KR101927669B1 - Method for providing customized health-care service algorithm - Google Patents

Abstract

The present invention relates to a method for providing a customized health-care service algorithm linked to a mobile terminal. According to an embodiment of the present invention, the method for providing a customized health-care service algorithm comprises: a subject evaluation step of evaluating a lifestyle of a subject in accordance with a predetermined standard by a health care service providing device; a content providing step of providing customized content, which is determined through a subject classification step and the subject evaluation step, to the subject; and a monitoring step of re-evaluating the lifestyle of the subject or comparing the customized content with new health examination data, which is collected from a national health insurance corporation server, after providing the customized content during a predetermined period.

Description

[0001] METHOD FOR PROVIDING CUSTOMIZED HEALTH-CARE SERVICE ALGORITHM [0002]

The present invention relates to a method of providing a personalized health care service associated with a mobile terminal. And more particularly, to a method for providing healthcare service content using a mobile device.

As a result of the health examination conducted by the National Health Insurance Corporation, the number of chronic patients is steadily increasing, and many of the people who have received health screening are suspected of having chronic diseases. However, management measures for people who have suspected chronic diseases are lacking.

Even if abnormal findings are found through the health screening provided by the present country, some health care services are provided only at the National Health Insurance Corporation and public health centers, and there is no other follow-up action.

In addition, the mortality rate of major chronic diseases is increasing with the increasing frequency of chronic diseases due to population aging, changes in lifestyles and eating habits, and environmental pollution, which will lead to a significant increase in social and economic burden of chronic diseases Therefore, prevention and management policies are needed.

In order to provide effective health care services to health check-ups, it is necessary to grasp current health conditions and risks more accurately.
Previous reference: Korean Patent Registration No. 10-1141425

The present invention is to analyze a health risk factor based on a user's health check result.

Also, the present invention is to provide healthcare service contents and recommendation algorithms suited to individual characteristics and needs so that correct lifestyle can be managed through data analysis collected by individual.

In addition, the present invention is to enhance health care services associated with ICT (Information and Communication Technology) -based user terminal devices to improve after-sales management of smart screening results such as user's lifestyle improvement and continuous self-management support.

A screening server for transmitting a screening result of a health insurance member according to an embodiment of the present invention; an integrated payroll information system for transmitting billing statement detail information; And a health message management device, the health message management device of the health message management system comprising: a communication module receiving the medical examination and examination result of the health insurance member from the integrated benefit information system; A storage module for storing a medical examination and a result of the examination of the health insurance member; And a processing module for analyzing the medical care and examination results of the health insurance subscribers stored in the storage module to classify the subjects corresponding to predetermined conditions and requesting the integrated payroll information system to provide medical information data of the classified subjects .

In addition, the processing module collects the results of the examination of the subject, the questionnaire, and the consultation result information to perform the metabolic syndrome task processing, and performs the corresponding group setting according to the risk factors, Subjects can be categorized.

The treatment module may be configured to: determine abdominal obesity, triglyceride, HDL, blood pressure, and blood glucose as a criterion for the risk factors, and the abdominal obesity, triglyceride, blood pressure, The HDL is set to a value lower than a preset value, and classified into a care group and a risk group according to the reference number of the risk factors.

In addition, the processing module collects the results of the examination of the subject, the questionnaire table, the consultation result information, and the billing specification to perform the chronic disease business processing, and subjects the subjects to the diabetes group, hypertension group, Classification can be done by dividing groups into multiple groups.

The processing module may further include: a determination unit configured to determine a start phrase structure and a source phrase structure of the contents in a predetermined format according to the classified subjects, and the body structure and contents of the contents are determined by considering a health risk factor, You can choose from preset formats and automatically recommend them.

Further, the processing module may set a format of the body structure of the contents as a purpose, a guide, and a flow of participation, and set predetermined contents corresponding to each of the flows in consideration of health risk factors or diseases, Can be automatically matched.

The processing module is further configured to: set a flow of the purpose, guidance, and participation, wherein the predetermined content corresponding to the purpose is selected to include a keyword relating to a health risk factor or disease of the subject, The set content is set as a question and answer format for the informative message, and the preset content corresponding to the participation is set as a hypertext structure for encouraging. In performing the automatic matching, Time, and status of the subject, but an answer can be set based on the season, the weather, and the consultation result information of the subject at the current time, taking into consideration the preference and preference of the subject.

