CN117316452A - Method and device for health assessment of hypertension and diabetes mellitus in closed-loop management scene - Google Patents
Method and device for health assessment of hypertension and diabetes mellitus in closed-loop management scene Download PDFInfo
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Abstract
The invention discloses a method for health assessment of hypertension and diabetes in a closed-loop management scene, which comprises the steps that a cloud service end obtains a patient risk level input by a doctor end, determines monitoring frequencies for monitoring various indexes of a patient based on the patient risk level, sends the monitoring frequencies to the patient end and the doctor end respectively, and obtains monitoring information of various indexes of the patient; the cloud service end sequentially performs standardization processing and variable assignment on each index information to obtain variable scores, obtains high-sugar integrated management scores based on the variable scores and index weights of each index information, and sends instruction suggestions corresponding to abnormal information appearing in the information of each index and based on the continuous high-sugar integrated management scores to the patient-intelligent acquisition end and the doctor end. The method is used for continuously monitoring, collecting and dynamically evaluating the health data of patients with hypertension and diabetes. The invention also provides a device for health assessment of hypertension and diabetes under the closed-loop management scene.
Description
Technical Field
The invention belongs to the field of chronic disease health evaluation systems, and particularly relates to a method and a device for health evaluation of hypertension and diabetes in a closed-loop management scene.
Background
Slow disease is the first risk factor that jeopardizes human life and health, hypertension and diabetes are the major risk factors in slow disease. According to the global disease burden 2019 report of Lancet, in 2019, hypertension caused 1080 ten thousand deaths, which is the first risk factor for death. So establishing an effective chronic disease management mechanism is not slow.
The traditional chronic disease management only focuses on the treatment link, the patient has no need of asking the body fluid after diagnosis and treatment, the follow-up situation is unknown, and the effect is very poor. The current advanced medical anti-fusion chronic disease management mechanism is characterized in that a hospital utilizes an Internet platform to dynamically connect with a patient, doctors can utilize mass data uploaded outside the hospital of the patient to perform more personalized outside-hospital management on the patient, compliance and management effects of the patients are improved, but new problems are derived, namely that the information of the patient cannot be effectively utilized due to the fact that medical staff in a primary hospital possibly exist in insufficient level or hands are insufficient, and the patient can gradually fall off. It is therefore desirable to provide intelligent assessment and recommendation for a patient that can help a physician process patient data quickly.
Chronic disease management is a long-term process and continuous data monitoring of the patient's risk factors is necessary. For the high-risk susceptible people, the change of dangerous factors is required to be continuously monitored, and once indexes such as blood pressure, blood sugar and the like are abnormal, intervention measures can be timely taken to avoid further development of the diseases; for patients suffering from diseases, through continuously uploaded data such as blood pressure and blood sugar, life behaviors, medication conditions and the like, doctors can provide more personalized management schemes for the patients through systematic recommendation, and can prevent complications, and meanwhile, dynamic evaluation results and analysis guidance can enable the patients to know about the health conditions of the patients and change the health conditions in a targeted manner, so that compliance of the patients is improved.
Chinese patent publication No. CN113360847 discloses a cardiovascular disease prediction system and a cardiovascular disease management system including the prediction system: the prediction evaluation part of the patent mainly utilizes basic information such as age, sex, heart rate, total cholesterol, high density lipoprotein cholesterol, whether diabetes mellitus exists, whether smoking exists, the number of smoking per day, the systolic blood pressure of blood pressure, whether coronary heart disease exists, whether past stroke or transient cerebral ischemia attack exists, whether left ventricular hypertrophy exists, whether valve disease exists and the like and clinical indexes to complete the prediction of 2 years of coronary heart disease of a patient who does not suffer from the disease, the prediction of atrial fibrillation within 10 years, the prediction of severe coronary heart disease within 10 years and the prediction of intermittent claudication probability within 4 years; and prediction of 5 years of stroke of residents suffering from the atrial fibrillation of patients, prediction of probability of heart failure of residents suffering from coronary heart disease or hypertension in the years, and prediction of probability of recurrence of coronary heart disease of residents suffering from coronary heart disease in 2 years; these are all cross-sectional predictions that help users to understand their own condition during group entry, whereas cardiovascular disease is often chronic and requires long-term management and control, and such predictions are not timely and effective for the clinician as a clinical indicator of the behavior of the patient after management and of some response effects.
In the prior art, the evaluation of chronic diseases such as hypertension and diabetes mellitus has the problems that continuous data are not utilized, only simple superposition of a plurality of questionnaires is adopted, the monitoring frequency is unreasonable, and the like. Therefore, there is a need to design an evaluation method that can reasonably and continuously monitor various data of patients during the long-term management of hypertension and diabetes and evaluate the health condition of the patients by using the continuous data.
Disclosure of Invention
The invention provides a method for health assessment of hypertension and diabetes in a closed-loop management scene, which can reasonably and continuously monitor various indexes of a hypertension and diabetes patient, and can continuously acquire data and dynamically and continuously assess the data.
