KR101439810B1 - System and method for health, disease-index formation and healthcare support of regional and workplace unit - Google Patents

Abstract

A system and a method for creating health and disease indexes and supporting healthcare in regional and workplace units according to the present invention includes the steps of: extracting necessary information, which includes qualification information, hospital medical information, and health screening information, according to target chronic diseases from a database of a healthcare support system by the healthcare support system; modeling a monitoring model according to the target diseases using the extracted necessary information; verifying the modeled monitoring model by taking into consideration data previously stored in the database and input information into an external terminal; and setting a monitoring index according to the target diseases using the modeled monitoring model that has been verified; and allowing a regional server or an enterprise server to share the set monitoring index.

Description

BACKGROUND OF THE INVENTION Field of the Invention The present invention relates to a system and method for providing health information,

The present invention relates to a method and system for healthcare support, and more particularly to a method and system for healthcare support that provides a more integrated, systematic, and efficient approach to chronic disease management.

According to the World Health Organization, chronic diseases are characterized by long-lasting and progressive progression, which is a cause of high mortality.

In Korea, the disease structure is gradually changing due to chronic diseases. In case of such chronic diseases, even if they develop due to a long incubation period, it is often the time to come to a late timing of the eye, so management of risk factors for prevention is becoming important.

Chronic disease progresses slowly, and the duration from the exposure of the risk factor to the onset of the disease is not only long, but also the progress from the onset to the aftereffect or disability is long. Therefore, .

Current status indicators of chronic diseases do not explain the relationship with risk factors for chronic disease management, and there are no monitoring indicators to manage chronic diseases efficiently.

Therefore, a more integrated, systematic and efficient method for chronic disease management is required.

SUMMARY OF THE INVENTION The present invention provides a method and system for healthcare support that provides a more integrated, systematic, and efficient approach to chronic disease management.

A method for supporting healthcare management according to an embodiment of the present invention is characterized in that a healthcare management system extracts necessary information for a target disease of a chronic disease including qualification, hospital medical treatment information and health examination information from the database of the healthcare support system ; Modeling a target disease-specific monitoring model using the extracted necessary information; Verifying the modeled monitoring model in consideration of data previously stored in the database and input information of an external terminal; Setting a monitoring index for each disease using the modeled monitoring model; And sharing the set monitoring metric with a local server or an enterprise server.

Extracting data stored in the database of the local server or the database of the enterprise server using the monitoring index, the data being determined to be dangerous or necessary to prevent; Classifying the extracted data by target diseases; And notifying the classified data of the contents that are judged to be dangerous or judged to be necessary when there is an input signal for each target disease.

In addition, the verifying may include: analyzing the analysis subject before the specific time point of the target disease in the database when the verification is performed in consideration of the pre-stored data; Identifying a dependent variable corresponding to a patient admitted after a specific time point of the target disease using the CPC code in the database; In the above-mentioned database, there is provided a method for diagnosing a pathological physiological condition including gender, age and income level, demographic and social condition data, health examination data including health examination data on smoking cessation and drinking, physical activity, obesity, blood pressure, Identifying an explanatory variable including state data; And verifying the modeled monitoring model in consideration of the dependent variable and the explanatory variable.

In addition, the step of setting the monitoring index for each target disease may be a preliminary step for setting the monitoring index for each target disease. The data may be reviewed to determine whether the complication is morbidity, Determining a subject; If complication is mild, it is judged whether it is hypertension, diabetes or dyslipidemia. If hypertension, diabetes and dyslipidemia are involved, the person is included in the prevention of cerebrovascular diseases and chronic kidney disease. A step of judging the target of prevention of complications; And judging the subject to be a target for prevention of hypertension, diabetes, and dyslipidemia including hypertension, diabetes, and dyslipidemia prevention target if the hypertension, diabetes, and dyslipidemia are not involved .

