CN112967769B - Data processing and data evaluation method for chain pharmacy diabetes follow-up visit data - Google Patents
Data processing and data evaluation method for chain pharmacy diabetes follow-up visit data Download PDFInfo
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- CN112967769B CN112967769B CN202110294461.4A CN202110294461A CN112967769B CN 112967769 B CN112967769 B CN 112967769B CN 202110294461 A CN202110294461 A CN 202110294461A CN 112967769 B CN112967769 B CN 112967769B
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The invention provides a data processing and data evaluation method for chain pharmacy diabetes follow-up visit data, which comprises the following steps: configuring a blood sugar value input module, and visually displaying the blood sugar value input module on a follow-up data input interface of the diabetes management system; storing the blood sugar value data currently input in the blood sugar value input module in a storage module, and marking corresponding follow-up time; calling the current blood sugar value data, and distributing a weight value of the blood sugar value according to the current blood sugar value data; historical blood sugar value data is retrieved, the detection frequency of the blood sugar value is obtained through statistics, and the weight value of the detection frequency of the blood sugar value is distributed according to the current blood sugar value data and the detection frequency of the blood sugar value; and a configuration medication compliance selection module which visually displays the medication compliance selection module on a follow-up data input interface of the diabetes management system and assigns a weighted value of the medication compliance for use according to a selection result of the medication compliance selection module.
Description
[ technical field ] A
The invention relates to the follow-up visit data processing field, in particular to a data processing and data evaluation method for chain pharmacy diabetes follow-up visit data.
[ background of the invention ]
At present, for daily management of diabetes patients, pharmacists in community chain drugstores usually obtain follow-up visit data of the diabetes patients through telephones, in-person follow-up visits and the like, and then document the diabetes patients according to the follow-up visit data. The follow-up data is processed and evaluated by a pharmacist according to own experience, on one hand, the data is processed and analyzed manually by the pharmacist, a large amount of time and energy are consumed, and the follow-up data is difficult to process and evaluate, on the other hand, the follow-up data is processed and evaluated by different pharmacists in a large difference mode without fixed data processing and evaluation specifications, and therefore the evaluation result lacks accuracy.
Therefore, it is necessary to build an intelligent data processing and evaluating engine through the diabetes management system, and apply a data processing and data evaluating method of chain pharmacy diabetes follow-up visit data in the intelligent data processing and evaluating engine.
[ summary of the invention ]
The invention has the main advantage of providing the data processing and data evaluation method for the chain pharmacy diabetes follow-up visit data, wherein the data processing and data evaluation method for the chain pharmacy diabetes follow-up visit data is applied to an intelligent data processing and evaluation engine built in a diabetes management system so as to automatically perform data processing and data evaluation on the follow-up visit data.
Another advantage of the present invention is to provide a method for data processing and data evaluation of chain pharmacy diabetes follow-up data, which is capable of performing data processing and data evaluation of follow-up data in multiple dimensions, such as blood glucose level, blood glucose level detection frequency, medication compliance, diet management execution, operation management execution, diabetes knowledge acquisition, glycated hemoglobin monitoring frequency, and/or complications.
Another advantage of the present invention is to provide a method for data processing and data evaluation of chain pharmacy diabetes follow-up visit data, wherein the method for data processing and data evaluation of chain pharmacy diabetes follow-up visit data can perform weight value assignment according to the follow-up visit data, and finally obtain the total weight values of the plurality of dimensions.
Another advantage of the present invention is to provide a data processing and data evaluation method for chain pharmacy diabetic follow-up visit data, which is suitable for a service scenario of a community chain pharmacy, and adds medication compliance, adaptability and normalization of diabetic patients, and sets a larger proportion of weight. The comprehensive evaluation of the detection, the medication, the living habits and other factors is realized, and meanwhile, the medication condition of the diabetic patient is mainly evaluated.
Accordingly, in accordance with the present invention, a method for data processing and data evaluation of chain pharmacy diabetes follow-up visit data having at least one of the foregoing advantages, comprises the steps of:
s101, configuring a blood sugar value input module, and visually displaying the blood sugar value input module on a follow-up data input interface of a diabetes management system;
s102, storing the blood sugar value data currently input in the blood sugar value input module in a storage module, and marking corresponding follow-up time;
s103, calling the current blood sugar value data, and distributing a weight value of the blood sugar value according to the current blood sugar value data;
s104, calling historical blood sugar value data, counting to obtain blood sugar value detection frequency, and distributing a weight value of the blood sugar value detection frequency according to the current blood sugar value data and the blood sugar value detection frequency; and
s105, configuring a medication compliance selection module, visually displaying the medication compliance selection module on a follow-up data input interface of the diabetes management system, and distributing a weighted value of medication compliance for use according to a selection result of the medication compliance selection module.