The embodiment of the invention provides a method for health assessment of hypertension and diabetes mellitus in a closed-loop management scene, which comprises the following steps:
the cloud service end obtains a patient risk grade based on a patient risk assessment result output by the doctor end, determines monitoring frequencies of basic information of the patient, clinical indexes and indexes related to active health behaviors based on the patient risk grade, and sends the monitoring frequencies to the patient end and the doctor end respectively;
the cloud server side obtains monitoring information which is output by the patient side and the intelligent acquisition terminal and aims at indexes related to active health behaviors, and monitoring information which is output by the doctor side and aims at basic information and clinical indexes of the patient;
The cloud service end sequentially performs standardization processing and variable assignment on each item of index information in the monitoring information to obtain variable scores corresponding to each item of index information, obtains high-sugar integrated management scores based on the variable scores and index weights corresponding to each item of index information, constructs a health assessment report based on continuous high-sugar integrated management score curves and instruction suggestions corresponding to abnormal information in a set time, and sends the health assessment report to a patient end and a doctor end respectively.
Further, a high sugar comprehensive management score is obtained based on the variable score and the index weight corresponding to each item of index information, wherein the method for determining the index weight comprises the following steps:
obtaining index risk grade scores of all indexes through expert evaluation, taking the ratio of the number of patients with reduced index variable scores based on the dry prognosis to the total number of patients as the change difficulty degree scores of all indexes, and obtaining importance scores of all indexes based on the index risk grade scores and the change difficulty degree scores of all indexes;
and obtaining the weights of the indexes through an AHP analytic hierarchy process based on the importance scores of the indexes.
Further, obtaining weights of the indexes through an AHP hierarchical analysis method based on importance scores of the indexes comprises the following steps:
Setting a corresponding relation between the ratio of the importance scores of any two indexes and the scale, wherein when the ratio of the importance scores of any two indexes is not lower than 1, the ratio of the importance scores of any two indexes not lower than 1 is divided into n ratio ranges, the n ratio ranges are corresponding to the set n scale values, and when the ratio of the importance scores of any two indexes is lower than 1, the reciprocal of the ratio of the importance scores of any two indexes is taken as the corresponding scale;
and constructing a judgment matrix of each index based on the scale by setting the corresponding relation between the ratio of the importance scores of any two indexes and the scale based on the importance scores of each index, and sequentially carrying out hierarchical single-order, consistency test, hierarchical total order and one-time test on the judgment matrix to obtain the final weight of each index.
Further, the importance score omega of the ith index is obtained based on the index risk level and the change difficulty score of each index i The method comprises the following steps:
ω i ＝α i +20β i
wherein alpha is i Index risk level, beta, for the ith index i And (5) the change difficulty degree score of the ith index.
Further, the cloud service end sequentially performs standardization processing and variable assignment on each item of index information contained in the monitoring information to obtain a variable score corresponding to each item of index information, and the cloud service end comprises the following steps:
Each index comprises a plurality of data item ranges, and each data item range is assigned step by step based on the risk degree of the patient so as to obtain the corresponding relation between each data item range and the variable score;
and carrying out standardization processing on each item of index information, and obtaining the variable score of each item of index information through the corresponding relation between each data item range and the variable score based on the data item range of the standardization processing result.
Further, obtaining a patient risk assessment result through doctor-side assessment includes:
when a patient enters a group for the first time, a doctor receives basic information of the patient and information of clinical indexes detected when the patient enters the group for the first time, and judges and obtains a patient risk assessment result by adopting a risk assessment standard based on the basic information of the patient and the information of the clinical indexes detected when the patient enters the group for the first time;
when monitoring a patient, a doctor side obtains monitoring information through a cloud server side, and reevaluates the risk level of the patient by adopting a risk evaluation standard based on the monitoring information, and the cloud server side adjusts the monitoring frequency through the reevaluated risk level.
Furthermore, the doctor side can also receive the monitoring frequency and monitoring information of the indexes related to the active health behaviors from the current time of the cloud server side;
The doctor side can also modify the guiding advice corresponding to the abnormal information, the modified guiding advice is sent to the patient side through the cloud service side, and the treatment scheme can also be provided or modified based on the health assessment report.
Further, the patient profile includes age, gender, family history, and psychological illness;
the clinical information comprises physical and chemical index data measured;
the monitoring information of the indicators related to the active health behavior comprises life habit indicator data of the patient, including smoking, drinking, exercise and diet indicator data, body mass index, medication compliance, blood pressure measurement compliance and blood glucose measurement compliance.
Furthermore, the change trend of the high sugar comprehensive management score along with the time and the change trend of each variable score of each index along with the time can be displayed through a doctor end and a patient end;
the doctor side can also display the distribution of the high sugar comprehensive management scores of all patients and the change trend of the variable scores of all indexes of all patients with time.
The specific embodiment of the invention also provides a device for health assessment of hypertension and diabetes in a closed-loop management scene, which comprises the following steps:
The cloud service end is used for obtaining a patient risk grade based on a patient risk assessment result from the doctor end, determining monitoring frequencies of basic information, clinical indexes and indexes related to active health behaviors of the patient according to the patient risk grade, respectively sending the monitoring frequencies to the patient end, the intelligent acquisition terminal and the doctor end, obtaining monitoring information, which is output by the patient end and the intelligent acquisition terminal, of the indexes related to the active health behaviors, and monitoring information, which is output by the doctor end, of the basic information and the clinical indexes of the patient;
the method is also used for sequentially carrying out standardization processing and variable assignment on each item of index information in the monitoring information to obtain a variable score corresponding to each item of index information, obtaining a high-sugar integrated management score based on the variable score and the index weight of each item of index, constructing a health evaluation report based on a continuous high-sugar integrated management score curve in a set time and a guiding suggestion corresponding to abnormal information, and respectively sending the health evaluation report to a patient-intelligent acquisition end and a doctor end;
the doctor end is used for receiving the monitoring frequency of the cloud service end, sending monitoring information aiming at basic information and clinical indexes of a patient to the cloud service end, and sending a patient risk assessment result to the cloud service end;
The patient end is used for receiving the monitoring frequency of the cloud server end, sending monitoring information of indexes related to the active health behaviors to the cloud server end and receiving a health evaluation report from the cloud server end.