In addition, the step of setting the monitoring index according to the subject disease is a monitoring index for hypertension, diabetes, and dyslipidemia, and includes the number of subjects for hypertension, diabetes, dyslipidemia prevention, Obesity rate, abdominal obesity rate, and screening rate, and as a monitoring index for cardiovascular and chronic kidney disease, the number of subjects for prevention of complications, the prevalence of hypertension without complication, the incidence of hypertension without complication, the prevalence of diabetes without complication, The incidence of dyslipidemia without complication, the incidence of dyslipidemia without complication, the incidence of metabolic syndrome without complication, smoking rate, loneliness rate, recommended physical activity unperformed rate, obesity rate, abdominal obesity rate, Complications The incidence rate of hospitalization is set up, and the integrated detection rate, detection rate, secondary screening rate, Sugeomryul of river behaviors dangerous characters, regular inspection method sugeomryul cancer type-specific positive test rate may correspond to the steps for setting up the cancer detection rate, and positive predictive value.

A healthcare support system according to an embodiment of the present invention includes a communication module for transmitting and receiving various data including a local server or an enterprise server and a monitoring index; A monitoring model module for modeling a target disease-specific monitoring model using the necessary information extracted and extracted from the target disease of the chronic disease including the qualification, hospital medical information, and health examination information in the database; An indicator selection module for verifying the monitoring model modeled by the monitoring model module in consideration of data previously stored in the database and input information of an external terminal for selecting a monitoring indicator; A monitoring index module for setting a monitoring index for each disease by using the modeled monitoring model verified by the index selection module; And a management module for sharing the monitoring indicator set by the monitoring indicator module with a local server or an enterprise server so that the local server or the enterprise server applies the monitoring indicator. A local server receiving the monitoring index from the communication module of the health information system and applying the stored monitoring indicator to the stored local resident data; And an enterprise server receiving the monitoring index from the communication module of the health information system and applying the information to the stored information of the enterprise office.

According to the present invention, the monitoring model for chronic diseases is modeled by using health insurance related data, and the monitoring index for each chronic disease is set, thereby making it possible to efficiently manage chronic diseases at the regional and enterprise level.

BRIEF DESCRIPTION OF THE DRAWINGS A brief description of each drawing is provided to more fully understand the drawings recited in the description of the invention.
1 is a diagram illustrating a system for supporting healthcare management according to an embodiment of the present invention.
2 to 5 are flowcharts illustrating a method for supporting healthcare management according to an embodiment of the present invention.
6 to 18 are flowcharts illustrating a method for supporting healthcare management according to an embodiment of the present invention.

Specific structural and functional descriptions of the embodiments of the present invention disclosed herein are for illustrative purposes only and are not to be construed as limitations of the scope of the present invention. And should not be construed as limited to the embodiments set forth herein or in the application.

The embodiments according to the present invention are susceptible to various changes and may take various forms, so that specific embodiments are illustrated in the drawings and described in detail in this specification or application. It is to be understood, however, that it is not intended to limit the embodiments according to the concepts of the present invention to the particular forms of disclosure, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

The terms first and / or second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are intended to distinguish one element from another, for example, without departing from the scope of the invention in accordance with the concepts of the present invention, the first element may be termed the second element, The second component may also be referred to as a first component.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ", or" having ", or the like, specify that there is a stated feature, number, step, operation, , Steps, operations, components, parts, or combinations thereof, as a matter of principle.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as ideal or overly formal in the sense of the art unless explicitly defined herein Do not.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, the present invention will be described in detail with reference to the preferred embodiments of the present invention with reference to the accompanying drawings. Like reference symbols in the drawings denote like elements.

1 is a diagram illustrating a health care support system for supporting health care according to an embodiment of the present invention. The health management support system 10 for health management includes a health information system 100, a local server 200, an enterprise server 300, a regional headquarters server 400, a branch server 500, and a network. At this time, the regional headquarters server 400 may include a server in the Seoul area headquarters, a server in Kyungin area headquarters, a server in Daegu area headquarters, a server in Daejeon area, a server in Gwangju area and a server in Busan area. ) May be located in a more detailed subregion under each regional headquarters server.

