Universal Access–Oriented Emergency Response for Older Adults in Rural Thailand: A Cyber-Physical-Human IoT Case Study

Universal Access–Oriented Emergency Response for Older Adults in Rural Thailand: A Cyber-Physical-Human IoT Case Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Universal Access–Oriented Emergency Response for Older Adults in Rural Thailand: A Cyber-Physical-Human IoT Case Study Nicharee Jaikummwang, Ratree Kummong, Kanyarat Phuengbanhan, Supattra Tantijariyapan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8609067/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose The purpose of this study was to design, deploy, and evaluate a universal access–oriented emergency healthcare system that reduces barriers related to age, digital literacy, infrastructure limitations, and organizational fragmentation in rural settings. The research question addressed how a human-centric cyber-physical-human Internet of Things (IoT) platform can support equitable access to emergency healthcare for older adults in low-resource communities. Methods A Smart Information Management System and Emergency Medical Call System (SIMS–EMCS) was developed using a user-centered co-design approach. The platform integrates structured elderly health data management with real-time emergency communication through a widely used social messaging interface. The system was deployed in a rural subdistrict in Thailand and evaluated through real emergency use and controlled drills. Usability and system performance were assessed using a structured questionnaire administered to healthcare personnel, rescue teams, and community volunteers. Results The system achieved a 99.2% end-to-end communication success rate and reduced average emergency response time by 35% compared with conventional phone-based reporting. Usability evaluation yielded a “Good” overall score (mean 4.01 ± 0.65), indicating broad acceptability across users with diverse digital literacy levels. Conclusion The findings demonstrate that effective emergency healthcare access can be achieved without assuming high technical expertise or continuous connectivity, supporting core principles of universal access. SIMS–EMCS provides a replicable, human-centric model for inclusive emergency healthcare delivery in low- and middle-income country contexts. Universal Access Elderly Emergency Care Human-Centric IoT Accessibility Rural Health Systems Cyber-Physical-Human Systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Thailand, like many countries in the Asia–Pacific region, is undergoing a rapid demographic transition toward an aging society [ 1 ]. Rural communities face a dual challenge: a growing proportion of older adults living with chronic conditions and functional decline, alongside limited access to timely and coordinated emergency medical services [ 2 ]. In such settings, emergency response delays are frequently exacerbated by fragmented communication and the absence of interoperable, real-time health information systems that link patients, first responders, and healthcare facilities. Although Thailand has implemented national electronic medical record (EMR) systems, these platforms are largely designed for hospital-centric workflows and rarely interoperate seamlessly with community-level emergency mechanisms [ 3 ]. As a result, rural emergency coordination continues to rely on ad hoc practices—such as personal telephone calls, handwritten notes, or verbal reporting—which often lead to inconsistent information exchange, incomplete patient context, and delayed mobilization of rescue teams [ 4 ]. These limitations highlight a persistent gap between institutional health information systems and frontline emergency response in rural areas. Experiences from other low- and middle-income countries (LMICs) indicate that mobile and pervasive health platforms can help bridge this gap by extending digital communication and structured data management beyond hospital environments [ 5 ]. Recent work in human-centered pervasive health emphasizes that advanced technologies, including AI and multimodal analytics, deliver practical benefits only when embedded within real workflows and socio-cultural contexts [ 6 ]. Similarly, privacy-preserving and distributed architectures—such as federated learning and blockchain-based auditing—have been proposed for healthcare IoT, underscoring the importance of secure and scalable system design in resource-constrained settings [ 7 ]. However, in many rural deployments, the primary barriers are not sensing accuracy or advanced analytics, but rather communication, coordination, and workflow integration. Most existing solutions therefore operate either as standalone health record systems or as isolated emergency alert tools, with limited integration between structured patient data and real-time emergency communication [ 8 ]. Adoption challenges are further amplified when systems require new software installation or complex interfaces, which disproportionately affect elderly users and community volunteers with heterogeneous digital literacy [ 9 ]. While prior research has advanced wearable and physiological sensing technologies [ 10 ], these approaches alone do not address the broader socio-technical challenges of emergency coordination in rural communities. In Thailand, no widely adopted initiative has yet unified real-time emergency medical call workflows with structured elderly health data management through a technically and culturally adaptable IoT-based architecture [ 11 ]. Consequently, first responders often lack immediate