Wherein the processing module transmits a message suitable for a subject according to a time of receiving a health message including a season, a holiday, a time, and a day of week when the automatic matching is further considered considering the transmission time and the state, According to the seasons and holidays, the special message is sent according to the season, and it is defined as spring in March - May, spring in June - August, autumn in September - November, and winter in December - February. A special pre-set seasonal food and movement special message is transmitted. In the case of a holiday, a special message such as a preset nutrition corresponding to a holiday, a stress overcoming method, and a caution for long-time driving are transmitted. According to the life patterns of the subjects during the day, special messages containing information on nutrition are sent at 11:00 am and encouraged to drink at 5:00 pm You can send a special message that sends a special message and encourages you to exercise at 7 pm in the evening.

According to an embodiment of the present invention, a health risk factor can be analyzed based on a user's health check result.

According to an embodiment of the present invention, it is possible to provide a healthcare service content and a recommendation algorithm suited to individual characteristics and needs so that correct lifestyle can be managed through analyzing collected data for each individual.

According to an embodiment of the present invention, a health management service linked to a user terminal device based on ICT (Information and Communication Technology) is developed to enhance the follow-up management of a smart examination result such as lifestyle improvement and continuous self-management support .

1 is a block diagram of a health care service providing system according to an embodiment of the present invention.
2 is a block diagram illustrating an internal configuration of a healthcare service providing apparatus according to an embodiment of the present invention.
3 is a detailed block diagram of a risk prediction model selection module according to an embodiment of the present invention.
4 is a flowchart illustrating a method of providing a healthcare service according to an embodiment of the present invention.
5 is a general flowchart of a health care service providing system according to an embodiment of the present invention.
FIG. 6 is a diagram illustrating an example of an arbitration model algorithm in a health care service providing system according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. The embodiments of the present invention can be modified in various forms, and the scope of the present invention should not be construed as being limited to the following embodiments. This embodiment is provided to more fully describe the present invention to those skilled in the art. Thus, the shape of the elements in the figures has been exaggerated to emphasize a clearer description.

Other advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims.

Unless defined otherwise, all terms (including technical or scientific terms) used herein have the same meaning as commonly accepted by the generic art in the prior art to which this invention belongs. Terms defined by generic dictionaries may be interpreted to have the same meaning as in the related art and / or in the text of this application, and may be conceptualized or overly formalized, even if not expressly defined herein I will not

The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. As used herein, the terms' comprise 'and / or various forms of use of the verb include, for example,' including, '' including, '' including, '' including, Steps, operations, and / or elements do not preclude the presence or addition of one or more other compositions, components, components, steps, operations, and / or components. The term 'and / or' as used herein refers to each of the listed configurations or various combinations thereof.

It should be noted that the terms such as '~', '~ period', '~ block', 'module', etc. used in the entire specification may mean a unit for processing at least one function or operation. For example, a hardware component, such as a software, FPGA, or ASIC. However, '~ part', '~ period', '~ block', '~ module' are not meant to be limited to software or hardware. Modules may be configured to be addressable storage media and may be configured to play back one or more processors. ≪ RTI ID = 0.0 >

Thus, by way of example, the terms 'to', 'to', 'to block', 'to module' refer to components such as software components, object oriented software components, class components and task components Microcode, circuitry, data, databases, data structures, tables, arrays, and the like, as well as components, Variables. The functions provided in the components and in the sections ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ' , '~', '~', '~', '~', And '~' modules with additional components.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings attached hereto.

FIG. 1 is a diagram illustrating a healthcare service providing system 1 according to an embodiment of the present invention.

1, a healthcare service providing system 1 according to an embodiment of the present invention includes a health care service providing apparatus 200, a National Health Insurance Corporation server 100, an external server 300, 400). Herein, the term 'server' refers to a logical unit, and it is obvious to a person skilled in the art that the present invention is not necessarily a physically separated component.

