WO2018117899A1

WO2018117899A1 - Decision support method and system in the remote monitoring of people at risk of cancer

Info

Publication number: WO2018117899A1
Application number: PCT/RU2016/000923
Authority: WO
Inventors: Айрат Мидхатович ХАНОВ
Original assignee: Айрат Мидхатович ХАНОВ
Priority date: 2016-12-23
Filing date: 2016-12-23
Publication date: 2018-06-28
Also published as: RU2681196C2; RU2016150847A; RU2016150847A3

Abstract

This technical solution relates to computing systems and methods in medicine, in particular to methods and systems for decision-making in the remote monitoring of the state of health of a user for the purposes of preventing cancer. A decision support method in the remote monitoring of people at risk of cancer, in which a set of reference value limits of indicators of a state of health is produced; a matrix of the health indicators of at least one user is generated; health indicator values of the at least one user are produced for a prespecified period; the health indicator values produced are analysed to see if they exceed the limits of the reference values produced earlier; a list of diagnostic measures for the at least one user is generated if at least one of the health indicator values of the user exceeds the reference value limits; the health indicator matrix of the at least one user is supplemented with new data produced after the diagnostic measures have been carried out by the user; notifications about the results of the diagnostic measures are generated and displayed to a consulting doctor for the purpose of taking medical decisions. The technical result consists in increasing the accuracy of assessing the degree of risk of disease of a user.

Description

METHOD AND SYSTEM OF SUPPORT OF DECISION-MAKING DURING REMOTE MONITORING OF PEOPLE IN THE GROUP

ONCOLOGICAL RISK TECHNICAL FIELD

[0001] This technical solution relates to computer systems and methods in medicine, and in particular to methods and systems for decision making with remote monitoring of human health in order to prevent cancer.

BACKGROUND

[0002] Currently, the basis of modern medical individual cancer prevention is a continuous monitoring process and the implementation of a personal method for examining high-risk individuals.

[0003] In practice, the late diagnosis of precancerous diseases and malignant tumors has three main reasons that need to be and can be corrected:

• late visit to the doctor due to careless attitude to one’s health or increased fear of cancer,

• low level of knowledge about risk factors and early symptoms of cancer; · insufficient oncological alertness among doctors of the general treatment network, i.e. not every doctor can provide expert advice on the prevention and early diagnosis of cancer.

[0004] Currently, additional opportunities have appeared to solve these problems. These include remote monitoring (monitoring) of the state of human health through information and communication technologies. SUMMARY OF THE INVENTION

[0005] This technical solution is aimed at eliminating the disadvantages inherent in solutions known from the prior art.

[0006] An object of the invention is to remotely monitor a user's health status for cancer prevention.

[0007] The technical result is to increase the accuracy of assessing the degree of risk of disease in a user.

[0008] Also an additional technical result is to increase the efficiency of decision-making by a doctor and increase the speed of monitoring a user.

[0009] The indicated technical result is achieved thanks to the decision support method for remote monitoring of people included in the cancer risk group, in which a set of limits of reference values of health indicators is obtained; form a matrix of health indicators of at least one user; receive from a predetermined period the values of the health indicators of at least one user; carry out the analysis of the obtained values of health indicators for their going beyond the previously obtained abstract values; form a list of diagnostic measures for at least one user if at least one of the values of the health indicators goes beyond the reference values; supplement the matrix of health indicators of at least one user with new data obtained after the user performs diagnostic measures; form and display notifications about the results of diagnostic measures to the attending physician for making medical decisions.

[00010] In some embodiments, the limit of the reference value of a health indicator is a range or a maximum value or a minimum value. [00011] In some embodiments, for each health indicator, set their own range of reference values.

[00012] In some embodiments, the analysis of the obtained values of the health indicators is carried out to determine whether they go beyond the previously obtained abstract values by checking whether the obtained value falls within the limits of the reference values.

[00013] In some embodiments, each health indicator is associated with a diagnostic event in the database.

[00014] In some embodiments, the diagnostic measure is a urinalysis or a biochemical blood test or organ ultrasound or tomography.

[00015] In some embodiments, a set of tasks for performing diagnostic actions is generated and executed on the workflow management server.

[00016] In some embodiments, if different values of health indicators that are outside the reference values require the execution of the same diagnostic event, repeated events are excluded from the generated set.

[00017] In some embodiments, each diagnostic event includes a required date and time for execution.