Particularly, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s106, configuring a diet management execution condition selection module, visually displaying the diet management execution condition selection module on a follow-up data input interface of the diabetes management system, and distributing a weight value of the diet management execution condition according to a selection result of the diet management execution condition selection module.
Particularly, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s107, configuring a motion management execution condition selection module, visually displaying the motion management execution condition selection module on a follow-up data input interface of the diabetes management system, and distributing a weight value of the motion management execution condition according to a selection result of the motion management execution condition selection module.
Particularly, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s108, configuring a diabetes knowledge grasping condition selection module, visually displaying the diabetes knowledge grasping condition selection module on a follow-up data input interface of a diabetes management system, and distributing a weight value of the diabetes knowledge grasping condition according to a selection result of the diabetes knowledge grasping condition selection module.
Particularly, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s109, configuring a glycosylated hemoglobin monitoring frequency selection module, visually displaying the glycosylated hemoglobin monitoring frequency selection module on a follow-up data input interface of a diabetes management system, and distributing a weight value of the glycosylated hemoglobin monitoring frequency according to a selection result of the glycosylated hemoglobin monitoring frequency selection module.
Particularly, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s110, calculating the sum of all weight values distributed in the executed steps to serve as a total weight value, setting a follow-up period according to the total weight value, and visually displaying the follow-up period on a display interface of the diabetes management system.
In particular, in the step S101, the blood glucose level input means includes a fasting blood glucose level input unit for inputting a fasting blood glucose level in the follow-up data and a postprandial blood glucose level input unit for inputting a postprandial blood glucose level in the follow-up data.
In particular, in step S102, the blood glucose level data includes the fasting blood glucose level and the postprandial blood glucose level.
In particular, in step S103, if both the fasting blood glucose level and the postprandial blood glucose level do not reach the standard, a weight value of the blood glucose level is assigned to be 20; assigning a weight value of blood glucose value of 10 if one of said fasting blood glucose value and said postprandial blood glucose value meets the standard and the other does not meet the standard; if both the fasting blood glucose value and the postprandial blood glucose value reach the standard, assigning a weight value of the blood glucose value to be 0; when at least one of the fasting blood glucose level and the postprandial blood glucose level has a null value that is not input, a weight value of the assigned blood glucose level is 10.
In particular, in step S104, if both fasting blood glucose level and postprandial blood glucose level in the current blood glucose value data reach standards and the counted blood glucose level detection frequency is 1 time per 2 weeks, a weight value of the blood glucose level detection frequency is assigned as 20; if the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data reach the standard and the counted blood glucose value detection frequency is 1 time per 1 month, the weight value of the blood glucose value detection frequency is assigned to be 10; if at least one of the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data does not reach the standard and the statistically obtained detection frequency of the blood glucose value is more than 4 times per 1 week, distributing the weight value of the detection frequency of the blood glucose value to be 20; if at least one of the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data does not reach the standard and the counted blood glucose value detection frequency is 1-3 times per 1 week, distributing the weight value of the blood glucose value detection frequency to be 10; if at least one of the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data does not reach the standard and the counted blood glucose value detection frequency is less than 1 time per 1 week, the weight value of the blood glucose value detection frequency is assigned to be 0; if the blood sugar value detection frequency is not obtained through statistics, the weight value of the blood sugar value detection frequency is assigned to be 0.
The above and other advantages of the invention will be more fully apparent from the following description and drawings.
The above and other advantages and features of the present invention will be more fully apparent from the following detailed description of the invention, the accompanying drawings and the claims.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic diagram of the steps of a method for data processing and data evaluation of chain pharmacy diabetes follow-up visit data according to an embodiment of the present invention;
FIG. 2 schematically illustrates a blood glucose value input module visually displayed on a follow-up data input interface of the diabetes management system;
FIG. 3 illustrates an exemplary medication adherence selection module visually displayed on a follow-up data entry interface of a diabetes management system;
FIG. 4 illustrates an exemplary diet management performance selection module visually displayed on a follow-up data input interface of the diabetes management system;
FIG. 5 exemplarily illustrates that the exercise management performance selection module is visually displayed on a follow-up data input interface of the diabetes management system;
FIG. 6 illustrates an exemplary diabetes knowledge management selection module visually displayed on a follow-up data entry interface of the diabetes management system;
FIG. 7 is an exemplary illustration of a glycated hemoglobin monitoring frequency selection module visually displayed on a follow-up data entry interface of the diabetes management system.
