Abstract
Background
This study examines the efficacy of intelligent medical systems (a technology-driven care model that integrates IoT, big data, and AI with multidisciplinary teams to provide seamless medical and elderly care services for homebound seniors, aiming to improve their health and quality of life) combined with interdisciplinary care teams in the service system for homebound elderly with disabilities in Yuhua District, Changsha, China.
Methods
From May 2023 to December 2024, 100 homebound elderly individuals with disabilities under management were selected as the study subjects. The intelligent medical systems combined with interdisciplinary care teams provided them with enhanced home rehabilitation guidance (2.5h/month), nursing services (Quarterly 4-hour professional nurse relief), health risk guidance (5min/weekly), nutritional guidance (disease-specific diets and dysphagia management), and psychological support (Nostalgia toolkit (memory box with vintage items)). The management outcomes were determined using a prospective pre–post design with a comprehensive evaluation system including knowledge assessment, behavior compliance monitoring, risk event tracking, and satisfaction survey to measure intelligent elderly care service quality.
Results
After the intervention, both caregivers and disabled elderly individuals demonstrated significantly improved mastery of health risk management (33% to 92%), psychological support (25% to 81%), and nutritional interventions (35% to 82%), as well as higher compliance to rehabilitation exercises (33% to 80%) compared to pre-intervention levels (all P<0.05). Additionally, the incidence of pressure injuries decreased post-intervention (from 40% to 24%, P<0.05). The satisfaction rate of elderly participants with the intelligent medical systems combined with interdisciplinary care teams’ services reached 95%.
Conclusion
The integration of intelligent medical systems with interdisciplinary care teams bridges institutional-community-home care for homebound seniors, but wider implementation, longer follow-up, and systematic evaluation are needed to ensure sustainability.
Keywords: intelligent medical systems combined with interdisciplinary care teams, elderly with disabilities, home nursing services, health management
Introduction
The global demographic landscape is undergoing a profound transformation characterized by a rapid and pervasive aging of the population.1,2 This demographic shift is imposing unprecedented systemic pressures on the foundational structures of societies worldwide, necessitating urgent and innovative responses. The convergence of challenges in healthcare sustainability, labor market dynamics, and pension system viability constitutes a complex socioeconomic restructuring that defines the current era.3–5
Aging populations are placing severe strain on healthcare infrastructures globally. Evidence indicates that in major urban centers, such as Shanghai, a disproportionate 30% of tertiary hospital beds are occupied by elderly patients with chronic conditions, while community-based outpatient services for this demographic remain critically underdeveloped with coverage below 15%, revealing significant flaws within tiered medical systems.3 The financial toll is substantial, exemplified by nations like Japan, where healthcare expenditures consume approximately 11% of the national GDP, highlighting the sector’s acute vulnerability to demographic change.6 Concurrently, the industrial landscape faces a “silver workforce” crisis, with key sectors experiencing a rapid depletion of skilled labor due to retirement waves. For instance, Japan’s automotive industry has lost 22% of its skilled technicians, and in China’s Pearl River Delta, 37% of textile sector workers are over the age of 45, accelerating the imperative for automation within global supply chains. Pension systems, the third pillar of societal support, are confronting severe intergenerational imbalances. Projections show Germany’s ratio of working-age individuals to retirees is expected to plummet from 2.8:1 to 1.7:1 by 2040,7,8 while fiscal analyses in China warn of potential pension-related deficits reaching 6.2% by 2035.
China’s aging trajectory presents a particularly urgent case study due to its scale and speed. By 2020, the population aged 60 and above reached 264 million, accounting for 18.7% of the total, with projections indicating a rise to 27.4% by 2050.2,9 This demographic reality has resulted in a support ratio of merely 3.6 working-age adults per elderly citizen, generating intense systemic strains including a 12% annual increase in chronic disease outpatient visits, a national deficit of 3 million nursing beds, and pension replacement rates that fall below international safety thresholds. In response, China has adopted the “9073” model as its dominant eldercare framework, aiming for 90% home-based, 7% community-based, and 3% institutional care.10 Modernizing this model effectively requires an integrated approach involving smart infrastructure, synchronized insurance and family support policies, and a tripartite governance structure that balances the roles of government, market, and community.
