CN115312207A - Chronic disease monitoring and management method based on intelligent health terminal - Google Patents
Chronic disease monitoring and management method based on intelligent health terminal Download PDFInfo
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- CN115312207A CN115312207A CN202210978999.1A CN202210978999A CN115312207A CN 115312207 A CN115312207 A CN 115312207A CN 202210978999 A CN202210978999 A CN 202210978999A CN 115312207 A CN115312207 A CN 115312207A
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Abstract
The invention relates to the technical field of disease monitoring, in particular to a chronic disease monitoring and management method based on an intelligent health terminal. The method comprises the following steps: and (3) classification step: acquiring information of a target person, generating a classification category according to the information of the target person and generating a corresponding control target value according to the classification category; a plan generating step: acquiring detection information of a target person, generating a monitoring plan according to the classification type and the detection information, and prompting the target person to acquire actual data of the target person according to the monitoring plan; a comparison step: and comparing the control target value with the actual data, and prompting the target personnel when the comparison result is abnormal. In the prior art, the self-management will of the patient is poor, and the effect of limited-number medical advice is small. Compared with the prior art, the method and the system can effectively reduce the time cost of self-management so as to improve the self-management will, and prompt the target personnel without the limitation of the management period and times so as to change the self body state more timely.
Description
Technical Field
The invention relates to the technical field of disease monitoring, in particular to a chronic disease monitoring and management method based on an intelligent health terminal.
Background
The chronic diseases are all called chronic non-infectious diseases, are not specific to a certain disease, but are generalized and general names of diseases which have hidden onset, long course of disease, prolonged illness, lack of exact etiology evidence of infectious organisms, complex etiology and are not completely confirmed. Currently, self-management is an effective method for controlling chronic disease conditions, but the main scenario of self-management is outside hospitals. Outside hospitals, patients only have the ability to collect physical data, but do not have the ability to effectively manage and analyze data. Therefore, frequent visits to the hospital for offline consultation are required. On the one hand, the patient's will be less willing to manage himself/herself, and on the other hand, even if certain information can be obtained by the medical orders, the limited number of medical orders plays a minor role in the management period of up to several years. Therefore, how to allow patients to conduct autonomous management will play an important role in managing and controlling the state of an illness.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a chronic disease monitoring and management method based on an intelligent health terminal.
In order to solve the technical problems, the invention provides the following technical scheme:
a chronic disease monitoring and management method based on an intelligent health terminal comprises the following steps: and (3) classification step: acquiring information of a target person, generating a classification category according to the information of the target person and generating a corresponding control target value according to the classification category; a plan generating step: acquiring detection information of a target person, generating a monitoring plan according to the classification type and the detection information, and prompting the target person according to the monitoring plan to acquire actual data of the target person; a comparison step: and comparing the control target value with the actual data, and prompting the target personnel when the comparison result is abnormal.
During actual execution, the target personnel are classified according to the information of the target personnel, and corresponding control targets are generated according to the classification types. And generating a monitoring plan according to the detection information and classification type of the target personnel. The detection information can be obtained by the self-detection of the target person or the detection of going to a hospital. And prompting parameters needing to be monitored and monitoring time of the target personnel by the monitoring plan so as to obtain actual data of the target personnel. And comparing the control target value with the actual data. And when the comparison result is abnormal, prompting the target person. In conclusion, the target personnel can clearly know the content of the parameters to be monitored according to the monitoring plan and can timely know the parameter change of the target personnel according to the comparison result, so that the parameter change can be timely managed and controlled. Thus, the target person does not need to frequently visit the hospital for consultation. On one hand, the time cost of self-management of the target personnel is reduced, so that the self-management willingness of the target personnel is improved, on the other hand, the prompt of the target personnel is not limited by the management period and times, so that the target personnel can know the change of the body state of the target personnel more timely.
Further, the "classifying step" further includes the steps of: acquiring information of a target person, wherein the information of the target person comprises age, medical history, disease state and physical state; generating classification categories according to the information of the target person, wherein the classification categories comprise old patients, young mild patients, young severe patients and special patients; and generating a corresponding control target according to the classification category.