In the present invention, the health care service providing apparatus 200 collects the collected data through the external server 300 based on the Big Data accumulated in the National Health Insurance Corporation server 100, The risk prediction model is verified and processed, and the optimal risk prediction model is set and utilized. The above system suggests a way to utilize the Big Data of the National Health Insurance Corporation efficiently in connection with the pilot project related to health using mobile devices. 1, the National Health Insurance Corporation server 100 is located outside the healthcare service providing apparatus 200 or is located inside the healthcare service providing apparatus 200 separately from the one shown in FIG. 1 .

The apparatus 200 for providing a healthcare service according to an embodiment of the present invention may measure health risk factors (for example, high blood pressure, dyslipidemia , High blood glucose, abdominal obesity, etc.), and a content recommendation algorithm based on risk assessment results. The health care service provision apparatus 200 is connected to the National Health Insurance Corporation server 100 and the external server 300 through a network. Here, the network is a concept including a known wired / wireless Internet, a mobile communication network, or a combination thereof. The healthcare service provision apparatus 200 can exchange data with the terminal device 400 through a mobile network.

The National Health Insurance Corporation server (100) is a server operated by the National Health Insurance Corporation. It is a server that provides general health examination data including health examination results of examinees, health examination medical history data, And stores examination result information including data. For example, the National Health Insurance Corporation server 100 may store information of subscribers registered in the National Health Insurance Corporation in the form of a general check database, a document database, a specification database, and a medical record database. 1, the National Health Insurance Corporation server 100 is located outside the health care service providing apparatus 200, but may be located inside the health care service providing apparatus 200. [

The external server 300 is a server that provides an academicly verified risk prediction model to an external server. A server holding domestic and foreign reports and theses, or a search engine capable of searching for such specialized documents. Any form of server that includes domestic and international reports and dissertations on risk prediction models is possible.

For example, the healthcare service provision apparatus 200 can collect a risk prediction model for domestic and overseas type 2 diabetes, ischemic stroke (cerebral infarction), ischemic heart disease, and cardiovascular disease from the external server 300, And the optimal risk prediction model can be set by performing verification and processing through the big data. Specifically, the healthcare service providing apparatus 200 receives a term indicating a comprehensive disease risk prediction such as a "risk prediction model", a "risk score model" and a "risk scoring system" in the external server 300, cox proportional hazard model, "" survival analysis model, "and so on.

More specifically, the Framingham Diabetes Risk Score Study was used as a predictor of type 2 diabetes risk, the PROCAM study was used to predict ischemic stroke risk, the Chinese multi -provincial study, a risk prediction model for SCORE study can be used to collect and validate each risk prediction model. For example, the ischemic stroke prediction model may include the PROCAM study model and the Chinese cohort study model, the ischemic heart disease prediction model may include the Chinese multi-provincial study model, the Chinese cohort study model, May include the USA-PRC cohort study model, the SCORE study model, and the Framingham heart study model.

Here, the health care service providing apparatus 200 may be collected by an external input by the user through the information described in the paper or the like, for example, the risk prediction model.

The terminal device 400 according to the present invention can be installed and operated by installing an application program. The terminal device 400 includes a display module (not shown) And may be a mobile device having a network connection means. However, the present invention is not limited thereto, and any means capable of transmitting and receiving data through a network can be a terminal device 400 of the present invention.

The terminal device 400 of the present invention may be a terminal possessed by a person who has undergone the health checkup, but is preferably a terminal device such as a "PC " when used by a person in charge of the health insurance management corporation and may be connected to an internal internal network . Therefore, although the terminal device 400 used by the person in charge of the organization can exist outside the engine, it is preferable that the terminal device 400 exist inside the company to prevent problems such as security.

FIG. 2 is a block diagram illustrating an internal configuration of a healthcare service providing apparatus 200 according to an exemplary embodiment of the present invention. FIG. 3 is a block diagram illustrating a detailed configuration of a risk prediction model selection module 230 according to an exemplary embodiment of the present invention. .

2, the health care service provision apparatus 200 includes a communication module 210, a data collection module 220, a risk prediction model selection module 230, a subject determination module 240, a subject classification module 250, An evaluation module 260, a content providing module 270, a monitoring module 280, and a database 290.

The communication module 210 exchanges data with the National Health Insurance Corporation server 100, the external server 300, and the terminal device 400. The communication module 210 receives from the National Health Insurance Corporation server 100 the examination result information including the information on the general health examination data, the information on the health checkup survey data, the information on the statement and the medical record, And transmits and receives various data to and from the server 300 and the terminal device 400.