In the embodiment of the present invention, 'network', 'communication network' and 'communication' can be used in the same sense. The three terms refer to wired and wireless local and wide area data transmission and reception networks capable of transmitting and receiving a file between a user terminal, a terminal of another user, and a server.

It will be apparent to those skilled in the art that the term "server" or "module" is used herein to describe a logical unit and is not necessarily a physically separate component.

The health information system 100 includes a communication module 110, a monitoring index module 120, a monitoring model module 130, an index selection module 140, a management module 150, and a database DB 160 .

The communication module 110 is connected to the local server 200, the enterprise server 300, the regional headquarters server 400, the branch server 500, and various data (e.g., various kinds of data including monitoring indices) And transmits and receives the data.

The monitoring model module 130 extracts necessary information for the target diseases of chronic diseases including qualifications, hospital medical care information and health examination information from the database 160, Model. The qualification includes a date of birth including a date of birth, a place of residence, a place of work, insurance, an income, etc. The hospital medical information includes a hospital name, a type, a disease name, a symptom, Smoking status, drinking status, diet, physical activity, abdominal obesity, blood pressure, blood sugar, blood lipid, and the like. The target disease-specific monitoring model will be described in FIGS. 6 to 19. FIG.

The index selection module 140 performs a function of verifying the monitoring model modeled by the monitoring model module 130 in consideration of data stored in the database 160 and input information of an external terminal for selecting a monitoring index do. At this time, the pre-stored data may correspond to the national health insurance information, and the input information of the external terminal may correspond to the expert verification information of each disease inputted from the external terminal.

The monitoring index module 120 performs a function of setting a monitoring index for each disease using the modeled monitoring model verified by the index selection module 140.

The management module 150 may share the monitoring index set by the monitoring index module 120 with the regional server 200, the regional headquarters server 400, the branch server 500, or the enterprise server 300 .

At this time, the local server 200 or the enterprise server 300 performs the function of preventing the chronic disease of the local residents or the corporate employees applying the monitoring index.

The method for supporting healthcare in the health care support system 10 according to the present invention will be described in more detail below.

2 to 5 are flowcharts illustrating a method for supporting healthcare management according to an embodiment of the present invention. The method for supporting the health care can be performed in the health care support system 10 shown in FIG.

2, the health information system 100 of the health care support system 10 extracts the necessary information for the target disease of the chronic disease including the qualification, the hospital medical care information, and the health examination information in the database 160 S10). The qualification includes a date of birth including a date of birth, a place of residence, a place of work, insurance, an income, etc. The hospital medical information includes a hospital name, a type, a disease name, a symptom, Smoking status, drinking status, diet, physical activity, abdominal obesity, blood pressure, blood sugar, blood lipid, and the like.

The monitoring model for each disease is modeled using the extracted necessary information (S20). Modeling using the extracted necessary information will be described with reference to FIG. 6 to FIG.

The modeled monitoring model is verified by considering the data stored in the database 160 and the input information of the external terminal (S30). At this time, the pre-stored data may correspond to the national health insurance information, and the input information of the external terminal may correspond to the expert verification information of each disease inputted from the external terminal.

A monitoring index for each disease is set using the modeled monitoring model (S40). In this case, the monitoring index for each target disease can be set by the verification process. For example, a monitoring index for hypertension, diabetes, dyslipidemia, a monitoring index for cardiovascular and chronic kidney disease, and a monitoring index for cancer 3 It can be set as branches.

Herein, the monitoring index for hypertension, diabetes, and dyslipidemia is a monitoring index for the hypertension, diabetes, dyslipidemia (or abbreviated old) prevention target business, the wide tone rate, the recommended physical activity nonperformance rate, the obesity rate, Obesity rate, and screening rate, and as a monitoring index for the cerebral blood vessel and chronic kidney disease, the number of subjects for the complication prevention business, the prevalence of hypertension without complication, the incidence of hypertension without complication, the prevalence of diabetes without complication, the incidence of diabetes without complication, The incidence of dyslipidemia without complication, the incidence of metabolic syndrome without complication, the rate of smoking, the rate of loudness, the recommended physical activity incomplete rate, obesity rate, abdominal obesity rate, screening rate, unadjusted rate, , And as a monitoring index for the cancer, the integrated detection rate, the detection rate, the secondary inspection rate, the health behavior Here you can set the sugeomryul of periodic sugeomryul cancer type test methods tested positive rate, cancer detection rate, positive predictive stars.

