CN109243564A - A kind of drug risk assessment system - Google Patents
A kind of drug risk assessment system Download PDFInfo
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- CN109243564A CN109243564A CN201811113677.0A CN201811113677A CN109243564A CN 109243564 A CN109243564 A CN 109243564A CN 201811113677 A CN201811113677 A CN 201811113677A CN 109243564 A CN109243564 A CN 109243564A
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Abstract
A kind of drug risk assessment system, including information collection module, reliability evaluation module, for the information collection module for collecting risk medication information, the risk medication information includes risk prescription information, accumulation of risks temporal information, risk times for spraying；The reliability evaluation module is used to assess risk medication information substitution reliability growth classics parameter model, obtains average time between failures, and then obtain system reliability Growth Evaluation result.Above-mentioned technical proposal is assessed by introducing reliability growth classics parameter model, can be provided technical indicator for the risk prevention system in entire medical diagnosis and treat system, be provided visual suggestion for pharmaceutical drugs management.It is calculated by the reliability to every risk, to achieve the effect that visual cues systematic risk.
Description
Technical field
The present invention relates to use caused by human risks in medical diagnosis and treatment systems design area more particularly to a kind of diagnosis and treatment process
Medicine risk evaluating system.
Background technique
Unreasonable medication phenomenon still remains in the actual clinical treatment in China, is embodied in and uses taboo to special population
The unreasonable combined effect of drug, overdose, repeat administration, drug etc. [3].It is exactly all that Irrational Use of Drugs, which sums up,
Non-security as caused by human factor, effective, economic, appropriate medication [4].The convalescence of this research emphasis concern cerebral infarction
Clinical application situation, i.e., it has often been said that stroke in convalescent stage rational use of medicines situation.Cranial vascular disease (Cerebrovascular
Disease, CVD) it ranked third position, and the adult first cause disabled in the cause of disease of world population's death, while still
Cause the one of the major reasons [5] of elderly population cognition dysfunction, the disturbance of emotion.Cerebral infarction, be known as in Chinese medicine " apoplexy " or
" stroke ", China are the high-incidence countries of cranial vascular disease, and annual disease incidence is about (185~219)/100,000, estimate annual new hair
There are about 2,000,000 for CVD case, will have 1,500,000 people to die of cranial vascular disease every year, and survivor has 7,000,000 people [6], thus may be used
See that rapid growth trend is presented in the disease incidence of Chinese cranial vascular disease, the death rate, and cerebral apoplexy has seriously threatened in China always
The health [7] of year crowd.Since the related laws and regulations of China's Drug Administration and measure are incomplete, regional plus remote segment
It will all be led with the factors such as thin of lack of standardization, patient's rational use of medicines consciousness of the shortage of poverty-stricken area medical resource, disease treatment
Cause on the clinical treatment of cerebral infarction medication it is unreasonable
Summary of the invention
It is capable of the data of analysis system risk medication for this reason, it may be necessary to provide one kind and provides reliability growth judgement assessment
As a result system, the safety of quantization system improve safe medication reliability.
To achieve the above object, a kind of drug risk assessment system is inventor provided, including information collection module, reliable
Property Growth Evaluation module,
The information collection module includes risk prescription letter for collecting risk medication information, the risk medication information
Breath, accumulation of risks temporal information, risk times for spraying；The reliability evaluation module is used to substitute into risk medication information
Reliability growth classics parameter model is assessed, and average time between failures is obtained, and then is obtained system reliability growth and commented
Estimate result.
Specifically, the risk prescription information further includes risk classifications, specifically includes pharmacology risk classifications, physiology risk class
Type, the pharmacology risk classifications are more than two medicament compatibilities that mistake is had recorded in doctor's advice or prescription, the physiology risk
Type is the taboo medicament being had recorded in doctor's advice or prescription to specific crowd；It further include categorization module, the categorization module is used for
Statistic of classification is carried out to risk medication information according to different risk classifications, the reliability evaluation module is also used to not
Risk medication information with risk classifications substitutes into reliability growth classics parameter model, calculates separately system reliability Growth Evaluation
As a result.
It specifically, further include trend test module, test of fitness of fot module, the trend test module is used for collection
The risk medication information data arrived carries out trend test, and the test of fitness of fot module is used to believe the risk medication being collected into
Breath data are fitted goodness inspection, in the case where the two meets simultaneously, reliability evaluation module risk medication information
Risk medication information is just used for the assessment of reliability growth classics parameter model.