BRIEF DESCRIPTION OF THE DRAWINGS

[00018] The features and advantages of this technical solution will become apparent from the following detailed description and the accompanying drawings, in which:

[00019] In FIG. 1 shows a block diagram of an implementation of a decision support method for remote monitoring of people in a cancer risk group;

[00020] FIG. 2 shows a decision support system for remote monitoring of people in the cancer risk group; [00021] In FIG. Figure 3 shows an infological database model that includes a matrix of user health indicators;

[00022] FIG. 4 example of the implementation of the analysis of the values of user health indicators;

5 [00023] FIG. 5 example of the implementation of loading values of health indicators into the matrix of patient health indicators;

[00024] FIG. Figure 6 shows an example of a data model of a decision support method for remote monitoring of people in the cancer risk group;

s [00025] FIG. 7 shows a flow diagram of a method for generating and tracking diagnostic and organizational measures.

DETAILED DISCLOSURE OF TECHNICAL SOLUTION

15 [00026] Below will be described the concepts and definitions necessary for the detailed disclosure of the ongoing technical solution.

[00027] This technical solution can be implemented as a distributed computer system.

[00028] In the case where the solution is implemented by means of 20 computing systems, a system means a computer system, a computer (electronic computer), CNC (numerical control), RV (programmable logic controller), computerized control systems, and any other devices capable of perform a given, clearly defined sequence of 25 operations (actions, instructions).

[00029] An instruction processing device is understood to mean an electronic unit or an integrated circuit (microprocessor) that executes machine instructions (programs).

[00030] An instruction processing device reads and executes machine instructions (programs) from one or more data storage devices. AT the role of a storage device may be, but not limited to, hard disks (HDD), flash memory, ROM (read only memory), solid state drives (SSD), optical media (CD, DVD, etc.).

[00031] A program is a sequence of instructions for execution by a computer control device or an instruction processing device.

[00032] Electronic medical record (EMC) - a set of electronic personal medical records (EPMZ) related to one user, collected, stored and used within a single medical organization. The rules for the collection, storage and use of EMC, as well as the right of access to it, are established by a medical organization on the basis of the national standard GOST R 52636-2006 "Electronic medical history. General provisions", as well as in accordance with legislative requirements and regulatory documents of the Ministry of Health and Social Development of the Russian Federation. The term EMC is an analogue of the international term Electronic Medical Record (EMR).

[00033] Localization - (lat. Localis - local, from locus - place), assignment of something to a specific place, restriction of the spread of any phenomenon.

[00034] Oncology - biomedical science that studies the causes, the mechanisms of development and manifestation of tumors and develops methods for their diagnosis, treatment and prevention.

[00035] Hereditary predisposition - inherited willingness to develop a particular property, quality, function, structure. Most often, the term denotes in medicine a predisposition to disease, dysfunction, and deficiency.

[00036] Oncomarkers are specific substances, waste products of a tumor, or substances produced by normal tissues in response to invasion of cancer cells that are found in blood and / or urine of patients with cancer and some other diseases not related to oncology. The detection of tumor markers makes it possible to suspect the presence of a tumor in the body at an early stage, to conduct large-scale screening studies and monitor the dynamics of the disease during treatment.

[00037] An infological model (information-logical model) is a subject-oriented domain model that is independent of the type of DBMS and defines the totality of information objects, their attributes and relationships between objects, the dynamics of changes in the domain, and the nature of the information needs of users.

[00038] A decision support method for remote monitoring of people at risk of cancer is shown in FIG. one.

[00039] Preliminary data is obtained about the user in the following areas: hereditary risks, past illnesses, general state of health, general condition of organs and systems, occupational hazards and the environment, bad habits and nutrition, health history (history).

[00040] The hereditary risk data contains a list of the user's relatives who have experienced cancer, their relationship and specific diseases.

[00041] The data of past diseases contain a list of past diseases, and for each disease, the diagnosis and age of the user during the disease is given.

[00042] Data on the general state of well-being of the user contain data on complaints of the user. For example, is there an unmotivated increase in body temperature, unmotivated weakness, fatigue, is there a lack of appetite, is there a pallor of the skin, an increase in lymph nodes, is there headache, tinnitus, is there shortness of breath, are there nose bleeds, is there a bad blood clotting, prolonged bleeding with cuts, causeless weight loss.

[00043] The data on occupational hazards and the environment contain data on whether there is contact with the products of the chemical industry, work in the open air, work in the coal industry, participate in tobacco production, contact with herbicides, work in dusty environments, inhalation of harmful substances, contact on oil refining, production of lubricants, contact with dyes, whether there is an effect of ionizing radiation - radiation.