The set monitoring indicator is shared with the local server 200 or the enterprise server 300 (S50). The monitoring indicator may be shared with the local server 200 or the enterprise server 300 through the sharing and the monitoring indicator may be applied to the local server 200 or the enterprise server 300. [

3, after the step S50, the local server 200 or the enterprise server 300 may use the monitoring index to store data stored in the database of the local server 200 or the database of the enterprise server 300 (S110), and classifies the extracted data according to the target disease (S120). At this time, the extracted data may correspond to the information of the resident or the information of the employee stored in the local server 200 or the enterprise server 300, and the subjects may be classified according to the target disease.

The local server 200 or the enterprise server 300 may determine that the input signal is a dangerous signal for the target disease or that the classified data (eg, a resident or an employee) (E.g., text service, application service, e-mail, etc.) (S130).

FIG. 4 corresponds to a specific embodiment of S30. In S30, when the data is verified by considering the pre-stored data, the analysis target person before the specific time point according to the target disease is grasped in the database 160 (S210). For example, an analysis subject such as a cerebrovascular disease patient, a diabetic disease patient, or the like before a certain time point in the database 160 before 2004 can be grasped.

In the database 160, the dependent variable corresponding to the patient admitted after the specific point in time according to the target disease is identified using the CP code (S220). For example, patients admitted to hospital with cerebrovascular disease and diabetic disease after 2005 can be identified.

In the database, pathological physiological state data including sex, age and income level, demographic data including health status data, health examination data including smoking cessation, drinking and physical activity, obesity, blood pressure, blood glucose, (S230), and verifies the modeled monitoring model by considering the dependent variable and the explanatory variable (S240). For example, in all genders and age groups, the incidence of cardiovascular disease incidence is lower than that of non-examinees, which may mean that the screening is effective in preventing cerebrovascular disease.

FIG. 5 is a preliminary step for setting the target disease-specific monitoring index. (S310), the data of the database (the local server 200 or the enterprise server 300) is examined to determine whether the complication is morbid (S320) If it is determined that the person is the subject of treatment and rehabilitation business (S330), it is determined whether the complication is morbid (S320). If the complication does not correspond to the morbid complainant, it is determined whether the patient is suffering from hyperglycemia (hypertension, diabetes, dyslipidemia) In the case of hypertension (hypertension, diabetes, dyslipidemia), the subject is judged to be a subject for prevention of complications including subjects for cerebrovascular and chronic renal disease prevention (S350), hypertension (hypertension, diabetes, dyslipidemia (S360), the subject is judged to be a target for prevention of hyperglycemia (hypertension, diabetes, dyslipidemia) including hypertension, diabetes, and dyslipidemia prevention target business (S360).

After the preliminary step, the number of subjects of the hyperlipidemia (hypertension, diabetes, dyslipidemia) prevention target and the number of subjects of the complication prevention target may be used as a monitoring index of the target disease. For example, the number of subjects of the hyperlipidemic (hypertension, diabetes, dyslipidemia) prevention target can be set as a monitoring index for hypertension, diabetes and dyslipidemia, And may be set as a monitoring index for disease.

6 to 18 are views showing a monitoring model for each disease of a method for health care according to an embodiment of the present invention.

Figure 6 shows an obesity monitoring model. In the above-mentioned obesity monitoring model, the health behavior factors causing obesity are 'inadequate diet' and 'low physical activity'. Obesity can be prevented through the inspection service, and obesity complication can be prevented through the management service.

