Research on the Innovation of Community Smart Elderly Medical Service Model Based on Health Big Data
Publicado en línea: 19 mar 2025
Recibido: 15 oct 2024
Aceptado: 05 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0396
Palabras clave
© 2025 Lizhi Hu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Currently, aging has become a major problem encountered in the development process of countries around the world, and the rapid growth of the elderly population has made the development of the traditional model of old-age care face great challenges [1–2]. Data from the seventh population census show that by the end of 2020, the elderly population aged 65 and above in China rose to 180 million, accounting for about 13% of the total number of people. The significant deepening of aging poses a great challenge for China to effectively solve problems in the aging process [3–5]. Children have their own family career life, which is stressful and difficult to cope with in many aspects, and they may not invest enough energy in the elderly, and they have less and less time to support the elderly, coupled with the deterioration of their physical functions, thus invariably increasing a number of problems, such as self-care problems, and they are more in need of comprehensive community-based intelligent aging services [6–8]. Nowadays, the rapid development of artificial intelligence and big data, the concern of the elderly group is not only limited to eating well and wearing well, but also put forward high requirements for personalized pension services, the traditional way of pension mode is difficult to adapt to the requirements of the new era, and it is even more difficult to protect the needs of the elderly themselves, therefore, the development of community wisdom pension services is imminent [9–12].
With the full advent of the digital era, emerging technologies represented by big data, Internet of Things, cloud computing, etc. are embedded in different situations in society, officially opening the curtain of the development of digital society. As a derivative of the organic integration of the senior care service industry and big data technology, community smart aging is a new intelligent aging mode following family aging, institutional aging and traditional community aging, and it is a major initiative to break through the dilemma of unbalanced development of supply and demand for senior care and make up for the problem of fragmentation of resource distribution at the current stage of China’s aging [13–16]. Utilizing the logic of digital governance to promote the innovative development of China’s intelligent elderly care, it provides a development direction and technical path for China’s intelligent elderly care industry and the response to the aging problem in the new era [17–18].
The explosive development of information intelligence technology also promotes the rapid development of the smart elderly industry, in which the way information intelligence technology empowers the elderly industry, the underlying logic of its operation and the role it plays are very worthy of scholars to study and explore. Literature [19] systematically reviews the development and cutting-edge research in the field of smart home, and compares the characteristics of smart home products for the elderly in China and the West, pointing out that China’s smart home products for the elderly are policy-driven and quasi-public attributes, and therefore, while China’s smart home products for the elderly are developing rapidly, there is also a lack of demand and disordered development and other problems. Literature [20] examined the current status of the educational practices for the elderly provided by the community-based smart aging platform, and argued that along with the rapid development of the aging conversation process, the smart community platform and information technology should be fully utilized to provide aging services for the elderly while also being able to support the acquisition of knowledge and information for the elderly. Literature [21] deeply analyzed the analysis reports of the smart elderly industry, and found that the smart elderly services meet the psychological needs of the elderly to a certain extent, in which the modern service level is the direction that focuses on the need to optimize and improve. Literature [22] proposed a geriatric care management system with the Internet of Medical Things (IoMT) as the underlying architecture and evaluated it with real cases, finding that less time is spent on performing comprehensive surveillance in an IoMT-based environment, which is positive for improving the effectiveness and efficiency of daily operations. Literature [23] built a community data visualization platform covering smart service, smart medical care, smart supervision and smart warning based on the collected physiological and psychological characteristics of the elderly, which improves the supervision, warning efficiency of the elderly smart community.
In-depth understanding of the current situation of the development of elderly services and optimization of elderly services needs to be combined with the model of the development of elderly services, the management of elderly services and the evaluation of elderly services to carry out indepth research at the level of the development of elderly services. Literature [24] combined GIS technology and spatial analysis methods, based on the nearest neighbor index and geographic concentration index and other indicators to examine and measure the balance, proximity and concentration of the spatial and temporal distribution of smart healthy aging demonstration bases in each province of China, pointing out that China’s smart aging bases present spatial aggregation characteristics, assembled in the central and eastern regions, and given the market-oriented, policy and technology-driven policies, to to further promote the wisdom of senior living bases. Literature [25] assessed the depression level and cognitive functioning of older adults using interview methods, standardized Mini-Mental State Examination (MMSE) and Geriatric Depression Scale (GDS) tools, revealing that nursing home residents suffer from psychiatric disorders and are in great need of mental health support and assistance. Literature [26] tries to study how to formulate policies to avoid problems such as the decline in the quality of elderly services brought about by the marketization of elderly services from the government’s point of view, introduces a theoretical model in the case of asymmetric information, and analyzes it in comparison with the theoretical model in the case of complete information, and concludes that the government needs to pay attention to the design of ex ante policies, which can help to reduce the risk of the quality of elderly services. Literature [27] analyzed the quantitative data based on factor analysis, confidence test and multiple regression model, confirmed the factor of built environment, and pointed out that the quality of senior living of the elderly can be assessed through the elements of indicators such as room distance, space, barrier-free design, indoor environment, daily care service, cleaning service and so on. Literature [28] introduces a model for assessing how information technology affects senior living services, which includes subjective preference assessment elements of stakeholders as indicators, and develops a detailed discussion with practical cases and theoretical analysis. Literature [29] summarized the research related to the abuse (R-REM) phenomenon among the elderly in nursing homes, pointing out that the frequency of R-REM phenomenon is observed in nursing homes at about 12%-23%, the main way of expression is physical and verbal humiliating abuses, and the abusers are mainly males, with an average age of about 80 years old, and often accompanied by dementia, and the study deepened people’s and scholars’ understanding and knowledge of the R-REM The study deepens people’s and scholars’ understanding and knowledge of the phenomenon of R-REM.
