CN108122607A - Patient Experience evaluation and test optimization service system is carried out based on big data - Google Patents
Patient Experience evaluation and test optimization service system is carried out based on big data Download PDFInfo
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- CN108122607A CN108122607A CN201810029738.9A CN201810029738A CN108122607A CN 108122607 A CN108122607 A CN 108122607A CN 201810029738 A CN201810029738 A CN 201810029738A CN 108122607 A CN108122607 A CN 108122607A
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Abstract
The present invention is proposed a kind of evaluated and tested based on big data progress Patient Experience and optimizes service system, including：Patient's master data acquisition module for patient registration's identity information account, after identity information verification verification, evaluates Hospital medical situation, collects several evaluation of patient data, forms big data acquisition storehouse item by item；Data sort out decimation blocks, after scoring for patient each evaluation column, pass through data preparation classifying mode, data are subjected to categorizing operation, the data wherein sorted out experience scoring, emergency treatment experience scoring, discharge comprehensive grading and later maintenance for experience scoring of being hospitalized, outpatient service and score, and invalid score data is rejected after data are sorted out；Assessment indicator analysis module for during patient is hospitalized, the different section office of different Patient Experiences to carry out scoring operation to hospital department, is sent to the data-optimized recommending module of analysis after the fraction of evaluation is sorted out according to department.
Description
Technical field
The present invention relates to computer big data analysis fields more particularly to a kind of big data that is based on to carry out Patient Experience evaluation and test
Optimize service system.
Background technology
As people give more sustained attention health, after sick Finding case physical condition is abnormal, Shi Biyao
Comprehensive inspection is carried out to hospital, delicate variation is occurring for doctor-patient relationship, and in this delicate variation, patient wishes
To better medical services, the pleasure of body and mind is enjoyed, due to being growing more intense for competition, hospital is also required to continuously improve itself
Respective services, but patient can't establish a kind of effective communication mechanism with hospital, cause the anxiety of doctor-patient relationship, patient
Also lack the objective basis of selection in face of various types hospital so as at a loss as to what to do, while hospital also has no way of finding out about it and itself needs to change
Into link, conclude computing by certain simple in the prior art, obtain some coarse evaluating datas, can not give patient with
Judge more accurately to see a doctor and move in experience.This just there is an urgent need for those skilled in the art solve it is corresponding the technical issues of.
The content of the invention
Present invention seek to address that technical problem in the prior art, especially innovatively proposes a kind of based on big data
Carry out Patient Experience evaluation and test optimization service system.
It is excellent based on big data progress Patient Experience evaluation and test the present invention provides one kind in order to realize the above-mentioned purpose of the present invention
Change service system, including：
Patient's master data acquisition module, it is right after characteristic information verification verification for collecting the characteristic information of patient
Hospital medical situation is evaluated item by item, and gridding covering is carried out to hospital, and hospital is divided into several grids, parallel acquisition doctor
The patient data of section office of institute collects several evaluation of patient data, forms big data acquisition storehouse, is cleared up for invalid data
It deletes；
Data sort out decimation blocks, after scoring for patient each evaluation column, are sorted out by data preparation
Data are carried out categorizing operation by mode, wherein the data sorted out experience evaluation for experience evaluation of being hospitalized, outpatient service, emergency treatment experience is commented
Valency, discharge overall merit and later maintenance evaluation, invalid evaluating data is rejected after data are sorted out；
Assessment indicator analysis module, for being hospitalized period in patient, the different section office of different Patient Experiences, to section of hospital
Room carries out scoring operation, and the data-optimized recommending module of analysis is sent to after the fraction of evaluation is sorted out according to department；
Analyze data-optimized recommending module, for by the data of hospital department assessment indicator analysis module carry out analysis and it is excellent
Change, demand user is recommended after the data after optimization are judged according to the criterion of setting.
Described carries out Patient Experience evaluation and test optimization service system based on big data, it is preferred that patient's master data
Acquisition module includes：
The data that patient evaluates item by item carry out typing operation by index information, when performing evaluating data operation, using elder generation
Internal data evaluation is arrived in peripheral data evaluation again, gradually deep evaluation level, which is evaluated as guiding link of being admitted to hospital,
The stage started to moving in hospital establishes parameter typing model, and foreground service link, the state of an illness are just sentenced according to parameter typing model
Link, link of whether performing the operation, anaesthetic injection pain link, nursing links, property safety link, link of periodically making the rounds of the wards, medication are changed
Cyclome section, auxiliary examination link, logistics support link, the data parameters of drug price link and Drug price addition link are adopted
Collection after terminating to peripheral data evaluation, carries out hospital internal data evaluation, which is evaluated as hospital staff couple
The evaluating data of hospital's entirety performs link by parameter typing model to job enthusiasm link, hospital policy, work performs ring
Section, the skilled link of technical ability, personnel's communication efficiency link, patients' privacy link of protection, diagnosis communication link and auxiliary technical ability link
Data parameters carry out typing.