access to essential patient information—such as comorbidities, medications, functional status, and prior interventions—at the point of care, limiting informed triage and on-site decision-making [ 12 ]. To address this gap, this study introduces the Smart Information Management System and Emergency Medical Call System (SIMS–EMCS), an integrated Cyber-Physical-Human IoT platform designed for rural, low-resource environments. The system adopts a three-layer architecture—comprising human and physical interaction, communication, and cloud-based data services—to support low-bandwidth operation, role-based access, and context-informed information exchange. To maximize accessibility and minimize training requirements, SIMS–EMCS leverages the widely adopted Line Official Account (Line OA) as a human-facing IoT gateway, enabling culturally familiar and ubiquitous communication between community members and emergency responders [ 13 ]. The system was developed using a user-centered co-design approach, involving healthcare personnel, municipal rescue teams, and community volunteers throughout design, implementation, and pilot deployment in a rural subdistrict with a high proportion of elderly residents [ 14 ]. System evaluation focused on usability, operational reliability, and emergency response performance, with particular emphasis on reducing response latency and improving access to structured patient information under real-world conditions [ 15 ]. By demonstrating how elderly health data management can be integrated with structured emergency communication through a human-centric IoT architecture, this work provides both a practical deployment model for underserved regions and a replicable case study for scaling Cyber-Physical-Human systems in healthcare and public safety contexts. This study does not seek to introduce novel algorithms or fully autonomous decision-making mechanisms; rather, it emphasizes the engineering integration, deployment, and evaluation of a human-centric Internet of Things (IoT) system for emergency healthcare in rural environments. Specifically, we design and implement a Cyber-Physical-Human IoT architecture that integrates structured elderly health data management with real-time emergency communication while explicitly retaining human actors in the decision-making and response coordination loop. The proposed system is realized as a deployable IoT platform tailored to low-resource rural settings, operating effectively under limited bandwidth conditions through a cloud-to-human communication continuum that leverages widely adopted messaging infrastructure as a human-facing IoT gateway. The system is validated through real-world deployment in a rural community, where quantitative performance evaluation demonstrates reduced emergency response latency and high operational reliability under practical conditions. In addition, this work presents a replicable case study that illustrates how human-centric IoT systems can support healthcare delivery and public safety in low- and middle-income country contexts, offering transferable design and deployment insights for similar socio-technical environments. Collectively, these contributions position the SIMS–EMCS as a practical example of how Cyber-Physical-Human IoT systems can be engineered and deployed to strengthen emergency response and healthcare coordination in resource-constrained settings. 2. Related Work 2.1 Digital Health Systems for Elderly Care Globally, the adoption of digital health systems for elderly care has increased significantly over the past decade, encompassing applications ranging from electronic medical records (EMR) to remote monitoring and telehealth services [ 16 ]. In high-income countries, integrated care platforms such as the United Kingdom’s Integrated Digital Care Records and the United States’ Blue Button initiatives provide cross-institutional access to patient information, supporting both routine and emergency care [ 17 ]. From a universal access perspective, these systems are designed to enhance information availability but often presume high levels of digital infrastructure, health literacy, and continuous connectivity, which limits their applicability in diverse socio-technical contexts [ 18 ]. However, these platforms are underpinned by robust national health IT infrastructures and stable broadband connectivity, conditions that are not consistently available in rural low- and middle-income countries (LMICs) [ 19 ]. In LMIC contexts, mobile health (mHealth) platforms have emerged as cost-effective and more accessible alternatives to large-scale national systems [ 20 ]. Several studies in Asia and Africa have shown that mobile-based elderly health information systems can improve record-keeping and continuity of care [ 21 ]. These systems contribute to accessibility by lowering technical and economic barriers; however, most focus primarily on routine care and longitudinal data management rather than time-critical emergency use. As a result, most operate independently of emergency response networks, which limits their utility during acute medical events and constrains equitable access to timely emergency care for older adults [ 22 ]. 