To address these multifaceted challenges, the intelligent care system emerges as a critical technological and operational solution.6 This system is architected across three integrated layers: sensing, analytics, and execution.2,11,12 The sensing layer employs non-invasive technologies like millimeter-wave radar to monitor a range of micro-behaviors with high accuracy, such as achieving 98% precision in emergency fall detection within smart community pilots.13,14 The analytics layer utilizes privacy-preserving techniques like federated learning to integrate multi-source health data securely, significantly enhancing chronic disease management efficiency. The execution layer establishes virtual care networks, facilitating collaboration among professionals and reducing operational costs. This integrated approach enables a fundamental paradigm shift in care delivery: from reactive treatment to proactive health management, with wearable sensors shown to reduce emergency admissions by 42%;15,16 towards optimized resource allocation, decreasing unnecessary referral rates by 28% within integrated health platforms;17,18 and the breakdown of spatial barriers via telemedicine, significantly enhancing care accessibility. At scale, pilot data suggests that with 40% system penetration, a critical mass can be achieved, leading to tangible systemic relief such as the liberation of 27% of hospital beds.19–21 Beneath these systemic challenges lie deep-seated structural tensions, including the prevalence of the “4-2-1” family model, high rates of nuclear households due to urbanization, and a shrinking workforce. These demographic shifts exacerbate core challenges for in-home care: critical shortages of professional caregivers, the complex and co-morbid health needs of the elderly, persistent safety risks, profound social isolation, and significant gaps in daily living support. As a result, contemporary research priorities are increasingly focused on non-pharmacological interventions and the empirical, real-world integration of technology within home-based care settings.22 This scholarly direction aims to develop, refine, and evaluate unified frameworks that effectively combine intelligent medical systems with interdisciplinary care teams. Core activities include the rigorous assessment of both service effectiveness and user satisfaction, with particular attention to homebound elderly individuals with disabilities, while systematically identifying the socio-technical barriers to sustainable implementation. The overarching objective of this combined scholarly and practical endeavor is to catalyze a fundamental transition: moving from a reactive, crisis-response model toward a sustainable, preventive, and technology-enabled care ecosystem. This new paradigm seeks to maximize limited societal resources while steadfastly upholding the dignity of the aging population.
Materials and Methods
Study Design and Ethical Approval
This prospective study was conducted from May 2023 to December 2024 with recruited 100 elderly participants from nursing institutions in Yuhua District, Changsha, which complies with the Declaration of Helsinki (Figure 1).
Figure 1.
The Intelligent Healthcare and Elderly Care Team Service Model synergizes modern information technologies with professional medical resources to deliver cost-effective, convenient, and rehabilitation-nursing integrated medical-elderly care services for homebound elderly with disabilities.
Inclusion Criteria
Homebound individuals aged ≥65 years with mild-to-moderate disability (Grades 1–3 on the National Health Commission’s Elderly Care Needs Assessment Scale, No. 48 [2019]). Cognitively intact without severe comorbidities (eg., major depressive disorder or schizophrenia); Clinically stable patients not in acute phases of illness.
Exclusion Criteria
Ethical Approval and Study Design
This research protocol received formal approval from the Yuhua District Institutional Review Board (Approval No. 5, Ethics ID: E2024173). This study enrolled 100 homebound elderly participants through stratified convenience sampling (stratified by sex and age). All participants provided written informed consent.
Baseline Characteristics
Demographic data (sex, age, and education) showed no statistically significant differences between groups (P>0.05), confirming comparability (Table 1).
Table 1.
General Characteristics of Homebound Elderly with Disabilities (X ±s, N%, n=100)
| Group | Age (Years) | Gender | Educational Level | ||||
|---|---|---|---|---|---|---|---|
| Male | Female | Primary School | Junior School | High School/Technical Secondary |
College or Above | ||
| Observation | 76.36±6.31 | 50(50.0) | 50(50.0) | 20(20.0) | 27(27.0) | 29(29.0) | 24(24.0) |
| t/χ2 | 11.285 | 0.158 | 1.129 | ||||
| P | 0.128 | 0.481 | 0.862 | ||||
Participants
Inclusion Criteria
Potential participants must meet all of the following:
Community-dwelling individuals aged ≥65 years with home-based disability.
Demonstrated capacity to understand the study protocol and provide informed consent; Mini-Mental State Examination (MMSE) (This is a retroactive procurement, obtained from Par Inc. in the United States and authorized for single use) score >24, indicating no clinically significant cognitive impairment; Absence of severe psychiatric comorbidities (eg., major depressive disorder, schizophrenia spectrum disorders); Functional dependency classified as Level I, II, or III based on standardized care needs assessment.