According to an embodiment of the present invention, the period for receiving the examination result information from the National Health Insurance Corporation server 100 is set and the examination result information is received for each period (for example, 3 months, 6 months, 1 year, etc.) can do. This is because, if data is frequently received from the outside, there may be a performance or capacity problem in the system itself. Therefore, a period for receiving data from the external server 300 or the National Health Insurance Corporation server 100 is set in a predetermined cycle .

The data collection module 220 collects general health checkup data, health checkup survey data, specification and medical record information of health checkup recipients from the communication module 210, You can adjust the value. At certain times, the health checkup items may change or the collected data variables / values may differ depending on the policy change of the National Health Insurance Corporation or changes in the data / value collection policy. For example, in the past data policy, data were recorded as 1 (none) and 2 (as) in the family history of a specific disease, but according to the changed data policy, 0 (none) , 1 (yes), the past data value should be adjusted according to the changed data policy.

According to one embodiment of the present invention, the general health screening data collected by the data collection module 220 includes height, weight, waist circumference, visual acuity, hearing, blood pressure, body mass index, urinary protein, hemoglobin level, Cholesterol levels, triglyceride levels, HDL-cholesterol levels, LDL-cholesterol serum creatinine levels, gonadal sperm filtration rate, AST levels and gamma GTP levels.

In addition, the health screening survey data can be used to diagnose and treat diseases such as tuberculosis, hepatitis, hepatic disease, hypertension, history of heart disease, stroke, diabetes and cancer, history of liver disease, hypertension, stroke, heart disease, And the medical record data may be data including a date of initiation of treatment, a form code, a hospital code, a hospital cord, a number of days of hospitalization, a day of hospitalization, a date of first hospitalization, . Here, the health examination data can be collected based on data of the date on which the subject has recently examined the health examination among the collected data. For example, if the collected health checkup data has data for 2015 and data for 2017, the data for 2017, which is the latest date, is determined as the basic data.

The risk prediction model selection module 230 selects a representative risk prediction model for each disease. Referring to FIG. 3, the risk prediction model selection module 230 includes a prediction model collection module 231, a prediction model parameter extraction module 232, a variable scraping module 233, a prediction model reliability calculation module 234, And a selection module 235.

The prediction model collection module 231 collects the academicly verified risk prediction models from the external server 300 through the communication module 210. Specifically, risk prediction models for type 2 diabetes, ischemic stroke (cerebral infarction), ischemic heart disease, and cardiovascular disease are collected. Here, the external server 300 is a search engine having a database including a database including Medline, Google Scholar, and domestic and overseas reports and theses, a term indicating a comprehensive disease risk prediction model for each disease, a health risk prediction prediction performance prediction prediction model And searches the external server 300 and collects the academicly verified risk prediction models.

According to an embodiment of the present invention, a process of collecting the risk prediction model from the external server 300 by the prediction model collection module 231 will be described below. First, the risk prediction model is firstly classified through searching the keyword related to the disease from the external server. Then, risk prediction models are collected through a second classification to confirm that the variables required by the risk prediction model can be provided through the data collected by the data collection module.

The predictive model parameter extracting module 232 extracts a predictive model parameter used in the risk prediction from the collected risk prediction model, and the variable scraping module 233 extracts a variable corresponding to the extracted predictive model variable from the health check result information And the prediction model reliability calculation module 234 calculates the reliability of the risk prediction model based on the calculated result by matching the scraped variable with the prediction model parameter.

For example, the predictive model parameter extraction module 232 may be used to predict a risk predictor model that is collected in a type 2 diabetes risk prediction model, such as fasting blood sugar level, body mass index (BMI), systolic blood pressure, high density lipoprotein (HDL) Triglyceride levels and family history of type 2 diabetes mellitus as predictive model variables. The variables scraping module 233 then determines the variables corresponding to the predictive model variables (fasting blood glucose level, height / weight, blood pressure, HDL-cholesterol, triglyceride levels, and family history of diabetes ). Thereafter, the predicted model reliability calculation module 234 matches the scraped variables to the prediction model variables, and calculates the resultant values.

