CN108877954B - Medication compliance assessment method - Google Patents
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- CN108877954B CN108877954B CN201810579343.6A CN201810579343A CN108877954B CN 108877954 B CN108877954 B CN 108877954B CN 201810579343 A CN201810579343 A CN 201810579343A CN 108877954 B CN108877954 B CN 108877954B
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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
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
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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
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
Abstract
The invention discloses a medication compliance evaluation method, which comprises the steps of firstly recording the actual medication time and the medication times of a patient; calculating the actual medication completion rate W and the actual medication time accuracy rate Q through the actual values and the set values of the medication times and the medication time; determining the weights alpha and beta of an evaluation model according to clinical data of a patient and pharmacokinetic parameters of the patient to obtain a calculation model of the patient, wherein the calculation model of the patient is as follows: AI ═ α · W + β · Q; and (3) obtaining a medication compliance index AI by applying the actual medication completion rate and the medication time accuracy rate to the calculation model of the patient, and judging the medication compliance condition according to the size of the medication compliance index AI, wherein the closer the AI value is to 100%, the better the compliance is. The method of the invention can accurately, timely and variously reflect the medicine taking condition of the patient, and fully consider the influence of different diseases and different drug treatments on the calculation compliance, so that the index has more practical significance in guiding the clinical disease management.
Description
Technical Field
The invention belongs to the field of medicine detection, relates to a medicine detection evaluation method, and particularly relates to a medicine compliance evaluation method.
Background
Non-compliance of patients with treatment regimens prescribed by their physicians can result in increased costs of healthcare, poor clinical prognosis, increased mortality, and wasted medication. Non-compliance refers to the inability to follow a prescription to take a given medication at a prescribed time, to take a suggested dose, and thus to take under medication, over medication, or to take less than optimal control of the condition.
In the clinical study phase of a drug, accurately measuring compliance may have benefits such as: increased statistical reliability of clinical studies; the conclusions of the clinical study are more accurate. There are a number of important benefits to accurately assessing compliance during the treatment phase, such as: the interference of high and low drug compliance is eliminated so as to accurately judge the effectiveness of a treatment scheme, the self effect and the toxic and side effect of the drug; timely warning patients that the compliance deterioration will possibly lead to the development of drug resistance; and identifying side effects associated with overdosing.
Currently in the medical field, there are direct and indirect methods of assessing patient compliance. Direct measurement methods assess patient medication by measuring the concentration of a drug in blood or urine. The advantage is relative accuracy and the disadvantage is that only one positive or negative result is provided. Meanwhile, the cost is high, the operability is poor, and any rule on the medication compliance of patients cannot be prompted.
There are many ways of indirect assessment. Commonly used are:
1. evaluation by doctor; the patient is allowed to record a dosing log and then evaluated according to the record; the patient is asked at the time of inquiry, and the patient is allowed to self-evaluate the medication.
2. The patient was followed up by counting the number of remaining pellets (pill count). Compliance (number of actual hair grains-number of remaining grains)/(number of grains to be taken daily x distance last day of follow-up visit)
3. The medical treatment location Ratio (MPR). MPR is the number of days since the last time taken enough medicine/this time taken medicine was last time taken medicine
The method of patient self-complaint obviously has the problem that the method is not accurate enough due to the influence of subjective factors depending on doctor evaluation, and simultaneously, doctors often have difficulty in spending enough time to deeply communicate with patients and find whether compliance problems exist or not due to the reasons of limited clinic time, the trust degree of doctors and patients and the like, and if so, the real reasons are what. The method relying on counting the remaining drug particles and MPR are actually the result of the evaluation and lack of precision. For some chronic diseases such as hypertension and diabetes, the time interval between taking the medicine again and taking the medicine last time may be longer, such as serious compliance problems, and clinical prognosis may be seriously influenced. More importantly, the simple evaluation result is not helpful for understanding the reason of the real influence on the medicine taking rule of the patient and the lack of compliance, and the targeted intervention is difficult to achieve.
In the prior art, the medicine taking condition of a patient is recorded and judged through an intelligent medicine bottle, but a universal quantitative standard of medication compliance is still lacked.