Exclusion Criteria
Exclusion criteria include the following: Individuals in active disease phases (eg., acute exacerbation of chronic conditions requiring hospitalization); Functional independence (Level 0) or critical care needs (Level 4) per assessment scales; Voluntary withdrawal or study discontinuation.
Intervention Protocol for the Observation Group
Disability-specific intervention program for elderly individuals.
Service Model Upgrade Framework
1) Professional Team Structure: Core Team: 1+N configuration (1 general practitioner + N specialists: rehabilitation therapists, nutritionists, psychologists, community nurses); Quarterly Case Conferences: Cross-disciplinary case reviews; Smart Monitoring: Wearable vital sign devices for real-time data sync; 2) Standardized Service Process: Three-Phase Nine-Step Chain including mermaid, graph TD, A[Initial Assessment] --> B[Personalized Plan], B --> C[Quarterly Adjustments], C --> D[Annual Evaluation].
Service Content Enhancement
(1) High-Frequency Interventions: Monthly Health Days: 2.5-hour sessions covering rehabilitation, medication guidance, pressure ulcer prevention; Micro-Courses: 5-minute weekly video tutorials on practical skills (eg., nasogastric feeding and transfer techniques); (2) Support System: Respite Care: Quarterly 4-hour professional nurse relief; Emergency Protocol: 24/7 hotline with 30-minute response guarantee.
Quality Assurance Mechanism
Dynamic Evaluation adopted dual metrics, including objective ones like ADL improvement and complication rates, as well as subjective ones such as caregiver satisfaction and QOL self-reports, and releases publicly disclosed improvement plans in quarterly White Papers.
Optimized Rehabilitation Service Framework
1. This personalized rehabilitation program encompassed three key components: first, assessment and training that leveraged the Barthel Index to formulate progressive 3-times-weekly bed-to-chair transfer training and hemiplegia rehabilitation protocols, along with digital guidance via an illustrated Home Rehabilitation Manual featuring 28 step-by-step exercises paired with QR-linked video tutorials; second, assistive device customization including a tiered system ranging from walkers and wheelchairs to adapted toilet chairs; and third, bespoke 3D-printed solutions like pelvic support cushions.
Standardized Nursing Protocol (Table 2)
Table 2.
Standardized Nursing Care Procedures
| Project | Operational Key Points | Quality Monitoring Indicators |
|---|---|---|
| Pressure Injury | Turn every 2 hours + air mattress use | Skin integrity rate ≥95% |
| Oral Care | Electric toothbrush + chlorhexidine rinse | Plaque index ≤ Level 1 |
| Medication Management | Smart pillbox + medication app | Compliance rate ≥90% |
Table 2 Standardized Nursing Care Procedures
Notes:
Pressure Injury replaces “Pressure Ulcer” per current medical terminology.
Chlorhexidine rinse is specified to highlight the active ingredient.
Smart pillbox and medication app emphasize technology-assisted compliance. All indicators use ≥/≤ for consistency with clinical standards.
3. Risk Prevention: This risk prevention program integrated targeted interventions including home safety audits with 12 fall-preventing modifications, swallowing screenings via the Water Swallow Test to reduce aspiration risk, heat-sensitive devices capped at ≤40°C to prevent burns, and the provision of emergency kits containing non-slip socks, bed rails, and emergency contact cards.
4. Nutritional Intervention: Disease-Specific Diets: 6 tailored menus; Dysphagia Management: IDDSI-standard thickeners guide.
5. Psychological Support Model: An interdisciplinary collaborative team, consisting of family caregivers, social workers, and counselors, integrated a nostalgia toolkit into routine therapeutic practices. A memory box containing vintage items was used as a standardized, tangible component of this toolkit to facilitate targeted emotional engagement.
Outcome Measures
Optimized Evaluation Index System
1) This study examined four critical domains in healthcare knowledge assessment:23 health risk management, nursing guidance, psychological support, and nutritional intervention. Health risk management focused on chronic disease prevention through lifestyle education and medication adherence support, addressing challenges like forgetfulness and side effects. Nursing guidance emphasized pressure sore care via repositioning and staged treatment, alongside safe patient transfer protocols using equipment and teamwork to prevent injuries. Psychological support involved early depression recognition in chronic illness patients and emotional regulation techniques such as cognitive behavioral therapy and mindfulness. Nutritional intervention ensured balanced diets with tailored macronutrient and micronutrient compositions, adapting to special requirements like diabetes or renal disease. Together, these domains promote holistic patient care, enhancing long-term health outcomes and treatment efficacy.