[ detailed description ] embodiments
For better understanding and implementation, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The terms "comprises," "comprising," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
With reference to fig. 1 to 7 of the accompanying drawings of the present specification, a data processing and data evaluation method of chain pharmacy diabetes follow-up visit data according to an embodiment of the present invention is illustrated. The data processing and data evaluation method of chain pharmacy diabetes follow-up data is applied to an intelligent data processing and evaluation engine built in a diabetes management system to automatically perform data processing and data evaluation on the follow-up data, wherein the data processing and data evaluation method of chain pharmacy diabetes follow-up data can perform data processing and data evaluation on the follow-up data in multiple dimensions such as blood sugar value, blood sugar value detection frequency, medication compliance, diet management execution condition, operation management execution condition, diabetes knowledge mastering condition, glycosylated hemoglobin monitoring frequency and/or complication condition, and the like, and can perform weight value distribution according to the follow-up data to finally obtain the total weight values of the multiple dimensions.
It is worth mentioning that the data processing and data evaluation method for diabetes follow-up visit data of chain pharmacy is suitable for the service scene of the community chain pharmacy, the medication compliance, adaptability and normalization of the diabetes patients are added, and a larger proportion of weight is set. The method mainly evaluates the medication condition of the diabetic while realizing comprehensive evaluation of multiple factors such as detection, medication, living habits and the like.
Specifically, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data comprises the following steps:
s101, configuring a blood sugar value input module 10, and visually displaying the blood sugar value input module 10 on a follow-up data input interface 1 of the diabetes management system.
In step S101, as shown in fig. 2 of the present specification, the blood glucose level input module 10 includes an fasting blood glucose level input unit 101 and a postprandial blood glucose level input unit 102, where the fasting blood glucose level input unit 101 is used for inputting a fasting blood glucose level in the follow-up data, and the postprandial blood glucose level input unit 102 is used for inputting a postprandial blood glucose level in the follow-up data. The fasting blood glucose level input means 101 and the postprandial blood glucose level input means 102 are visually displayed on the follow-up data input interface 1 at the same time.
Further, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s102, storing the blood sugar value data currently input in the blood sugar value input module 10 in a storage module, and marking corresponding follow-up time.
It will be appreciated that the blood glucose value data for each follow-up is stored in the memory module, and the blood glucose value data for each follow-up must be tagged with the follow-up time in order to be distinguishable. The blood glucose value data includes the fasting blood glucose value and the postprandial blood glucose value.
Further, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s103, calling the current blood sugar value data, and distributing a weight value of the blood sugar value according to the current blood sugar value data.
In step S103, it is determined whether the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data reach the standard, respectively, wherein the fasting blood glucose value is greater than 6.1, which is unqualified, otherwise, which is qualified, wherein the postprandial blood glucose value is greater than 7.8, which is unqualified, otherwise, which is qualified. Assigning a weight value of blood glucose value of 20 if both the fasting blood glucose value and the postprandial blood glucose value do not meet the standard; assigning a weight value of blood glucose value of 10 if one of said fasting blood glucose value and said postprandial blood glucose value meets the standard and the other does not meet the standard; assigning a weight value of blood glucose value of 0 if both the fasting blood glucose value and the postprandial blood glucose value meet the standards; when at least one of the fasting blood glucose level and the postprandial blood glucose level has a null value that is not input, a weight value of the assigned blood glucose level is 10.
Further, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
and S104, calling historical blood sugar value data, counting to obtain blood sugar value detection frequency, and distributing a weight value of the blood sugar value detection frequency according to the current blood sugar value data and the blood sugar value detection frequency.