Described carries out Patient Experience evaluation and test optimization service system based on big data, it is preferred that the data sort out sampling
Module includes：
It is calculated by the peripheral data evaluation and internal data evaluation that are obtained in patient's master data acquisition module all kinds of
Achievement data compares analysis using importance matrix analysis, cluster and carries out classification sampling to hospital's qualitative index；To patient service
Know from experience link and hospital's interior quality perceives link and sequence analysis is compared；With reference to the Patient Experience of medical act process link
It perceives, sample out contribution link and link of losing points will be sorted out in peripheral data evaluation and internal data evaluation, drawing influences patient
The main Score factor of experience and factor of losing points；To sorting out defect factors analysis and the assessment of efficiency disturbance degree in sampling process；So
The data sending after sampling will be sorted out afterwards to assessment indicator analysis module.
Described carries out Patient Experience evaluation and test optimization service system based on big data, it is preferred that the assessment indicator analysis
Module includes：
Medical satisfaction evaluation and test weight is established, score value of the patient in consulting services section office is calculated, first calculates patient's typing number
According to index average,Wherein, n is clinical samples number, and subscript i, j are data evaluation
Sequence number, j=1+i, Pki, PkjThe project of satisfaction evaluation, M are carried out for patientsFor patient participate in evaluation and test tag set, s ∈ 1,
2,…n}；
Module deviation product computing two-by-two is carried out to each patient, then summation operation is being carried out, is calculating as follows：
Wherein SQ[qi,qj]
It is evaluation keyword qiAnd qjSimilarity；
Evaluating data is normalized
Wherein, asIt clicks on and submits for patient
Evaluate collection, C is harmonic factor, dkIt is the binaryzation page parameter for evaluating keyword, z is the keyword for evaluating selection, WijFor
Medical satisfaction class weight.
Described carries out Patient Experience evaluation and test optimization service system based on big data, it is preferred that the analysis is data-optimized
Recommending module includes：
Calculate distinguishing valueWherein, FijTo participate in having for evaluation set in classification of assessment device
Imitate accuracy, tijIt is emotional category；T is emotional category space, is T={ sense organ (Sense), emotion (Feel), thinking
(Think), action (Act), relation (Relate) },It is to participate in medical satisfaction evaluation in emotional category tijOn classification just
True rate；
Use distinguishing value WijAnalysis optimization must be carried out, optimization data are then recommended into target patient by weight model,
The complex optimum is recommended to analyze weight model
Wherein Ri、RjExpired
The Term Weight of meaning degree evaluation, α is weight factor, and λ is average variation probability, CavgFor average satisfaction size.
In conclusion by adopting the above-described technical solution, the beneficial effects of the invention are as follows：
The big data medical patient experience optimization service system of the present invention, can precisely gather Patient Experience and quality of medical care is commented
Valency information, is analyzed by cloud platform, is provided detailed medical care evaluation monitoring report, and is changed for quality-improving, the Continuous Quality of hospital
Into offer evidence-based foundation；The investigation and analysis of third party's Patient Experience, comprehensive hospital quality estimating, section office's deep layer attributional analysis, quality
The services such as preferential improvement selection, hospital department quality sustained improvement monitoring are promoted, so as to be provided for patient in the service more optimized
Hold, recommend more accurately evaluating data.
1st, platform building and O＆M service：It is hospital including data cloud, collection terminal, application end, analysis end (three end of a cloud)
It establishes Patient Experience data cloud management system, establish Hospital Employee morale index test and appraisal database, and maintenance is provided and is taken with upgrading
Business with HIS (without docking).
2nd, data acquisition service：It is made of terminal device, optimizing and analyzing model and Test and analysis system, with regard to patient in hospital body
The data source tested, sample size calibration, sample size acquisition, intelligent data analysis etc. provide specialized service；
3rd, attributional analysis and emphasis monitoring service：By to hospital market feature, brand situation and emphasis medical act
The test and appraisal of process link and Hospital Connotation quality management module, the complete qualitative control present situation for reflecting hospital and section office.And it searches
The short slab loophole of problem link and management aspect proposes preferential recommendation on improvement and emphasis monitoring implementation effect, helps hospital and each
Section office promote hospital's quality；
4th, performance promotes service：According to analysis result, performance analysis is provided for hospital and section office, helps hospital and section office excellent
Change performance management, achieve the purpose that Dynamic Performance monitors.