In the absence of a universal quantification criterion: the medicine taking time data are discrete time points, and unprocessed time points are difficult to effectively assist managers such as doctors to make decisions;
if only the binary record is taken or not taken, the fluctuation of the medicine taking time cannot be reflected. However, for some diseases, for some drugs, fluctuations in dosing time are also critical for controlling the condition, such as antiviral treatments;
results from different studies are difficult to compare in the absence of universal quantification standards;
more importantly, there is no prospective index that can dynamically evaluate the drug compliance of patients and perform targeted management based on the drug compliance.
If a better method is available, more timely, more accurate and more refined information about the medicine taking condition of the patient can be provided, great help can be provided for the doctors and the patients to find the problems in the early stage and solve the problems in the early stage.
Disclosure of Invention
The invention aims to combine the medication completion rate and the medication time accuracy rate of a patient, and provides a medication compliance evaluation method, the medication compliance evaluated by the method can accurately judge the medication condition of the patient in various aspects, and for different diseases, different medicaments can optimize a compliance quantification method, such as antiviral treatment medicaments, so that the compliance index has important clinical guidance significance in guiding the medication of the patient, intervening and adjusting the medication in time, and performing difference evaluation aiming at different medicaments.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for evaluating drug compliance, comprising the steps of:
a. recording the actual medication time and the number of times of medication of the patient;
b. calculating the actual medication completion rate W according to the actual medication times and the set medication application times;
c. calculating the actual medication time accuracy Q according to the actual medication time and the set application time;
d. determining the weights alpha and beta of an evaluation model according to clinical data of a patient and pharmacokinetic parameters of the patient to obtain a calculation model of the patient, wherein the calculation model of the patient is as follows: AI ═ α · W + β · Q;
e. and (3) applying the actual medication completion rate and the medication time accuracy rate to a calculation model of the patient to obtain a medication compliance index AI (adherence index), and judging the medication compliance condition according to the size of the medication compliance index AI, wherein the closer the AI value is to 100%, the better the compliance is.
Preferably, the actual dose completion rate
D is the actual number of doses taken over the compliance calculation period;
n is the number of medications taken within the compliance calculation period.
Preferably, the actual time accuracy of administration
i represents the ith dose and ranges from 1 to D, ViThe value of the fluctuation condition of the ith administration is 1 or 0, and the judgment is carried out by the following method:
if | Ti-T0If L is less than or equal to L, then Vi1; otherwise Vi0, wherein TiFor the ith actual time of administration, T0And L is the maximum allowable error of the set medication time and is a constant, and when all the actual medication times are within the maximum allowable error range of the medication time, the accuracy rate Q of the actual medication time is 1.
Preferably, the actual medication completion rate weight α and the actual medication time accuracy rate weight β satisfy: α + β is 1, 0< α ≦ 1; β < α.
Preferably, the actual medication time and number of times the patient took the medication can be recorded manually, by software, by smart medication devices, or by smart vials.
Preferably, when a user takes a plurality of medicines, the medication adherence index AI can be calculated for each medicine separatelykThen, the medication compliance indexes of all the medicines are weighted and averaged to obtain a comprehensive average medication compliance index
Wherein M is the number of the medicines to be taken together and is a positive integer, AIkIs the drug compliance index of the kth drug, k ranges from 1 to M, thetakIs the weight of the compliance index, θ, for the kth drugkIs constant, which is determined from pharmacokinetic or clinical data.
The invention has the beneficial effects that:
the medication compliance evaluated by the method can accurately judge the medication condition of patients in various aspects, and different alpha and beta can be selected according to different diseases (such as antiviral treatment) and different drug metabolism (such as drugs with short half-life), so that the calculated compliance index has more guiding significance in guiding clinic.
Drawings
FIG. 1 is a flowchart of the method for evaluating drug compliance of the present invention
FIG. 2 is a graph showing the time record of the administration of the drug for approximately 30 days in the patient 1 in the example.
FIG. 3 is a graph showing the time record of the administration of the drug for approximately 30 days by patient 2 in the example.