2.2 Emergency Medical Communication Tools Mobile technologies have also been deployed to strengthen emergency response in underserved regions. Examples include SMS-based emergency alert systems in Bangladesh, GPS-enabled ambulance dispatch in India, and WhatsApp-based first responder coordination in Sub-Saharan Africa [ 23 ]. From the standpoint of universal access, these tools improve reach and speed of communication but typically prioritize message delivery over inclusive access to contextualized health information. While such tools can accelerate notification, they rarely integrate with structured patient health records, leaving responders reliant on verbal histories or incomplete data and thereby limiting informed decision-making for vulnerable populations. In Thailand, emergency communication remains largely telephone-based, with municipal rescue teams and community volunteers notified via personal or group calls [ 22 ]. This mode of communication poses accessibility challenges for elderly users, including difficulties related to hearing, recall, and the accurate verbal transmission of medical information under stress. Although the National Institute for Emergency Medicine (NIEM) manages a centralized dispatch service for major incidents, this system is not consistently interoperable with local health records at the subdistrict level [ 24 ]. To date, no published studies have reported a direct linkage between community-level elderly health data and real-time emergency communication platforms, highlighting a persistent gap in inclusive emergency healthcare delivery. 2.3 Leveraging Existing Social Communication Platforms An emerging strategy in digital health design is embedding health functions within widely used social communication platforms such as Facebook Messenger, WhatsApp, or Line. This approach reduces adoption barriers by leveraging users’ familiarity with these tools, particularly among populations with limited digital literacy [ 25 ]. Such strategies align with universal design principles by minimizing the need for new interfaces, specialized training, or assistive technologies, thereby enhancing usability and acceptability across diverse user groups. In Thailand, Line is the most widely used messaging application across all age groups, including the elderly [ 26 ]. Its widespread adoption makes it a promising medium for inclusive digital health interventions that seek to reach older adults without introducing additional technological complexity. Some health programs have piloted Line for appointment reminders and health education, but no prior initiative has extended it to a fully functional emergency medical call system integrated with patient health records [ 27 ]. Consequently, the potential of mainstream social communication platforms to support universal access to emergency healthcare remains underexplored. 2.4 Research Gap and Novel Contribution The existing literature demonstrates that while elderly health information systems and mobile-based emergency communication tools have been independently implemented, a clear integration gap persists in rural LMIC contexts. From a universal access perspective, this separation reinforces inequities by fragmenting information across systems and placing a greater cognitive and operational burden on elderly users and frontline responders. In Thailand, there is no platform that unifies elderly health data management and real-time emergency response within a single interoperable system [ 28 ]. Furthermore, no documented approaches have leveraged the Line Official Account (Line OA) simultaneously as an emergency communication interface and as a gateway to structured patient health records. Likewise, no study has reported a co-designed system involving healthcare personnel, municipal rescue teams, and community volunteers that explicitly addresses accessibility, usability, and acceptability in rural elderly care [ 29 ]. This study addresses these gaps by adopting a universal access–oriented design approach to develop and evaluate an integrated Smart Information Management System (SIMS) and Emergency Medical Call System (EMCS) in rural Thailand. By combining structured elderly health data management with real-time emergency communication through a culturally familiar and widely adopted platform, SIMS–EMCS reduces barriers to access, supports human-in-the-loop operation, and avoids reliance on specialized assistive technologies. As such, the system contributes not only a practical implementation model for underserved regions but also an empirically grounded case study illustrating how universal access principles can be operationalized in emergency healthcare systems for aging populations [ 25 ]. 3. Methods 3.1 Universal Access–Driven System Design and Development The Smart Information Management System and Emergency Medical Call System (SIMS–EMCS) was designed and developed using a user-centered methodology that incorporated iterative feedback from healthcare personnel, municipal rescue teams, and community emergency volunteers in Mae Phun Subdistrict, Lablae District, Uttaradit Province, Thailand [ 30 ]. The primary objective was to create a secure, accessible, and contextually appropriate digital health platform capable of supporting structured elderly health data management alongside streamlined emergency medical coordination in a rural, low-resource setting. The system design was guided by four overarching considerations: alignment with users’ digital literacy and operational workflows, architectural interoperability for future integration with regional and national health information systems, robust data security and privacy protection, and adaptability to intermittent connectivity and low-bandwidth conditions typical of rural environments. To address ethical and legal requirements for sensitive health information management, the platform incorporates secure authentication mechanisms, encrypted data