot in both standard flows of procedures as well as remedies in cases of complications. In an embodiment the images or video could include timestamps.
  • the classification of the procedure may include video, photographic, sound, text, sensor data, as well as any combination thereof.
  • FIG. 8 shows an exemplary embodiment of a data-management-system including a centralized-database 32, such as in FIG. 6, connected with a data-analytics-module 36, a search module 37, and a data-verification-module 38.
  • the data-analytics-module 36 may include tools for selecting specific data in the procedure-records by various parameters, such as anesthiology related data, patient's blood pressure, patient's age, type of procedure, etc.
  • the data-analytics- module may include tools enabling users to perform certain analytics operations and protocols (e.g., data-mining, machine learning, text analysis, Al-based decision support systems) on the selected data.
  • the data-analytics-unit may be configured to find "correlations" between various medical-parameters, patient-parameters, and medical-outcomes.
  • the search-module 37 may enable users to search data in the procedure-records according to parameters such as recordnumber, surgery-type, data-type, patient's current medical conditions, medical history, etc.
  • the data-verification-module 38 may enable users to select certain data (e.g., set of procedure-records) and to validate the integrity of the data, i.e., to ensure that the selected data was not altered or corrupted. If the selected data is directly stored on blockchain, then the integrity of the data is ensured by the integrity of the blockchain. If counterparts of the data are stored on the blockchain then the data integrity of the counterpart-datasets may be evaluated periodically (or at specific times) by comparing the counterpart-datasets (stored on the records- database, not on blockchain) with the corresponding blockchain stored dataset.
  • data e.g., set of procedure-records
  • This latter embodiment is particularly useful in situations where the block size of the blockchain implementation is small and cannot fit a full medical procedure record / surgical record dataset; in this case, a hash value calculated based on the dataset is stored on the blockchain together with a location to the dataset, whereas the actual medical procedure record dataset / surgical procedure data can be stored off the blockchain, in a separate database system. Users wishing to verify the integrity of the data can compare the hash value of the dataset per the blockchain entry with the hash value of the dataset stored off the blockchain; should these not match, the dataset is rejected as not the genuine entry.
  • the data-verification-module may enable users to validate the selected data by calculating the hashes of the datasets and comparing the calculated hashes with the corresponding hashes stored on the blockchain. Please note that the calculated hashes are calculated at the time when the verification is performed (i.e., verification-time) whereas the hashes stored on blockchain were calculated and stored at a blockchain-storing- time which may be essentially the time when the procedure-record was formed.
  • FIG. 9 shows an exemplary embodiment of a method for securing the integrity of the data in the records-database.
  • the data in the data-classes 35 is grouped and/or organized in a plurality of datasets 41 (i.e., dataset-1, dataset-2, ... dataset-n).
  • Each of the datasets may be secured via the blockchain by calculating a hash 42 for the dataset and storing the hash on the blockchain 43 as a block-hash 44.
  • copies of the datasets may be stored directly on the blockchain.
  • the datasets may have substantially the same size (e.g., 1 MB, or lOOkB), the size being predetermined so as to be suitable for storing on the blockchain.
  • Data-management-systems such as the one described with reference to FIG. 6 may be difficult to implement because they require that a majority or a large number of service providers (e.g., hospitals, private practices, healthcare companies) agree to have a third party (e.g., administrator of the centralized-database) copy and store their medical data on a system controlled by the third party. Understandably, many healthcare providers are reluctant to give their data to someone else.
  • the data-management-system described below with reference to FIG. 10 circumvents the problem described above, i.e., having data owned by service-providers copied, stored, and managed by someone else.
  • FIG. 10 shows an exemplary embodiment of a data-management-system connected with a plurality of data-acquisition-systems 51.
  • Each of the data-acquisition-systems 51 is configured to acquire sensor and imaging data generated during a specific medical procedure performed on a certain patient.
  • data-acquisition-system-1 may be used to acquire data during a medical-procedure-1 performed on patient-1 by operator-1 of a service-provider- 1; whereas data-acquisition-system-2 may be used to acquire data during a medical-procedure- 2 performed on patient-2 (which is different from patient-1) by operator-2 (different from operator-1) of a service-provider-2 (which may be different form service-provider-1).
  • Each of the data-acquisition-systems may perform the operations described above with reference to FIG. 3 or the operations described with reference to FIG. 5., thereby forming their own procedure-data-structures (including de-identified data) which may be stored in databases 52 owned by the service-providers performing the medical procedure.