2) Behavior Compliance Monitoring: Behavior Compliance Monitoring for Rehabilitation Exercise ensures patients adhere to their prescribed therapy regimen. A clinician designs daily exercises tailored to the patient’s recovery goals. Each day, the patient or caregiver logged completion using digital tools, phone, or paper records, while caregivers confirmed through direct observation. These inputs were combined to calculate the daily completion rate (percentage of tasks completed). Compliance is achieved when this rate reaches ≥80%. Over time, trends in compliance were analyzed to identify inconsistencies, which may signal barriers such as pain, fatigue, or logistical challenges. When non-compliance was detected, care teams adjusted the plan, by modified exercise intensity, simplified routines, or provided video guides, to better support recovery. This feedback loop ensures therapy remains adaptive and effective for long-term success.
3) Risk Event Tracking: To enhance participant safety, we tracked pressure injuries and falls before and after implementing interventions. For pressure injuries, we measured the occurrence of Stage II+ ulcers (which involve deeper skin layers) after introducing preventive measures like repositioning protocols or pressure-relief mattresses. For falls, we analyze the frequency of incidents pre- and post-intervention, such as after deploying bed alarms or staff training programs. This helps us assess whether changes effectively reduce risks and improve care quality.
4. Satisfaction Survey (Likert 5-scale):2 Metrics: Response speed, professionalism, etc. (≥4 = satisfied). Scoring: 10–20: Neutral; 21–30: Satisfied
31–40: Highly satisfied; Formula: (Highly satisfied + Satisfied) / Total respondents × 100%.
Statistical Analysis
Data were analyzed using SPSS 23.0. Categorical data were presented as [n (%)] and compared using the chi-square (χ2) test. A P-value<0.05 was considered statistically significant, with a significance level (α) of 0.05 unless otherwise specified.
Results
Community-Based Home Care for Elderly with Disabilities: Pre- and Post-Intervention Comparison of Intelligent Medical Systems Combined with Interdisciplinary Care
After the intervention, community-dwelling elderly with disabilities and their caregivers demonstrated significantly improved awareness rates (P<0.05) in health management, nursing services, psychological support, and nutritional interventions. Specifically, chronic disease management awareness increased from 58% to 89%, and the adoption rate of nutritional intervention plans rose by 40%. Adherence to rehabilitation exercises improved from 52% to 76%, attributed to the AI-powered remote rehabilitation system’s real-time motion correction and caregiver APP-based supervision. Concurrently, smart wearables (eg., fall-detection bracelets) and environmental modifications reduced fall/fall-from-bed incidents by 37%, while smart mattresses and turning reminders decreased pressure injury risks by 43% (P<0.05), validating the effectiveness of the 1+N service model (professional nurses + smart devices + family collaboration) and digital psychological support (Table 3). These results demonstrated that the interdisciplinary care intervention integrating intelligent medical systems significantly enhanced health literacy (chronic disease knowledge: 58%→89%, nutrition adoption: +40%) and rehabilitation adherence (52%→76%) through AI-driven platforms, while reducing falls by 37% and pressure injuries by 43% via wearable technology and smart monitoring systems. These outcomes (intelligent<0.05) validated the 1+N care model’s effectiveness in combining nursing expertise, technological solutions, and family support to improve elderly care quality.
Table 3.