In another example, the risk prediction model of ischemic stroke may predict the risk of ischemic stroke by considering gender, age, current smoking status, past history, and systolic blood pressure, and the predictive model of ischemic heart disease risk is age, current smoking, total cholesterol And systolic blood pressure.

The prediction model selection module 235 classifies the risk prediction model into each disease, and selects a risk prediction model with the highest reliability for the risk prediction model as the representative risk prediction model for each disease. Here, the reliability evaluation is performed by analyzing the ROC curve (Receiver Operation Characteristic Curve), and the AUC value (Area Under the Curve) is selected as the high risk prediction model as the representative risk prediction model. For example, for the risk prediction models A and B of A, the AUC value is 0.771, and the B value of AUC is 0.845, the higher the AUC value is selected as the representative risk prediction model for cardiovascular disease.

The subject-person determining module 240 classifies the subjects who have undergone the health check-up for a predetermined period from the National Health Insurance Corporation server 100, and diagnoses hypertension or diabetes The subjects who are diagnosed are excluded from the risk group. The predetermined period is generally set to be within the last one year. However, if the collected information is too large, the preset period may be set to a period shorter than one year, and if the collected information is too small, the predetermined period may be set to a period longer than one year.

The subject classification module 250 is configured to classify at least one of a plurality of health risk factors including high blood pressure, dyslipidemia, high blood sugar and abdominal obesity according to predetermined criteria on the basis of the result of the health examination classification of the subject decision module 240 Determine the type of risk group subject, including health risk factors. The reason why the risk group is classified by type is to provide the contents to the subject according to the type of the health risk factors included in the subject. For example, the number of types classified into 4 or 5 health risk factors can be 14 to 15, but the scope of the present invention is not limited to the above-mentioned number of categories, It is clear that the number of ties can be changed through design changes.

Alternatively, the subject can be classified according to the number of steps (number of steps). For example, a group with more than 10,000 remunerations per day is classified as one group, a group with more than 5,000 salaries and less than 9,999 salaries as two groups, and a group with less than 5,000 salaries as three groups.

The assessment module 260 evaluates the lifestyle of the subjects being classified. Lifestyle habits assessed by the assessment module 260 may be diet, stress level and management methods, nicotine dependence test, and alcohol dependence test. The lifestyle assessment provides a predetermined evaluation item to the subject and provides customized lifestyle management contents to the subject based on the input of the subject.

The content providing module 270 provides the content to the target person determined through the above process. According to the result of the evaluation module 260, the contents can be provided according to a disease whose lifestyle and risk of the subject is predicted. The content providing module 270 may also provide content that includes information about topic keywords (e.g., fine dust, swine flu, etc.) to users as well as the results of the evaluation module 260 .

The monitoring module 280 provides the content determined by the subject to the subject for a predetermined period of time, and then monitors the content by comparing with the lifestyle of the subject or the new health examination data. This is to concentrate on the variation of the numerical value rather than to concentrate on the numerical value by using the characteristic of the health examination performed at the predetermined period, so as to provide a service suitable for the subject.

FIG. 4 is a flowchart illustrating a method for providing a healthcare service according to an embodiment of the present invention, and FIG. 5 is a flowchart illustrating an overall process of providing a healthcare service according to an embodiment of the present invention.

Referring to FIGS. 4 and 5, a method of providing healthcare services is as follows.

According to an embodiment of the present invention, a data collection step S10, a risk prediction model selection step S20, a subject determination step S30, a subject classification step S40, a subject evaluation step S50, S60) and a monitoring step (S70).

The data collection step (S10) collects the health examination data including the general health examination data, the health examination medical history data, the specification and the medical care record data from the National Health Insurance Corporation server (100).

In the step S20 of selecting a risk prediction model, a risk prediction model for type 2 diabetes, ischemic stroke, ischemic heart disease, and cardiovascular disease is collected from the external server 300, and a representative risk prediction Select a model.

According to an embodiment of the present invention, the risk prediction model selection step (S20) collects the risk prediction models for each disease from the external server 300 and uses the collected risk prediction models The results of this study are summarized as follows. First, the predicted model parameters were extracted, the scored health examination variables were matched with the predicted model parameters extracted from the collected health examination data, and the scrapped health examination variables were matched to the predicted model parameters. The reliability of the model is evaluated, and the risk prediction model with the highest reliability can be selected as the representative risk prediction model for each disease.