Optionally, the reliability growth classics parameter model is assessed, obtained average time between failures θcAre as follows:
θc(tj)=tj/N(tj) (j=1,2 ..., n)
Wherein tjTo accumulate test period t1,t2,...,tn, N is cumulative failure number.
It is different from the prior art, above-mentioned technical proposal is assessed by introducing reliability growth classics parameter model, energy
Technical indicator enough is provided for the risk prevention system in entire medical diagnosis and treat system, provides visual suggestion for pharmaceutical drugs management.
It is calculated by the reliability to every risk, to achieve the effect that visual cues systematic risk.
Detailed description of the invention
Fig. 1 is drug risk appraisal procedure flow chart described in specific embodiment；
Fig. 2 is system transient failure rate curve graph described in specific embodiment；
Fig. 3 is the bar chart of the MTBF value of each failure type described in specific embodiment；
Fig. 4 is drug risk assessment system module map described in specific embodiment；
Fig. 5 is medicine assisting in diagnosis and treatment system module figure described in specific embodiment；
Fig. 6 is drug risk data query interface described in specific embodiment；
Fig. 7 is that risk doctor's advice described in specific embodiment counts interface；
Fig. 8 is risk order data administration interface described in specific embodiment.
Specific embodiment
Technology contents, construction feature, the objects and the effects for detailed description technical solution, below in conjunction with specific reality
It applies example and attached drawing is cooperated to be explained in detail.
Referring to Fig. 1, the present embodiment includes the following steps that S100 is received for a kind of drug risk appraisal procedure in the present invention
Collect risk prescription information, the risk medication information includes risk prescription information, accumulation of risks temporal information, risk medication time
Number, S102 substitute into AMSAA reliability growth classics parameter model according to risk medication information and are assessed, and S104 obtains system can
By property Growth Evaluation result.In our embodiment, drug risk is the objective feelings of the medicining condition relative to manual operation
Condition assessment, therefore, if there is the means of science steadily objectively to be assessed, it will help improve the treatment effect of medical system
Fruit.
We will carry out a definition to drug risk data in the following embodiments, we are known as drug risk data
Or risk medication information includes risk prescription information, mainly there are two risk classifications for tool, specifically include pharmacology risk classifications, life
Risk classifications are managed, the pharmacology risk classifications are more than two medicament compatibilities that mistake is had recorded in doctor's advice or prescription, described
Physiology risk classifications are the taboo medicament being had recorded in doctor's advice or prescription to specific crowd；It further include step, according to different wind
Dangerous type carries out statistic of classification to risk medication information, substitutes into reliability growth warp to the risk medication information of different risk classifications
Allusion quotation parameter model calculates separately system reliability Growth Evaluation result.Specifically, the method for the present invention can be adapted for having doctor
In the arbitrary system that teacher's prescription information is collected, for being directed to the prescription of doctor, it is able to carry out record, and carry out for prescription
It divides or keyword is taken passages.For example, record risk medication information.To use system for the cerebral infarction convalescence clinic road of applicant
For diameter system, when the prescription entry that doctor issues in cerebral infarction convalescence clinical path violates in rational use of medicines rule base
When regular, this record is just defined as drug risk data/risk medication information, that is, generates or there may be Irrational Use of Drugs feelings
The order data of condition.Such as: activity liver patient disabling is explicitly pointed out in the contraindication of drug ethyldopa, if doctor does not have
Patient's past medical history is known about, to there is the patient of activity hepatopathy to issue ethyldopa, then this is just recorded conduct by system
Drug risk database is recorded in drug risk data.Drug risk data management module in systems can be according to above-mentioned wind
Dangerous type carries out classification storage, convenient for later use reliability growth classics parameter model come to the drug risk data of acquisition into
Row analysis, and result is fed back into authorities so that hospital makes medication adjustment in time, while it is reasonable also to allow seminar to improve
The function of Drug use administration system.
By consulting the cases history of attached convalescent home's Medical Records Dept. Patients with Cerebral Infarction and documents and materials and consulting being combined to face
Medical personnels, the systems such as bed doctor, clinical pharmacist devise drug interaction examination, adverse reaction examination, conscience kidney function
Can not full medication examination, indication examine, injection compatibility of drugs examinations, the elderly's medication examination, drug usage and dosage examination, give
Medicine approach examines, repeated drug taking examines, contraindication examines that the risk project of totally ten aspects examines.