Figure 7 shows a hypertension monitoring model. In the above hypertension monitoring model, the factors causing the hypertension are 'drinking', 'low physical activity', 'obesity' ('inappropriate diet', 'low physical activity' causes obesity) And the occurrence of hypertensive complications can be prevented through management services and therapies.

Figure 8 shows a diabetic monitoring model. The above-mentioned diabetes monitoring model is based on the fact that factors causing the occurrence of diabetes are 'inadequate diet', 'low physical activity', 'obesity' ('improper diet' and 'low physical activity' cause obesity) And prevent the occurrence of diabetic complications through management services and treatment services.

Figure 9 shows an abnormal lipidemia monitoring model. The above abnormal lipidemia monitoring model is based on the fact that factors causing the occurrence of dyslipidemia are 'inadequate diet', 'low physical activity', 'obesity' ('inappropriate diet', 'low physical activity' ('Inadequate diet' and 'low physical activity' cause diabetes). The screening service can prevent dyslipidemia and can prevent the occurrence of dyslipidemia due to management services and treatment services.

10 shows a model for monitoring cardiovascular diseases. In the cardiovascular disease monitoring model, the factors causing the cardiovascular disease were 'smoking', 'drinking', 'inappropriate diet', 'low physical activity', 'obesity' ('inappropriate diet', 'low physical activity' (Diabetes mellitus), diabetes mellitus (inappropriate diet, low physical activity causing diabetes), dyslipidemia (inappropriate diet, low physical activity causing dyslipidemia), chronic kidney disease Disease ', and it can prevent cardiovascular diseases through a screening service, and can prevent the occurrence of cerebrovascular disease complications through management services and treatment services.

Figure 11 shows a model for monitoring chronic kidney disease. In the cardiovascular disease monitoring model, the factors causing the cardiovascular disease were 'smoking', 'drinking', 'inappropriate diet', 'low physical activity', 'obesity' ('inappropriate diet', 'low physical activity' (Hypertension), 'hypertension' ('inadequate diet', 'low physical activity' causes hypertension), 'diabetes' ('inappropriate diet', 'low physical activity' causes diabetes), 'dyslipidemia' 'Inadequate diet' and 'low physical activity' cause abnormal dyslipidemia). It can prevent chronic kidney disease through the screening service and prevent the complication of chronic kidney disease through management service and treatment service.

Figure 12 shows a model for monitoring chronic liver disease. In the chronic liver disease monitoring model, the factors causing chronic liver disease are 'drinking', 'inappropriate diet', 'low physical activity', 'obesity' ('inappropriate diet', 'low physical activity' , 'Hypertension' ('inadequate diet', 'low physical activity' causes hypertension), 'diabetes' ('inappropriate diet', 'low physical activity' causes diabetes), 'dyslipidemia' , 'Low physical activity' induces abnormal dyslipidemia). It can prevent chronic liver disease through screening / vaccination service, and can prevent chronic liver disease complication through management service and treatment service.

Figure 13 shows a low bone density monitoring model. The low bone density monitoring model is based on the fact that factors causing low bone density are 'smoking', 'drinking', 'inappropriate diet', 'low physical activity', 'obesity' ('inappropriate diet', 'low physical activity' (Diabetes) ('inappropriate diet', 'low physical activity' leads to diabetes), 'dyslipidemia' ('inappropriate', 'inadequate diet', 'low physical activity' Diabetes, and low physical activity cause abnormal dyslipidemia). Through the screening service, low bone density can be prevented. Management services and treatment services can prevent the occurrence of fractures.

Figure 14 shows an osteoarthritis monitoring model. The above osteoarthritis monitoring model is based on the observation that factors causing osteoarthritis are 'inadequate diet', 'low physical activity', 'obesity' ('inappropriate diet', 'low physical activity' induces obesity), 'hypertension' ('Low physical activity' causes hypertension), 'diabetes' ('inappropriate diet', 'low physical activity' causes diabetes), 'dyslipidemia' ('inappropriate diet', 'low physical activity' It can prevent osteoarthritis through the screening service and prevent the occurrence of osteoarthritis complications through management services and treatment services.