The study first proposes a reengineering design for the medical information service platform and uses the elderly community in City A as the research object. The elderly service data of A city is mined, and at the same time, the joint analysis method is used to conduct quantitative research on the consumer preference of the elderly community, so as to obtain the consumer’s choice preference data, and to summarize the needs of the elderly, the community service center for the elderly, and the demand. Summarize the demand points of elderly people and community service centers, analyze the needs of the core users of the elderly through the KANO model, and summarize the type of demand analyzed by the KANO model. On this basis, the medical service interaction design is carried out according to the needs, and the design evaluation of the practice content is carried out through the SUS usability scale and the effectiveness comparison test.
Medical and nursing information technology for senior care services is an innovative way of implementing the policy of combining medical and nursing care with intelligent automation. It informatizes the process of interaction between service recipients (the elderly) and service providers (medical service centers and third-party service organizations) covered by the community-based home care service. Through the analysis and processing of big data, it generates reasonable suggestions, reassembles the elements of community pension resources, improves the efficiency of the original community pension service, and realizes the closed-loop management of regional collaborative medical and nursing informatization pension service supported by remote diagnosis and treatment and emergency aid service, with community basic medical care as the bottom of the network, home health care as the foundation, third-party service institutions as the auxiliary, and comprehensive treatment in tertiary hospitals as the center. Closed-loop management of elderly care services is shown in Figure 1. The establishment of medical and nursing informatization platform on the one hand can share the pressure of large medical institutions, so that high-quality medical resources sink into the community, on the other hand, can make public health service institutions and large medical institutions and the community to realize the flow of patient information, which can help to unify the standardized management of each medical service sector, and guarantee the efficiency and quality of service.
Closed-loop management of medical and nursing care information
The medical and nursing information platform is mainly composed of three parts: an information collection system, a health information database, and a health management service system. Physiological indexes of the elderly are collected through intelligent wearable devices, medical checkups at community medical service centers and hospital visits; the collected data are encrypted and transmitted through the network, and stored in the background in the pension information cloud, and the cloud database classifies and transmits the processed information to the electronic health record database and the electronic medical record database through the analysis of big data; through the sharing and exchange of data, the health information is processed in a combination of artificial and automated ways by relying on the manual interpretation of experts and the software intelligent analysis; the elderly, their children, pension service institutions and medical institutions can process the health information through the SMS network. Through data sharing and exchange, relying on experts’ manual interpretation and software intelligent analysis, the health information of the elderly is processed in a combination of manual and automated way; the elderly, their children, senior care service organizations and medical institutions can obtain the required information through SMS gateway, intelligent APP or PC.
The advanced information processing platform is a tool for service provision, while elderly care is the focus of community-based home care. Data is the foundation of the platform, and it is essential for quality medical resources to overcome time and space limitations and reach the grassroots community. The medical and nursing information platform improves the quality of traditional senior care services by collecting, analyzing, and applying data. The platform is supported by a three-tier pension and health service structure built on the community pension service system, regional medical service system, and hospital expert medical system.
The service process of the medical and nursing information platform mainly involves four main bodies of interest: the elderly themselves, community service centers, third-party service organizations (logistics and distribution, third-party payment, pharmacy and e-commerce), and large tertiary comprehensive hospitals. The types of processes mainly include community monitoring and consultation for chronic disease, medicine prescription delivery, and imaging examination delivery.
A business intelligence analysis system based on data warehouse technology is the process of extracting information and knowledge from massive data. In the process of establishing smart healthy aging data, data from five departments, including A City Health Planning Commission, senior care institutions, A City Development and Reform Commission, A City Tourism Bureau, and medical and health care institutions, are extracted and aggregated in the Smart Recreation and Nursing Shared Data Center. The Smart Recreation Shared Data Center innovatively proposes a set of process solutions from standards to the creation of meta-models, which, together with the big data cleansing, integration, conversion and data service interfaces, can effectively solve the problem of a large amount of basic data being duplicated in the management of various systems without synchronization, and there are a large number of data inconsistencies and the problem of “information silos”.
Using distributed streaming computing tool Stom, data from different business systems are integrated and cleaned into the shared data center, and data analysis, data storage and pre-processing are carried out through storage and analysis technology, and some data are aggregated and transformed. Some of the missing data in the healthcare organization data was deleted. For some particularly abnormal data (e.g., the daily accommodation fee of a hotel provided by the Tourism Bureau exceeded RMB 2,000, which is an obvious entry error), the data were interpolated after communication with the relevant hotels. Some of the lesser medical organization data (e.g. only registration, not processed after prescribing medication) were not processed. The data will be statute and sent to the shared data exchange center, based on which the data analysis will be carried out.
The basic related concepts of density clustering algorithm are as follows:
Core point: for a given point
Direct Density Reachable: given a set of points
Density reachable: if there exists a chain of points
Density connectivity: if there exists a point
Noise: points that are not included in any class are called noise.