But since the energy and time of research is limited, it is incomplete that the application is substantially carried out adverse drug reaction, darling renal function
Medication study in terms of medication, drug interaction, indication examination four.And it is defined as four kinds of drug risk data types, medicine
The infull medication B of object adverse reaction A, darling renal function, drug interaction C, indication examine D, and wherein AC can be referred to as pharmacology
Risk classifications, BD can be referred to as physiology risk classifications.Each type can be related to a variety of drugs, specific drug risk reason,
The drug being related to is recorded according to practical medication.
This research by the examination management module in path by the data of rational use of medicines rule base come to canonical prescriptions,
Rational drug use is examined.When the rule during the prescription entry that doctor issues in the paths breaks the rules library, just this is remembered
Record is defined as fail data, and system can be automatically logged into fail data library.Such as: drug ethyldopa is wanted in medication contraindication
Activity liver patient has been asked to disable, if doctor has issued ethyldopa to the patient for having activity hepatopathy, system just will
This record is recorded in fail data library as fail data.We can also define the type of risk: further, described
Risk prescription information further includes risk classifications, specifically includes pharmacology risk classifications, physiology risk classifications, the pharmacology risk classifications
To have recorded more than two medicament compatibilities of mistake, Shu Xue-tong as shown in Table, aspirin compatibility in doctor's advice or prescription
Adverse reaction can be generated, the physiology risk classifications are the taboo medicament being had recorded in doctor's advice or prescription to specific crowd；Also wrap
Step is included, statistic of classification is carried out to risk medication information according to different risk classifications, the risk medication to different risk classifications
Information substitutes into reliability growth classics parameter model, calculates separately system reliability Growth Evaluation result.
Illustrated in below table of the invention: drug interaction, hepatic and kidney function obstacle, the elderly's medication risk
Type, totally 15 kinds of fail data types.
1 failure type table of table
The World Health Organization (WHO) is physiological age >=65 year old to the definition of the elderly
Data in table 1 are in addition to needing the database information in clinical path, it is also necessary to the patient information data with hospital
Library, pharmacy databases, information about doctor database etc. are associated.
It specifically, further include trend test step, test of fitness of fot step, to collection after the collection of risk medication information
The data arrived carry out trend test, and the test of fitness of fot, in the case where the two meets simultaneously, risk medication information is just used for
The assessment of reliability growth sight spot parameter model.
In the embodiment being introduced below, we will focus on how introduction carries out actual appraisal procedure, such as we can
With design data sheet, generation number, the out-of-service time, work accumulated time, processing time of every kind of fail data are calculated.Here
Generating number is number of the system grabs to failure type；Out-of-service time be containing this failure type prescription assigning the time with it is upper
Secondary fail data prescription assigns the time difference between the time；Work accumulated time is that this failure prescription assigns time and experiment opening
The time difference of beginning；Handling the time is the time used in the processing of doctor after there is prescription danger hidden danger.
Analysis assessment is carried out using AMSAA model for this system data.The advantages of AMSAA model is mean time between failures
The point estimation precision of time (MTBF) is higher, and can provide the interval estimation of current MTBF.
System reliability Analysis in Growth
(1) trend test
U method of inspection is taken, step:
Test statistics μ is calculated, for fixed time test, test statistics is
M=n=Failure count in formula.
According to given level of significance α, the tables of critical values for looking into trend test statistic μ can obtain the critical of test statistics
Value μ1-α/2.By test statistics μ and critical value μ1-α/2It is compared.As μ <-μ1-α/2When, show have with level of significance α/2
Apparent reliability growth trend；As μ > μ1-α/2When, show there is apparent reliability decreasing trend with level of significance α/2；
As-μ1-α/2 < μ < μ1-α/When 2, no apparent reliability variation tendency is shown with level of significance α.
(2) test of fitness of fot
It generallys use Cramer-Von Mises method of inspection and is fitted goodness inspection.
Utilize the test number t of sequence1<t2<…<tnDigital simulation goodness inspection statistics valueFor
It is tabled look-up according to the level of significance α of the selected test of fitness of fot and obtains test of fitness of fot statisticFace
Dividing valueIfIt then indicates not refusing AMSAA model, it is on the contrary then refuse AMSAA model, the model cannot be used
Fitting system.