Figure 15 shows a breast cancer monitoring model. The breast cancer monitoring model is based on the fact that factors causing breast cancer are 'drinking', 'improper diet', 'low physical activity', 'obesity' ('inappropriate diet', 'low physical activity' (Diabetes mellitus), diabetes mellitus ('inappropriate diet', 'low physical activity', or 'low body physical activity' Activity can lead to abnormal dyslipidemia). Breast cancer can be prevented through the screening service, and breast cancer complication can be prevented through the examination / management service and treatment service.

16 shows a colon cancer monitoring model. The colorectal cancer monitoring model is based on the observation that factors causing colon cancer are 'smoking', 'drinking', 'inappropriate diet', 'low physical activity', 'obesity' ('inappropriate diet' (Diabetes) ('inappropriate diet', 'low physical activity' leads to diabetes), 'dyslipidemia' ('inappropriate', 'inadequate diet', 'low physical activity' Diabetes' and 'low physical activity' cause abnormal dyslipidemia). It can prevent colon cancer through the screening service and can prevent the complication of colon cancer through the examination / management service and treatment service.

Figure 17 shows a stomach cancer monitoring model. The above colon cancer monitoring model can prevent the gastric cancer and H.Pylori problem through the use of the 'smoking' and 'improper diet' factors that cause gastric cancer, and it can prevent gastric cancer complication Can be prevented.

Figure 18 shows a cervical cancer monitoring model. The above cervical cancer monitoring model can prevent the cervical cancer through the screening / vaccination service and prevent the incidence of the cervical cancer complication through the examination / management service and the treatment service .

The present invention can also be embodied as computer-readable codes on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored.

Examples of the computer-readable recording medium include DISK, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like. Or in the form of a carrier wave (e.g., transmission over the Internet).

The computer readable recording medium may also be distributed over a networked computer system so that computer readable code can be stored and executed in a distributed manner. And functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers skilled in the art to which the present invention pertains.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. Accordingly, the true scope of the present invention should be determined by the technical idea of the appended claims.

10: Health care support system 100: Health information system
110: communication module 120: monitoring indicator module
130: Monitoring model module 140: Indicator selection module
150: Management module 160: Database
200: Local server 300: Enterprise server
400: Regional Headquarters Server 500: Branch Office Server

Claims (4)

