CN-122025043-A - Intelligent outpatient experience perception analysis method, system and storage medium based on user real-time feedback

CN122025043ACN 122025043 ACN122025043 ACN 122025043ACN-122025043-A

Abstract

The invention discloses an intelligent outpatient experience perception analysis method, system and storage medium based on user real-time feedback, wherein the method comprises the steps of scanning and collecting spatial information data of an outpatient area of a hospital, and constructing an outpatient spatial map model based on the spatial information data; and constructing a clustered data cluster by adopting a space-time clustering algorithm based on the feedback information data, mapping the clustered data cluster to the outpatient space map model, and generating a corresponding real-time dynamic thermodynamic diagram. According to the intelligent outpatient experience perception analysis method, system and storage medium based on the user real-time feedback, the outpatient point can be locked according to the user real-time feedback, and the outpatient efficiency and the patient satisfaction degree are improved.

Inventors

LIN ZIANG
Jia June
GAO BAOLI
LIU ZHE

Assignees

山东大学齐鲁医院

Dates

Publication Date: 20260512
Application Date: 20251226

Claims (10)

1. An intelligent outpatient experience perception analysis method based on user real-time feedback, which is characterized by comprising the following steps: Scanning and collecting spatial information data of an outpatient area of a hospital, and constructing an outpatient spatial map model based on the spatial information data; Collecting feedback information data of an outpatient user; And constructing a clustered data cluster by adopting a space-time clustering algorithm based on the feedback information data, mapping the clustered data cluster to the outpatient space map model, and generating a corresponding real-time dynamic thermodynamic diagram.
2. The intelligent outpatient experience awareness analysis method of claim 1, wherein the feedback information data comprises positioning information data and negative feedback data, wherein the negative feedback data comprises a negative feedback time.
3. The intelligent outpatient experience awareness analysis method of claim 2, wherein the feedback information data further comprises user group attribute data, wherein the user group attribute data comprises at least gender information.
4. The intelligent outpatient experience perception analysis method according to claim 3, wherein said constructing a clustered data cluster using a spatio-temporal clustering algorithm based on said feedback information data comprises minimizing a sum of squares of errors in the clusters by the clustering algorithm, i.e. solving: Wherein, the A kth cluster data cluster in the cluster data cluster set, which includes all data points in the cluster data cluster; for the feature vector of the i-th data point in the cluster data cluster, For the x-axis coordinates of the positioning information data in the i-th data point, For the y-axis coordinates of the positioning information data in the i-th data point, For the z-axis coordinates of the positioning information data in the i-th data point, Is the negative feedback time in the ith data point, where, A dimension of the space is constituted and, Constructing a time dimension; The calculation formula of the center of the kth cluster data cluster is as follows: Wherein, the For the kth cluster data cluster Number of data points contained.
5. The intelligent outpatient experience awareness analysis method of claim 4, wherein different weight coefficients can be set for the time dimension and the space dimension when constructing a cluster data cluster.
6. The intelligent outpatient experience awareness analysis method of claim 1, further comprising identifying feedback rush hour and feedback rush hour areas based on the real-time dynamic thermodynamic diagram and generating a feedback period profile and a feedback area profile.
7. The intelligent outpatient experience awareness analysis method of claim 6, further comprising reproducing a real-time scenario of a feedback rush hour and/or a feedback rush hour in combination with the real-time dynamic thermodynamic diagram and the accessed outpatient area image video data.
8. The intelligent outpatient experience awareness analysis method of claim 7, further comprising simulating resource scheduling based on the identified feedback rush hour or feedback rush hour area to verify feasibility and effectiveness of the resource scheduling.
9. An intelligent outpatient experience sensing system based on user real-time feedback, the system comprising: The environment construction module is used for scanning and collecting spatial information data of an outpatient area of the hospital and constructing an outpatient spatial map model based on the spatial information data; the user feedback module comprises a feedback terminal for users, wherein the feedback terminal is used for acquiring and transmitting feedback information data of the users; The data processing module is used for receiving feedback information data sent by the feedback terminal, constructing clustered data clusters by adopting a space-time clustering algorithm based on the feedback information data, mapping the clustered data clusters to the outpatient space map model, and generating corresponding real-time dynamic thermodynamic diagrams.
10. A computer readable storage medium storing one or more instructions adapted to be loaded by a processor to perform the method of any one of claims 1 to 8.