The Maximum-likelihood estimation of MTBF
Assuming that the Reliability Growth Analysis of repairable system is in the truncation of moment T, the system successive out-of-service time is successively
For 0=t0<t1<t2<…<tn<T (n≥1)
The Maximum-likelihood estimation of a, β is
The unbiased esti-mator of βFor
Corresponding, it is estimated as a, θ (T)
Relevant parameter calculates
(1) test trend statistic μ value is calculated according to formula (1),
Table look-up to obtain the corresponding μ in α=0.05α/2=-1.960, therefore work as μ < μα/2When, system shows a marked increase.
(2) the transient failure rate λ of moment t is calculated with following equation(t)
In formula: a is scale parameter, a > 0；M is growth rate, 0 < m < 1.
(3) calculating of a, m
The least-squares estimation (Least Squares Estimation, LSE) of parameter is by accumulation test period t1,
t2,...,tnAnd its corresponding cumulative failure times N (t1),N(t2),...,N(tn) obtain accumulation MTBF:
θc(tj)=tj/N(tj) (j=1,2 ..., n) (7)
Had according to AMSAA model:
Therefore, residual sum of squares (RSS) are as follows:
The least-squares estimation (LSE) of available a and m in the smallest situation of residual sum of squares (RSS) are as follows:
According toIt calculatesValue
Therefore, available system transient failure rate curve graph is as shown in Figure 2.
System reliability Analysis in Growth based on AMSAA model
Analysis assessment is carried out using AMSAA model to the implementation phase of MES.The advantages of AMSAA model is that the point of MTBF is estimated
It is higher to count precision, and the interval estimation of current MTBF can be provided.Test of fitness of fot etc. is as shown in table 2.
2 reliability growth result of table and Trend judgement table
Upper table calculating process is referring to table 2.
According to table 2, the bar chart for the MTBF value for improving each failure type in front and back is drawn as shown in Fig. 3.
System transient failure rate curve comparison is drawn reliability growth trend test by the calculating for carrying out foregoing character again
Figure is as shown in Figure 3
By above-mentioned design, the drug risk appraisal procedure of the present invention program can be by collecting the diagnosis and treatment in the testing time
Prescription judges the type of error and number of errors of diagnosis and treatment prescription by set rule, according to number of errors and testing time
Etc. indexs combination reliability growth model, be able to reflect out under Different Rule, the doctor in system abide by the rule effect it is good
It is bad, such as reliability growth, then it is assumed that rule is executed well, and the probability of whole medical treatment system errors prescription can be smaller,
The management level for also reflecting the medical system simultaneously is higher.And conversely, at least can if the result of reliability growth is undesirable
Enough reflect that the medication rule is performed not properly, is said toward small, need it is subsequent strengthen in doctor group it is certain specifically
The warning of medication rule is said toward big to be better protected from the birth of malpractice, can reflect the risk management of medical system
It is horizontal insufficient, need management level further note that can also preferably be held by alteration ruler itself
Row.Speech and in short, above-mentioned drug risk appraisal procedure better solved drug risk assessment quantification problem.With good
Practical function.
In the embodiment shown in fig. 4, the present invention program also describes a kind of drug risk assessment system, can run on
It is stored on the computer processor of aforementioned drug risk appraisal procedure operation program, executes use by working medias such as processors
The technical solution of medicine risk assessment may include in the present embodiment that 400 information collect mould with oh risk assessment application system
Block, 402 reliability evaluation modules,
The information collection module includes risk prescription letter for collecting risk medication information, the risk medication information
Breath, accumulation of risks temporal information, risk times for spraying；The reliability evaluation module is used to substitute into risk medication information
Reliability growth classics parameter model is assessed, and average time between failures is obtained, and then is obtained system reliability growth and commented
Estimate result.
Specifically, the risk prescription information further includes risk classifications, specifically includes pharmacology risk classifications, physiology risk class
Type, the pharmacology risk classifications are more than two medicament compatibilities that mistake is had recorded in doctor's advice or prescription, the physiology risk
Type is the taboo medicament being had recorded in doctor's advice or prescription to specific crowd；It further include 404 categorization modules, the categorization module
For carrying out statistic of classification to risk medication information according to different risk classifications, the reliability evaluation module is also used to
Reliability growth classics parameter model is substituted into the risk medication information of different risk classifications, calculates separately system reliability growth
Assessment result.