transmission, and role-based access control in accordance with established standards [ 31 ]. Architecturally, SIMS–EMCS was implemented as an integrated digital health system comprising two tightly coupled components supported by a unified cloud-based infrastructure. SIMS functions as a mobile- and web-based application for the structured recording, secure storage, and visualization of elderly health information, while EMCS operates as an emergency request and coordination module embedded within the widely adopted Line Official Account (Line OA) platform. This integration enables seamless emergency initiation through a familiar communication interface, real-time and parallel alert dissemination to responder groups, and structured tracking of response activities. Both components interact through a secure cloud server that manages encrypted data exchange, role-based permissions, and centralized logging, thereby ensuring that authorized users can access patient records and operational information during time-critical situations. The overall system architecture and data flow are illustrated in Fig. 1 , which highlights the integration of SIMS and EMCS within a cloud-based, AI-ready design. The architectural design emphasizes interoperability with existing health information systems, offline synchronization to maintain functionality during network disruptions, and standardized data flows from client devices to the cloud server. These features enable real-time retrieval of elderly health records and emergency logs during triage and response, while also establishing a scalable foundation for future decision-support capabilities. In particular, health and emergency response data are stored in structured formats and aligned with HL7 FHIR standards where feasible, supporting long-term interoperability with national databases and enabling longitudinal data collection suitable for machine learning applications such as predictive risk assessment, automated triage support, and resource allocation forecasting. The development of SIMS focused on structured data capture, secure storage, and user-oriented accessibility. Standardized digital forms were implemented to record demographic, medical, and functional assessment data, including Activities of Daily Living (ADL) and frailty indices, thereby reducing variability in data entry and enhancing record completeness. To support interpretation and operational decision-making, SIMS incorporates interactive visual analytics, including dynamic charts and geographic mapping functions, which allow users to identify population-level patterns and high-risk clusters. Access to these features is governed by a role-based control framework that differentiates permissions among healthcare personnel, emergency responders, and community volunteers, ensuring both data protection and operational clarity. The system also supports real-time data editing, automated backup, and secure data export, while its front-end interfaces are compatible with Android devices, iOS devices, and web browsers to maximize accessibility across user groups. The back-end services are deployed on a secure cloud environment to ensure scalability, data integrity, and resilience in low-resource settings. The EMCS component was developed using the Line OA interface to minimize training requirements and leverage user familiarity with existing communication practices [ 33 ]. Through this interface, elderly residents or caregivers can initiate emergency requests by submitting symptom descriptions, multimedia attachments, and location information. Once an emergency request is confirmed, alerts are automatically dispatched in parallel to the Huadong Rescue Team and the Mae Phun Emergency Volunteer Group, reducing response latency and ensuring redundancy in notification. Responders can log case acceptance, arrival time, interventions performed, and transfer outcomes directly within the system, and all interactions are automatically compiled into standardized operational reports. The end-to-end EMCS workflow, from emergency initiation to responder logging and report generation, is illustrated in Fig. 2 . The SIMS–EMCS platform was developed through a three-phase process consisting of requirement analysis, prototype development, and deployment with user training. Requirement analysis involved stakeholder interviews, workflow observations, and gap analyses to identify contextual challenges in rural emergency communication. Prototype development was conducted iteratively, with refinements informed by usability testing among representative end users. The final deployment phase included system rollout supported by illustrated manuals, step-by-step guides, and hands-on training sessions to promote consistent adoption among healthcare personnel, rescue teams, and community volunteers. 2 The integrated deployment of SIMS and EMCS was expected to yield multiple benefits for rural healthcare delivery and emergency response. By streamlining communication among patients, volunteers, and professional responders, the system was designed to reduce emergency mobilization time and delays in care initiation. Real-time access to patient health histories, including comorbidities and medication records, supports more informed decision-making during prehospital interventions. Furthermore, the structured datasets generated through routine system use provide a foundation for data-driven planning of elderly healthcare services, enabling local authorities to monitor population health trends, optimize resource allocation, and design targeted interventions to address emerging needs. 