[00044] The data on bad habits and nutrition contain data on the user's smoking, alcohol consumption.

[00045] The data on the health history (history) contain complete additional information about the user, which contains weight values, laboratory test results, functional test results, tumor markers, description of moles.

[00046] All data obtained can come from various sources of information: questionnaires (questionnaires, diaries), personal medical devices, results of medical examinations, results of laboratory tests, and opinions of medical consultants.

[00047] In some embodiments, user data is received from a user interface unit to a physician interface unit through a server on which all information is stored. User data can be entered by him in advance, if the data are physiological, then measured using sensors containing means for storing history or a certain period of time. The server may be kept for processing physiological parameters.

[00048] Then, all received user data is converted by means of conversion (or transformations), which provide the conversion of data into a format intended for loading into the matrix of user health indicators and in the document repository (if they are received from external sources).

[00049] Rules for transforming input XML documents can be generated using an extensible Stylesheet Language Transformations (XSLT) language. Data selection rules can be generated in the XPath query language. Moreover, in some embodiments, the data may be in the format of Excel, DBF, CSV, and others, without limitation.

[00050] Step 101: obtain a set of limits of reference values of health indicators;

[00051] In a quantitative type of user analysis, results are provided in the form of a numerical value. Moreover, there is a range of norms, as well as average values of indicators of health conditions. Reference value in analyzes is a medical term used to evaluate results in laboratory studies. The reference value of an indicator is a value that is within the normal range. Limits in the general case can be determined by a range (minimum and maximum normal value), minimum or maximum value. These data were obtained by examining a healthy part of the population. For example, the reference values of thyroid hormones are as follows. For thyroid hormone (triiodothyronine), normal values will be in the range 1.2–2.8 mMU / L, and for thyroxine (total), in the range 60.0–160.0 nmol / L. This is how the index of TSH analysis (thyrotropin) may look: the limits of the reference values are 0.5-5.0 μIU / ml, and the result itself is 2.0. As can be seen from the last example, the figure obtained in the study is in the normal range. In this technical solution, reference values can be obtained from an external data source, or form in the process of functioning of the system. Reference values can also be obtained from the user interface block.

[00052] Step 102: form a matrix of health indicators of at least one user;

[00053] The matrix of health indicators of a user who is a patient can be part of the database stored on the data storage device and be associated with the patient's information object (IO) in the infological model.

[00054] The matrix of health indicators can be implemented through a scalable table and look like this:

[00055] An infological database model including a matrix of user health indicators is shown in FIG. 3.

[00056] Step 103: obtaining, with a predetermined period, the health indicators of at least one user;

[00057] The values of health indicators are received on a data processing device via a wireless data transmission channel from a user interface unit, which is intended for user interaction. The specified interface unit may include a display, controller, USB or any other connector for connecting a user's computer. [00058] The period for obtaining various health indicators may be different. For example, weight should be measured daily, and a blood test every three months. In some embodiments, the implementation period may be predetermined and fixed.

[00059] Step 104: carry out the analysis of the obtained values of health indicators with a view to their going beyond the previously obtained reference values.

[00060] The analysis of the obtained values of the health indicators is carried out by checking whether the obtained value falls within the limits of the reference values or not. This check can be carried out by comparing the health indicator and the limit of reference values. If the values of the health indicator go beyond the reference values, then it is of interest for diagnosis, therefore, such a indicator can be marked, for example, by the symbol “*” or some flag.

[00061] Step 105: form a list of diagnostic measures for at least one user if at least one of the values of the health indicators is outside the reference values;

[00062] In the database of health indicators, each value of the patient’s health indicator is associated with a diagnostic measure. For example, a diagnostic measure such as a Holter pressure study corresponds to such a health indicator as pressure in the database. If the user has a pressure higher than normal, then he is prescribed the above diagnostic measure. Thus, they carry out the formation of a list of diagnostic measures based on all health indicators that have gone beyond the reference values.

[00063] In FIG. Figure 5 shows a method for forming a list of diagnostic measures based on health indicators whose values went beyond the reference. Yu [00064] In some embodiments, the diagnostic measures may be a urinalysis, a biochemical blood test, an ultrasound of any organ, tomography, and the like.

[00065] The input parameters for the formation of the list are a set of user health indicators, comprising: the name of the indicator; indicator value; link to the list of events.