In the step S104, if the fasting blood glucose value and the postprandial blood glucose value in the current blood glucose value data both reach the standard and the counted blood glucose value detection frequency is 1 time every 2 weeks, the weight value of the blood glucose value detection frequency is assigned to be 20; if the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data reach the standard and the counted blood glucose value detection frequency is 1 time per 1 month, the weight value of the blood glucose value detection frequency is assigned to be 10; if at least one of the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data does not reach the standard, and the counted blood glucose value detection frequency is more than 4 times per 1 week, distributing a weight value of the blood glucose value detection frequency to be 20; if at least one of the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data does not reach the standard and the counted blood glucose value detection frequency is 1-3 times per 1 week, distributing the weight value of the blood glucose value detection frequency to be 10; if at least one of the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data does not reach the standard and the counted blood glucose value detection frequency is less than 1 time per 1 week, the weight value of the blood glucose value detection frequency is assigned to be 0; if the blood sugar value detection frequency is not counted, the weight value of the blood sugar value detection frequency is assigned to be 0.
Further, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s105, configuring a medication compliance selection module 20, visually displaying the medication compliance selection module 20 on the follow-up data input interface 1 of the diabetes management system, and allocating a weighted value of medication compliance for use according to a selection result of the medication compliance selection module 20.
In the step S105, as shown in fig. 3 of the present specification, the medication compliance selection module 20 includes a selection item "medication not missed", a selection item "medication missed is not more than 3 times", and a selection item "medication missed is more than 3 times". If the selection result of the medication compliance selection module 20 is 'medication not missed', the weight value of the medication compliance for distribution is 20; if the selection result of the medication compliance selection module 20 is that the missed medication is less than or equal to 3 times, the weight value of the medication compliance for distribution is 10; if the selection result of the medication compliance selection module 20 is that the missed medication is more than 3 times, the weight value of the medication compliance for distribution is 0; if the selection result of the medication compliance selection module 20 is unselected, the weighted value of the medication compliance for allocation is 10.
Further, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s106, configuring the diet management execution condition selection module 30, visually displaying the diet management execution condition selection module 30 on the follow-up data input interface 1 of the diabetes management system, and distributing the weight value of the diet management execution condition according to the selection result of the diet management execution condition selection module 30.
In step S106, as shown in fig. 4 of the present specification, the diet management performance selection module 30 includes the selection item "never", the selection item "occasionally" and the selection item "strictly". If the selection result of the diet management execution condition selection module 30 is "never", the weight value of the assigned diet management execution condition is 10; if the selection result of the diet management execution condition selection module 30 is "occasional", the weight value of the assigned diet management execution condition is 5; if the selection result of the diet management execution condition selection module 30 is "strict", the weight value of the assigned diet management execution condition is 0; if the selection result of the diet management execution condition selection module 30 is unselected, the weight value of the assigned diet management execution condition is 10.
Further, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s107, configuring the exercise management execution condition selection module 40, visually displaying the exercise management execution condition selection module 40 on the follow-up data input interface 1 of the diabetes management system, and distributing a weight value of the exercise management execution condition according to the selection result of the exercise management execution condition selection module 40.
In step S107, as shown in fig. 5 of the present specification, the exercise management performance selection module 40 includes the selection item "never", the selection item "occasionally", and the selection item "strictly". If the selection result of the motion management execution case selection module 40 is "never", the weight value of the motion management execution case is assigned to be 10; if the selection result of the motion management execution case selection module 40 is "occasional", a weight value of the motion management execution case is assigned to be 5; if the selection result of the motion management execution condition selection module 40 is "strict," the weight value of the motion management execution condition is assigned to be 0; if the selection result of the motion management execution selecting module 40 is unselected, the weight value of the motion management execution is assigned to be 10.
Further, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s108, configuring a diabetes knowledge grasping condition selection module 50, visually displaying the diabetes knowledge grasping condition selection module 50 on a follow-up data input interface 1 of the diabetes management system, and distributing a weight value of the diabetes knowledge grasping condition according to a selection result of the diabetes knowledge grasping condition selection module 50.
In the step S108, as shown in fig. 6 of the present specification, the diabetes knowledge grasping condition selecting module 50 includes the selection item "grasping", the selection item "learning portion", and the selection item "completely unknown". If the selection result of the diabetes knowledge mastery case selection module 50 is "mastery", the weight value for assigning the diabetes knowledge mastery case is 0; if the selection result of the diabetes knowledge mastery case selection module 50 is "learning part", the weight value for assigning the diabetes knowledge mastery case is 5; if the selection result of the diabetes knowledge grasping condition selecting module 50 is "completely unknown", the weight value for assigning the diabetes knowledge grasping condition is 10; if the selection result of the diabetes knowledge grasping condition selecting module 50 is unselected, the weight value for assigning the diabetes knowledge grasping condition is 10.