Further, the plan generating step further comprises the following steps: acquiring detection information of a target person, and generating a monitoring plan according to the classification type and the detection information, wherein the monitoring plan comprises a week monitoring project, a month monitoring project and a random monitoring project; and prompting the target personnel according to the monitoring plan to acquire actual data of the target personnel.
Furthermore, the week monitoring project comprises a once-weekly monitoring project, a once-every-two-week or once-three-week monitoring project; the random monitoring items comprise palpitation and dizziness monitoring, illness monitoring, pre-exercise monitoring, post-drinking monitoring and pre-driving monitoring.
Further, once-a-week monitoring items comprise fasting monitoring, lunch monitoring and dinner monitoring; the monitoring items once every two or three weeks include fasting monitoring, after breakfast monitoring, before lunch monitoring, after lunch monitoring, before dinner detecting, after dinner monitoring, before sleep monitoring.
Further, the comparison step further comprises the following steps: comparing the actual data with the control target value item by item, uploading a comparison result, and prompting a target person when the comparison result is abnormal; and calculating the difference between the actual data to obtain the daily fluctuation, uploading the daily fluctuation, and prompting the target personnel when the daily fluctuation is abnormal.
Further, the method also comprises the following warning steps: the warning step comprises the following steps: comparing the actual data with the lowest warning value, and warning the target personnel when the comparison result is less than the lowest warning value; and comparing the actual data with the maximum warning value, and warning the target personnel when the comparison result is greater than the maximum warning value.
Compared with the prior art, the invention has the following advantages:
on one hand, the time cost of the target personnel can be effectively reduced, the self-management will of the target personnel is improved, and on the other hand, the change of the physical state of the target personnel can be prompted in time.
The invention can clearly know the body state of the target person on one hand, and on the other hand, when the body state is well developed, the invention is a positive incentive to the target person, thereby further improving the self-management willingness of the target person.
Most of the actual data acquired by the method is related to the diet state, on one hand, the related data is used for helping target personnel to control chronic diseases, on the other hand, the diet influences the value of the actual data faster, the target personnel can realize the adjustment effect more quickly, and when the actual data develops to a good state, the method is positive encouragement for the target personnel, so that the willingness of self management of patients is further improved.
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FIG. 1: and (4) an overall flow chart.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
A chronic disease monitoring and management method based on an intelligent health terminal comprises the following steps: and (3) classification step: acquiring information of a target person, generating a classification category according to the information of the target person and generating a corresponding control target value according to the classification category;
a plan generating step: acquiring detection information of a target person, generating a monitoring plan according to the classification type and the detection information, and prompting the target person according to the monitoring plan to acquire actual data of the target person;
a comparison step: and comparing the control target value with the actual data, and prompting a target person when the comparison result is abnormal.
Specifically, the classifying step further includes the steps of: acquiring information of a target person, wherein the information of the target person comprises age, medical history, disease state and physical state;
generating classification categories according to the information of the target person, wherein the classification categories comprise old patients, young mild patients, young severe patients and special patients;
and generating a corresponding control target according to the classification category.
Specifically, the plan generating step further includes the steps of: acquiring detection information of a target person, and generating a monitoring plan according to the classification type and the detection information, wherein the monitoring plan comprises a week monitoring project, a month monitoring project and a random monitoring project;
and prompting the target personnel according to the monitoring plan to acquire actual data of the target personnel.
The week monitoring project comprises a once-weekly monitoring project, a once-every-two-week or once-three-week monitoring project; the weekly monitoring items comprise fasting monitoring, lunch monitoring and dinner monitoring; the monitoring items once every two weeks or three weeks include fasting monitoring, after breakfast monitoring, before lunch monitoring, after lunch monitoring, before dinner detecting, after dinner monitoring, before sleep monitoring. The random monitoring items comprise palpitation and dizziness monitoring, illness monitoring, pre-exercise monitoring, post-drinking monitoring and pre-driving monitoring.
