Community old-age care service management method based on big data

Abstract

The invention relates to the technical field of big data analysis and community old-age service management, in particular to a community old-age service management method based on big data, which comprises the following steps: step 1, collecting basic information of old people; step 2, a data storage and integration module; step 3, a data analysis module; step 4, a service resource configuration module; step 5, a service supervision and feedback module; by collecting and analyzing big data information such as living habits, physical conditions and medical service requirements of the elderly, personalized elderly care service scheme customization can be realized, the pertinence and effectiveness of the service are effectively improved, the health risk degree of the elderly is evaluated, the influence degree of different factors on the health risk is determined through big data analysis, and the health risk management efficiency is improved. The health problem of the old people can be found and processed in time, the life quality and happiness of the old people are improved, meanwhile, the nursing burden of families and society is relieved, and the community nursing service is more user-friendly and intelligent.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis and community elderly care service management, and specifically to a community elderly care service management method based on big data. Background Art

[0002] As the global population continues to age, community-based elderly care, as a model of elderly care that combines the advantages of family-based elderly care and institutional-based elderly care, is increasingly gaining widespread attention. In community-based elderly care services, it is necessary to provide the elderly with a variety of services including life care, medical care, cultural entertainment, etc.

[0003] The existing community elderly care service management system has great limitations in predicting the health risks and needs of elderly care services, allocating resources, and monitoring service quality, making it difficult to achieve refined management and personalized services. Therefore, a community elderly care service management method based on big data is proposed. Summary of the invention

[0004] In view of this, the present invention provides a community elderly care service management method based on big data to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0005] The technical solution of the present invention is implemented as follows: A community elderly care service management method based on big data, comprising the following steps:

[0006] Step 1: Collect basic information of the elderly;

[0007] Step 2: Data storage and integration module;

[0008] Step 3: Data analysis module;

[0009] Step 4: Service resource configuration module;

[0010] Step 5: Service supervision and feedback module.

[0011] Further preferably, in step one, basic information, family and economic information, and service demand information of the elderly are collected.

[0012] Further preferably, in step 2, a database system is selected based on the information of the elderly to store the collected information, and then the collected raw data is cleaned, data of different formats and sources are standardized, and data collected from multiple channels are integrated to form a complete elderly information file.

[0013] Further preferably, in step three, health risks are predicted based on the health data of the elderly, service demands are clustered according to the needs of the elderly, and then the service quality is evaluated based on the services received by the elderly.

[0014] Further preferably, the comprehensive health risk index can be expressed by the following formula:

[0015]

[0016] in:

[0017] HRI is a comprehensive health risk index, which is used to quantitatively assess the health risk level of the elderly;

[0018] n is the number of factors used to assess health risk;

[0019] w i is the weight of the i-th risk factor, and the impact of different factors on health risks is determined through big data analysis;

[0020] f i is the quantitative value of the ith risk factor.

[0021] Further preferably, in the step four, medical care resource allocation, life service resource allocation and human resource allocation are included. According to the health risk prediction results, medical care resources in the community are allocated, according to the service demand cluster analysis results, life service resources are allocated, and community elderly care service personnel are arranged according to the workload and professional requirements of each elderly care service.

[0022] Further preferably, the medical resource allocation can be expressed by the following formula:

[0023]

[0024] in:

[0025] N hp The number of medical staff that the community needs to deploy;

[0026] p is the number of elderly groups classified according to health risks;

[0027] r k is the proportional coefficient of the kth elderly group’s demand for medical staff, which is determined by analyzing the group’s average frequency of health service needs and the time required for each service;

[0028] N ok is the number of people in the kth elderly group.

[0029] Further preferably, in step five, a complete service supervision mechanism is established to supervise the entire process of elderly care services through various means, a multi-channel feedback collection mechanism is established to provide feedback on opinions and suggestions on elderly care services, and the collected feedback information is processed and analyzed.

[0030] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0031] By collecting and analyzing big data information such as the living habits, physical conditions, and medical service needs of the elderly, the present invention can realize personalized customization of elderly care service plans, effectively improve the pertinence and effectiveness of services, assess the health risk level of the elderly, determine the impact of different factors on health risks through big data analysis, promptly discover and deal with health problems of the elderly, improve the quality of life and happiness of the elderly, and at the same time reduce the elderly care burden on families and society, making community elderly care services more humane and intelligent.

[0032] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0035] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0036] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0037] like Figure 1 As shown, an embodiment of the present invention provides a community elderly care service management method based on big data, comprising the following steps:

[0038] Step 1: Collect basic information of the elderly;

[0039] Step 2: Data storage and integration module;

[0040] Step 3: Data analysis module;

[0041] Step 4: Service resource configuration module;

[0042] Step 5: Service supervision and feedback module.

[0043] In one embodiment, in step one, basic information, family and economic information, and service demand information of the elderly are collected; information of the elderly is collected to build a basis for personal files of the elderly, which is used to accurately identify each elderly person in the system; health information is used to predict health risks and develop personalized health management plans; by collecting family and economic information, more care and help services are provided; for elderly people with financial difficulties, the community can provide free or low-cost elderly care services, and ask the elderly about their needs for community elderly care services.