It specifically, further include 406 trend test modules, 408 test of fitness of fot modules, the trend test module is used for
Trend test is carried out to the risk medication information data being collected into, the test of fitness of fot module is used for the risk being collected into
Medication information data is fitted goodness inspection, and in the case where the two meets simultaneously, reliability evaluation module risk is used
Risk medication information is just used for the assessment of reliability growth classics parameter model by medicine information.
Optionally, the reliability growth classics parameter model is assessed, obtained average time between failures θcAre as follows:
θc(tj)=tj/N(tj) (j=1,2 ..., n)
Wherein tjTo accumulate test period t1,t2,...,tn, N is cumulative failure number.
In further embodiments, as shown in figure 5, above-mentioned drug risk assessment system, can be used as embedded system (son
System) design is in the medical consultations system of more high-level, and therefore, the present invention also provides a kind of medicine assisting in diagnosis and treatment systems to set
Meter, including following module, regular library module 500, sufferer information logging modle 502, prescription information logging modle 504, sufferer letter
Breath logging modle and prescription information logging modle are outputed for recording sufferer information, prescription for recording doctor for specific sufferer
Prescription, for general prescription, need to record prescription outputs doctor and object sufferer, sufferer information record packet
The various sufferer relevant informations such as age, gender, medical history, contraindication have been included, and these information also believe aforementioned drug risk
Foundation is provided to the judgement that people's medication fault is physiology risk classifications in breath.Such as forbid the wrong medicine of open the elderly
Whether can be then greater than 65 years old by age information and the rule judgement of prescriptions keyword while satisfaction is to search.And it is advising
Then in library module, rule base is just used to record the above-mentioned various keyword decision rules for forbidding occurring simultaneously, if in prescription information
And there is the keyword for meeting block rule simultaneously in object sufferer information, then the prescription is necessarily judged as wrong prescription, and single
Occurs the case where keyword for the certain medications for forbidding compatibility in a prescription information.
It further include error statistics module 506 in our assisting in diagnosis and treatment system to preferably carry out assisting in diagnosis and treatment,
In error statistics module, the medication prescription information for being judged as wrong prescription according to rule base can be carried out collect statistics, system
Meter method include classified according to medication type of mistake, the medication rule that violates is classified, is counted according to doctor, root
Count according to doctor team etc..The statistical result of above-mentioned error statistics module can be transferred in display module, be opened up
Show, is known by user.Error statistics module can pass through embedded form function (as described above table 1, table 2 etc.), built-in system
It is accomplished come the mode for realizing and exporting certain subfield data to count plug-in unit.
In rule base, we can also modify in itself to rule, modify while occurring being determined as mistake prescription
Keyword rule, can also increase or delete the rule of judgement, and rule base itself is made to can satisfy the needs of medical technology development.
And after having the help of error statistics module, drug risk evaluation subsystem can preferably carry out accordingly may be used
It is analyzed by property, specifically, display module may include the interactive interface with user, our diagnosis and therapy system receives user to risk
The specific statistic of classification of medication information selects, and such as classifies according to doctor's classification, according to risk classifications, according to rule classification,
The test accumulated time and test failure number that corresponding statistical result is obtained according to specific classification substitute into drug risk and assess subsystem
Average time between failures θ can be obtained in system by modelc:
θc(tj)=tj/N(tj) (j=1,2 ..., n)
Wherein tjTo accumulate test period t1,t2,...,tn, N is cumulative failure number.Certain drug risk assesses subsystem
System still is able to complete the other function that above scheme is recorded.
By above-mentioned design, the medicine assisting in diagnosis and treatment system of the present invention program can by interface alternation, typing doctor and
Patient information is recorded with corresponding prescription information, preferably can be carried out statistical classification to wrong medicine information, be met auxiliary
Help the demand of error prompting in medical diagnosis, and further when classification data by drug risk evaluation subsystem institute in application,
Present system scheme can preferably judge the relevant fail-safe analysis evaluation result of each index during medication.
In some other specific embodiment part of the invention, in order to effectively extract the design of rational use of medicines rule base
Including two parts, first is that drug data acquisition management module, second is that conventional therapy and prescription data management module.
In drug data acquisition management module, by package insert, Pharmacopoeia of People's Republic of China (2015 editions), middle Chinese
People republic pharmacopeia clinical application notice (version in 2015), Chinese rehabilitation of stroke patients treatment guidelines (2011 editions), China are acute scarce
Hemorrhagic cerebral apoplexy diagnosis and treatment guide (2014 editions), country defend the cerebral infarction class disease rational use of medicines guide of the newest publication of planning commission, such as: high
Blood pressure rational use of medicines guide[36]The tutorial message used Deng drug in authoritative information carries out basic data acquisition.When due to research
Between and energy limited research only accomplish to the above standard midbrain infarction treat common drug information carry out edit.