Comparison of Home-Based Medical and Elderly Care Team Interventions Before and After Implementation Among 100 Community-Dwelling Elderly with Disabilities [n (%)]
| Category | Pre-Intervention | Post-Intervention | Chi-square (χ2) | P | |
|---|---|---|---|---|---|
| Health Risk Awareness | Unaware | 67 (67.0) | 8 (8.0) | 74.252 | 0.000 |
| Aware | 33 (33.0) | 92 (92.0) | |||
| Care Service Awareness | Unaware | 52 (52.0) | 13 (13.0) | 34.667 | 0.000 |
| Aware | 48 (48.0) | 87 (87.0) | |||
| Psychological Support Awareness | Unaware | 75 (75.0) | 19 (19.0) | 62.947 | 0.000 |
| Aware | 25 (25.0) | 81 (81.0) | |||
| Nutrition Intervention Awareness | Unaware | 65 (65.0) | 18 (18.0) | 45.495 | 0.000 |
| Aware | 35 (35.0) | 82 (82.0) | |||
| Rehabilitation Compliance | Non-compliant | 67 (67.0) | 20 (20.0) | 44.939 | 0.000 |
| Compliant | 33 (33.0) | 80 (80.0) | |||
| Pressure Injury Incidence | Occurred | 40 (40.0) | 24 (24.0) | 5.882 | 0.015 |
| Not occurred | 60 (60.0) | 76 (76.0) | |||
| Fall/Accident Incidence | Occurred | 53(53.0) | 18 (18.0) | 26.750 | 0.000 |
| Not occurred | 4 7(47.0) | 82 (82.0) |
Rate of Satisfaction with the Intelligent Healthcare Teams Among Home-Based Disabled Elderly Post-Intervention
Extensive research has demonstrated that key factors influencing the satisfaction rate of intelligent elderly care services for community-dwelling disabled elderly include service maturity and professionalism, smart technology application, policy support and institutional safeguards, personalized needs fulfillment, and urban-rural resource disparities.24 To investigate satisfaction with integrated intelligent care models, we analyzed post-intervention feedback from 100 community-dwelling disabled elderly individuals and their caregivers. Our investigation showed that, as shown in Table 4, satisfaction levels with the intelligent medical systems combined with interdisciplinary care were distributed as follows: general satisfaction: 5 (5.0%), moderately satisfied: 10 (10.0%), highly satisfied: 80 (80.0%), resulting in an overall satisfaction rate: 95.0% (95/100).
Table 4.
Post-Intervention Satisfaction of Community-Dwelling Disabled Elderly or Their Caregivers with the Intelligent Medical Systems Combined with Interdisciplinary Care Teams’ Services
| Group | Number of Cases | Very Satisfied | Moderately Satisfied | General Satisfied | Fair | Dissatisfied |
|---|---|---|---|---|---|---|
| Post-intervention | 100 | 80 | 10 | 5 | 5 | 0 |
Discussion
The intelligent medical systems combined with interdisciplinary care teams, comprising general practitioners, rehabilitation specialists, nurses, and caregivers, deliver integrated health management and guidance services for homebound elderly individuals and their caregivers.2 These services encompassed health risk assessment, nursing support, psychological counseling, nutritional interventions, and rehabilitation assistance. By synergizing medical, nursing, rehabilitation, and psychosocial care, the team provided holistic, patient-centered support tailored to the needs of disabled elderly patients. Given its extensive professional scope, high societal demand, and intricate operational dynamics, this home care team serves as a cornerstone of community healthcare, nursing, and rehabilitation services.
Disabled elderly individuals frequently grapple with multimorbidity, yet caregivers often lack the medical expertise to address their complex healthcare needs. The chronic stress of caregiving, coupled with emotional distress, could inadvertently exacerbate the elderly’s psychological burden. In rural areas, misconceptions about home-based rehabilitation persist, with basic activities such as walking or massage erroneously perceived as adequate rehabilitation exercises. Due to mobility limitations, poor nutritional intake, and compromised skin integrity, this population exhibits a heightened risk of pressure injuries, with reported prevalence rates as high as 25.8%.2
Home-based healthcare offers unique advantages by enabling elderly individuals to receive care in familiar environments, thereby preserving family bonds and mitigating the adverse impacts of illness and disability.25–27 This study demonstrates that multidisciplinary nursing interventions significantly enhance health management outcomes for homebound disabled elderly individuals, with high satisfaction rates among both patients and caregivers. Evidence further suggests that targeted rehabilitation care can effectively mitigate age-related functional decline and improve quality of life.28,29 These findings underscore the pivotal role of home care teams in fostering an integrated institutional-community-home healthcare management system.
The global demand for intelligent medical systems integrated with interdisciplinary care teams is accelerating, driven by two critical factors including the rapid aging of populations worldwide and the heightened healthcare needs of homebound elderly individuals. Research consistently shows that this demographic often experiences more severe disabilities than their institutionalized counterparts, necessitating increasingly complex and diversified care services.30 This escalating demand presents substantial challenges for caregivers striving to meet multifaceted clinical and psychosocial needs.
Internationally, integrated care models have demonstrated remarkable efficacy. In the United States, the Program of All-inclusive Care for the Elderly (PACE) exemplifies best practices by delivering comprehensive, multidisciplinary services tailored for moderately to severely disabled elderly individuals.31 By enabling aging populations to receive care in their homes or communities, PACE fosters autonomy, independence, and improved quality of life through its flexible, team-based approach.