The DBSCAN algorithm discovers the peak density points by finding the local density value points and also selects the local clustering radius based on the found peak density points white wash [30]. The specific three steps of the algorithm proposed in this paper are described below. Density peak point discovery The basic requirement of cluster analysis is that the distance of objects within a cluster is as small as possible, and the distance of objects between different clusters is as large as possible. From the definition of density-based clusters, clusters can be viewed as high-density regions separated by low-density regions [31]. From this, it can be deduced that the peak density points have two typical characteristics, one is that the peak density points have higher local densities; the other is that the distance to other inter-cluster objects is larger than the distance between non-peak density points and other inter-cluster objects in the same cluster. The peak density point can be found by calculating these two characteristics of each data point. Definition 1, Neighborhood of a data point: Given the minimum number of points ( Definition 2, local density of data points: Given the minimum number of points ( That is, Neighborhood expansion of peak density points In this paper, we propose a neighborhood expansion algorithm that automatically selects the local radius, which better solves the problem of inaccurate clustering results caused by using the global cluster radius. The specific steps of expanding the neighborhood of density peak points are as follows: take any one density peak point Calculate the mean Perform a neighborhood query for each data point in Return to step 2 until Cluster merging and conflict handling After the neighborhood expansion of each density peak point, the clusters containing the same elements need to be merged. When merging clusters, two main cases need to be considered: (1) two density peak points are in the same cluster, the clusters where these two density peak points are located can be merged directly. (2) The clusters of two density peak points have intersection but do not belong to the same cluster, and the clusters they each belong to have common intersection, so the clusters where the value points are located cannot be directly merged. This is the conflict problem in cluster merging. In order to solve this kind of conflict problem, we propose a solution. Before merging two clusters, first examine whether its intersection contains density core points, if it contains density core points this indicates that the two clusters belong to the first case, you can directly merge the two clusters: if it does not contain any density core points belongs to the first case, at this time, we need to further determine its intersection of elements of the affiliation problem. The affiliation function is defined as:
KANO model is a structural analysis method. KANO model is able to calculate the relationship between the degree of availability of each feature of a product and user satisfaction, and is a useful tool for categorizing and ranking the importance of user requirement items [32]. The Kano model classifies various attributes related to user satisfaction. There are five types of attributes: essential attributes, desired attributes, charismatic attributes, undifferentiated attributes, and reverse attributes.
The Kano model is able to categorize product functional attributes and then identify the relationship between functional attributes and user satisfaction. The research method is divided into five steps:
Identify the functional requirement items of users using the product through multiple user research methods. Design a Kano questionnaire based on the sorted out functional requirement items. Distribute and collect Kano questionnaires. Count the results of the Kano questionnaire and categorize each requirement item in the way shown in Table 1. M: Must-have attribute, A: Attractive attribute, O: Expected attribute; I: No-difference attribute, R: Reverse attribute, and Q: There is a problem with this answer. Categorize each requirement item according to Kano attributes to determine the design method of the function for design practice. When the method of taking the largest value to determine the attribute category of the requirement item, the maximum value has more than one, and in this case it is impossible to determine the Kano attribute of the changed item. To avoid this situation, some experts have improved the Kano model. Berger proposed calculating the user relative satisfaction coefficient, which can be used to determine the categorization to which the functionality belongs. When the product provides this feature, the Better coefficient is calculated in equation (5):
Kano attribute classifications
| User demand | Not providing this functionality | |||||
|---|---|---|---|---|---|---|
| Like | Of course | It doesn’t | Tolerable | Dislike | ||
| Provide this feature (reverse problem) | Like | Q | A | A | A | O |
| Of course | R | I | I | I | M | |
| It doesn’t | R | I | I | I | M | |
| Tolerable | R | I | I | I | M | |
| Dislike | R | R | R | R | Q | |
When the product does not provide this feature, the Worse factor is calculated as shown in equation (6):
A, O, M, and I represent the number of a functional requirement attributed to categories A, O, M, and I, respectively. After calculating the Better-Worse coefficients, the results of the coefficient calculations for each functional requirement item are grouped into a coordinate graph to classify the attributes of the requirement item.
Kano model to structured questionnaire as a tool, the investigator should design the appropriate questionnaire, collate the results and then categorize the various demand attributes to clarify the product positioning and enhance the user experience [33]. The application of the Kano model is mainly divided into the following steps:
Pre-preparation First of all, we should clarify the purpose of the research, determine the target object, after determining, is to obtain user requirements. There are usually several ways to obtain user requirements: Desktop research method. Collect and find information from the Internet, literature collection and research. User interview method. First determine whether the type of user interview is structured or semi-structured, after that, design the question outline, select the user and get the user’s permission, conduct one-on-one interviews, handwrite or record during the conversation, and collect the user’s usage and demand points. After the end of each interviewer’s conversation, the user’s demand points are analyzed to determine their needs. Questionnaire research method. According to the content of the research and the target research object, design the questionnaire outline, analyze the statistical data results, and derive the user demand points. The functional requirements obtained from user research should be collected and organized, and all of them should be sorted out by removing repetitive and unreasonable parts. The number of demanded items should not be too high, preferably less than 30, as it may lead to too many Kano questions, which may bore the user and result in inaccurate questionnaire results. Data collection using Kano questionnaire According to the functional requirements items derived from user research, the design of Kano questionnaire is to take the user requirements obtained earlier as the question stem, and design questions in both positive and negative directions for each requirement point, to identify the user’s attitudes and experiences when changing the functionality to have or not to have. Designing two questions for each demand item, the amount of questions may be large, and there is repetition in some of the contents, which can easily cause user fatigue and annoyance, and this situation will affect the effect of the user’s answer, resulting in inaccurate results of the questionnaire. Analyze and organize the collected data After collecting the data, the score of each function in the five dimensions is counted. Using the formula in section 2, the data of each item is calculated and the quartile diagram is drawn. According to the calculation of the weight of the requirement item, the weight of each requirement item is calculated to determine the importance of the requirement item, and the larger the value of the weight indicates the higher the importance of the requirement item.