5th, satisfaction investigation service：Including the side such as outpatient service and inpatient experience, degree of recognition, loyalty, Employees ' Satisfaction Degree
The investigation and analysis in face, are promoted for hospital qualityization and improve management system and provide reference frame.
6th, serviced using promotion：Carry out evaluating result report deciphering, the training of Patient Experience Data Analyst of Patient Experience
Experts lecture foster, in terms of Patient Experience etc..
7th, management consulting service：Primarily directed to hospital in the relevant issues that find in the process of management, provide Management Advisory Services,
Reengineering teaches the services such as training.
Description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination accompanying drawings below to embodiment
Substantially and it is readily appreciated that, wherein：
Fig. 1 is overview flow chart of the present invention；
Fig. 2 and Fig. 3 is effect diagram of the present invention.
Specific embodiment
The embodiment of the present invention is described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar element or has the function of same or like element.Below with reference to attached
The embodiment of figure description is exemplary, and is only used for explaining the present invention, and is not considered as limiting the invention.
As shown in Figure 1, Patient Experience evaluation and test optimization service system, bag are carried out based on big data the present invention provides one kind
It includes：
Patient's master data acquisition module, it is right after characteristic information verification verification for collecting the characteristic information of patient
Hospital medical situation is evaluated item by item, and gridding covering is carried out to hospital, and hospital is divided into several grids, parallel acquisition doctor
The patient data of section office of institute collects several evaluation of patient data, forms big data acquisition storehouse, is cleared up for invalid data
It deletes；
Data sort out decimation blocks, after scoring for patient each evaluation column, are sorted out by data preparation
Data are carried out categorizing operation by mode, wherein the data sorted out experience evaluation for experience evaluation of being hospitalized, outpatient service, emergency treatment experience is commented
Valency, discharge overall merit and later maintenance evaluation, invalid evaluating data is rejected after data are sorted out；
Assessment indicator analysis module, for being hospitalized period in patient, the different section office of different Patient Experiences, to section of hospital
Room carries out scoring operation, and the data-optimized recommending module of analysis is sent to after the fraction of evaluation is sorted out according to department；
Analyze data-optimized recommending module, for by the data of hospital department assessment indicator analysis module carry out analysis and it is excellent
Change, demand user is recommended after the data after optimization are judged according to the criterion of setting.
Described carries out Patient Experience evaluation and test optimization service system based on big data, it is preferred that patient's master data
Acquisition module includes：
The data that patient evaluates item by item carry out typing operation by index information, when performing evaluating data operation, using elder generation
Internal data evaluation is arrived in peripheral data evaluation again, gradually deep evaluation level, which is evaluated as guiding link of being admitted to hospital,
The stage started to moving in hospital establishes parameter typing model, and foreground service link, the state of an illness are just sentenced according to parameter typing model
Link, link of whether performing the operation, anaesthetic injection pain link, nursing links, property safety link, link of periodically making the rounds of the wards, medication are changed
Cyclome section, auxiliary examination link, logistics support link, the data parameters of drug price link and Drug price addition link are acquired
(it is subdivided into link of being admitted to hospital, link of seeing and treating patients, link of making the rounds of the wards, central aspects, nursing links, auxiliary inspection link, attitude, complaint pipe
Reason, price perceive, medical environment and logistics service, patient safety, medical ethics and atmosphere, surgery anesthesia link this 13 links), externally
It encloses after data evaluation terminates, carries out hospital internal data evaluation, it is whole to hospital which is evaluated as hospital staff
The evaluating data of body performs link by parameter typing model to job enthusiasm link, hospital policy, work performs link, skill
It can skilled link, personnel's communication efficiency link, patients' privacy link of protection, diagnosis communication link and the data for aiding in technical ability link
Parameter carries out typing.