[0044] In one embodiment, in step two, a database system is selected based on the information of the elderly to store the collected information, and then the collected raw data is cleaned, the data in different formats and sources are standardized, and the data collected from multiple channels are integrated to form a complete elderly information file.

[0045] In one embodiment, in step three, health risks are predicted based on the health data of the elderly, service demands are clustered according to the needs of the elderly, and then the service quality is evaluated based on the services received by the elderly. Data mining and machine learning algorithms are used to predict the possible health risks of the elderly in the future based on the health data of the elderly, and cluster analysis is performed on the service demand data of the elderly. The elderly with similar needs are divided into different groups, and more targeted elderly care service packages are designed and provided according to the characteristics of different groups.

[0046] In one embodiment, the comprehensive health risk index can be expressed by the following formula:

[0047]

[0048] in:

[0049] HRI is a comprehensive health risk index, which is used to quantitatively assess the health risk level of the elderly;

[0050] n is the number of factors used to assess health risk;

[0051] w i is the weight of the i-th risk factor, and the impact of different factors on health risks is determined through big data analysis;

[0052] fi is the quantitative value of the ith risk factor.

[0053] In one embodiment, step four includes allocation of medical care resources, allocation of life service resources and allocation of human resources. According to the health risk prediction results, medical care resources in the community are allocated. According to the service demand cluster analysis results, life service resources are allocated. According to the workload and professional requirements of various elderly care services, community elderly care service personnel are arranged. In combination with the daily health management needs of the elderly, corresponding health management sites are set up, equipped with necessary health monitoring equipment and professional medical staff. According to the needs of the elderly for cultural and entertainment activities, corresponding activity venues and facilities are allocated, and diversified cultural and entertainment activities are organized. Professional training and skills improvement are regularly conducted for elderly care service personnel to improve the professional quality and service level of service personnel.

[0054] In one embodiment, medical resource allocation can be expressed by the following formula:

[0055]

[0056] in:

[0057] N hp The number of medical staff that the community needs to deploy;

[0058] p is the number of elderly groups classified according to health risks;

[0059] r k is the proportional coefficient of the kth elderly group’s demand for medical staff, which is determined by analyzing the group’s average frequency of health service needs and the time required for each service;

[0060] N ok is the number of people in the kth elderly group.

[0061] In one embodiment, in step five, a complete service supervision mechanism is established to supervise the entire process of elderly care services through various means, and a multi-channel feedback collection mechanism is established to provide feedback on opinions and suggestions on elderly care services, and the collected feedback information is processed and analyzed; the processing results are fed back to the elderly and their families, and the elderly care services are optimized and improved based on the feedback information.

[0062] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A community elderly care service management method based on big data, characterized by: The following steps are involved: Step 1: Collect basic information of the elderly; Step 2: Data storage and integration module; Step 3: Data analysis module; Step 4: Service resource configuration module; Step 5: Service supervision and feedback module.

2. A community elderly care service management method based on big data according to claim 1, characterized in that: In step one, basic information, family and economic information, and service demand information of the elderly are collected.

3. A community elderly care service management method based on big data according to claim 1, characterized in that: In the step 2, a database system is selected to store the collected information based on the information of the elderly, and then the collected raw data is cleaned, data of different formats and sources are standardized, and data collected from multiple channels are integrated to form a complete information file for the elderly.

4. The community elderly care service management method based on big data according to claim 1 is characterized by: In the step three, health risks are predicted based on the health data of the elderly, service demands are clustered according to the needs of the elderly, and then the service quality is evaluated based on the services received by the elderly.

5. A community elderly care service management method based on big data according to claim 4, characterized in that: The comprehensive health risk index can be expressed by the following formula: in: HRI is a comprehensive health risk index, which is used to quantitatively assess the health risk level of the elderly; n is the number of factors used to assess health risk; w i is the weight of the i-th risk factor, and the impact of different factors on health risks is determined through big data analysis; f i is the quantitative value of the ith risk factor.

6. The community elderly care service management method based on big data according to claim 1 is characterized by: In the step four, including the allocation of medical care resources, life service resources and human resources, the medical care resources in the community are allocated according to the health risk prediction results, the life service resources are allocated according to the service demand cluster analysis results, and the community elderly care service personnel are arranged according to the workload and professional requirements of each elderly care service.

7. A community elderly care service management method based on big data according to claim 6, characterized in that: The medical resource allocation can be expressed by the following formula: in: N hp The number of medical staff that the community needs to deploy; p is the number of elderly groups classified according to health risks; r k is the proportional coefficient of the kth elderly group’s demand for medical staff, which is determined by analyzing the average frequency of health service demand for this group and the time required for each service; N ok is the number of people in the kth elderly group.

8. The community elderly care service management method based on big data according to claim 1 is characterized by: In step five, a comprehensive service supervision mechanism is established to supervise the entire process of elderly care services through various means, a multi-channel feedback collection mechanism is established to provide feedback on and suggestions for elderly care services, and the collected feedback information is processed and analyzed.

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