Specifically, the comparison step further comprises the following steps:
comparing the actual data with the control target value item by item, uploading a comparison result, and prompting a target person when the comparison result is abnormal;
and calculating the difference between the actual data to obtain the daily fluctuation, uploading the daily fluctuation, and prompting the target personnel when the daily fluctuation is abnormal.
Further comprises a warning step:
the warning step comprises the following steps:
comparing the actual data with the lowest warning value, and warning the target personnel when the comparison result is less than the lowest warning value;
and comparing the actual data with the maximum warning value, and warning the target person when the comparison result is greater than the maximum warning value.
In practical implementation, the method can be loaded on an existing intelligent terminal, for example: the mobile phone, the computer, the tablet and the like can be used for data input and storage. The target person inputs age, medical history, disease state and physical state, and is classified according to the input information, specifically, the target person can be classified into an old patient, a young mild patient, a young severe patient and a special patient. Wherein, a special patient refers to a person whose body presents corresponding pathological phenomena due to special reasons, such as: the physical state of the pregnant woman presents a pathological phenomenon corresponding to diabetes due to the pregnancy. Each class corresponds to a corresponding control target.
And acquiring the detection information of the target person. The detection information includes at least recent physical data of the target person, such as: diabetics are at blood sugar level for nearly a few days, and hypertensive patients are at blood pressure for nearly a few days. And carrying out secondary classification on the target personnel according to the classification type and the detection information. For example: special patients-control smooth, special patients-control unstable, special patients-parameters meet the standard. Similarly, the aforementioned categories also exist for elderly patients, young mild patients, and young severe patients. Specifically, how to judge whether to control stably according to the detection information and whether the parameter reaches the standard can be judged according to the existing medical theory, which is not described herein again. And generating a monitoring plan according to the classification type and the detection information. The monitoring plan comprises a weekly monitoring project, a monthly monitoring project and a random monitoring project. The week monitoring project comprises a once-weekly monitoring project, a once-every-two-week or once-three-week monitoring project. Wherein the weekly monitoring items comprise fasting monitoring, lunch monitoring and dinner monitoring. The monitoring items once every two or three weeks include fasting monitoring, after breakfast monitoring, before lunch monitoring, after lunch monitoring, before dinner detecting, after dinner monitoring, before sleep monitoring. In actual conditions, the diet status has a great influence on the human body. Therefore, the influence of diet on human body can be accurately known. The random monitoring items comprise palpitation and dizziness monitoring, illness monitoring, pre-exercise monitoring, post-drinking monitoring and pre-driving monitoring. The random monitoring project only carries out monitoring when the target person carries out corresponding actions. Wherein, the parameter targeted by the monthly monitoring item is a key intervention parameter of the corresponding disease, for example: the monthly monitoring item of diabetes is the value of glycated hemoglobin. Therefore, the content required to be monitored by the target personnel is prompted according to the monitoring plan so as to obtain the actual data of the corresponding project.
In actual execution, the control target value at least comprises the following values: fasting value, postprandial value, preprandial value, random value, pre-sleep value, monthly value. Comparing the actual data with the control target value item by item, for example: comparing the actual data obtained by fasting monitoring with fasting value, comparing the actual data obtained by monitoring after lunch and breakfast with postprandial value, and so on. And uploading the comparison result, and performing corresponding judgment by an internet doctor after uploading to obtain a corresponding on-line medical advice. Meanwhile, when the comparison result is abnormal, the intelligent terminal can correspondingly prompt the target personnel. For example: when monitoring diabetes, the actual data obtained by fasting monitoring is compared with fasting value, and after the comparison result is uploaded, an internet doctor can judge whether dawn phenomenon or hematoxylin phenomenon exists, and provide corresponding opinion according to the dawn phenomenon or hematoxylin phenomenon. Meanwhile, calculating the difference between the actual data obtained in the same day specifically comprises: the difference between the pre-meal actual data and the post-meal actual data, for example: a difference between the actual value monitored before lunch and the actual value monitored after lunch. A difference between a maximum value in the actual data and a minimum value in the actual data. Uploading the two difference values as daily fluctuation, and performing corresponding judgment by an internet doctor after uploading to obtain corresponding online medical advice. Meanwhile, when the daily fluctuation is abnormal, the target person is prompted. For example: when the diabetes mellitus is monitored, if the difference between the actual numerical value monitored before the lunch and the actual numerical value monitored after the lunch is too large (the difference between the actual numerical value monitored before the lunch and the actual numerical value monitored after the lunch is not larger than 2.2mmol/L according to the existing medical theory), corresponding prompt is carried out on a target person. Meanwhile, corresponding orders are given by Internet doctors.