Further, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s109, configuring a glycosylated hemoglobin monitoring frequency selection module 60, visually displaying the glycosylated hemoglobin monitoring frequency selection module 60 on a follow-up data input interface 1 of the diabetes management system, and distributing a weighted value of the glycosylated hemoglobin monitoring frequency according to a selection result of the glycosylated hemoglobin monitoring frequency selection module 60.
In step S109, as shown in fig. 7 of the present specification, the glycated hemoglobin monitoring frequency selection module 60 includes a selection item "1 time in 3 months", a selection item "1 time in half year to 1 year", and a selection item "never". If the selection result of the glycated hemoglobin monitoring frequency selection module 60 is "1 time in 3 months", the weight value for assigning the glycated hemoglobin monitoring frequency is 10; if the selection result of the glycated hemoglobin monitoring frequency selection module 60 is "1 time in half a year to 1 year", the weight value for assigning the glycated hemoglobin monitoring frequency is 5; if the selection result of the glycated hemoglobin monitoring frequency selection module 60 is "never", the weight value for assigning the glycated hemoglobin monitoring frequency is 0; if the selection result of the glycated hemoglobin monitoring frequency selection module 60 is unselected, the weight value for assigning the glycated hemoglobin monitoring frequency is 0.
Further, the data processing and data evaluation method for chain pharmacy diabetes follow-up visit data further comprises the following steps:
s110, calculating the sum of all weight values distributed in the executed steps to serve as a total weight value, setting a follow-up period according to the total weight value, and visually displaying the follow-up period on a display interface of the diabetes management system.
It is understood that step S106, step S107, step S108 and/or step S109 are selectively performed steps, in some embodiments, and may not be performed in some embodiments, and therefore, the total weight value is the sum of all weight values assigned in the performed steps. Preferably, if the total weight value is less than or equal to 30, setting the follow-up period to be 1 time per quarter; if the total weight value is between 30 and 50, setting a follow-up period to be 1 time per month; if the total weight value is more than or equal to 50, setting the follow-up period to be 2 times per month.
Through the above detailed description of the embodiments, those skilled in the art will clearly understand that the embodiments may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. Based on such understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, wherein the storage medium includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a compact disc-Read-Only Memory (CD-ROM) or other Memory, a magnetic disk, or any other computer-readable medium capable of storing data.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A data processing and data evaluation method of chain pharmacy diabetes follow-up visit data is suitable for an intelligent data processing and evaluation engine of a diabetes management system and is characterized in that,
the method comprises the following steps:
s101, configuring a blood sugar value input module, and visually displaying the blood sugar value input module on a follow-up data input interface of a diabetes management system;
s102, storing the blood sugar value data currently input in the blood sugar value input module in a storage module, and marking corresponding follow-up time;
s103, calling the current blood sugar value data, and distributing a weight value of the blood sugar value according to the current blood sugar value data;
s104, calling historical blood sugar value data, counting to obtain blood sugar value detection frequency, and distributing a weight value of the blood sugar value detection frequency according to the current blood sugar value data and the blood sugar value detection frequency;
and
s105, configuring a medication compliance selection module, visually displaying the medication compliance selection module on a follow-up data input interface of the diabetes management system, and distributing a weighted value of the medication compliance according to a selection result of the medication compliance selection module;
s106, configuring a diet management execution condition selection module, visually displaying the diet management execution condition selection module on a follow-up data input interface of the diabetes management system, and distributing a weight value of the diet management execution condition according to a selection result of the diet management execution condition selection module;
s107, configuring a motion management execution condition selection module, visually displaying the motion management execution condition selection module on a follow-up data input interface of the diabetes management system, and distributing a weight value of a motion management execution condition according to a selection result of the motion management execution condition selection module;
s108, configuring a diabetes knowledge mastery condition selection module, visually displaying the diabetes knowledge mastery condition selection module on a follow-up data input interface of a diabetes management system, and distributing a weight value of the diabetes knowledge mastery condition according to a selection result of the diabetes knowledge mastery condition selection module;
s109, configuring a glycosylated hemoglobin monitoring frequency selection module, visually displaying the glycosylated hemoglobin monitoring frequency selection module on a follow-up data input interface of a diabetes management system, and distributing a weighted value of the glycosylated hemoglobin monitoring frequency according to a selection result of the glycosylated hemoglobin monitoring frequency selection module;
s110, calculating the sum of all weight values distributed in the executed steps to serve as a total weight value, setting a follow-up period according to the total weight value, and visually displaying the follow-up period on a display interface of the diabetes management system.