And the intelligent acquisition terminal is used for sending monitoring information of indexes related to the active health behaviors to the cloud server.
Compared with the prior art, the invention has the beneficial effects that:
according to the monitoring system and the monitoring method, the monitoring frequency of the indexes related to the active health behaviors of the patient is determined through the patient risk level provided by the cloud service end, so that the system provided by the invention can provide more reasonable monitoring frequency for each index of the patient based on the specific health condition of the patient, and the monitoring frequency is adjusted based on the change of the health condition of the patient.
The invention also constructs the high sugar comprehensive management score based on each index information and each index weight in the monitoring information, can accurately evaluate the overall state of the hypertension diabetes patient in a period of time through the continuous high sugar comprehensive management score curve in the set time, and respectively sends the continuous high sugar comprehensive management score curve in a period of time to doctors and patients, so that the doctors can judge the health condition of the patient after intervention to provide data basis for the intervention in the next stage, and can timely send abnormal information and guidance comments to the patients and doctors respectively, thereby timely performing the intervention when facing emergency conditions.
Drawings
FIG. 1 is a flow chart of a method for health assessment of hypertension and diabetes in a closed loop management scenario provided by an embodiment of the present invention;
fig. 2 is a schematic diagram of a device for health assessment of hypertension and diabetes in a closed-loop management scenario according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application.
In order to provide reasonable monitor plans of various indexes for different patients with hypertension and accurately provide the overall health state of the patients with hypertension and diabetes and guidance comments for abnormal conditions for a period of time for doctors based on the acquired data so as to provide data basis for the treatment of the next stage or enable the doctors or the patients with hypertension and diabetes to carry out emergency treatment on emergencies, the invention provides a method for carrying out health assessment on the hypertension and diabetes in a closed-loop management scene, as shown in fig. 1, which comprises the following steps:
S1, a cloud service end obtains a patient risk level based on a patient risk assessment result: the doctor end judges and obtains a patient risk assessment result by adopting a risk assessment standard based on the basic information and clinical information of the doctor, and the cloud service end obtains a patient risk grade based on the patient risk assessment result from the doctor end, and the specific steps are as follows:
when the hypertension and diabetes patient provided by the embodiment of the invention is subjected to management for the first time, a manager is required to collect essential basic information of the hypertension and diabetes patient and input the essential information into a doctor terminal, wherein the basic information of the patient comprises the following steps: identification number, name, gender, contact phone, height, weight, waistline, management classification (hypertension and diabetes), account number and password are also required. According to the basic information of the patient, corresponding clinical examination and examination are carried out on the patient to obtain clinical information, a manager inputs the basic information and the clinical information into an evaluation list, a doctor obtains a patient risk evaluation result for the patient based on the evaluation list, in one embodiment, the patient risk evaluation result is further evaluated according to blood pressure data or blood sugar data of the patient in a period of time, the patient risk evaluation result is sent to a cloud service end, and the cloud service end determines a patient risk level based on the patient risk evaluation result provided by the doctor.
In one embodiment, the patient risk assessment results from the physician-side assessment include: when a patient enters a group for the first time, a doctor receives basic information of the patient and information of clinical indexes detected when the patient enters the group for the first time, and judges and obtains a patient risk assessment result by adopting a risk assessment standard based on the basic information of the patient and the information of the clinical indexes detected when the patient enters the group for the first time; when monitoring a patient, a doctor side obtains monitoring information through a cloud server side, and reevaluates the risk level of the patient by adopting a risk evaluation standard based on the monitoring information, and the cloud server side adjusts the monitoring frequency through the reevaluated risk level.
In a specific embodiment, the risk assessment criteria is determined as "guidelines for prevention and treatment of hypertension 2018 in China", clinical guidelines for prevention and treatment of type 2 diabetes in elderly people in China (2022 edition), and the patients with hypertension are equally divided into four cardiovascular risk level levels of low risk, medium risk, high risk and very high risk according to the number of blood pressure levels and risk factors and whether there are target organ lesions and complications according to "guidelines for prevention and treatment of hypertension 2018 in China; according to the clinical guidelines for prevention and treatment of type 2 diabetes in elderly people in China (2022 edition), diabetics are classified into strict control and general control according to fasting blood glucose levels. In this embodiment, the high risk and the very high risk of hypertension are combined into the high risk, and the three levels of the high risk, the medium risk and the high risk are classified into the low risk and the medium risk as the risk assessment results, and the monitoring frequency of each index of the patient is changed along with the change of the level in the risk assessment results.