Detailed Description
The invention will now be illustrated by way of example with reference to the accompanying drawings,
as shown in fig. 1, a method for evaluating drug compliance, the method comprising the steps of:
a. recording the actual medication time and the number of times of medication of the patient;
b. calculating the actual medication completion rate W according to the actual medication times and the set medication application times;
c. calculating the actual medication time accuracy Q according to the actual medication time and the set application time;
d. determining the weights alpha and beta of an evaluation model according to clinical data of a patient and pharmacokinetic parameters of the patient to obtain a calculation model of the patient, wherein the calculation model of the patient is as follows: AI ═ α · W + β · Q;
e. and (3) applying the actual medication completion rate and the medication time accuracy rate to a calculation model of the patient to obtain a medication compliance index AI (adherence index), and judging the medication compliance condition according to the size of the medication compliance index AI, wherein the closer the AI value is to 100%, the better the compliance is.
Actual rate of completion of medication
D is the actual number of doses taken over the compliance calculation period;
n is the number of medications taken within the compliance calculation period.
Actual time accuracy of medication
i represents the ith dose and ranges from 1 to D, ViThe value of the fluctuation condition of the ith administration is 1 or 0, and the judgment is carried out by the following method:
if | Ti-T0If L is less than or equal to L, then Vi1 is ═ 1; otherwise Vi0, wherein TiFor the ith actual time of administration, T0And L is the maximum allowable error of the set medication time and is a constant, and when all the actual medication times are within the maximum allowable error range of the medication time, the accuracy rate Q of the actual medication time is 1.
Therefore, the drug compliance Index (Adhernce Index)
The first part of the formula evaluates whether the medicine taking condition is satisfied (namely the medicine taking completion rate), the second part evaluates the medicine taking time fluctuation condition (namely the medicine taking time accuracy rate), and the medicine taking compliance index is comprehensively judged and calculated according to the two conditions.
As a more preferable example, when only the influence of two factors, i.e., the actual medication completion rate and the actual medication time accuracy rate, on the medication compliance is considered, the actual medication completion rate weight α and the actual medication time accuracy rate weight β satisfy: alpha + beta is 1, 0< alpha is less than or equal to 1, the compliance is better, the larger the AI value is, the closer to 100% is the final value, and the perfect state is equal to 100%; the maximum medication tolerance error L is determined by pharmacokinetic or clinical trial data analysis optimization. The following examples illustrate
If the patient takes the medicine each time and on time, the compliance parameter is the compliance index AI ═ α + β ═ 100%
If the patient takes the medication each time, but the timing of the medication administration fluctuates too much, then the second part of the above equation
It will be less than 1 and will,
will be less than beta, then the compliance index is not 100% although the patient takes the drug daily. The first part of the formula quantifies whether the patient takes medicine or not, and the second part quantifies whether the patient takes medicine on time or not; and combining the compliance of taking medicine or not with the compliance of taking medicine or not on time according to the weight of each part to calculate the compliance index.
As shown in FIG. 2, the administration time of the patient 1 is recorded about 30 days, and the patient 1 takes the antiviral drug every day, and the administration time is very consistent every day, so that the administration rule can reduce the possibility of the generation of the viral drug resistance. Fig. 3 is a record of the administration time of the patient 2 for about 30 days, and the patient 2 is also administered with the antiviral drug every day, but the administration time fluctuates greatly, and the administration rule increases the probability of viral drug resistance.
If the doctor consults according to the conventional method, it may be difficult to find the problem of fluctuation of the administration time. Since the patient is actually taking the medicine every day, the patient himself or herself is likely to be completely unaware of the difference in the timing of taking the medicine. If the traditional pil count or MPR calculation method is adopted and the medicine taking result is simply checked, both patients have 100% compliance; but clearly the two patients have different rates of viral resistance. If the compliance index is calculated according to the technical scheme of the invention, the compliance index of the first patient is 100 percent; and the second patient can be less than beta due to the fluctuation of the medicine taking time, so that the compliance index is less than 100 percent, and the clinical prognosis condition is more consistent with the actual medicine taking condition of the second patient. From the perspective of the physician, by looking at whether the problem is found in the first or second part of the compliance index, the physician can also help them quickly determine where the potential problem is and how to perform targeted intervention.