Through the scoring value of each investigator’s willingness to choose their own purchase of the simulated retirement community, so that we can obtain 315*18=5670 data about the joint preference model. Parameter estimation of these data was carried out through least squares regression, or OLS, in the SPSS software, and the resulting statistical analysis is shown in Table 2. From the results presented in the table, it can be found that the significant p-value of the model is 0.010**, which presents significance at the benchmark of α = 0.05, indicating that the model basically meets the statistical requirements.
The overall statistic is combined to analyze the output
| Attribute | Level | Coefficient | Standard error | T value | P value |
|---|---|---|---|---|---|
| - | const | -5.439 | 0.396 | 14.522 | 0.000*** |
| Medical service | Hospital level | 1.356 | 0.255 | 5.85 | 0.002*** |
| Hospital level + Psychological consultation | 1.394 | 0.255 | 6.013 | 0.002*** | |
| Safety management | Basic safety equipment + Professional care worker | -0.075 | 0.255 | -0.272 | 0.776 |
| Basic safety equipment + Custom workers | -0.582 | 0.255 | -2.442 | 0.067* | |
| Regional position | Residential area | 0.01 | 0.328 | 0.074 | 0.965 |
| downtown | -0.201 | 0.275 | -0.744 | 0.48 | |
| Catering service | General voluntary meal | -0.669 | 0.255 | -2.817 | 0.045** |
| Star buffet | 0.068 | 0.255 | 0.338 | 0.771 | |
| Accommodation environment | Double or more | -1.11 | 0.255 | -4.702 | 0.007*** |
| Single room | -0.085 | 0.255 | -0.314 | 0.746 | |
| Entertainment | Spontaneous organization | -0.2 | 0.32 | -0.625 | 0.546 |
| Price | Flat with the market average | 0.585 | 0.275 | 2.349 | 0.079* |
| Below the market average | 0.721 | 0.328 | 2.391 | 0.076* | |
| Variance analysis | Sum of squares | freedom | Mean square | F value | Significance |
| Regression | 0.722 | 11 | 0.161 | 7.532 | 0.01** |
| Residual error | 0.034 | 2 | 0.004 | ||
| Total | 0.701 | 15 | |||
| The OLS regression model is fitting for excellence | R value | R square | Adjusted r | Standard error | |
| 0.973 | 0.943 | 0.832 | 1.081 | ||
Note: represent 1%, 5%, and 10% significance levels, respectively.
Cluster analysis results
Selected a community in A city, the elderly population of 410 people to carry out a survey, this paper for cluster analysis is based on the DBSCAN clustering algorithm, due to the output of the analysis of the data are percentage values, and thus no further standardization process. In the SPSS software to import the original data, set the predictive model plate DBSCAN cluster analysis algorithm in the K = 3 to carry out cluster analysis experiments, the final experimental data as shown in Table 3. Among the three categories of consumers in the senior living community, the first category has the largest number of consumers, 185, accounting for 45.12%; the second category has 142 consumers, accounting for 34.63%; and the third category has the smallest number of consumers, 83, accounting for 20.24%.
The results of the analysis of variance for the fields are shown in Table 4, which shows that there is a significant effect of all variables except regional location and recreational activities, and the order of importance of each attribute on the results of the cluster analysis is as follows: health care services (127.735) > price (67.987) > accommodation environment (13.493) > safety management (10.235) > catering services (3.852) > Recreational activities (1.183) > Area location (0.69).
Consumer segmentation
After obtaining the classification results of the three types of consumer groups through the cluster analysis method, this paper further conducted a joint analysis of each type of consumer group separately to explore the attribute preferences of different types of consumers, and the results of the collated data of the joint analysis of the three types of consumers are shown in Table 5, in which the value of the cluster centroid of each type of cluster analysis is the value of the attribute importance ratio in the table. The largest number of the first category of consumers, accounting for nearly half of the respondents, up to about 45%, observing the data in the table can be seen that the most prominent importance of attributes is medical services, far exceeding the overall importance value of 34.797%; the second category of consumers with the second largest proportion, in the overall total of about 37% of the consumers. According to the data in the table, the requirements of this category of consumers are significantly higher than the other two categories of consumers for accommodation, food and beverage services, safety management, but the importance of medical services only accounts for 13.527%, so we can say that the second category of consumers belongs to the enjoyment type of consumers. The third category of consumers has the smallest number, accounting for only 18%. When choosing a senior living community, this group of consumers first focuses on the price, and the importance of the price attribute accounts for 20.829%, which is significantly higher than that of the other two groups of consumers, 4.282% and 9.777%, and then focuses on the accommodation environment, health care services, regional location, food and beverage services, and does not have a large perception of safety management, so we can define the third group of consumers as price-oriented consumers. Consumers.
Consumer clustering results for old-age communities
| Cluster category | Frequency | Percentage % |
|---|---|---|
| 1 | 185 | 45.12 |
| 2 | 142 | 34.63 |
| 3 | 83 | 20.24 |
| Total | 410 | 100.0 |
Field difference analysis results
| Clustering category (mean ± standard deviation) | Testing | ||||
|---|---|---|---|---|---|
| Class 1(n=185) | Class 2(n=142) | Class 3(n=83) | F | P | |
| Medical service | 32.157±6.738 | 11.827±5.161 | 13.454±5.11 | 127.735 | 0 |
| Safety management | 10.532±5.836 | 14.887±8.179 | 7.52±4.206 | 10.235 | 0 |
| Regional position | 10.455±5.088 | 11.473±6.442 | 12.62±7.229 | 0.69 | 0.426 |
| Catering service | 10.957±4.982 | 15.655±9.504 | 11.986±6.787 | 3.852 | 0.016 |
| Accommodation environment | 13.632±6.796 | 22.031±9.295 | 13.711±6.441 | 13.493 | 0 |
| Entertainment | 6.146±4.719 | 7.15±5.879 | 8.561±5.683 | 1.183 | 0.22 |
| price | 9.967±4.83 | 11.038±4.481 | 26.686±8.765 | 67.987 | 0 |
Note: ***, **, * represent 1%, 5%, and 10% significance levels, respectively.