S2, determining monitoring frequency of various indexes of the patient based on the patient risk level, and obtaining monitoring information: the cloud service end determines monitoring frequencies of basic information, clinical indexes and indexes related to active health behaviors of the patient through the patient risk level, and sends the monitoring frequencies to the patient end and the doctor end respectively, and monitoring information is obtained.
In a specific embodiment, the present embodiment dynamically monitors the continuous health status of the patients with hypertension and diabetes mellitus by monitoring the high sugar integrated management index in real time, that is, monitoring information, the high sugar integrated management index provided in the present embodiment includes clinical indexes capable of reflecting management compliance and management effects, and the clinical indexes are related to the risk factors of hypertension and diabetes mellitus, so that 22 indexes are screened as the high sugar integrated management index by studying and interviewing doctors in related guidelines of hypertension and diabetes mellitus and application practice, the indexes can be divided into three dimensions of patient basic information, clinical indexes and indexes related to active health behaviors from the whole management process, and the cloud service end determines the monitoring frequency of the patient basic information, the clinical indexes and the indexes related to the active health behaviors according to the risk level of the patient and transmits the monitoring frequency to the patient end and the doctor end respectively, and in an embodiment, the data item range and the monitoring frequency of the 22 indexes are provided, as shown in table 1.
The indexes related to the active health behavior provided by the embodiment comprise life habit index data, body mass index, medication compliance, blood pressure measurement compliance and blood sugar measurement compliance; the lifestyle index data provided by the embodiment comprise smoking, drinking, exercise and diet data, and the data is derived from a behavior questionnaire of a small program end of a patient; the body mass index BMI data provided in this embodiment is derived from height and weight data uploaded by a doctor end or a patient applet end; the medication compliance data provided by this embodiment is derived from a medication compliance questionnaire at the patient applet end; the blood pressure measurement compliance data provided by the embodiment are derived from the blood pressure record of the patient and the blood pressure monitoring plan of the patient at the cloud service end; the blood glucose measurement compliance data provided by the embodiment is derived from the blood glucose record of the patient and the blood glucose monitoring plan of the patient at the cloud service end.
The clinical index provided in this embodiment includes physical and chemical index data measured in a hospital, and the management effect of the patient after intervention and self-management by a doctor and a manager is reflected by the physical and chemical index data, where the physical and chemical index data includes a blood pressure control condition, a current blood pressure condition, a blood sugar control condition, a current blood sugar condition, blood fat, homocysteine, uric acid, left ventricular hypertrophy, carotid plaque, and microalbuminuria. The control conditions of blood pressure and blood sugar refer to the average blood pressure and blood sugar level of the patient within one year, which are important indexes reflecting the long-term management effect, the control management is carried out according to the control target of the guideline, the occurrence of complications can be effectively prevented, the patient is in a relatively stable health state, the current blood pressure and blood sugar conditions are important indexes reflecting the short-term control effect of the patient, blood fat (total cholesterol and high-density lipoprotein cholesterol) homocysteine and uric acid are important indexes for diagnostic evaluation in the guideline, and target organ damages such as left ventricular hypertrophy, carotid intimal layer thickness and trace albuminuria are strong predictors of cardiovascular events, and have great influence on the health condition of the patient.
The blood pressure control condition data provided by the embodiment are derived from the initial diagnosis blood pressure grade input by a doctor and the blood pressure condition record in one year of a database; the blood pressure condition provided by the embodiment is derived from the latest blood pressure data, and is uploaded through a doctor end, a patient end and an intelligent acquisition terminal, wherein the intelligent acquisition terminal comprises an intelligent instrument end; the blood sugar control condition data provided by the embodiment is derived from the initial blood sugar recorded by the doctor and the blood sugar condition record in one year of the database; the blood glucose condition provided by this embodiment is derived from the latest blood glucose data, and is uploaded through doctor's terminal and patient's applet terminal, the blood lipid data is derived from total cholesterol and high density lipoprotein cholesterol data that doctor's terminal uploaded, the homocysteine is derived from homocysteine data that doctor's terminal uploaded, uric acid is derived from uric acid data that doctor's terminal uploaded, left ventricular hypertrophy is derived from electrocardiogram data of patient's uploaded by doctor's terminal, carotid plaque data is derived from carotid intima-media thickness data obtained by carotid ultrasound that doctor's terminal uploaded, and trace albuminuria data is derived from trace albuminuria and albumin/creatinine ratio data that doctor's terminal uploaded.
S3, obtaining a high sugar comprehensive management score based on the variable score and the index weight corresponding to each index: the cloud service end sequentially performs standardization processing and variable assignment on each item of index information contained in the monitoring information to obtain variable scores, and obtains high-sugar comprehensive management scores based on the variable scores and index weights of each item of index information.
After the high-sugar comprehensive management index provided by the embodiment of the invention is sent to the cloud service end, firstly cleaning data and standardization are carried out, abnormal data which do not accord with the actual scene are cleaned, and the cleaned data are standardized, for example, the contraction pressure reaches four digits, the height is only two digits, and the like; some data units may not be uniform, some patients will enter heights as 1.7m, some will enter heights as 170cm, and they need to be standardized as 1.7ml when calculating BMI; still other data such as height and age need to be extracted from the identification card number, and all the steps of the standardized processing need to be completed before the calculation of the high sugar integrated management index.
The high sugar comprehensive management score=full score- Σ (variable scores of various indexes of each index weight), and the higher the high sugar comprehensive management score is, the better the health condition of the hypertension and diabetes management of patients is represented.