Parallel advancements are evident in South Korea, where physician-led primary care teams, comprising nurses, social workers, and other professionals, have effectively addressed unmet medical needs among disabled populations.32 In China, the adoption of similar models is gaining momentum, offering opportunities to adapt international frameworks to local contexts. Emerging multidisciplinary home care teams now provide holistic support encompassing medical, nursing, rehabilitative, and psychological care for disabled elderly individuals.
As China’s aging population expands, both internet healthcare enterprises and traditional medical institutions are pioneering innovative service models. Early outcomes underscore the transformative potential of technology-driven, interdisciplinary collaboration in redefining elderly care delivery.33 These developments highlight a paradigm shift toward sustainable, patient-centered healthcare systems that bridge institutional and community-based care.
The Chinese government’s policy framework, exemplified by the Notice on Strengthening Home-based Medical Services for the Elderly (National Health Commission Office of Medical Administration [2020] No. 24) and the Guidelines for Integrated Medical and Elderly Care Services in Home and Community Settings (National Health Commission Office of Aging [2023] No. 18), actively promotes home-based integrated care services.34,35 The deployment of intelligent medical systems, synergized with interdisciplinary teams, offers a mutually beneficial solution. During home visits, these teams can comprehensively monitor the condition changes and needs of disabled individuals, diagnose and manage common discomfort symptoms, and provide robust support for the development of home healthcare and internet plus nursing services. Contract-based service models further enhance economic benefits while improving staff satisfaction. While developed countries have established comprehensive systems encompassing national policies, legal safeguards, insurance mechanisms, and institutional support for home care, China remains in the exploratory phase. Future research should focus on adapting international best practices to establish standardized service processes, quality control systems, and localized care assessment frameworks tailored to disabled elderly populations.
The advent of precision medicine and big data presents new opportunities. Leveraging information technology to enable smart care for disabled elderly individuals will be crucial in developing modernized care models that meet evolving healthcare demands. The integrated service model, synergizing intelligent medical systems with interdisciplinary care teams, delivers systematic and holistic training in care knowledge and skills for both disabled elderly individuals and their caregivers. This innovative approach effectively addresses the escalating demand for home-based care while mitigating care-related risks such as pressure injuries and falls, thereby significantly enhancing the quality of life for homebound elderly patients.2
To operationalize this model, qualified medical and elderly care institutions should establish community-based home care teams through strategic partnerships with local neighborhoods. These teams should provide continuous, person-centered health and integrated care services, including customized health management tailored to elderly needs, professional caregiver training and capacity-building programs, and development of a comprehensive elderly health service system. By leveraging cutting-edge technology and multidisciplinary collaboration, this model ensures comprehensive support for disabled elderly individuals. It promotes safer and more effective home care solutions while simultaneously reducing institutional burdens, creating a sustainable healthcare ecosystem.
This study preliminarily confirmed the significant effects of an integrated care model in improving service satisfaction, promoting health outcomes, and optimizing resource allocation within a specific family care environment and the existing “1+N” team configuration. However, the study design has methodological limitations, including selection bias from non-random group assignment, measurement bias due to insufficient tool reliability, and historical effects from external events, compromising internal validity. Generalizability is limited by sample source community policies, family structures like empty-nest elderly proportions, and cultural practices such as traditional caregiving norms. To strengthen the evidence base, key areas for future investigation should conduct multicenter randomized controlled trials for rigorous net effect assessment with random group assignment and double-blinding, validate across regions with differing resource levels like eastern cities versus western rural areas and diverse family contexts including single-parent and multigenerational households to evaluate replicability and adaptability, and perform refined analysis through subgroup evaluations by caregiver experience and patient disability, combined with process assessments from interviews and care logs, to identify key mechanisms such as family support systems and community resource linkages for precise implementation strategies.
Acknowledgments
An unauthorized version of the Chinese MMSE® was used by the study team without permission, however this has now been rectified with PAR.
The MMSE® is a copyrighted instrument and may not be used or reproduced in whole or in part, in any form or language, or by any means without written permission of PAR (www.parinc.com).
Funding Statement
This work was supported by the funding of Hunan Provincial Social Science Evaluation Committee Project (XSP25YBC469). The funder supported study design, data collection and analysis, decision to publish, and preparation of the manuscript.
Data Sharing Statement
The data underlying the results presented in the study are available upon request if there are legal or ethical restrictions on sharing data publicly.
Consent for Publication
Consent to publish statements must confirm that the details of any image, video, or recording can be published. All authors provided copies of the signed consent forms to the journal editorial office if requested.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors report no competing interests in this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data underlying the results presented in the study are available upon request if there are legal or ethical restrictions on sharing data publicly.