[00066] The output parameters after the implementation of the list formation can be: a return code (for example, 1 - list formation was successful; 2 - error in the input data); URL link to the list of diagnostic measures that the user can open on his interface unit.

[00067] For example, if the blood sugar values measured by a home blood glucose meter and transferred to the system are constantly outside the reference values, a blood test for glycated hemoglobin is prescribed.

[00068] A set of tasks for the implementation of diagnostic and organizational measures is generated and performed on the workflow management server. If different values of health indicators that went beyond the reference values require the execution of the same diagnostic measure, repeated measures are excluded from the set.

[00069] An example of a data model for a decision support method for remote monitoring of people at risk of cancer is shown in FIG. 6. For example, in the infological model, the information object "Contractor" contains the name; Contact details; role; age unique patient code

[00070] FIG. 7 shows a flow diagram of a method for generating and tracking diagnostic and organizational measures. [00071] A list of diagnostic measures is preliminarily formed, each having a date and time for completion.

[00072] The workflow management server is designed to formulate tasks for process participants (doctor, patient). The main functions of the server are: forming a task in the User’s Personal Account (LC) on the interface unit; generating and sending email notifications. The user is instructed, for example, to fill the jar with urine at home, and then bring it to the laboratory, describes the state in which the user should be, for example, on an empty stomach, etc. He also needs to transfer data through the user interface unit to the system.

[00073] Step 106: supplement the matrix of health indicators of at least one user with new data obtained after the user performs diagnostic measures.

[00074] After the user loads the names and values of indicators, the date of measurement of indicators into the remote monitoring system through the user interface unit, a record of health indicators is automatically added to the matrix of health indicators using the unique identifier of this user, and the matrix is updated.

[00075] Step 107: display notifications of the results of diagnostic measures to the attending physician for making medical decisions.

[00076] The attending physician also forms a task in his personal account: to view the results of diagnostic measures. This task forms the workflow management server automatically after the results are loaded into the user health matrix. The result of the event may be the appearance of a new “column” in the matrix of user health indicators. [00077] In some embodiments, alerts are issued to a physician, for example, by light emission, health indicators that are potentially hazardous to a user's health.

[00078] The data input / output device described above in the method may be a multimedia component, a sensor component, an audio component depending on the method step and its implementation.

[00079] The above method can be automatically implemented on the remote monitoring system 200 shown in FIG. 2.

[00080] The system 200 may be implemented using a mobile phone, a personal or laptop computer, or using other computing means suitable for performing the necessary program-logic functions or method steps described above.

[00081] The system 200 may include one or more of the following components: a processor 218, a controlled video camera 203, a memory 204, a power component 206, a media component 208, an input / output (I / O) interface 212, a sensor component 214, an audio component 210, data transfer component 216.

[00082] In some embodiments, the processing component that contains the processor 218 mainly controls all operations of the system 200, for example, display, data transfer, camera operation, and recording operation. The processing component may include one or more processors that implement instructions to complete all or part of the steps of the above methods. In addition, the processing component may include one or more modules for a convenient interaction process between the processing component and other components. For example, the processing component may include a multimedia module for conveniently facilitating interaction between the multimedia component 208 and the processing component. In said system 200, a set of reference limits is obtained by processor 218 values of health indicators, after which a matrix of health indicators of at least one user is formed, then, at a predetermined period, the health indicators of at least one user are obtained, then the obtained values of health indicators are analyzed for their going beyond the limits of previously obtained abstract values, then form a list of diagnostic measures for at least one user if at least one of the values of the health indicators comes out beyond the reference values, after which they supplement the matrix of health indicators of at least one user with new data obtained after the user performed diagnostic measures, and as a result, notifications about the results of diagnostic measures to the attending physician are generated and displayed for medical decisions.

[00083] The memory 204 is configured to store various types of data to support the operation of the system 200. Examples of such data include instructions from any application or method, image, video, etc. In the system 200, the memory stores an electronic medical record with data patient, as well as those formed in further localization. The memory 204 may be implemented in the form of any type of volatile memory, non-volatile memory, or a combination thereof, for example, Static Random Access Memory (RAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Permanent Storage Device (ROM), Permanent Storage Device (ROM), magnetic memory, flash memory, magnetic or an optical disc.

[00084] In some embodiments, power component 206 provides electricity to various components of system 200. Component 206 The power supply may include a power management system, one or more power supplies, and other nodes for generating, controlling, and distributing power to the system 200.