Description

Intelligent outpatient experience perception analysis method, system and storage medium based on user real-time feedback Technical Field The invention relates to the technical field of intelligent medical treatment, in particular to an intelligent outpatient experience perception analysis method, system and storage medium based on user real-time feedback. Background At present, the problem of insufficient route guidance of patients in hospital outpatient service generally exists, a doctor often lacks clear cognition on medical service flow and department space distribution, route consultation demands are frequently generated, such as repeated searching of manual guidance, wrong visit of departments, repeated route adjustment and the like, so that the medical efficiency and medical experience of the patients are directly affected. Finally, the patient needs to repeatedly turn back the diagnosis guiding table to obtain the staged path guidance, so that service breakpoints and medical experience loss are caused, and the discretized service supply mode not only aggravates the medical care resource load, but also aggravates the anxiety emotion of the patient. In view of the above problems, hospitals currently use patient satisfaction to measure and improve medical service quality. Patient satisfaction is one of the important indicators of the quality of medical service that reflects the patient's comprehensive assessment of the medical facility's level of service. As a user of medical service, a patient has important reference value for evaluating the overall service quality of a hospital, and through satisfaction survey, the hospital can discover problems in the service in time, determine the key direction of quality improvement and effectively track the improvement effect. However, the conventional satisfaction investigation method has the defects that firstly, the questionnaire is designed in a fixed mode, options are tedious and lack of flexibility, the content is tedious and easy to enable interviewees to generate listless emotion, secondly, the questionnaire is low in collection efficiency and low in filling quality, so that investigation flows into a form, the problems of insufficient objectivity and low accuracy exist, meanwhile, the information feedback period is long, the actual condition of the satisfaction degree of a patient is not easily mastered by a hospital manager in time, real feeling of the patient is difficult to fully and accurately reflect in real time, timeliness of quality management of the hospital is affected to a certain extent, in addition, the problems encountered by the patient in the out-patient treatment process cannot be accurately and timely reserved and reproduced, targeted improvement and adjustment of the problems of the patient are not facilitated, resource allocation and scheduling are also not flexibly adjusted, and medical resource utilization rate and outpatient efficiency and medical experience of the patient are seriously affected. Accordingly, there is a need to devise an intelligent outpatient experience awareness system and analysis method that addresses at least some of the above problems and deficiencies. Disclosure of Invention In view of the defects existing in the prior art of outpatient service, the invention provides an intelligent outpatient service experience sensing system based on real-time feedback of a user, which can lock an outpatient service pain point according to the real-time feedback of the user and improve the outpatient service efficiency and the satisfaction degree of the patient. In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme: An intelligent outpatient experience perception analysis method based on user real-time feedback, the method comprising: Scanning and collecting spatial information data of an outpatient area of a hospital, and constructing an outpatient spatial map model based on the spatial information data; Collecting feedback information data of an outpatient user; And constructing a clustered data cluster by adopting a space-time clustering algorithm based on the feedback information data, mapping the clustered data cluster to the outpatient space map model, and generating a corresponding real-time dynamic thermodynamic diagram. According to one aspect of the invention, the feedback information data comprises positioning information data and negative feedback data, wherein the negative feedback data comprises a negative feedback time. According to one aspect of the invention, the feedback information data further comprises user group attribute data, wherein the user group attribute data comprises at least gender information. In accordance with one aspect of the invention, the construction of clustered data clusters using a spatio-temporal clustering algorithm based on the feedback information data includes minimizing the sum of squares of errors within the clust