The subject decision step S30 is a step of predicting the risk of each disease based on the collected representative health risk prediction model based on the selected disease risk prediction model and comparing the collected health health data with the predetermined risk level Determine who will include the health screening data.

According to an embodiment of the present invention, the subject decision step S30 is a step of classifying the subject who has undergone the health examination for a predetermined period based on the collected health examination data, The risk of each disease is predicted by a representative representative risk prediction model, and the subjects who have exceeded the predetermined criteria are classified into the second category and those classified through the second classification are classified into the subjects who are diagnosed with hypertension or those who are diagnosed with diabetes And can be classified through the tertiary classification.

In the subject classification step S40, a subject who includes at least one health risk factor among the health risk factors including high blood pressure, dyslipidemia, high blood sugar and abdominal obesity according to a predetermined criterion, Classify according to the number.

According to one embodiment of the present invention, the subject classification step S40 is a step of classifying at least one of health risk factors including high blood pressure, dyslipidemia, high blood sugar and abdominal obesity according to predetermined criteria based on the subject's health examination data The number of health risk factors can be classified according to the number of health risk factors.

The subject evaluation step (S50) evaluates the lifestyle of the classified subject according to predetermined criteria.

According to an embodiment of the present invention, the subject evaluation step (S50) is a step of evaluating a subject based on an input of a subject, a diet evaluation and stress management evaluation, an alcohol dependency evaluation and a nicotine dependence evaluation, Based on the evaluation, it is classified into three stages. First, the location-based alcohol counseling center is introduced if the subject's alcohol dependence evaluation result is suspected, and if the nicotine dependence evaluation is suspected, the location based smoking cessation center is introduced can do.

The content providing step S60 provides the subject with the customized content determined through the subject classification step S40 and the subject person evaluation step S50.

According to one embodiment of the present invention, the content providing step S60 is a step of providing different contents according to the time when the contents are provided to the subject, and the first half of the predetermined period in which the contents are provided to the subject, The second half of the predetermined period of providing the knowledge type information and providing the contents to the subject can provide action type information on health risk factors and lifestyle. Here, the first half and the second half are terms of the first half and the second half of the period in which the contents are provided to the subject, which does not mean that the first half and the second half are the same.

On the basis of the information input from the terminal device and the information input by the subject on Wednesday, information on the health of the patient, Management information, Thursday provides smoking, drinking and stress management information based on the information entered by the subject and contents including mission information for self-monitoring on Friday. The odd-numbered parking in the second half of the predetermined period provides information on eating habits, and the even-numbered parking provides contents including practical information for increasing physical activity. On Thursdays, smoking, drinking and stress management information, Or provide a quiz about health care based on the provided content. The presented point-in-time portions herein may be design modifications by those skilled in the art.

The monitoring step S70 provides the customized contents for a preset period of time and then reevaluates the lifestyle of the subject or compares the lifestyle with the new health checkup data collected from the National Health Insurance Corporation server 100. [

FIG. 6 is a diagram illustrating an example of an arbitration model algorithm in a health care service providing system according to an embodiment of the present invention.

Referring to FIG. 6, an arbitration model algorithm showing an arbitration step including a management factor arbitration step (not shown) and a management timing arbitration step (not shown) of the healthcare service provision system 1 can be confirmed.

According to an embodiment of the present invention, after the content providing step S60, the management factor arbitration step or the management timing arbitration step may be included.

The management factor intervention step includes a health risk management factor message based on a health risk factor including a blood sugar level and a blood pressure value of a subject, a nutrition management factor message based on a nutrition management factor including a caloric value and a nutrient value of the subject, To the terminal device, an arbitration message classified into an activity amount management factor message based on the activity amount management factor including the number of steps of the activity amount management factor.

Here, the health risk management factor message provides a message indicating that the blood glucose level or the blood pressure level is within the normal blood glucose level range or the normal blood pressure range based on the information inputted by the subject, and the normal blood glucose level range or the normal blood pressure range Or nutritional management factor message is a message that informs the target calorie intake calorie calculation message or a message for recognition of excessive intake or lack of nutrient intake of the subject to be. The activity amount management factor message is a message that determines the average activity amount of the subject based on the number of steps of the subject and encourages the increase of the activity amount of the subject according to the determined amount of activity.