The values of α and β may be related to the pharmacokinetics of the particular drug being administered. For example, if the half-life of the drug to be taken is short, the time for taking the drug is important for maintaining stable blood concentration, and the value of beta is relatively large; if the difference between the medication times of the medication taken by several hours has no effect on the blood level, the difference is very small or even zero, and generally the beta is less than alpha.
The choice of alpha and beta is also related to the clinical condition of the patient. For example, the medicine is also an antiviral medicine, and is applied to the treatment of AIDS patients, the requirement on timely medicine taking is higher, so that the generation of drug-resistant viruses is reduced as much as possible, and beta is larger; for cold patients, beta may be smaller.
It is worth mentioning that as a new index, the values of α and β will vary depending on the specific clinical application, but can be determined by optimization of the clinical trial data analysis.
Based on clinical data, for general diseases and drug treatments, α ═ 0.9, β ═ 0.1, and L ═ 1 hour can be selected; for treatments that are susceptible to drug resistance, such as antiviral treatment, α -0.7, β -0.3, and L-0.5 hours may be selected.
The actual medication time and the number of times of medication of the patient can be recorded manually or can be recorded by intelligent medication equipment or an intelligent medicine bottle. When clinical data is used to determine α and β, once it is known whether the patient should take the drug each time, and the actual time of taking the drug differs from the time of taking the drug, different combinations of selected values of α, β and L can be tried to calculate the compliance index. After a certain scale of patient data (number of people, follow-up time, clinical prognosis, etc.), the clinical prognosis of the patient can be correlated with the calculated compliance index, and the compliance index obtained by combining selected values of alpha, beta and L is calculated to have the highest correlation coefficient with the clinical prognosis (such as the generation of drug resistance, such as mortality) so as to determine the appropriate alpha, beta and L. It is worth noting that the choice of clinical prognostic indicators will vary, as will the optimal combinations of values for α, β and L. The invention not only quantifies the compliance assessment method, but also closely combines the calculation of the compliance index with the specific clinical application. The selected values of the alpha, the beta and the L can change along with the change of clinical prognostic indicators, and the invention adapts to the monitoring requirements of different prognostic indicators, thereby having important guiding significance in the aspect of more accurately managing diseases.
When a user takes a plurality of medicines, the medication compliance index AI can be calculated for each medicine separatelykThen, the medication compliance indexes of all the medicines are weighted and averaged to obtain a comprehensive average medication compliance index
Wherein M is the number of the medicines to be taken and is a positive integer, AIkIs the drug compliance index of the kth drug, k ranges from 1 to M, thetakIs the weight of the compliance index, θ, for the kth drugkIs constant, which is determined from pharmacokinetic or clinical data.
θkThe value of (c) can be obtained by averaging each drug. For example, a patient may take two medications simultaneously, each with a theta of 50%. Clinically, theta can also be selected according to actual requirements, for example, for an AIDS patient with elevated blood fat, theta with a larger value can be selected for antiviral drugs, and theta with a smaller value can be selected for drugs for controlling blood fat. In addition, the method of clinical trial can also be adopted,selecting different theta weighted average combinations, wherein the theta combination with the best prediction clinical prognosis is the best theta combination.
Claims (4)
1. A method for evaluating drug compliance, comprising the steps of:
a. recording the actual medication time and the actual medication times of the patient through intelligent medication equipment;
b. calculating the actual medication completion rate W according to the actual medication times and the set medication application times;
c. calculating the actual medication time accuracy Q according to the actual medication time and the set application time;
d. determining the weights alpha and beta of an evaluation model according to clinical data of a patient and pharmacokinetic parameters of the patient to obtain a calculation model of the patient, wherein the calculation model of the patient is as follows: AI ═ α · W + β · Q;
e. the actual medication completion rate and the medication time accuracy rate are used for obtaining a medication compliance index AI by using a calculation model of the patient, and the medication compliance condition is judged according to the size of the medication compliance index AI, wherein the closer the AI value is to 100%, the better the compliance is;
wherein the actual dose completion rate
D is the actual number of doses taken over the compliance calculation period;
n is the number of times of administration within the compliance calculation period;
actual medication time accuracy
i represents the ith dose and ranges from 1 to D, ViThe value of the fluctuation condition of the ith administration is 1 or 0, and the judgment is carried out by the following method:
if | Ti-T0If L is less than or equal to L, then Vi1 is ═ 1; otherwise Vi0, wherein TiFor the ith actual time of administration, T0And L is the maximum allowable error of the set medication time and is a constant, and when all the actual medication times are within the maximum allowable error range of the medication time, the accuracy rate Q of the actual medication time is 1.