Joint analysis of the output of three types of consumers
| Attribute | The first category of consumer importance(%) | The second type of consumer importance(%) | Category iii consumer importance(%) |
|---|---|---|---|
| Medical service | 34.79721 | 13.52674 | 15.17438 |
| Safety management | 13.17163 | 15.58721 | 10.24 |
| Regional position | 13.09488 | 13.17326 | 15.34 |
| Catering service | 11.59721 | 15.35465 | 13.70563 |
| Accommodation environment | 15.27163 | 22.7314 | 15.43063 |
| entertainment | 7.78558 | 9.85 | 9.28063 |
| Price | 4.28186 | 9.77674 | 20.82873 |
Based on the results of the survey, the needs of the elderly and the community were obtained and summarized, and the results were obtained as shown in Tables 6 and 7. There are a total of 28 needs of the elderly, and there are 8 needs of the community service community. Elderly people’s needs are summarized Summarization of the needs of community elderly service centers
Induction of old age
| Target | Specific demand | Numbering |
|---|---|---|
| Expand information access | 1. We know the pension policy and the endowment information | P1 |
| 2. Understand the content of the pension service | P2 | |
| Improve the flexibility of the service | 1. The process of simplifying the pension application process | P3 |
| 2. Providing ancillary applications | P4 | |
| Improve initiative, effective intervention and prevention | 1. The body monitors the body’s indicators | P5 |
| 2. Health investigation | P6 | |
| 3. Door-to-door | P7 | |
| 4. Care service | P8 | |
| 5. Emergency assistance for 5.24 hours | P9 | |
| 6. Health lectures/counseling | P10 | |
| Scientific understanding and management of its own state | 1. The medical archives are established | P11 |
| 2. Health guidance | P12 | |
| 3. Physical exercise | P13 | |
| 4.Warning | P14 | |
| Provide mental care | 1. Child care | P15 |
| Diverse forms of activity | 1. Cultural entertainment | P16 |
| 2. Old education | P17 | |
| 3. Secondary employment | P18 | |
| 4. Knowledge lecture | P19 | |
| Psychological dredging | 1. Psychological counseling | P20 |
| 2. Legal advice/rescue | P21 | |
| 3. Cognitive guidance | P22 | |
| Provide information connectivity | 1. Communication between the services and health medical information | P23 |
| 2. Provide online service feedback channels | P24 | |
| Reduce service costs | 1. We will introduce volunteer services and provide free service | P25 |
| mprove the comprehensive level of service personnel | 1. To improve the professional level and moral level of service personnel | P26 |
| Improve the service | 1. The company provides a one-to-one long-term signing service | P27 |
| 2. Provide professional life - health - counseling platform | P28 |
Community service center demand induction
| Target | Specific demand | Numbering |
|---|---|---|
| Service content | 1. The content of the pension service | T1 |
| 2. Optimize service application process | T2 | |
| Service cost | 1. The project reduces service costs by volunteer, providing low or unpaid services | T3 |
| Communication | 2. Communicate with the patient’s family | T4 |
| Service content diversification | 3. Enrich the content of old-age services, especially health and mental health | T5 |
| Convenience and flexibility | 1. The content and opinions of the online feedback service | T6 |
| 2.Pay for online services | T7 | |
| Active service | 1. The company provides active services based on the elderly body monitoring data and reports | T8 |
The main user group of the community elderly service system design is the elderly, through the previous questionnaire survey and interviews, summarize the demand points, in order to verify the priority of these requirements in a more scientific way, the following Kano model is used to prioritize the requirements, in order to provide a basis for the subsequent design practice. Classification of requirement points by Kano model First of all, through the design of the questionnaire form to determine the Kano category of demand points, standardized questionnaires are divided into two dimensions, one is if the product / service to meet the needs of the user what will happen? The other thing is what will happen if the product/service does not meet the user’s needs? At the same time, the satisfaction level is divided into five levels from “very satisfied” to “very dissatisfied”. Based on the results of the questionnaire, we summarize the types of requirement points against the two-dimensional classification table of Kano attributes.Kano model classifies requirements into six categories, which are: Attractive Attributes (A), Expected Attributes (O), Must-have Attributes (M), Non-differentiated Attributes (I), Reverse Attributes (R), and Questionable Outcomes (Q). According to the Kano model standardized questionnaire form design of the elderly community old service needs evaluation questionnaire, this time 410 questionnaires were issued, 380 were recovered, with an effective rate of 93%, the questionnaire results against the evaluation results against the table obtained as shown in Table 8: Analysis of satisfaction coefficients of demand points The evaluation table of each demand is summarized using the questionnaire results, and the impact of each demand point on user satisfaction is analyzed using the satisfaction coefficient. The coefficient of user satisfaction (CSI) is divided into user satisfaction increase index (SI) and user dissatisfaction decrease index (DSI). The corresponding formulas (7) and (8) are calculated:
The old man needs statistics
| Numbering | A | O | M | I | R | Kano category |
|---|---|---|---|---|---|---|
| P1 | 6 | 21 | 6 | 5 | 1 | O |
| P2 | 5 | 12 | 17 | 4 | 1 | M |
| P3 | 3 | 12 | 19 | 6 | 0 | M |
| P4 | 15 | 11 | 6 | 5 | 2 | A |
| P5 | 3 | 11 | 20 | 5 | 1 | M |
| P6 | 9 | 14 | 8 | 6 | 0 | O |
| P7 | 4 | 11 | 18 | 3 | 1 | M |
| P8 | 9 | 15 | 7 | 5 | 1 | O |
| P9 | 6 | 8 | 7 | 4 | 0 | M |
| P10 | 3 | 6 | 10 | 16 | 0 | I |
| P11 | 16 | 9 | 7 | 5 | 0 | O |
| P12 | 6 | 101 | 17 | 4 | 0 | M |
| P13 | 4 | 6 | 8 | 17 | 1 | O |
| P14 | 14 | 10 | 8 | 5 | 0 | A |
| P15 | 6 | 8 | 16 | 5 | 1 | M |
| P16 | 5 | 8 | 14 | 6 | 3 | M |
| P17 | 15 | 8 | 7 | 5 | 0 | A |
| P18 | 4 | 6 | 9 | 16 | 2 | M |
| P19 | 8 | 8 | 7 | 14 | 0 | I |
| P20 | 14 | 6 | 7 | 5 | 2 | A |
| P21 | 10 | 15 | 7 | 5 | 0 | O |
| P22 | 5 | 5 | 6 | 18 | 2 | I |
| P23 | 5 | 6 | 10 | 14 | 1 | I |
| P24 | 6 | 8 | 8 | 13 | 2 | I |
| P25 | 8 | 17 | 7 | 5 | 0 | O |
| P26 | 7 | 10 | 16 | 4 | 0 | O |
| P27 | 7 | 14 | 7 | 9 | 1 | M |
| P28 | 14 | 10 | 8 | 6 | 0 | A |
In addition, Ti values are introduced, Ti values denote Max(SIx, DSIx) to visualize the degree of satisfaction impact, Ti values may be duplicated, so with the help of an adjustment factor k. The usual value of k is taken as 0- undifferentiated demand (I). Mandatory demand (M), 1-expected demand (O), 1.5-Charming demand (A). Substituting the data from Table 8 into the formula, the results obtained are shown in Table 9.
Satisfaction coefficient value
| Demand | SI | DSI | Ti | K | Demand | SI | DSI | Ti | K | Demand | SI | DSI | Ti | K |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| P1 | 0.71 | -0.71 | 0.71 | 1 | P11 | 0.68 | -0.43 | 0.68 | 1.5 | P21 | 0.68 | -0.59 | 0.68 | 1 |
| P2 | 0.45 | -0.76 | 0.76 | 0.5 | P12 | 0.84 | -0.92 | 0.84 | 0.5 | P22 | 0.29 | -0.32 | 0.32 | 0 |
| P3 | 0.38 | -0.78 | 0.78 | 0.5 | P13 | 0.29 | -0.40 | 0.40 | 0 | P23 | 0.31 | -0.46 | 0.46 | 0 |
| P4 | 0.7 | -0.46 | 0.7 | 1.5 | P14 | 0.65 | -0.49 | 0.65 | 1.5 | P24 | 0.4 | -0.46 | 0.46 | 0 |
| P5 | 0.36 | -0.79 | 0.79 | 0.5 | P15 | 0.4 | -0.69 | 0.69 | 0.5 | P25 | 0.68 | -0.65 | 0.68 | 1 |
| P6 | 0.62 | -0.59 | 0.62 | 1 | P16 | 0.39 | -0.67 | 0.67 | 0.5 | P26 | 0.46 | -0.70 | 0.70 | 0.5 |
| P7 | 0.42 | -0.81 | 0.81 | 0.5 | P17 | 0.66 | -0.43 | 0.66 | 1.5 | P27 | 0.57 | -0.57 | 0.57 | 1 |
| P8 | 0.67 | -0.61 | 0.67 | 1 | P18 | 0.29 | -0.43 | 0.43 | 0 | P28 | 0.63 | -0.47 | 0.63 | 1.5 |
| P9 | 0.56 | -0.6 | 0.6 | 0.5 | P19 | 0.43 | -0.41 | 0.43 | 0 | |||||
| P10 | 0.26 | -0.46 | 0.46 | 0 | P20 | 0.63 | -0.41 | 0.63 | 1.5 |
The value of SI is generally positive, and the larger the value indicates that the greater the impact of the requirement item on user satisfaction, the faster the satisfaction enhancement effect will be; the value of DSI is generally negative, and the more negative it is, the greater the impact on satisfaction reduction will be, indicating that the requirement item will make the satisfaction drop faster. In a nutshell, the higher the absolute value of SI and DSI, the greater the impact on satisfaction or dissatisfaction, and the lower the absolute value, the less noticeable the effect. In order to see more intuitively the degree of influence of each demand point on user satisfaction/dissatisfaction, the distribution of each demand item (DSI, SI) in the four quadrants of the Better-Worse coefficient analysis is analyzed more centrally. The Better-Worse coefficient analysis is divided into four quadrants: the first quadrant is the expectation factor, DSI ∈ 0,0.5], SI ∈ [0.5,1]; the second quadrant is the charm factor, DSI ∈ [0,0.5], SI ∈ [0.5,1]; the third quadrant is the no-difference factor, DSI ∈ [0,0.5], SI ∈ [0,0.5]; and the fourth quadrant is the must-have factor, DSI ∈ [0.5,1], SI ∈ [0,0.5]. The specific distribution is shown in Figure 2.