Described carries out Patient Experience evaluation and test optimization service system based on big data, it is preferred that the data sort out sampling
Module includes：
It is calculated by the peripheral data evaluation and internal data evaluation that are obtained in patient's master data acquisition module all kinds of
Achievement data compares analysis using importance matrix analysis, cluster and carries out classification sampling to hospital's qualitative index；To patient service
Know from experience link and hospital's interior quality perceives link and sequence analysis is compared；With reference to the Patient Experience of medical act process link
It perceives, sample out contribution link and link of losing points will be sorted out in peripheral data evaluation and internal data evaluation, drawing influences patient
The main Score factor of experience and factor of losing points；To sorting out defect factors analysis and the assessment of efficiency disturbance degree in sampling process；So
The data sending after sampling will be sorted out afterwards to assessment indicator analysis module.
Described carries out Patient Experience evaluation and test optimization service system based on big data, it is preferred that the assessment indicator analysis
Module includes：
Medical satisfaction evaluation and test weight is established, score value of the patient in consulting services section office is calculated, first calculates patient's typing number
According to index average,Wherein, n is clinical samples number, and subscript i, j are data evaluation
Sequence number, j=1+i, Pki, PkjThe project of satisfaction evaluation, M are carried out for patientsFor patient participate in evaluation and test tag set, s ∈ 1,
2 ... n }；
Module deviation product computing two-by-two is carried out to each patient, then summation operation is being carried out, is calculating as follows：
Wherein SQ[qi, qj]
It is evaluation keyword qiAnd qjSimilarity；
Evaluating data is normalized
Wherein, asIt clicks on and submits for patient
Evaluate collection, C is harmonic factor, dkIt is the binaryzation page parameter for evaluating keyword, z is the keyword for evaluating selection, WijFor
Medical satisfaction class weight.
Described carries out Patient Experience evaluation and test optimization service system based on big data, it is preferred that the analysis is data-optimized
Recommending module includes：
Calculate distinguishing valueWherein, FijTo participate in having for evaluation set in classification of assessment device
Imitate accuracy, tijIt is emotional category；T is emotional category space, is T={ sense organ (Sense), emotion (Feel), thinking
(Think), action (Act), relation (Relate) },It is to participate in medical satisfaction evaluation in emotional category tijOn classification just
True rate；
Use distinguishing value WijAnalysis optimization must be carried out, optimization data are then recommended into target patient by weight model,
The complex optimum is recommended to analyze weight model
Wherein Ri、RjExpired
The Term Weight of meaning degree evaluation, α is weight factor, and λ is average variation probability, CavgFor average satisfaction size.
The optimization services method of the present invention, includes the following steps：
S1, patient registration's identity information account, by the characteristic information for collecting patient：Such as gender, age bracket, inhabitation
Ground carrys out institute's reason etc., and the Present Service Situation of Hospital medical is evaluated item by item, collects several evaluation of patient data, forms big number
According to acquisition storehouse；
After patient scores to each evaluation column, by data preparation classifying mode, data are sorted out by S2
Operation, wherein the data sorted out for be hospitalized experience evaluation, outpatient service experience evaluation, emergency treatment experience evaluation, discharge overall merit and
Later maintenance is evaluated, and invalid evaluating data is rejected after data are sorted out；
S3, during patient is hospitalized, the different section office of different Patient Experiences carry out hospital department scoring operation, will
The fraction of evaluation is sent to the data-optimized recommending module of analysis after being sorted out according to department；
The data of hospital department assessment indicator analysis module are analyzed and optimized by S4, by the data after optimization according to
The criterion of setting recommends demand user after being judged.
As shown in Figures 2 and 3, objective and fair justice, Patient Experience are realized by the evaluation and test optimization method：It exactly stands and is suffering from
Five sense organ (Sense) of person, emotion (Feel), thinking (Think), action (Act), relation (Relate) aspects, rationality
It is got through with perception, assigns the wider psychological feelings of patient behavior and social effect.I.e. patient is during life cycle or medical treatment
Most directly impression and psychology are felt by any process for perceiving or observing and patient.
Patient satisfaction rate：It is direct expression of the patient for medical act process link synthesis meeting presentation, it can be serious
Satisfaction that is real and objectively reacting some problem or process link.
Medical Quality index：It is by specifically being measured in service management in patient perceivable hospital, being capable of the anti-of objective reality
Reflect hospital's inherent quality and service quality.
Degree of recognition：Patient oneself approves hospital, is ready to select to hospital or health care again.
Loyalty：Patient not only oneself approves hospital, is also ready to recommend that kith and kin are to hospital or health care.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not
In the case of departing from the principle of the present invention and objective a variety of change, modification, replacement and modification can be carried out to these embodiments, this
The scope of invention is limited by claim and its equivalent.