Health care support system,
Extracting from the database of the health care support system necessary information for the target disease of the chronic disease including the qualification, the hospital treatment information and the health examination information;
Modeling a target disease-specific monitoring model using the extracted necessary information;
Verifying the modeled monitoring model in consideration of data previously stored in the database and input information of an external terminal;
Setting a monitoring index for each disease using the modeled monitoring model; And
And sharing the set monitoring metric with a local server or an enterprise server,
Wherein the setting of the target disease-
As a preliminary step for setting the target disease-specific monitoring index,
Reviewing the data in the database to determine whether the complication is morbid and judging the person to be treated and rehabilitated if the complication is morbid;
If complication is mild, it is judged whether it is hypertension, diabetes or dyslipidemia. If hypertension, diabetes and dyslipidemia are involved, the subject is included in the prevention of cerebrovascular diseases and chronic kidney disease. A step of judging the target of prevention of complications; And
And a step of judging the subject as a subject for prevention of hypertension, diabetes, and dyslipidemia including hypertension, diabetes, and dyslipidemia prevention target business if the hypertension, diabetes, and dyslipidemia are not involved,
Wherein the setting of the target disease-
Diabetes mellitus, hyperlipidemia, hyperlipidemia, hyperlipidemia, dyslipidemia, hypertension, diabetes mellitus, dyslipidemia prevention program, number of subjects, wide range of tone, recommended non-physical activity rate, obesity rate, abdominal obesity rate,
This study was performed to investigate the relationship between diabetes mellitus and cardiovascular risk factors. Methods: We retrospectively reviewed the medical records of patients with diabetes mellitus, chronic renal disease, and hypertension. The rate of obesity, the rate of abdominal obesity, the rate of abstinence, the rate of non - compliance, the rate of persistent cure, and the incidence of complications. The rate of incidence of complications, metabolic syndrome without complications, smoking rate,
The monitoring index for cancer is a step of setting integrated detection rate, screening rate, secondary screening rate, screening rate among health behavior risk, periodic screening rate, positive screening rate for each screening method, cancer detection rate, Methods for management. The method according to claim 1,
After said sharing step,
Extracting data stored in the database of the regional server or the database of the enterprise server using the monitoring indicator, the data being determined to be dangerous or necessary to be prevented;
Classifying the extracted data by target diseases; And
When the input signal is included in the target disease, notifying the classified data of the information that is determined to be dangerous or determined to be necessary for prevention,
Wherein the verifying step comprises:
In the case of verifying by considering the previously stored data,
Identifying an analysis subject before a specific time point according to a target disease in the database;
Identifying a dependent variable corresponding to a patient admitted after a specific time point of the target disease using the CPC code in the database;
In the above-mentioned database, there is provided a method for diagnosing a pathological physiological condition including gender, age and income level, demographic and social condition data, health examination data including health examination data on smoking cessation and drinking, physical activity, obesity, blood pressure, Identifying an explanatory variable including state data; And
And verifying the modeled monitoring model by considering the dependent variable and the explanatory variable. delete A communication module for transmitting and receiving various data including a monitoring indicator and a local server or an enterprise server; A monitoring model module for modeling a target disease-specific monitoring model using the necessary information extracted and extracted from the target disease of the chronic disease including the qualification, hospital medical information, and health examination information in the database; An indicator selection module for verifying the monitoring model modeled by the monitoring model module in consideration of data previously stored in the database and input information of an external terminal for selecting a monitoring indicator; A monitoring index module for setting a monitoring index for each disease by using the modeled monitoring model verified by the index selection module; And a management module for sharing the monitoring indicator set by the monitoring indicator module with a local server or an enterprise server so that the local server or the enterprise server applies the monitoring indicator.
A local server receiving the monitoring index from the communication module of the health information system and applying the stored monitoring indicator to the stored local resident data; And
And a company server receiving the monitoring indicator from the communication module of the health information system and applying the monitoring indicator to the stored information of the company employee,
The monitoring indicator module includes:
In order to set the monitoring index for each target disease, the data of the database is reviewed in advance and it is judged whether or not the complication is mental. If the complaint is mental complication, the person is judged to be the subject of treatment and rehabilitation, If diabetes or dyslipidemia occurs, the person is judged to be a target for prevention of complications including cardiovascular and chronic kidney disease prevention projects, and if they do not fall into the category of hypertension, diabetes or dyslipidemia, If hypertension, diabetes or dyslipidemia does not occur, the subject is judged to be a target for prevention of hypertension, diabetes and dyslipidemia, including hypertension, diabetes and dyslipidemia prevention.
The monitoring indicator module includes:
As a monitoring index for hypertension, diabetes, and dyslipidemia, the number of subjects to be screened for hypertension, diabetes, dyslipidemia prevention, a wide tone rate, a recommended physical activity nonperformance rate, an obesity rate , Abdominal obesity rate, and screening rate, and as a monitoring index for cardiovascular and chronic kidney disease, the number of subjects for the complication prevention project, the prevalence of hypertension without complication, the incidence of hypertension without complication, the prevalence of diabetes without complication, The incidence of dyslipidemia without complication, the incidence of dyslipidemia without complication, the rate of detection of metabolic syndrome without complication, the rate of smoking, the rate of loudness, the recommended physical activity incomplete rate, obesity rate, abdominal obesity rate, screening rate, unadjusted rate, Incidence rate, integrated monitoring index for cancer, rate of screening, screening rate of secondary screening, health Status Risk party of sugeomryul, regular sugeomryul cancer type inspection method by positive test rate, cancer detection rate, regional work units of health, disease index creation and health care support systems for Healthcare, characterized in that for setting the positive predictive value.

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