Demand satisfaction profile
Through the analysis of the demand satisfaction distribution chart, from the perspective of user dissatisfaction reduction index DSI, essential factors>expectation factors>charm factors, the fulfillment of essential needs will not greatly enhance user satisfaction, because it is rightfully exist for the user, but not to provide the words will maximize the reduction of the user’s dissatisfaction with the product or service; from the perspective of the increase in user satisfaction index SI From the perspective of user satisfaction increase index (SI), charisma factor>expectation factor>necessary factor, the realization of charisma factor can bring surprise to the user, resulting in a substantial increase in user satisfaction, and will not cause too much impact when it is not satisfied; at the same time, from the perspective of DSI and SI, the expectation factor has the greatest impact, both on user satisfaction and user dissatisfaction is very obvious, so the realization of the user’s expectation is the core of the system design. The core of the system design, but also the system design should pay the most attention to the demand point, the realization of the necessary factors is the foundation, the realization of the charm factor can enhance the user’s attraction to the system.
The user observation and service design process is analyzed and summarized to classify the behavior of elderly users during the process of receiving home care medical services into cognitive, assessment, participation, and feedback stages. Cognitive stage design strategy In the preliminary observation of users in the use of smart products, it was found that when making use of any new app, users encountered a cumbersome registration and login interface, and the fastest solution was to hand over the phone to young people for registration. At that stage, the usability design of the product or service is particularly important. The older user’s impression of the service remains based on fear that it is difficult to use and impossible to operate. When designing, the steps to access the service can be reduced and the application can be completed in the most central steps. At the same time, additional information can be provided during the operation to facilitate users’ understanding and use. Realize the independent operation of the product service system by elderly users. Evaluation stage design strategy After completing the behavior and achieving the goals in the above stage, the goal of the elderly group in this stage is to identify whether the medical service meets their needs. Elderly users will evaluate the detailed content of the service and consider whether the service meets their expectations with their own expectations. After completing the evaluation and judgment, when the service meets their expectations, the motivation to accept it will be triggered. In this stage, there is no interaction between the elderly users and the system, and the problem to be solved in this stage is to solve the doubts of the elderly users about the service and to promote the participation of the elderly users in the service. The strategy of this stage is that the system needs to collect and analyze the personal information of the users, complete the evaluation of the elderly users, and recommend practical and appropriate types of services. Design strategy for the participation phase After completing the evaluation of the services mentioned above, elderly users will participate in the services and interact with the product and service system. The main objective of this phase is to achieve the medical goal of elderly users’ participation in the service. Improving human-machine communication is the key point. When designing the system, it is necessary to take into account the user’s touchpoints in the whole process, and add auxiliary elements in difficult processes, such as sound prompts and question-and-answer sessions, so as to reduce the difficulty of accepting the service and improve the product’s ease of use. Feedback stage design strategy After completing the service, users will evaluate and give feedback on the service quality, effect and efficiency, etc. Positive feedback can deepen the users ’ emotion of participation in the service, while negative feedback can provide the direction for the improvement of the system, and dedicate a little bit of advice for the construction of a perfect service system of home-based elderly care products and services. In order to improve the viscosity of the elderly users to the service, the feedback of the elderly users can be viewed - evaluation - implementation of the path to adopt the recommendations to improve the sense of participation of the elderly users.
Design of the intelligent medical terminal for the elderly at home
In terms of functional design, the main modules covered are voice interaction module, CNC mechanical module (responsible for intelligent adjustment of the screen angle), micro integrated chip module, wireless Bluetooth module (responsible for transmitting health data) and power storage/supply module, as shown in Figure 3.
Design of drug management equipment
The drug management device includes wireless Bluetooth modules, power storage/supply modules, CNC electromechanical modules, camera modules, and voice announcement modules in terms of functional modules, as shown in Figure 4.
Infrared detection equipment design
The main function of the infrared out prompting device is to monitor the behavior of the elderly out and at home, so as to avoid the elderly being injured in home activities and not being able to remind and save them at the first time. Its main modules include wireless Bluetooth module, power storage/supply module, camera module, infrared detection module, and voice announcement module, as shown in Figure 5.
Intelligent medical terminal function frame
Drug management equipment functional framework
Infrared detection function frame
Technical Architecture
The medical and health function module is the most important function module of the smart aging platform, according to the overall system architecture, the medical and health function module is in the application layer of the platform, as shown in Figure 6.
According to the function of the medical health subsystem and the position of the application layer, the medical and health function module needs the real-time physical sign data of the elderly collected by the health information collection terminal in the smart elderly care terminal equipment, and the health information fusion result data in the data fusion module, and these data together with the basic information of the elderly are completed together with the functions of remote monitoring, health information, regular arrangement of physical examinations, appointment registration W and obtaining diagnosis and treatment results on behalf of the elderly, etc., these functions are different according to the applicable objects. It will be divided into the TV version of home care software, the mobile version of home care software, the community service elderly care center, and the web version.
Community service center for the elderly web end:
Community senior care services in Tun use the HTTP protocol to interact with the user function module, and use an easy UI framework for interface writing. On the basis of easy UI, use pushlet technology, which can update the abnormal alarm information in real time.
Remote monitoring function realization
The remote monitoring function mainly includes two types of monitoring, one is the need for emergency rescue for the elderly when their physical signs are abnormal, their actions are abnormal, or there is a disease abnormality in the data fusion results that requires emergency medical treatment, and this type of emergency rescue can be divided into three parts: abnormal alarms for the elderly, abnormal information pushing, and elderly rescue based on localization. The second category is when the data fusion results show that the elderly suffer from certain chronic diseases, which do not require emergency assistance, but still require the elderly to be diagnosed in a timely manner, and at the same time need to care for the elderly through other functions in the future care.