Claims (5)
1. one kind carries out Patient Experience evaluation and test optimization service system based on big data, which is characterized in that including：
Patient's master data acquisition module, for collecting the characteristic information of patient, after characteristic information verification verification, to hospital
Medical conditions are evaluated item by item, carry out gridding covering to hospital, hospital is divided into several grids, section of parallel acquisition hospital
The patient data of room collects several evaluation of patient data, forms big data acquisition storehouse, and carrying out cleaning for invalid data deletes
It removes；
Data sort out decimation blocks, after scoring for patient each evaluation column, by data preparation classifying mode,
Data are subjected to categorizing operation, wherein the data sorted out are experienced evaluation, emergency treatment experience evaluation for experience evaluation of being hospitalized, outpatient service, gone out
Institute's overall merit and later maintenance evaluation, invalid evaluating data is rejected after data are sorted out；
Assessment indicator analysis module, for patient be hospitalized during, the different section office of different Patient Experiences, to hospital department into
Row scoring operation, is sent to the data-optimized recommending module of analysis after the fraction of evaluation is sorted out according to department；
Data-optimized recommending module is analyzed, for the data of hospital department assessment indicator analysis module to be analyzed and optimized,
Demand user is recommended after data after optimization are judged according to the criterion of setting.
2. according to claim 1 carry out Patient Experience evaluation and test optimization service system based on big data, which is characterized in that institute
Stating patient's master data acquisition module includes：
The data that patient evaluates item by item carry out typing operation by index information, when performing evaluating data operation, using first periphery
Data evaluation arrives internal data evaluation again, gradually deep evaluation level, which is evaluated as guiding link of being admitted to hospital, to entering
The stage that firmly hospital starts establishes parameter typing model, according to parameter typing model to foreground service link, the state of an illness just sentence link,
Whether perform the operation link, anaesthetic injection pain link, nursing links, property safety link, link of periodically making the rounds of the wards, medication dressing ring
The data parameters of section, auxiliary examination link, logistics support link, drug price link and Drug price addition link are acquired, right
After peripheral data evaluation terminates, hospital internal data evaluation is carried out, which is evaluated as hospital staff to hospital
Whole evaluating data, link is performed to job enthusiasm link, hospital policy by parameter typing model, work performs link,
The skilled link of technical ability, personnel's communication efficiency link, patients' privacy link of protection, diagnosis communication link and the number for aiding in technical ability link
Typing is carried out according to parameter.
3. according to claim 2 carry out Patient Experience evaluation and test optimization service system based on big data, which is characterized in that institute
Stating data classification decimation blocks includes：
All kinds of indexs are calculated by the peripheral data evaluation and internal data evaluation that are obtained in patient's master data acquisition module
Data compare analysis using importance matrix analysis, cluster and carry out classification sampling to hospital's qualitative index；Patient service is known from experience
Link and hospital's interior quality perceive link and sequence analysis are compared；With reference to the Patient Experience sense of medical act process link
Know, sample out contribution link and link of losing points will be sorted out in peripheral data evaluation and internal data evaluation, drawing influences patient's body
The main Score factor tested and factor of losing points；To sorting out defect factors analysis and the assessment of efficiency disturbance degree in sampling process；Then
The data sending after sampling will be sorted out to assessment indicator analysis module.
4. according to claim 1 carry out Patient Experience evaluation and test optimization service system based on big data, which is characterized in that institute
Stating assessment indicator analysis module includes：
Medical satisfaction evaluation and test weight is established, calculates score value of the patient in consulting services section office, patient's logging data is first calculated and refers to
Mark average,Wherein, n is clinical samples number, and subscript i, j are data evaluation sequence number,
J=1+i, Pki, PkjThe project of satisfaction evaluation, M are carried out for patientsThe tag set of evaluation and test, s ∈ { 1,2 ... are participated in for patient
n}；
Module deviation product computing two-by-two is carried out to each patient, then summation operation is being carried out, is calculating as follows：
Wherein SQ[qi,qj] it is to comment
Valency keyword qiAnd qjSimilarity；
Evaluating data is normalized
Wherein, asThe evaluation submitted is clicked on for patient
Collection, C are harmonic factors, dkIt is the binaryzation page parameter for evaluating keyword, z is the keyword for evaluating selection, WijIt is full for medical treatment
Meaning degree class weight.