Because the important purpose of the high-sugar comprehensive management score provided by the specific embodiment of the invention is to monitor the management condition of patients in the chronic disease management process, help the hypertension and diabetes patients to improve the behavior habit of the patients in the chronic disease management process, and enable the health behavior to be positively fed back through the visual result of the management index, the calculation of the weight score of each index comprehensively considers the risk degree of each index on the health of the patients and the difficulty degree which can be changed through the management. The weighting assignment of each index provided by the embodiment of the invention adopts a subjective and objective combination method.
The embodiment of the invention provides a method for determining the weights of various indexes, which comprises the following steps: obtaining index risk levels of various indexes through expert evaluation, taking the ratio of the number of patients with reduced index variable scores based on the dry prognosis to the total number of patients as a change difficulty degree score of the various indexes, and obtaining importance scores of the various indexes based on the index risk levels of the various indexes and the change difficulty degree score; and obtaining the weights of the indexes through an AHP analytic hierarchy process based on the importance scores of the indexes.
In a specific embodiment, the risk level of the index is obtained by questionnaire investigation of a plurality of experts, and the questionnaire sets each index as 4 index risk levels of low index risk, medium index risk, high index risk and high index risk, wherein the index risk level score comprises: index low risk 1 point, index medium risk 2 points, index high risk 3 points, index extremely high risk 5 points, i index questionnaire average score as index risk grade score alpha i 。
The index provided in this embodiment is scored by analyzing the data of the same index before and after the administration of the patients in the group, and the calculation method is as follows, each index is calculated according to the rule of assigning index variable, and the percentage of the total patients in the group population, which is the percentage of the patients with the reduced variable score of the ith index (i.e. the progression to worse health), is calculated as beta i ，20β i As a score of how easy the i-th index is to change.
The index risk level and the change difficulty score based on each index provided in this embodiment obtain the importance score ω of the ith index i The method comprises the following steps:
ω i ＝α i +20β i
wherein beta is i Is item iIndex risk rating, beta i And (5) the change difficulty degree score of the ith index.
The embodiment obtains the weight of each index by using an AHP analytic hierarchy process, and the AHP analytic hierarchy process calculates the weight, and comprises four steps of constructing a hierarchical structure model, constructing a judgment matrix, ordering a hierarchical list, checking consistency, and checking total ordering and consistency of the hierarchical list. The target layer of the hierarchical structure model is the health condition (high sugar comprehensive management score), the criterion layer is 22 indexes, and the scheme layer is the same patient with different patients or different times; when the analytic hierarchy process is used for constructing the judgment matrix, the importance score omega of the ith index is obtained by utilizing the calculation i The specific scale value is based on the ratio omega of importance scores i /ω j The calculation rule of the scale when the ratio is greater than or equal to 1 is determined as follows: if the ratio is less than 1, ω is taken j /ω i Is used to construct a 22 x 22 judgment matrix based on the scale terms between the 22 terms.
| ω i /ω j | [1,2) | [2,3) | [3,4) | [4,5) | [5,6) | [6,7) | [7,8) | [8,9) | [9,25) |
| Scale with a scale bar | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
The hierarchical single-order is to solve the weights of the indexes by using a judgment matrix, wherein a summation method (firstly, standardizing each column of the judgment matrix, summing each row of the standardized judgment matrix, and finally standardizing the summation result) is adopted to calculate each weight of the quasi-measurement layer, and then consistency test is carried out, and the result is obtained after the hierarchical total-order and consistency test
In one embodiment, the weights of the various indicators provided in this embodiment are shown in table 2.
Table 2: each index weight
| Index (I) | Weighting of | Index (I) | Weighting of |
| Age of | 3.3 | Blood glucose measurement compliance | 3.9 |
| Sex (sex) | 1.5 | Blood pressure control conditions | 6.4 |
| Family history | 2.1 | Current blood pressure condition | 3.6 |
| Psychological disorders | 1.2 | Blood glucose control conditions | 6.1 |
| Smoking article | 5.2 | Current glycemic condition | 3.4 |
| Drinking wine | 5.1 | Blood fat | 6.4 |
| Exercise machine | 5.1 | Homocysteine | 5.8 |
| Diet and food | 5.3 | Uric acid | 2.6 |
| Body mass index BMI | 3.6 | Left ventricular hypertrophy | 5.3 |
| Medication compliance | 8.3 | Carotid plaque | 5.9 |
| Compliance with blood pressure measurement | 3.9 | Microalbuminuria | 6.0 |
In a specific embodiment, the cloud service provided in this embodiment sequentially performs standardization processing and variable assignment on each item of index information included in the monitoring information to obtain a variable score, including: each index comprises a plurality of data item ranges, and each data item range is assigned step by step based on the risk degree of the patient so as to obtain the corresponding relation between each data item range and the variable score; after each item of index information is subjected to standardization processing, the variable score of each item of index information is obtained based on the data item range where the standardization processing result is located through the corresponding relation between each data item range and the variable score, as shown in table 3.