The management timing arbitration step is a step of arbitrating the administration time from the time of providing contents to the subject to the daily message to be sent every day, the notification message sent by the preset time according to the setting of receiving the message of the target person, And provides an arbitration message classified into an automatic message sent by the manager and a manual message sent directly by the manager to the terminal.

Although not shown directly in FIG. 6, the health care service providing system 1 can provide customized information suitable for the terminal device based on the management factors such as the classification, the classification to which the subject belongs, and the like.

For example, nutrition, exercise, and stress that are sent in week 1, week 2, week 3 can be sent according to the appropriate message for the season, and messages sent after week 3 can be sent a special message according to the subject group .

In addition, for example, a person who is required to send a message to quit smoking or drinking during lifestyle habits may be sent a quit smoking message corresponding to a special message to the terminal device.

The foregoing detailed description is illustrative of the present invention. In addition, the foregoing is intended to illustrate and explain the preferred embodiments of the present invention, and the present invention may be used in various other combinations, modifications, and environments. That is, it is possible to make changes or modifications within the scope of the concept of the invention disclosed in this specification, within the scope of the disclosure, and / or within the skill and knowledge of the art. The embodiments described herein are intended to illustrate the best mode for implementing the technical idea of the present invention and various modifications required for specific applications and uses of the present invention are also possible. Accordingly, the detailed description of the invention is not intended to limit the invention to the disclosed embodiments. It is also to be understood that the appended claims are intended to cover such other embodiments.

1: Customized healthcare service algorithm delivery system
100: National Health Insurance Corporation server
200: Health care service algorithm providing device
210: Communication module
220: Data Acquisition Module
230: Risk prediction model selection module
240: target person determination module
250: Subject classification module
260: Evaluation module
270: Content providing module
280: Monitoring module
290: Database
300; External server
400: terminal device

Claims (9)