Furthermore this module is also associated with hospital information management system, hospital pharmacy database, clinical path dictionary library, with
Ensure the complete, accurate and authoritative of rule base data.
Medicine information database can also be associated with hospital pharmacy database information, the drug including drug by data-interface
It is number, drug trade name, Drug generic name, indication, usage and dosage, drug interaction, contraindication, adverse reaction, special
These partial contents of crowd's medication detailed rules and regulations.
Country is defended under the authoritative departments such as planning commission, State Administration of Traditional Chinese Medicine in conventional therapy and prescription data management module
The therapeutic scheme recommended in the diagnosis and treatment guide of the convalescent brain infarction of hair carries out typing, inquires for doctor and manages mould as examining
One of examination standard of block.Above-mentioned drug data acquisition management module can preferably meet rule base foundation and perfect need
It asks.
Summarize information above building rational use of medicines rule base, rational use of medicines rule base database form design is as shown in table 1.
1 rational use of medicines rule base database list of table
It further include examining management module design in other embodiments of this hair invention
This module includes that customized examination rule, medication adaptability examine two parts.
(1) customized examination rule
This module be in rational use of medicines rule base without reference to examination data supplement.Selection institute is to be added
Examine type, add the contents such as corresponding project name, logic setting, parameter setting, comment, then submission system and by
Administration office of the hospital is audited, pending to pass through rear write-in rational use of medicines rule base.
(2) Rational drug use examines
Come by examination standard of the information of rational use of medicines rule base to the doctor's advice information in cerebral infarction convalescence clinical path
It is examined, and provides detailed examination result, the result finally examined can directly carry out examining management data query.Due to
Related examine immediately is carried out to doctor's advice medicining condition and does not meet actual conditions, after all drug effect, see whether that there are bad
Reaction etc. needs the regular hour, so subsequent review mechanism is taken in the doctor's advice examination of this research.
It preferably, further include drug risk data management module by pre-defining medication in rational use of medicines management system
Risk data type, acquires the drug risk data in predetermined time period, and serviceability increases classical parameter model to this
Part drug risk data carry out Reliability Growth Analysis, and give clinician's drug safety correlation warning information in time, mention
The reasonability of Gao doctor's medication during cerebral infarction convalescent care.
Drug risk data management submodule includes: drug risk data query, risk doctor's advice statistics, drug risk data
Manage three parts.Analysis of Reliability Data is to be inferred under cerebral infarction convalescence clinical path system actual application environment
Doctor's medication reliability level, including data collection, trend test, the inspection of the goodness of fit, characteristic quantities calculate,
Such as the estimation four processes of average time between failures (Mean Time Between Failure, MTBF).MTBF is reliability
Increase the noun calculated and refers to that there are the average time intervals that the doctor's advice of drug risk occurs in our current research.
Because cerebral infarction convalescence clinical path system is integrated with HIS, EMR, LIS, RIS/PACS system application, close
The drug risk data management module of reason Drug use administration system can carry out history medical record data in these information systems of hospital
It calls, to analyze the not online preceding doctor's advice medicining condition of rational use of medicines management system.
(1) drug risk data query
Its function is screened to the drug risk doctor's advice of the cerebral infarction of selected period.The pass of inquiry is selected first
Key word, including the infull medication examination of drug interaction examination, adverse reaction examination, darling renal function, indication examination etc.；With
Family selects corresponding type according to demand, then sets the period to be examined, finally clicks search key, that is, show and inquired
The doctor's advice prescription data arrived, and show medication advisory information, system interface is as shown in Figure 6.
(2) risk doctor's advice counts
This module be used to the drug risk doctor's advice generated in the selected period is counted, select to be counted when
Between section, click confirming button, system can by this period generate drug risk doctor's advice show in the form of cake chart, including production
Type, the accounting situation of raw risk medication, interface are as shown in Figure 7.
(3) drug risk data management
The function of this module is to be arranged, sorted out to the drug risk doctor's advice of seclected time period, for use can in next step
Increase classical parameter model by property to be assessed and analyzed, acquires and arrange drug risk data.System interface is as shown in Figure 8.