Table 3 variable scores of index information
Data item assignment of each patient basic information:
age:
gender:
psychological disorders:
family history of hypertension, diabetes:
data item scores for each of the metrics related to active health behavior:
smoking:
and (3) drinking:
movement:
diet:
/>
medication compliance:
blood pressure measurement compliance: (number of blood pressure uploading/corresponding management level requirement number)
Blood glucose measurement compliance: (number of blood sugar uploads/7)
Body mass index BMI:
each clinical index data item is assigned as follows:
blood pressure control conditions:
current blood pressure conditions: (recent blood pressure)
Blood glucose control:
current glycemic condition: (recent blood sugar)
Blood lipid:
homocysteine:
uric acid:
left ventricular hypertrophy:
carotid plaque:
microalbuminuria:
s4, obtaining a health evaluation report based on the high sugar comprehensive management score and the guidance opinion on the abnormal information: and constructing a health assessment report based on the continuous high-sugar comprehensive management score and the guiding advice corresponding to the abnormal information appearing in the information of each index, and respectively sending the health assessment report to a patient-intelligent acquisition end and a doctor end.
After the cloud service end engine provided by the embodiment of the invention calculates and obtains the high-sugar comprehensive management index result, the system can also give the instruction of corresponding abnormal data, for example, the system can give the corresponding instruction for the conditions that certain index data in the result is abnormally higher and lower and has abnormal change, and finally the instruction is pushed to the patient applet end and the doctor end together with the high-sugar comprehensive management index result, and the manager can manually adjust if the manager feels that the high-sugar comprehensive management index result needs to be modified after consulting, for example, the patient contraction pressure exceeds 180, the instruction can be given automatically: the patient can sit for rest after 20 minutes, and if the systolic pressure exceeds 180mmHg or the diastolic pressure exceeds 110mmHg, the patient can ask the manager to seek medical advice in time; if the medication records of the patient show that the medication which is not in the current medication list appears, the patient is reminded of taking care of medication contraindications.
The cloud service end provided by the embodiment of the invention forms a health assessment report by the continuous high-sugar comprehensive management score and the guidance opinion formed in a period of time, and sends the health assessment report to the patient end and the doctor end respectively. The manager and doctor can also modify the guiding opinion through doctor end, and help the patient to personally improve his own active management behavior. Meanwhile, the distribution condition of the overall high sugar comprehensive management indexes of all patients in different fractional segments, the index entry integrity condition of each patient, the change trend of the average level of the high sugar comprehensive management indexes of the patients managed by the medical institution and the average score change trend of each single index are displayed on a statistical analysis page at a doctor end, so that the doctor is helped to have general knowledge on the overall management effect.
In a specific embodiment, both the doctor end and the patient end are provided with output display modules, the displayed content comprises a high sugar comprehensive management score change trend graph, meanwhile, the value and the score of each index can be seen by clicking in, the change trend of the value and the score of each index can be seen by clicking in a certain index, in addition, the personalized guidance opinion aiming at the health condition of the patient is displayed, the output trend graph defaults to a day, shows the trend of the last month, and can also be screened according to 3 months, 6 months, one year and all.
In a specific embodiment, the doctor end provided in this embodiment further includes 2 functions, one is to modify the guiding opinion of the patient, and the other is to have a statistical analysis page to display the overall high sugar integrated management index distribution condition of all patients managed by the hospital, the change trend of the high sugar integrated management index according to months, and the score change trend of each single item.
In a specific embodiment, the patient end provided in this embodiment provides the patient with a monitoring frequency of the indicators related to the active health behavior, that is, a monitoring plan of the indicators related to the active health behavior, and the patient inputs, to the patient end, monitoring information of the indicators capable of autonomously inputting information among the indicators related to the active health behavior, and the patient is further capable of detecting, by an intelligent instrument in the intelligent acquisition terminal, corresponding monitoring information of the indicators related to the active health behavior, and sending, by the intelligent instrument in the intelligent acquisition terminal, the monitoring information to the cloud service end.
Aiming at the problems that medical resources of basic hospitals in hypertension and diabetes mellitus management are weak, the evaluation of hyperglycemia and diabetes mellitus two common chronic diseases is complex and difficult to comprehensively evaluate, and continuous evaluation cannot be completed by using continuous data of patients, the invention combines the hypertension risk evaluation situation of the patients, establishes a proper monitoring plan of data required by the high-sugar comprehensive management index evaluation for the patients, adopts a standardized data acquisition mode and a calculation method, enables continuous data of the patients to be effectively acquired and utilized, helps doctors to efficiently manage a large number of patients, adopts a targeted diagnosis and treatment mode for abnormal data change of the patients by using the result of the management index, enables the patients to know the health condition of the patients more, and enhances the consciousness and the power of active management.
The invention also provides a device for health assessment of hypertension and diabetes in a closed-loop management scene, which is shown in fig. 2 and comprises a cloud service end, a doctor end, a patient end and an intelligent acquisition terminal, wherein the intelligent acquisition terminal, the doctor end and the patient end are respectively communicated with the cloud service end.
The intelligent acquisition terminal provided by the embodiment of the invention comprises a patient information identification module and a data uploading module, wherein the patient information identification module is used for identifying the personal identity and the management mechanism of a patient, and the data uploading module is used for checking the indexes related to the active health behaviors of the patient and uploading the monitoring information of the corresponding indexes related to the active health behaviors to the cloud server.
The patient side provided by the embodiment of the invention comprises a patient side data uploading module and a patient side high sugar comprehensive management index module.