  • a procedure-record-1 including de-identified procedure-data-structure acquired during medical-procedure-1 is stored on database-1, wherein database-1 is owned by service-provider-1 (e.g. a specific hospital).
  • a procedure-record-2 including deidentified procedure-data-structure acquired during medical-procedure-2 is stored on database-2, wherein database-2 is owned by service-provider-2 (e.g. a specific hospital).
  • Medical-procedures performed by the same service-provider e.g. another hospital
  • the data in the databases 52 may be rendered immutable via one or more blockchains 53 by the operations described above with reference to FIG. 3 or the operations described with reference to FIG. 5.
  • the blockchains employed by different databases may be different from each other (i.e. each database uses its own blockchain). Alternatively, all or part of the databases may employ a common blockchain.
  • the data-management-system may include a centralized-data-access-system 50 which may further include a website and/or a centralized-access-database.
  • the centralized-data- access-system 50 may include a database-access-module configured to connect the centralized- data-access-system 50 with a plurality of databases 52, each of the databases comprising a plurality of procedure-records.
  • the centralized-data-access-system is configured to enable users 54 (e.g., user-1 to user-5 in FIG. 10) to access the data in the databases 52 (e.g., database- 1 to database-n).
  • the centralized-data-access-system may enable users 54 to perform specific studies and data analytics on the all or some of the data in the databases 52.
  • Databases 52 may be owned and managed by different service-providers (e.g., different healthcare companies, different hospital systems, different provider offices, etc.).
  • the centralized-data-access-system may be owned and managed by a party (e.g., company or non-profit institution) who is legally bound to keep the data securely for the benefit of the public and the owners of the databases 52.
  • Different datasets in the databases may be assigned different permission-levels indicating the conditions in which a user can access the data-file and the specific operations a user can perform on a data-file.
  • a permission level for a certain data-file may prescribe that the data-file cannot be download from the database but may be used in analytics studies (e.g., analytics performed in the database with analytic-tools owned by the database owner) and the user can see the results of the study.
  • Each user may have certain permissionlevel with respect to a data-file in a certain database.
  • user-1 may be permitted to perform certain analytics-operations on some data-files in the database-n owned by hospital-n and to receive the results of the analytics-operations; user-1 may be allowed to see the data- files but may not be allowed to download the files from the database-n; user-2 may only have audit access, i.e., to compare hash values of records with existing records on the blockchain but may not actually see the contents of the datasets used to create the hash values.; other users may have full access. Editing a record would require creation of a new record with its own separate address pointer on the blockchain, address pointer to the record being updated, address pointer to the external database system (if applicable), and hash value. Variations thereof may be envisaged by those skilled in the art.
  • the centralized-data-access-system may include one or more computer servers including one or more processors and one or more memories (memory modules, database systems, cloud storage devices, etc.).
  • the computer servers may include connection-ports configured to send and receive information to/from the databases 52 and the users 54 via network connections and/or the internet.
  • the centralized-data-access-system may act as the interface between users 54 and databases 52.
  • the centralized-data-access-system may include a search-module enabling users 54 to search data in the databases 52.
  • the centralized-data- access-system may include a data-analytics-unit enabling users to select data and perform various analytics operations and studies on the data in the databases 52.
  • the centralized-data- access-system may include one or more websites enabling users to access and use the searchmodule and the data-analytics-module.
  • the centralized-data-access-system may include a classification-module for categorizing and forming classes/groups of medical-procedures into one or more data-classes.
  • FIG. 11 shows an exemplary embodiment of an interface of the centralized-data-access-system 50 displaying several data-classes 56 formed by the classification-module.
  • Each of the data-classes 56 may include pointers (or links) to data files and procedure-records stored in various databases 52.
  • the links / pointers may point to procedure-records and/or files stored on different databases 52 owned by different service-providers.
  • data-class-1 may include all procedurerecords for which the medical procedure is a brain surgery.
  • Data-class-1 may include a procedure-record (e.g.
  • Brain-Procedure-a which is stored in database-2 owned by service- provider-2
  • a procedure-record e.g. Brain-Procedure-b
  • a procedure-record e.g. Brain-Procedure-c
  • the centralized-data-access-system 50 is connected and provides access to various procedure-records in databases 52, wherein each of the databases is owned by a different service-provider (e.g. hospital, private practice, university).
  • a data-class may include all the procedure-records (in all databases, i.e., database-1 to database-n) for which a classification parameter has a certain parameter-value.