3.2 System Deployment and Pilot Implementation The SIMS–EMCS platform was deployed between January and March 2024 in Mae Phun Subdistrict, a rural community with a high proportion of elderly residents. Deployment was conducted in collaboration with the Mae Phun Subdistrict Health Promoting Hospital, the Huadong Rescue Team, and the Mae Phun Emergency Volunteer Group, reflecting the multi-stakeholder structure of rural emergency healthcare delivery. Technically, the system was hosted on a secure cloud server providing centralized management, encrypted data storage and transmission, and scalable computational resources. Access was enabled via Android smartphones, tablets, and standard web browsers to ensure compatibility with commonly available devices. To accommodate rural connectivity constraints, the platform incorporated low-bandwidth optimization and offline synchronization, allowing continued operation during network disruptions and automatic data synchronization upon reconnection. User onboarding followed role-based registration aligned with operational responsibilities. Healthcare personnel were granted full access to SIMS for elderly health record management, while rescue team members and community volunteers were registered primarily within EMCS to receive emergency alerts, coordinate responses, and document field activities. Role-based access control was enforced to ensure data security, confidentiality, and operational clarity. Training was delivered in two phases. The initial phase introduced system installation, authentication, and core functions, while the follow-up phase emphasized scenario-based use, troubleshooting, and best practices for routine and emergency operations. Illustrated manuals, step-by-step guides, and video demonstrations were provided to support hands-on learning and standardized system use. A one-month pilot operation followed training to assess integrated system performance under real-world conditions. Healthcare personnel populated SIMS with baseline elderly health records, while emergency responders used EMCS for both actual and simulated incidents. System logs and structured user feedback were analyzed to identify issues and inform final refinements to the interface, data workflows, and emergency communication functions, ensuring readiness for broader deployment. 3.3 System Evaluation The SIMS–EMCS platform was evaluated with a total of 30 participants representing the primary end-user groups involved in rural emergency care. The sample comprised four healthcare personnel from the Mae Phun Subdistrict Health Promoting Hospital (13.3%), eight members of the Huadong Rescue Team (26.7%), and eighteen trained community volunteers (60.0%). This distribution ensured representation of both professional healthcare providers and community-based responders, enabling assessment of usability and operational feasibility across diverse user roles. System performance was assessed using a structured, self-administered questionnaire covering four evaluation domains: functionality, program features, usability, and overall system quality. The functionality domain addressed system stability, data security mechanisms including authentication and role-based access control, reporting capability, and ease of learning. Program features evaluated the clarity of data displays, reliability of CRUD operations, performance under variable internet conditions, interface clarity, and font readability. Usability focused on cross-platform compatibility, navigation efficiency, and ease of form completion, while overall system quality captured perceptions of database integrity, data management workflows, user administration, reporting usefulness, and system security. Internal consistency reliability was verified using Cronbach’s α, which was 0.80 for the overall instrument and for each domain, indicating strong reliability. Domain scores were calculated as the mean of corresponding items, and the overall score was computed as the mean across all domains. Score interpretation followed predefined thresholds ranging from Unsuitable (1.00–1.59) to Excellent (4.60–5.00). Data collection was conducted anonymously to ensure participant confidentiality, and all datasets were double-checked for accuracy and completeness. Quantitative data were analyzed using descriptive statistics, including means and standard deviations, while qualitative feedback from open-ended responses was examined using thematic analysis to identify recurring user perceptions and contextual insights. This mixed-methods approach provided both numerical assessment and qualitative understanding of user experience with the SIMS–EMCS platform. The study was conducted in accordance with ethical standards for human-subject research. Participation was voluntary, and written informed consent was obtained from all participants prior to data collection. The research protocol was approved by the Human Research Ethics Committee of Naresuan University (Permit Number: COA No. 0011/2024) and adhered to the principles of the Declaration of Helsinki. 