Preferably, the corresponding prompting content may be preset according to the existing medical theory, for example: and comparing the actual data obtained by fasting monitoring with the fasting value, and if the difference is too large, directly prompting the target person to pay attention to the dawn phenomenon and the hematoxylin-jeopardy phenomenon, and carrying out corresponding annotation on the dawn phenomenon and the hematoxylin-jeopardy phenomenon. On one hand, the target person can be more easily aware of the physical condition of the target person. On the other hand, the diagnosis pressure of Internet doctors can be relieved to a certain extent.
Also comprises a warning step. Specifically, each time actual data is acquired, the actual data is compared with the corresponding minimum warning value and maximum warning value. And when the actual data is less than the minimum warning value, the intelligent terminal warns the target personnel. And when the actual data is larger than the maximum warning value, the intelligent terminal warns the target person. It should be noted that the minimum and maximum alarm values can be set according to the existing medical theory. For example: the lowest alert value of the diabetes is 3.9mmol/L, and the maximum alert value is 16.7mmol/L.
Meanwhile, when the actual data of the target person is acquired, the secondary classification of the target person is judged again according to the actual data and the classification category. And when the secondary classification result changes, prompting the target person. For example: when the control is stable from the special patient-control unstable state to the special patient-control unstable state, the intelligent terminal prompts the target personnel.
In summary, the present invention classifies the target person according to the information of the target person to obtain a classification category, which is a first classification of the target person. The classification category characterizes the current "disease state" of the target person. The target person can more clearly know the current disease condition according to the classification type. And classifying the target personnel into a second classification according to the classification type and the detection information. The second classification characterizes the current "control state" of the target person. The target personnel can more clearly know the current control condition according to the second classification. Therefore, by utilizing the two-time classification, the target person can fully know the disease condition of the target person. Meanwhile, a future control target is determined through the first classification, and a future monitoring plan is determined through the second classification. Therefore, the target person can clearly know the control condition of the target person on the disease by using the relative change between the actual data and the control target value. When the actual data gradually approaches the control target value, the method is a positive incentive for the target personnel. On the other hand, the second classification is also updated in time according to the current actual data condition of the target person. When the second classification yields a classification such as: when the control is changed from unstable control to stable control, the control is also a positive excitation for the target person.
It should be noted that the specific parameters for which the actual data are associated with the disease, such as: diabetes mellitus is aimed at blood sugar, and hypertension is aimed at blood pressure.
Preferably, the actual data of the family members can be synchronously acquired, so that a family member profile is established on the intelligent terminal according to the actual data of the family members, and each family member is given corresponding suggestions or prompts according to the processes. Therefore, the influence of the genetic characteristics of part of chronic etiology on other family members can be monitored in time, so that the other family members can be prevented in advance aiming at the corresponding chronic diseases. For example: diabetes has obvious genetic susceptibility, and the physical conditions of other family members can be known as early as possible by using the method, so that the possibility of early treatment of the disease is provided.