The setting of specific entry and collection process will be described in detail in the 4th part of chapter 3 in figure.
It should be noted that being not intended to limit although the various embodiments described above have been described herein
Scope of patent protection of the invention.Therefore, it based on innovative idea of the invention, change that embodiment described herein is carried out and is repaired
Change, or using equivalent structure or equivalent flow shift made by description of the invention and accompanying drawing content, it directly or indirectly will be with
Upper technical solution is used in other related technical areas, is included within scope of patent protection of the invention.
Claims (4)
1. a kind of drug risk assessment system, which is characterized in that including information collection module, reliability evaluation module,
For the information collection module for collecting risk medication information, the risk medication information includes risk prescription information, wind
Dangerous integration time information, risk times for spraying；The reliability evaluation module is used to substitute into risk medication information reliable
Property increase classical parameter model and assessed, obtain average time between failures, and then obtain system reliability Growth Evaluation knot
Fruit.
2. drug risk assessment system according to claim 1, which is characterized in that the risk prescription information further includes wind
Dangerous type, specifically includes pharmacology risk classifications, physiology risk classifications, and the pharmacology risk classifications are to record in doctor's advice or prescription
More than two medicament compatibilities of mistake, the physiology risk classifications are to have recorded in doctor's advice or prescription to specific crowd
Avoid medicament；It further include categorization module, the categorization module is used to carry out risk medication information according to different risk classifications
Statistic of classification, the reliability evaluation module, which is also used to substitute into reliability to the risk medication information of different risk classifications, to be increased
Long classics parameter model, calculates separately system reliability Growth Evaluation result.
3. drug risk assessment system according to claim 1, which is characterized in that further include trend test module, fitting
Goodness inspection module, the trend test module is used to carry out trend test to the risk medication information data being collected into, described
Test of fitness of fot module is used to be fitted the risk medication information data being collected into goodness and examine, and meets simultaneously in the two
In the case where, risk medication information is just used for reliability growth classics parameter by reliability evaluation module risk medication information
Model evaluation.
4. drug risk assessment system according to claim 1, which is characterized in that the reliability growth classics parameter mould
Type is assessed, obtained average time between failures θcAre as follows:
θc(tj)=tj/N(tj) (j=1,2 ..., n)
Wherein tjTo accumulate test period t1,t2,...,tn, N is cumulative failure number.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113488132A (en) * | 2021-06-16 | 2021-10-08 | 中南大学湘雅医院 | System for controlling risk of drug shortage |
| CN113488195A (en) * | 2021-06-11 | 2021-10-08 | 华中科技大学同济医学院附属协和医院 | Batch accurate medication decision support system |
| CN115359921A (en) * | 2022-10-20 | 2022-11-18 | 山东民昊健康科技有限公司 | Medical information storage sharing system based on data analysis |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107845411A (en) * | 2017-12-04 | 2018-03-27 | 青岛大学附属医院 | Clinical medication decision support system |
| CN108172302A (en) * | 2017-12-15 | 2018-06-15 | 广州市康软信息科技有限公司 | Rational use of medicines information monitoring method and system |
-
2018
- 2018-09-25 CN CN201811113677.0A patent/CN109243564A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107845411A (en) * | 2017-12-04 | 2018-03-27 | 青岛大学附属医院 | Clinical medication decision support system |
| CN108172302A (en) * | 2017-12-15 | 2018-06-15 | 广州市康软信息科技有限公司 | Rational use of medicines information monitoring method and system |
Non-Patent Citations (3)
| Title |
|---|
| 代凯等: "基于可靠性增长规划模型对脑梗死病临床用药监测与评价", 《海峡药学》 * |
| 罗强: "小型电磁阀可靠性增长试验的初步研究", 《阀门》 * |
| 董锡明: "《机车车辆运用可靠性工程》", 30 June 2002, 中国铁道出版社 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113488195A (en) * | 2021-06-11 | 2021-10-08 | 华中科技大学同济医学院附属协和医院 | Batch accurate medication decision support system |
| CN113488132A (en) * | 2021-06-16 | 2021-10-08 | 中南大学湘雅医院 | System for controlling risk of drug shortage |
| CN113488132B (en) * | 2021-06-16 | 2023-03-14 | 中南大学湘雅医院 | System for controlling risk of drug shortage |
| CN115359921A (en) * | 2022-10-20 | 2022-11-18 | 山东民昊健康科技有限公司 | Medical information storage sharing system based on data analysis |
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