A health care service providing apparatus,
A data collection step of collecting health examination data including general health checkup data, health checkup survey data, specifications and medical record data from the National Health Insurance Corporation server;
Classifying the risk prediction model first by classifying the external server into keyword search for type 2 diabetes, ischemic stroke, ischemic heart disease, and cardiovascular disease;
Determining whether a variable required by the risk prediction model can be provided through the health examination data collected in the step of collecting the health examination data;
Collecting the finally selected risk prediction model by secondary classification of the risk prediction model through the determining step;
Extracting predictive model parameters necessary for the risk prediction model from the collected risk prediction models; The risk prediction model for type 2 diabetes is based on the use of predictive model parameters such as fasting blood glucose, BMI, systolic blood pressure, HDL-level, triglyceride level, and family history of type 2 diabetes And the risk prediction model for the ischemic stroke is based on sex, age, smoking history, systolic blood pressure, and the like. The ischemic heart diseases are classified into age, smoking, total cholesterol and systolic blood pressure As a necessary predictive model variable -
Scraping the variable corresponding to the extracted predictive model variable from the health examination data;
Calculating a result value by matching the scraped variable with the prediction model variable;
Calculating reliability of the risk prediction model based on the calculated result;
A step of selecting a risk prediction model to select a representative risk prediction model for each disease;
The health examination data collected is used to predict the risk level of each disease on the basis of the selected representative disease risk prediction model for each disease, and the health examination data exceeding predetermined risk criteria among the collected subjects including the health examination data A target person determination step of determining a target person including the target person;
A subject who classifies the subject according to the number of health risk factors including at least one health risk factor among high risk factors including high blood pressure, dyslipidemia, high blood sugar and abdominal obesity according to a predetermined criterion Classification stage;
A subject evaluation step of evaluating the lifestyle of the subject classified in the subject classification step according to a predetermined criterion;
A content providing step of providing the target person with the customized content determined through the target person classification step and the target person evaluation step; And
And a monitoring step of providing the customized content for a predetermined period of time and then reevaluating the lifestyle of the subject or comparing it with new health checkup data collected from the National Health Insurance Corporation server. delete The method according to claim 1,
Wherein the data collection step comprises:
The general health screening data may include data on height, weight, waist circumference, visual acuity, hearing, blood pressure, body mass index, urinary protein, hemoglobin level, fasting glucose level, total cholesterol level, triglyceride level, HDL-cholesterol level, LDL-cholesterol level Data including serum creatinine levels, gonadal sperm filtration rate, AST levels and gamma GTP levels,
The health examination medical history data may be used for diagnosis and treatment of diseases such as tuberculosis, hepatitis, hepatopathy, hypertension, history of heart disease, stroke, diabetes and cancer, history of diseases related to liver, liver disease, hypertension, stroke, Data containing the amount of drinking water,
The specification and the medical record data are data including a date of initiation of treatment, a form code, a hospital bottle code, a wounded cord, a number of days of hospitalization, a number of days of hospitalization, a date of first hospitalization,
Wherein the health checkup data is collected based on data of a date on which the subject has recently undergone the health checkup among collected data. delete The method according to claim 1,
The subject-
Based on the collected health screening data, the subjects who underwent health screening for the predetermined period are first classified, and the subjects classified through the first classification are predicted by the disease risk prediction model for each disease, The subjects who exceeded the standard are classified into the second category and the subjects classified through the second classification are classified by the third classification except the subject who is diagnosed with hypertension or the subject who is diagnosed with diabetes,
Wherein the subject-
Based on the health examination data of the subject, a subject who includes at least one health risk factor, including high blood pressure, dyslipidemia, high blood sugar and abdominal obesity, according to predetermined criteria, A method of providing a customized healthcare service algorithm to classify according to the method. The method according to claim 1,
The subject-
Evaluating the eating habit and the stress, evaluating the alcohol dependence and the nicotine dependence on the input of the subject,
The subject is classified into three stages of attention, possibility and doubt through the alcohol dependence evaluation and the nicotine dependence evaluation, and when the subject has a suspected alcohol dependence evaluation result, the location based alcohol counseling center is introduced, and the nicotine A method of providing a customized health care service algorithm that introduces a location based smoking cessation center if the dependency assessment is suspected. The method according to claim 1,
The content providing step may include:
Providing different contents to the subject according to the time of providing the contents,
The first half of the predetermined period of providing the contents to the subject provides the knowledge type information about the health risk factors and the lifestyle and the second half of the predetermined period of providing the contents to the subject is the practice about the health risk factors and lifestyle Type information,
On the basis of the health information on the health risk factors on Monday, the eating habit information based on the information inputted by the subject, the information inputted by the subject on Wednesday, and the information inputted from the terminal device, On Thursday, based on the information inputted by the target person, smoking, drinking, stress management information and Friday are provided with contents including mission information for self-monitoring,
In the second half, contents including practice information for odd-numbered parking and practice-type information for increasing physical activity are provided for even-numbered parking, and smoking, drinking and stress management information is given every Thursday, A method for providing a customized health care service algorithm that provides a health care quiz based on the content provided. The method according to claim 1,
After the content providing step,
A health risk management factor message based on a health risk factor including a blood glucose level and a blood pressure level of the subject, a nutrition management factor message based on a nutrition management factor including a caloric value and a nutrient value of the subject, A management factor intervention step of providing an arbitration message classified into an activity amount management factor message based on an activity amount management factor including the activity amount management factor to the terminal device; or
A daily message to be sent to the subject, a daily message to be sent each day, a notification message to be sent according to a preset time according to whether the subject is set to receive a message, And a management time arbitration step of providing an arbitration message classified into an automatic message and a manual message directly sent by the manager to the subject, to the terminal device. 9. The method of claim 8,
Wherein the management factor arbitration step comprises:
The health risk management factor message provides a message indicating that the blood glucose level or the blood pressure value is within the normal blood glucose level range or the normal blood pressure range based on the information inputted by the subject and indicates a normal blood glucose level range or a normal blood pressure range value Is not a normal range through the terminal device,
The nutrition management factor message is a message for providing a message to guide the calculation of the calorie of the food consumed by the subject or for recognizing the excessive intake or lack of intake of the nutrient by the subject,
Wherein the activity amount management factor message is a message for determining an average activity amount of the subject based on the number of steps of the subject and encouraging an increase in the activity amount of the subject according to the determined amount of activity.

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