In conclusion, the invention enables the target personnel to clearly know the item content required to be monitored and prompt the target personnel in time. On the one hand, the target personnel do not need frequent offline consultation of the hospital, so that the time cost of the target personnel is reduced, the self-management willingness of the patient is further improved, and on the other hand, the change of the body state of the target personnel can be prompted in time. Meanwhile, the invention adopts a dual comparison mechanism of comparing actual data with a control target value and comparing actual data. On one hand, the target person can be clearly informed of the change of the body state of the target person, and on the other hand, when the actual data approaches the control target value, the target person is positively motivated, which is beneficial to further improving the willingness of the patient to manage himself. Secondly, most of the actual data acquired by the method is related to the diet state, on one hand, the influence of diet on chronic diseases is large, the related data is used for helping target personnel to control the chronic diseases, on the other hand, the numerical influence of diet on the actual data is fast, when the target personnel adjusts diet, the target personnel can quickly realize the adjustment effect, and when the actual data is developed to a good state, the method is a positive encouragement for the target personnel, so that the willingness of the patient to self-manage is further improved.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (7)
1. A chronic disease monitoring and management method based on an intelligent health terminal is characterized by comprising the following steps: the method comprises the following steps: and (4) classification: acquiring information of a target person, generating a classification category according to the information of the target person and generating a corresponding control target value according to the classification category;
a plan generating step: acquiring detection information of a target person, generating a monitoring plan according to the classification type and the detection information, and prompting the target person according to the monitoring plan to acquire actual data of the target person;
a comparison step: and comparing the control target value with the actual data, and prompting the target personnel when the comparison result is abnormal.
2. The chronic disease monitoring and management method based on the intelligent health terminal as claimed in claim 1, wherein: the "classifying step" further comprises the steps of:
acquiring information of the target person, wherein the information of the target person comprises age, medical history, disease state and physical state;
generating classification categories according to the information of the target person, wherein the classification categories comprise old patients, young mild patients, young severe patients and special patients;
and generating a corresponding control target according to the classification category.
3. The chronic disease monitoring and management method based on the intelligent health terminal as claimed in claim 1, wherein: the "plan generating step" further includes the steps of:
acquiring detection information of a target person, and generating a monitoring plan according to the classification type and the detection information, wherein the monitoring plan comprises a weekly monitoring project, a monthly monitoring project and a random monitoring project;
and prompting the target personnel according to the monitoring plan to acquire actual data of the target personnel.
4. The chronic disease monitoring and management method based on the intelligent health terminal as claimed in claim 3, wherein: the week monitoring project comprises a once-weekly monitoring project, a once-every-two-week or once-three-week monitoring project;
the random monitoring items comprise palpitation and dizziness monitoring, illness monitoring, pre-exercise monitoring, post-drinking monitoring and pre-driving monitoring.
5. The chronic disease monitoring and management method based on the intelligent health terminal as claimed in claim 4, wherein: the weekly monitoring items comprise fasting monitoring, lunch monitoring and dinner monitoring;
the monitoring items once every two weeks or three weeks comprise fasting monitoring, breakfast after monitoring, lunch before monitoring, lunch after monitoring, dinner before detecting, dinner after monitoring and sleeping before monitoring.
6. The chronic disease monitoring and management method based on the intelligent health terminal as claimed in claim 1, wherein: the step of comparing further comprises the steps of:
comparing the actual data with a control target value item by item, uploading the comparison result, and prompting the target personnel when the comparison result is abnormal;
and calculating the difference between the actual data to obtain the daily fluctuation, uploading the daily fluctuation, and prompting the target personnel when the daily fluctuation is abnormal.
7. The chronic disease monitoring and management method based on the intelligent health terminal as claimed in claim 1, wherein: further comprises a warning step:
the warning step comprises the following steps:
comparing the actual data with the lowest warning value, and warning the target person when the comparison result is less than the lowest warning value;
and comparing the actual data with the maximum warning value, and warning the target personnel when the comparison result is greater than the maximum warning value.
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| CN202210978999.1A CN115312207A (en) | 2022-08-16 | 2022-08-16 | Chronic disease monitoring and management method based on intelligent health terminal |
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| CN202210978999.1A CN115312207A (en) | 2022-08-16 | 2022-08-16 | Chronic disease monitoring and management method based on intelligent health terminal |
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