The patient-side data uploading module provided by the embodiment of the invention comprises uploading of blood pressure, blood sugar, weight, medication and life behavior data, and is used for receiving monitoring frequency of the cloud service side, and a patient uploads the data to the cloud service side in a numerical value, picture and other forms in a self-management process.
The patient-side high sugar integrated management index module provided by the embodiment of the invention is used for displaying the high sugar integrated management index and the change curve of the score of each index in different time intervals, and also comprises the data value of each index and personalized guidance opinion.
The doctor side provided by the embodiment of the invention comprises a patient management module, a doctor side high sugar comprehensive management index module, a statistical analysis module and a doctor side data uploading module.
The patient management module is mainly used for checking specific information of each patient, and comprises basic information of the patient, a monitoring plan, a checking record, personal monitoring data and medication conditions, wherein the content of the basic information is information when the patient is documented, the monitoring plan is used for displaying the monitoring frequency of various indexes of the current high-sugar comprehensive management index of the patient, the checking record is used for the history record of checking and checking of the patient in a hospital, the personal monitoring data is used for displaying the behavior record of autonomous management of the patient, the data comprise blood pressure, blood sugar, weight, movement and the like uploaded by the small program end of the patient, and the medication conditions comprise a medicine list and medication record of the patient.
The doctor-side high-sugar comprehensive management index module provided by the embodiment is used for displaying the change curves of the management indexes of the patient in different time intervals, the current data values and score conditions of various indexes, and personalized guidance opinions, and the doctor can modify the guidance opinions recommended by the system. The statistical analysis module provided in this embodiment is directed to all the managed patients, including a management situation overview unit of the patient and a high sugar integrated management index statistical unit, where the management situation overview unit is used to display the number of patients in each grade, and the high sugar integrated management index statistical unit is used to display the change situation of the high sugar integrated management index of the whole patient and the overall change situation of each index.
The doctor-side data uploading module is used for receiving the monitoring frequency of the cloud service side, sending monitoring information aiming at basic information and clinical indexes of a patient to the cloud service side, sending a patient risk assessment result to the cloud service side, and sending the basic information and the clinical information of the patient to the cloud service side.
The cloud service end provided by the embodiment of the invention comprises a data storage module and a calculation engine module.
The data storage module provided by the embodiment of the invention comprises a management information sub-module of a patient and a risk factor index data sub-module, wherein the management information sub-module of the patient is used for receiving monitoring information which is output by a patient end and aims at indexes related to active health behaviors, and monitoring information which is output by a doctor end and aims at basic information and clinical indexes of the patient, and sending the monitoring information to the calculation engine module, and the risk factor index data sub-module is used for receiving a patient risk assessment result from the doctor end and sending the risk assessment result to the calculation engine module;
the calculation engine module provided by the embodiment of the invention comprises a management level engine and a high-sugar comprehensive management index engine, wherein the management level engine is used for obtaining a patient risk level based on a patient risk assessment result from a doctor end, determining monitoring frequencies of basic information, clinical indexes and indexes related to active health behaviors of a monitored patient based on the patient risk level, and sending the monitoring frequencies to the patient end, an intelligent acquisition terminal and the doctor end respectively; the high-sugar integrated management index engine is used for sequentially carrying out standardized processing and variable assignment on each item of index information contained in the monitoring information to obtain a variable score, obtaining a high-sugar integrated management score based on the variable score and the index weight of each item of index information, obtaining a continuous high-sugar integrated management score in a set time, constructing a health evaluation report based on the continuous high-sugar integrated management score and a guiding suggestion corresponding to abnormal information appearing in the information of each item of index, and respectively sending the health evaluation report to a patient-intelligent acquisition end and a doctor end.
Claims (10)
1. A method for health assessment of hypertensive diabetes in a closed-loop management scenario, comprising:
the cloud service end obtains a patient risk grade based on a patient risk assessment result output by the doctor end, determines monitoring frequencies of basic information of the patient, clinical indexes and indexes related to active health behaviors based on the patient risk grade, and sends the monitoring frequencies to the patient end and the doctor end respectively;
the cloud server side obtains monitoring information which is output by the patient side and the intelligent acquisition terminal and aims at indexes related to active health behaviors, and monitoring information which is output by the doctor side and aims at basic information and clinical indexes of the patient;
the cloud service end sequentially performs standardization processing and variable assignment on each item of index information in the monitoring information to obtain variable scores corresponding to each item of index information, obtains high-sugar integrated management scores based on the variable scores and index weights corresponding to each item of index information, constructs a health assessment report based on continuous high-sugar integrated management score curves and instruction suggestions corresponding to abnormal information in a set time, and sends the health assessment report to a patient end and a doctor end respectively.
2. The method for health assessment of hypertension and diabetes in a closed-loop management scenario according to claim 1, wherein the method for determining the index weight comprises the steps of:
Obtaining index risk grade scores of all indexes through expert evaluation, taking the ratio of the number of patients with reduced index variable scores based on the dry prognosis to the total number of patients as the change difficulty degree scores of all indexes, and obtaining importance scores of all indexes based on the index risk grade scores and the change difficulty degree scores of all indexes;
and obtaining the weights of the indexes through an AHP analytic hierarchy process based on the importance scores of the indexes.