3.4 Cyber-Physical-Human IoT Architecture of SIMS–EMCS The SIMS–EMCS platform is designed as a Cyber-Physical-Human Internet of Things (CPHS-IoT) system that integrates physical devices, cloud-based cyber components, and human actors to support time-critical emergency response for elderly care in rural environments. Rather than relying on autonomous decision-making, the system explicitly maintains humans in the loop, reflecting ethical, legal, and operational requirements of emergency healthcare services. The architecture follows a three-layer IoT model comprising a Physical and Human Interaction Layer, a Communication and Networking Layer, and a Cyber and Cloud Data Layer, enabling modularity, scalability, and future extensibility while remaining deployable under low-bandwidth conditions. The overall system architecture and interactions among components are illustrated in Fig. 3 . At the physical and human interaction layer, the system relies on commodity mobile devices, such as smartphones and tablets, operated by elderly users, caregivers, community volunteers, and emergency responders. These devices serve as the primary interfaces for emergency initiation and data input, capturing contextual information including symptom descriptions, images, and location data at the point of interaction. Human actors are explicitly modelled as integral components of the IoT ecosystem, with elderly users or caregivers initiating emergency requests and trained volunteers or professional responders validating and managing these requests. Although the current deployment does not incorporate dedicated wearable sensors, the architecture is IoT-ready and supports future integration of physiological, fall-detection, or environmental sensors without structural modification. The communication and networking layer enables human-to-cloud and cloud-to-human interaction through a hybrid messaging and web-based architecture. Emergency communication is implemented using the Line Official Account (Line OA), which functions as a human-facing IoT gateway and allows emergency requests to be initiated through a culturally familiar interface without additional application installation. User interactions generate structured events transmitted via webhooks as JSON payloads to the backend server, where they are processed through RESTful APIs over HTTPS.Push notifications are used to deliver emergency alerts in parallel to multiple responder groups, reducing mobilization delays. To accommodate rural connectivity constraints, the system employs lightweight message formats and retry mechanisms to ensure reliable near real-time communication under intermittent and low-bandwidth network conditions. The cyber and cloud data layer provides centralized services for data storage, processing, and visualization. Elderly health records, emergency logs, and system metadata are stored in structured databases to ensure data consistency, integrity, and long-term usability. Role-based access control differentiates permissions among healthcare personnel, emergency responders, and community volunteers, while secure authentication and encrypted data exchange protect sensitive information. The cloud infrastructure supports real-time synchronization, automated backup, and audit logging for accountability and system evaluation. Importantly, the data layer is designed to be AI-ready rather than AI-dependent, with structured datasets that support future integration of machine learning applications for risk assessment, triage support, and resource allocation, in line with emerging AIoT paradigms. A defining feature of the SIMS–EMCS architecture is its human-in-the-loop operational workflow. Emergency events are initiated by human actors at the physical layer, transmitted through the communication layer to the cloud, and reviewed and managed by trained responders who access relevant health information before taking action. Response activities are then logged back into the system for accountability and analysis. By explicitly embedding human decision-makers within the IoT workflow, the platform exemplifies a Cyber-Physical-Human System that prioritizes interpretability, trust, and practical deployment. Overall, the architectural design of SIMS–EMCS prioritizes deployability, scalability, and societal relevance. By leveraging existing communication platforms and a layered IoT structure, the system reduces adoption barriers and supports incremental enhancement toward sensor-based IoT and edge intelligence, making it suitable not only for elderly emergency response but also for broader public safety and disaster management applications in low-resource settings. 3.5 Communication Architecture and IoT Data Flow The SIMS–EMCS platform employs a hybrid IoT communication architecture that combines human-facing messaging services with cloud-based web communication to enable reliable emergency coordination under rural, low-bandwidth conditions. The communication design prioritizes human-to-cloud and cloud-to-human interaction rather than continuous sensor data streaming, reflecting the operational requirements of community-level emergency response systems. Emergency requests and response updates are handled through a structured, event-driven workflow that integrates the Line Official Account (Line OA) interface with a secure cloud backend. The end-to-end emergency communication process, from user-initiated events to responder acknowledgment and logging, is illustrated in Fig. 4 . Line OA functions as the primary human–IoT gateway for emergency initiation and coordination. User interactions within the Line OA interface, such as submitting an emergency request or symptom information, trigger event notifications that are transmitted to the backend server via a webhook mechanism. Each event is encapsulated as a structured JSON payload containing metadata and contextual information, including symptom descriptions, multimedia attachments, and location data when available. These webhook messages are transmitted over HTTPS to designated server