5. according to claim 1 carry out Patient Experience evaluation and test optimization service system based on big data, which is characterized in that institute
Stating the data-optimized recommending module of analysis includes：
Calculate distinguishing valueWherein, FijFor in classification of assessment device participate in evaluation set effectively just
True rate, tijIt is emotional category；T be emotional category space, be T=sense organ (Sense), emotion (Feel), thinking (Think),
Action (Act), relation (Relate) },It is to participate in medical satisfaction evaluation in emotional category tijOn classification accuracy rate；
Use distinguishing value WijAnalysis optimization must be carried out, optimization data are then recommended into target patient by weight model, this is comprehensive
Closing optimization recommendation analysis weight model is
Wherein Ri、RjCarry out satisfaction
The Term Weight of evaluation, α are weight factors, and λ is average variation probability, CavgFor average satisfaction size.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109065149A (en) * | 2018-06-29 | 2018-12-21 | 康美药业股份有限公司 | A kind of integration method and system for health consultation platform |
| CN109376888A (en) * | 2018-10-09 | 2019-02-22 | 长安大学 | A kind of Forum on College Eating-room management system and management method based on cell phone application |
| CN110595496A (en) * | 2019-08-14 | 2019-12-20 | 中国平安财产保险股份有限公司 | Medical aid path recommendation method based on data analysis and related equipment |
| CN110993069A (en) * | 2019-10-10 | 2020-04-10 | 望海康信(北京)科技股份公司 | Hospital cost determination method and system |
| CN111354467A (en) * | 2020-03-03 | 2020-06-30 | 浙江省人民医院 | Comprehensive evaluation method and system applied to temperature nursing evaluation indexes |
| CN111462880A (en) * | 2020-04-02 | 2020-07-28 | 四川省陆大队科技有限公司 | System and method for medical service evaluation |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106780234A (en) * | 2017-02-25 | 2017-05-31 | 深圳市前海安测信息技术有限公司 | Doctor grading commending system and method based on data correlation |
| CN106920315A (en) * | 2017-03-09 | 2017-07-04 | 重庆医科大学附属第医院 | Medical care satisfaction investigation system |
-
2018
- 2018-01-12 CN CN201810029738.9A patent/CN108122607A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106780234A (en) * | 2017-02-25 | 2017-05-31 | 深圳市前海安测信息技术有限公司 | Doctor grading commending system and method based on data correlation |
| CN106920315A (en) * | 2017-03-09 | 2017-07-04 | 重庆医科大学附属第医院 | Medical care satisfaction investigation system |
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|---|---|---|---|---|
| CN109065149A (en) * | 2018-06-29 | 2018-12-21 | 康美药业股份有限公司 | A kind of integration method and system for health consultation platform |
| CN109376888A (en) * | 2018-10-09 | 2019-02-22 | 长安大学 | A kind of Forum on College Eating-room management system and management method based on cell phone application |
| CN110595496A (en) * | 2019-08-14 | 2019-12-20 | 中国平安财产保险股份有限公司 | Medical aid path recommendation method based on data analysis and related equipment |
| CN110993069A (en) * | 2019-10-10 | 2020-04-10 | 望海康信(北京)科技股份公司 | Hospital cost determination method and system |
| CN111354467A (en) * | 2020-03-03 | 2020-06-30 | 浙江省人民医院 | Comprehensive evaluation method and system applied to temperature nursing evaluation indexes |
| CN111462880A (en) * | 2020-04-02 | 2020-07-28 | 四川省陆大队科技有限公司 | System and method for medical service evaluation |
| CN111667898A (en) * | 2020-06-19 | 2020-09-15 | 健康互联(广州)信息科技股份有限公司 | Classification processing method of medical medication rule information |
| CN111667898B (en) * | 2020-06-19 | 2021-08-27 | 重庆中联信息产业有限责任公司 | Classification processing method of medical medication rule information |
| CN112712865A (en) * | 2020-12-24 | 2021-04-27 | 重庆至道医院管理股份有限公司 | Multi-source heterogeneous doctor-patient experience abnormal data fusion working system and working method |
| CN112848182A (en) * | 2020-12-31 | 2021-05-28 | 江苏南高智能装备创新中心有限公司 | Injection molding process optimization method and device based on big data analysis |
| CN112848182B (en) * | 2020-12-31 | 2023-03-03 | 江苏南高智能装备创新中心有限公司 | Injection molding process optimization method and device based on big data analysis |
| CN113674846A (en) * | 2021-09-18 | 2021-11-19 | 浙江远图互联科技股份有限公司 | Hospital intelligent service public opinion monitoring platform based on LSTM network |
| CN114936872A (en) * | 2022-05-11 | 2022-08-23 | 山东远盾网络技术股份有限公司 | Information analysis method based on big data |
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