3. The method for health assessment of hypertension and diabetes in a closed-loop management scenario according to claim 2, wherein the weights of the various indexes are obtained by an AHP hierarchical analysis method based on the importance scores of the various indexes, comprising:
setting a corresponding relation between the ratio of the importance scores of any two indexes and the scale, wherein when the ratio of the importance scores of any two indexes is not lower than 1, the ratio of the importance scores of any two indexes not lower than 1 is divided into n ratio ranges, the n ratio ranges are corresponding to the set n scale values, and when the ratio of the importance scores of any two indexes is lower than 1, the reciprocal of the ratio of the importance scores of any two indexes is taken as the corresponding scale;
And constructing a judgment matrix of each index based on the scale by setting the corresponding relation between the ratio of the importance scores of any two indexes and the scale based on the importance scores of each index, and sequentially carrying out hierarchical single-order, consistency test, hierarchical total order and one-time test on the judgment matrix to obtain the final weight of each index.
4. The method for health assessment of hypertension and diabetes in a closed-loop management scenario according to claim 2, wherein the importance score ω of the ith index is obtained based on the index risk level and the change difficulty score of each index i The method comprises the following steps:
ω i ＝α i +20β i
wherein alpha is i Index risk for the ith indexGrade, beta i And (5) the change difficulty degree score of the ith index.
5. The method for health assessment of hypertension and diabetes in a closed-loop management scenario according to claim 1, wherein the cloud service side sequentially performs standardization processing and variable assignment on each item of index information contained in the monitoring information to obtain a variable score corresponding to each item of index information, and the method comprises the following steps:
each index comprises a plurality of data item ranges, and each data item range is assigned step by step based on the risk degree of the patient so as to obtain the corresponding relation between each data item range and the variable score;
And carrying out standardization processing on each item of index information, and obtaining the variable score of each item of index information through the corresponding relation between each data item range and the variable score based on the data item range of the standardization processing result.
6. The method for health assessment of hypertension and diabetes in a closed-loop management scenario according to claim 1, wherein the patient risk assessment result is obtained by doctor-side assessment, comprising:
when a patient enters a group for the first time, a doctor receives basic information of the patient and information of clinical indexes detected when the patient enters the group for the first time, and judges and obtains a patient risk assessment result by adopting a risk assessment standard based on the basic information of the patient and the information of the clinical indexes detected when the patient enters the group for the first time;
when monitoring a patient, a doctor side obtains monitoring information through a cloud server side, and reevaluates the risk level of the patient by adopting a risk evaluation standard based on the monitoring information, and the cloud server side adjusts the monitoring frequency through the reevaluated risk level.
7. The method for health assessment of hypertension and diabetes in a closed-loop management scenario according to claim 1, wherein the doctor side is further capable of receiving monitoring frequency and monitoring information of the index related to active health behavior from the current time of the cloud service side;
The doctor side can also modify the guiding advice corresponding to the abnormal information, the modified guiding advice is sent to the patient side through the cloud service side, and the treatment scheme can also be provided or modified based on the health assessment report.
8. The method of health assessment of hypertensive diabetes in a closed-loop management scenario of claim 1, wherein the patient basis information includes age, gender, family history, and psychological illness;
the clinical information comprises physical and chemical index data measured;
the monitoring information of the indicators related to the active health behavior comprises life habit indicator data of the patient, including smoking, drinking, exercise and diet indicator data, body mass index, medication compliance, blood pressure measurement compliance and blood glucose measurement compliance.
9. The method for health assessment of hypertension and diabetes in a closed-loop management scenario according to claim 1, wherein the trend of the overall management score of high sugar over time and the trend of the variable scores of the indexes over time can be displayed by both doctor side and patient side;
the doctor side can also display the distribution of the high sugar comprehensive management scores of all patients and the change trend of the variable scores of all indexes of all patients with time.
10. A device for health assessment of hypertensive diabetes in a closed-loop management scenario, comprising:
the cloud service end is used for obtaining a patient risk grade based on a patient risk assessment result from the doctor end, determining monitoring frequencies of basic information, clinical indexes and indexes related to active health behaviors of the patient according to the patient risk grade, respectively sending the monitoring frequencies to the patient end, the intelligent acquisition terminal and the doctor end, obtaining monitoring information, which is output by the patient end and the intelligent acquisition terminal, of the indexes related to the active health behaviors, and monitoring information, which is output by the doctor end, of the basic information and the clinical indexes of the patient;
the method is also used for sequentially carrying out standardization processing and variable assignment on each item of index information in the monitoring information to obtain a variable score corresponding to each item of index information, obtaining a high-sugar integrated management score based on the variable score and the index weight of each item of index, constructing a health evaluation report based on a continuous high-sugar integrated management score curve in a set time and a guiding suggestion corresponding to abnormal information, and respectively sending the health evaluation report to a patient-intelligent acquisition end and a doctor end;
the doctor end is used for receiving the monitoring frequency of the cloud service end, sending monitoring information aiming at basic information and clinical indexes of a patient to the cloud service end, and sending a patient risk assessment result to the cloud service end;
The patient end is used for receiving the monitoring frequency of the cloud server end, sending monitoring information of indexes related to the active health behaviors to the cloud server end and receiving a health evaluation report from the cloud server end.
And the intelligent acquisition terminal is used for sending monitoring information of indexes related to the active health behaviors to the cloud server.
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