2. The method of claim 1, wherein the method comprises: the actual medication completion rate weight alpha and the actual medication time accuracy rate weight beta satisfy: α + β is 1, 0< α ≦ 1; β < α.
3. The method of claim 1, wherein the method comprises: the maximum allowable error L of the medication time is determined according to pharmacokinetic or clinical trial data analysis optimization.
4. The method of any one of claims 1 to 3, wherein: when a user takes a plurality of medicines, the medication compliance index AI can be calculated for each medicine separatelykThen, the medication compliance indexes of all the medicines are weighted and averaged to obtain a comprehensive average medication compliance index
Wherein M is the number of the medicines to be taken together and is a positive integer, AIkIs the drug compliance index of the kth drug, k ranges from 1 to M, thetakIs the weight of the compliance index, θ, for the kth drugkIs constant, which is determined from pharmacokinetic or clinical data.
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| CN108877954B (en) * | 2018-06-07 | 2022-05-17 | 中国医学科学院北京协和医院 | Medication compliance assessment method |
| CN110718304B (en) * | 2019-10-10 | 2022-09-27 | 电子科技大学 | Method for monitoring medication compliance of AIDS patient |
| CN111145845A (en) * | 2019-12-20 | 2020-05-12 | 四川大学华西第二医院 | Block chain based anti-tumor drug grading management and tracking medication compliance system |
| CN113380408B (en) * | 2021-06-04 | 2023-06-02 | 合肥巧士健康科技有限责任公司 | Medicine taking behavior analysis system based on behavior-time guardrail |
| CN113488133A (en) * | 2021-07-13 | 2021-10-08 | 中国人民解放军联勤保障部队第九六〇医院 | Medicine taking reminding mobile phone application software for resisting helicobacter pylori infection |
| CN113707263B (en) * | 2021-08-31 | 2023-09-12 | 平安科技（深圳）有限公司 | Drug effectiveness evaluation method and device based on group division and computer equipment |
| CN114420244A (en) * | 2022-02-28 | 2022-04-29 | 北京大学第三医院（北京大学第三临床医学院） | Medication analysis method and device for Parkinson disease patient |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104537265A (en) * | 2015-01-15 | 2015-04-22 | 曹庆恒 | Method, system and equipment for intelligently computing personalized rational medication time |
| CN105701356A (en) * | 2016-02-24 | 2016-06-22 | 黄文佳 | Patient medication compliance judging method |
| CN107229833A (en) * | 2017-06-14 | 2017-10-03 | 重庆新三禾网络科技有限公司 | Methods of marking and system are followed the trail of in a kind of medication |
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| US20120203573A1 (en) * | 2010-09-22 | 2012-08-09 | I.D. Therapeutics Llc | Methods, systems, and apparatus for optimizing effects of treatment with medication using medication compliance patterns |
| CN106599554A (en) * | 2016-12-01 | 2017-04-26 | 广东省中医院 | Method for monitoring medication based on renal function level of patient |
| CN107391941A (en) * | 2017-07-26 | 2017-11-24 | 上海科瓴医疗科技有限公司 | A kind of method and system for improving compliance |
| CN108877954B (en) * | 2018-06-07 | 2022-05-17 | 中国医学科学院北京协和医院 | Medication compliance assessment method |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104537265A (en) * | 2015-01-15 | 2015-04-22 | 曹庆恒 | Method, system and equipment for intelligently computing personalized rational medication time |
| CN105701356A (en) * | 2016-02-24 | 2016-06-22 | 黄文佳 | Patient medication compliance judging method |
| CN107229833A (en) * | 2017-06-14 | 2017-10-03 | 重庆新三禾网络科技有限公司 | Methods of marking and system are followed the trail of in a kind of medication |
Non-Patent Citations (1)
| Title |
|---|
| 患者用药依从性及其影响因素分析;张新平 等;《中国药房》;20061231;第17卷(第10期);第791-793页 * |
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