2. The chain pharmacy diabetes follow-up data processing and data evaluation method according to claim 1,
in the step S101, the blood glucose level input means includes a fasting blood glucose level input unit for inputting a fasting blood glucose level in the follow-up data and a postprandial blood glucose level input unit for inputting a postprandial blood glucose level in the follow-up data.
3. The chain pharmacy diabetes follow-up data processing and data evaluation method according to claim 2,
in step S102, the blood glucose value data includes the fasting blood glucose value and the postprandial blood glucose value.
4. The chain pharmacy diabetes follow-up data processing and data evaluation method according to claim 3,
in step S103, if both the fasting blood glucose level and the postprandial blood glucose level do not reach the standard, assigning a weight value of the blood glucose level of 20; assigning a weight value of blood glucose value of 10 if one of said fasting blood glucose value and said postprandial blood glucose value meets the standard and the other does not meet the standard; assigning a weight value of blood glucose value of 0 if both the fasting blood glucose value and the postprandial blood glucose value meet the standards; when at least one of the fasting blood glucose level and the postprandial blood glucose level has a null value that is not input, a weight value of the assigned blood glucose level is 10.
5. The chain pharmacy diabetes follow-up data processing and data evaluation method according to claim 4,
in step S104, if both fasting blood glucose value and postprandial blood glucose value in the current blood glucose value data reach standards and the counted blood glucose value detection frequency is 1 time per 2 weeks, assigning a weight value of the blood glucose value detection frequency of 20; if the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data reach the standard and the counted blood glucose value detection frequency is 1 time per 1 month, the weight value of the blood glucose value detection frequency is assigned to be 10; if at least one of the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data does not reach the standard, and the counted blood glucose value detection frequency is more than 4 times per 1 week, distributing a weight value of the blood glucose value detection frequency to be 20; if at least one of the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data does not reach the standard and the counted blood glucose value detection frequency is 1-3 times per 1 week, distributing the weight value of the blood glucose value detection frequency to be 10; if at least one of the fasting blood glucose value and the postprandial blood glucose value in the blood glucose value data does not reach the standard and the counted blood glucose value detection frequency is less than 1 time per 1 week, the weight value of the blood glucose value detection frequency is assigned to be 0; if the blood sugar value detection frequency is not counted, the weight value of the blood sugar value detection frequency is assigned to be 0.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1676093A (en) * | 2005-04-27 | 2005-10-05 | 张厚玫 | Diabetes monitoring instrument |
| CN102542146A (en) * | 2010-12-30 | 2012-07-04 | 浙江大学 | Network evaluation system for healthy life management of type II diabetes |
| CN104899816A (en) * | 2015-05-23 | 2015-09-09 | 深圳市前海安测信息技术有限公司 | Primary medical platform and diabetes patient monitoring method based on same |
| CN107292115A (en) * | 2017-07-12 | 2017-10-24 | 冯培根 | Artificial intelligence fititious doctor diagnosis and therapy system and method based on diabetes |
Family Cites Families (3)
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|---|---|---|---|---|
| CN106413549B (en) * | 2013-11-28 | 2019-09-24 | 豪夫迈·罗氏有限公司 | For analyzing the method and apparatus method, system and computer program product for the physiological measure of user being continuously monitored |
| CN104361229A (en) * | 2014-11-11 | 2015-02-18 | 姚定国 | Follow-up interaction system for diabetes doctor |
| CN105160199A (en) * | 2015-09-30 | 2015-12-16 | 刘毅 | Continuous blood sugar monitoring based method for processing and displaying diabetes management information with intervention information |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1676093A (en) * | 2005-04-27 | 2005-10-05 | 张厚玫 | Diabetes monitoring instrument |
| CN102542146A (en) * | 2010-12-30 | 2012-07-04 | 浙江大学 | Network evaluation system for healthy life management of type II diabetes |
| CN104899816A (en) * | 2015-05-23 | 2015-09-09 | 深圳市前海安测信息技术有限公司 | Primary medical platform and diabetes patient monitoring method based on same |
| CN107292115A (en) * | 2017-07-12 | 2017-10-24 | 冯培根 | Artificial intelligence fititious doctor diagnosis and therapy system and method based on diabetes |
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