  • the classification parameters may include, but not be limited to: the type of medical procedure; patient's age; procedure's date; type of equipment used during the medical-procedure; physiological parameters recorded by sensors during the medical procedure (patient's blood pressure, cranial pressure, body temperature, heart rate, etc.).
  • the classification-parameter for the data-classes shown in FIG. 11 is the type of medical procedure (e.g., brain surgery, open heart surgery, and spine surgery).
  • the classification-parameters may be chosen so that the formed data-classes are the optimal "data units" for performing analytics and scientific studies. For example, a person who wants to study a specific occurrence during open heart surgery is most likely to want to consider the data in the procedure-records for open heart surgery.
  • the data in a data-class may be analyzed via data analytics procedures (e.g., data-mining, machine learning, text analytics, etc.) with the purpose of finding relationships between various medical-parameters, patientparameters and medical-outcomes.
  • FIG. 12 shows an exemplary embodiment of a data-acquisition-system connected with a data-management-system, wherein the data-acquisition-system is configured to be included in a portable surgical system 60 such as the one disclosed in the international patent application PCT/US2017/042266 titled "Ultraportable System for Intraoperative Isolation and Regulation of Surgical Site Environments" and filed on July 14, 2017 by D. Teodorescu et al.
  • the portable surgical system may be used to perform surgery in environments other than operating rooms, such as in the field, outdoors, tents, cottages, residential rooms, etc.
  • the data-acquisition-system When used in field applications (e.g., outdoors, remote areas) the data-acquisition-system most often cannot be connected with databases in real time, such as the ones described with reference to FIGS. 2 to 11, because in the field there may be no available internet or network connections (neither cable connections nor Wi-Fi connections).
  • the data-acquisition- system may include one or more memories 61 and/or a satellite-connection-device 62.
  • the memories are configured to store the data generated by the sensors, by the imaging system, and/or data input by the operators.
  • the memories may store the data while there is no connection with the data-management-system.
  • a connection with the data-management- system e.g., the data-acquisition-system is connected to internet via a cable, WiFi, or by a satellite connection, or some other means
  • the data in the memories may be sent to the databases of the data-management-system (see e.g., databases described with reference to FIGS. 2, 6, 7, 10, and 11) and stored on the databases in a corresponding procedure-record hashed, and have a corresponding block-hash created.
  • satellite connections are useful because they provide a way to transmit data to databases from the field (e.g., remote areas).
  • the satellite- connection-device may be configured to send (via a satellite connection) to the data- management-system the data generated by the sensors, by the imaging system, and/or data input by the operators. This way data generated during medical procedures performed in the field may be sent to the databases of the data-management-system even in the absence of cable or Wi-Fi connections to the internet.

Abstract

La présente invention divulgue un système de gestion de données pour gérer des données acquises durant des procédures médicales. Le système de gestion de données peut inclure une base de données centralisée et/ou une pluralité de bases de données stockant des enregistrements de procédure. Chacun des enregistrements de procédure comprend des données acquises par un système d'acquisition de données durant une procédure médicale réalisée sur un patient spécifique. Le système de gestion de données permet à des utilisateurs d'accéder à des données, de rechercher des données, et de réaliser une analyse sur des données dans les bases de données. Le système peut en outre inclure une chaîne de blocs configurée pour rendre immuable les données dans la base de données centralisée et dans des bases de données. Le système de gestion de données peut inclure un système centralisé d'accès à des données permettant à des utilisateurs d'accéder à la pluralité de bases de données même lorsque chacune des bases de données est exploitée par différents prestataires de soins de santé et lorsque les enregistrements de procédure dans les bases de données sont détenus par les différents propriétaires. Le système d'acquisition de données peut faire partie d'un système d'intervention chirurgicale portable pour réaliser une intervention chirurgicale sur place.
PCT/US2022/041266 2022-08-23 2022-08-23 Systèmes et procédés d'utilisation d'une chaîne de blocs pour sécuriser des données acquises dans des intervention chirurgicales et dans d'autres procédures médicales WO2024043878A1 (fr)

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US20200146638A1 (en) * 2012-07-16 2020-05-14 Valco Acquisition Llc As Designee Of Wesley Holdings, Ltd Medical procedure monitoring system
US20190156923A1 (en) * 2017-11-17 2019-05-23 LunaPBC Personal, omic, and phenotype data community aggregation platform
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