Location diagram of medical and health function module
After outputting the design scheme of intelligent elderly guardianship service system based on multimodal theory, in order to help the elderly users better understand the service, the author produced a service demonstration video for display and supplemented with explanatory notes, and at the same time produced the SUS usability scale, which was distributed to six elderly users who had participated in the user interviews, and invited the elderly to evaluate the existing service design scheme, and based on the results of the evaluation, the service design scheme was launched. Based on the evaluation results, the corresponding interviews were conducted, and the data statistics are shown in Table 10, with Q as the question, M as the mean, Y1 as user 1, Y2 as user 2, Y3 as user 3…and so on, and the secondary usability scoring criteria are shown in Table 11.
SUS availability scale
| Q | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Scoring | Evaluation |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Y1 | 4 | 3 | 5 | 2 | 5 | 1 | 5 | 1 | 4 | 1 | 93 | A+ |
| Y2 | 5 | 4 | 3 | 1 | 4 | 2 | 5 | 1 | 5 | 1 | 83 | B |
| Y3 | 4 | 3 | 4 | 2 | 4 | 1 | 5 | 1 | 5 | 1 | 85.4 | B+ |
| Y4 | 4 | 3 | 5 | 1 | 4 | 2 | 5 | 2 | 4 | 1 | 92 | A+ |
| Y5 | 5 | 1 | 5 | 1 | 5 | 1 | 4 | 1 | 3 | 1 | 91.2 | A- |
| Y6 | 3 | 2 | 5 | 1 | 3 | 1 | 5 | 2 | 5 | 1 | 91.2 | A- |
| M | 4.2 | 2.6 | 4.5 | 1.3 | 4.2 | 1.3 | 4.8 | 1.3 | 4.3 | 1 | 89.3 | A- |
Availability criteria
| Very different meaning | Comparison different meaning | Neutrality | Comparative consent | Very agree | Unfilled |
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | Default 3 |
SUS provides an overall usability assessment measure with a total of 10 questions, of which odd items are positive statements and even items are negative statements. The fourth, fifth, and tenth items represent “validity” and “ease of learning”; Items 2, 3, 7 and 8 represent “efficiency of use” and “usability”; The 1st, 6th, and 9th items represent “satisfaction”. The statistical results show that the service system’s design scheme has an average score of 89.3 points and an average rating of A-, which indicates that the overall availability of the system scheme is high. Among them, the deviation of items 2 and 5 is high, and it can be inferred that there are still some deficiencies in the ease of learningand usability of the product, which can be optimized and modified in the subsequent iteration process to improve the integrity of the service.
After the data was tallied, further comparative tests were conducted to further understand the effectiveness of the system interactions in this paper in terms of enhancing the user experience. In this evaluation, it was decided to intercept the more feasible parts of the service content through different channels of medical services to conduct a comparison test between the traditional technology and the system of this paper. The user response speed was recorded separately, and the test results are shown in Table 12 and Table 13:
Online medical service
| User | Traditional technical response time(s) | The system response time of this article(s) |
|---|---|---|
| User 1 | 1.5 | 1.4 |
| User 2 | 1.7 | 1.5 |
| User 3 | 1.7 | 1.7 |
| User 4 | 1.5 | 1.5 |
| User 5 | 1.4 | 1.2 |
| User 6 | 1 | 1 |
| Mean | 1.47 | 1.38 |
Offline medical services
| User | Traditional technical response time(s) | The system response time of this article(s) |
|---|---|---|
| User 1 | 2.9 | 1.1 |
| User 2 | 2.4 | 1.1 |
| User 3 | 3.2 | 1.3 |
| User 4 | 2.7 | 1.1 |
| User 5 | 2.9 | 1.2 |
| User 6 | 2.5 | 1.2 |
| Mean | 3.85 | 1.08 |
According to the data in the above tables, it can be found that under the online channel, the effect of using the traditional technology and the system of this paper, respectively, the effect of the speed of business processing is not a big gap, and there is only a gap of 0.09s in terms of the average value. In contrast, under the offline scenario, there is a significant decline in the reception efficiency of the elderly for the traditional technology, the gap reaches 1.3s, according to the test observation, some of the elderly are more hesitant to accept the information about whether to continue to do business. Under the conditions of the system interaction in this paper, the response speed of the system is comparable to that when it is online, and the gap between the use of the two types of services is large under the same channel. According to the results of this test, it can be learned that the service mode of the research system in this paper is feasible to enhance the convenience of the elderly group to handle the consultation, medicine collection, and the speed of seeing the doctor.
This paper applies the conjoint analysis method, a quantitative analysis method that can study consumer preference, to analyze the consumer preference of senior living communities in a city as a case study. 380 valid questionnaires were collected, and the results were obtained according to the joint analysis preference model; the demand points of the elderly and the community elderly service center were summarized, and the Kano model was used to calculate the importance of the demand with the elderly as the core users, and the service system architecture was constructed on the basis of the service system, and the medical system of the elderly end was used to carry out the design of the mobile end product mainly. Through comparative experiments in different channels, we practiced designing community healthcare services for elderly users. The results show that the significant P-value of the model of the research object in this paper is 0.010**, which presents significance at the level of α=0.05, so the model basically meets the requirements. The empirical study based on the attribute preference of senior living community found that medical service and accommodation environment are the key factors influencing consumers to buy senior living community products, and factors such as catering service and price also play a more or less influential role. Through the Kano model to analyze the needs of the core user elderly people, and the Kano type generalization of the needs, the results show that the realization of the user’s expected needs is the core of the system design, the realization of the necessary factors is the foundation, and the realization of the charm factors can enhance the user’s attraction to the system. In the design practice, the gap between the traditional service channel technology proposed in this paper and the proposed service system is significant. It shows that the service system in this paper is feasible.