[00085] In some embodiments, the multimedia component 208 includes 5 a screen providing an output interface between the system and the user. In some implementations, the screen may be a liquid crystal display (LCD) or touch panel (SP). If the screen includes a touch panel, the screen may be implemented as a touch screen for receiving an input signal from a user. The touch panel includes one or more touch sensors in the sense of gesturing, touching and sliding the touch panel. The touch sensor can not only feel the touch or the gesture of turning over, but also determine the duration of time and pressure associated with the operation mode of touch and sliding. After determining the risk of development

15 oncological diseases on the basis of the oncological risk values for each location on the multimedia component 208 display the risk of developing the oncological disease, the oncological risk for each location, an additional parameter characterizing the increased risk of developing the oncological disease for each location, characteristic

20 hereditary predispositions for the development of cancer for each location on the data input / output device.

[00086] An I / O interface 212 provides an interface between a processing component and a peripheral interface module.

[00087] The sensor component 214 comprises one or more sensors and

25 is configured to provide various aspects of assessing the state of the system 200. For example, the sensor component 214 can detect on / off states of the system 200, the relative position of components, for example, the display and keypad of the system, a change in position of the system or one component of the system, presence or absence contact between the user and the system, as well as orientation or acceleration / deceleration of the system. The sensor component 214 includes a proximity sensor configured to detect the presence of an object in the vicinity when there is no physical contact. The sensor component 214 includes an optical sensor (for example, CMOS or CCD image sensor) configured to be used in visualization of the application. In some embodiments, the sensor component comprises an acceleration sensor, a gyroscope, a magnetic sensor, a pressure sensor, or a temperature sensor.

[00088] The communication component 216 (data transmission component) is configured to facilitate wired or wireless communication between the system and other devices. The system can access a wireless network based on communication standards, but not limited to, such as WiFi, 2G or 3G, or combinations thereof. In one exemplary embodiment, the data transmission component 216 receives a broadcast signal or broadcast, related information from an external broadcast control system via a broadcast channel. In one embodiment, the data transmission component 216 comprises a near field communication module (NFC) to facilitate short-range communication, or a radio frequency identification (RFID) module, an infrared data transmission module (IrDA), or ultra-wideband (UWB) technology, or a module Bluetooth (BT) or other means suitable for wireless data transmission.

[00089] In an exemplary embodiment, the memory 204 includes instructions that are executed by a processor 218 of the system 200 to implement the methods described above to improve the reliability of detection of a target surveillance object. For example, a non-volatile computer-readable medium may be a ROM, random access memory (RAM), compact disk, magnetic tape, floppy disks, optical storage devices and the like.

Claims

FORMULA

A way to support decision-making with remote monitoring of people included in the cancer risk group, including the following steps:

• receive a set of limits of reference values of health indicators;

• form a matrix of health indicators of at least one user;

• receive at a predetermined period the values of the health indicators of at least one user;

• carry out the analysis of the obtained values of health indicators for their going beyond the previously obtained abstract values;

• form a list of diagnostic measures for at least one user if at least one of the values of the health indicators goes beyond the reference values;

• supplement the matrix of health indicators of at least one user with new data obtained after the user performs diagnostic measures;

• form and display notifications about the results of diagnostic measures to the attending physician for making medical decisions.

The method according to claim 1, characterized in that the limit of the reference value of the health indicator is a range or a maximum value or a minimum value.

The method according to claim 1, characterized in that for each health indicator they set their own interval for obtaining its value.

4. The method according to claim 1, characterized in that they analyze the obtained values of the health indicators for their going beyond the previously obtained abstract values by checking whether the obtained value falls within the limits of the reference values. 5 5. The method according to claim 1, characterized in that each indicator of the state of health is associated in the database with a diagnostic measure.

6. The method according to claim 1, characterized in that the diagnostic measure is a urinalysis or biochemical blood test or ultrasound of the organ or tomography,

s 7. The method according to claim 1, characterized in that a set of diagnostic measures is generated and launched on the workflow management server.

8. The method according to claim 1, characterized in that, if different values of health indicators that are beyond the reference values,

15 prescribe the execution of one and the same diagnostic measure, repetitive measures are excluded from the set.

9. The method according to claim 1, characterized in that each diagnostic measure includes the necessary date and time of execution.

20 10. The decision support system for remote monitoring of people included in the cancer risk group includes:

• one or more data processing devices;

• one or more data storage devices;

• where the steps of claim 1 are stored on at least one data storage device 25 and are executed on at least one data processing device.