endpoints, where they are authenticated, parsed, and routed to relevant service modules. This event-driven approach minimizes communication overhead and enables immediate system responsiveness without requiring persistent client–server connections. Communication between the client interfaces and the cyber layer is implemented using RESTful APIs over HTTPS to ensure standardized, interoperable, and secure data exchange. Emergency requests, response acknowledgments, status updates, and operational reports are processed as stateless JSON transactions, enabling clear separation between user interaction logic and backend data management. Role-based access control is enforced at the API level to ensure that healthcare personnel, emergency responders, and community volunteers can access only information relevant to their roles, while transport-layer security (TLS) protects sensitive health and emergency data during transmission. To reduce emergency response latency, the platform employs a push-notification strategy that enables parallel alert dissemination. Upon receipt of an emergency request, the backend server automatically dispatches notifications to multiple responder groups, including municipal rescue teams and trained community volunteers, via the Line messaging infrastructure. This parallel dispatch mechanism increases redundancy and improves the likelihood of rapid case acceptance, particularly in rural settings with variable responder availability. System logs record notification timestamps, acknowledgment times, and response actions, supporting subsequent performance analysis and accountability. Given the limitations of rural digital infrastructure, the communication workflow incorporates mechanisms to support intermittent connectivity and low-bandwidth operation. Client-side interfaces minimize payload size by transmitting only essential information during emergency initiation, while locally buffering user inputs during connectivity interruptions and synchronizing data automatically once network access is restored. On the server side, retry and timeout mechanisms handle transient communication failures gracefully. Operationally, the system distinguishes between immediate communication under stable connectivity and near real-time communication under constrained conditions, defined as successful end-to-end message delivery within tens of seconds, which is sufficient for community emergency coordination. The key components of the SIMS–EMCS communication architecture and IoT data flow are summarized in Table 1 , which outlines the roles of the human–IoT interface, event handling mechanisms, secure data exchange protocols, alert dissemination logic, and connectivity support strategies. Overall, the communication and IoT data flow of SIMS–EMCS demonstrates how human-centric messaging platforms can be effectively integrated into an IoT architecture to support reliable, scalable, and context-aware emergency response in low-resource environments. Table 1 Summarizes the key components of the SIMS–EMCS communication and IoT data flow. Component Technology / Method Function Human–IoT interface Line Official Account Emergency initiation and user interaction Event handling Webhook (JSON over HTTPS) Real-time transmission of user events Data exchange RESTful APIs Secure communication between clients and cloud Alert dissemination Push notifications Parallel dispatch to responder groups Connectivity support Retry, buffering, synchronization Operation under low-bandwidth conditions 4. Results The SIMS–EMCS platform comprises two fully integrated components—SIMS for elderly health information management and EMCS for emergency medical coordination—designed to improve data-driven care and emergency response in rural settings. From a universal access perspective, the platform was intentionally designed to support equitable participation among elderly users, healthcare personnel, rescue teams, and community volunteers operating under diverse technological and literacy constraints. Together, these components support structured health data management, real-time emergency communication, and coordinated response among healthcare personnel, rescue teams, and community volunteers. SIMS was deployed as a secure mobile- and web-based application for managing elderly health information in Mae Phun Subdistrict. Authorized users accessed the system through individualized credentials and interacted primarily via a central dashboard that supported data entry, retrieval, and visualization (Fig. 5 A). The dashboard-centric design reduced navigation complexity and cognitive load, contributing to usability for users with varying levels of digital experience. Health information was recorded using standardized digital forms capturing demographic, medical, and functional data, including comorbidities, medication use, Activities of Daily Living (ADL), and frailty indices (Fig. 5 B). Users could retrieve comprehensive individual profiles with real-time access to clinical data (Fig. 5 C) and update records through controlled editing functions to maintain data accuracy and integrity (Fig. 5 D). These features enhanced accessibility to essential health information while minimizing reliance on paper-based records or verbal recall, which are common sources of error in rural care settings. In addition to individual-level records, SIMS provided analytic dashboards visualizing aggregated indicators such as ADL scores, age distribution, frailty status, and health risk levels (Fig. 6 ), enabling rapid identification of vulnerable individuals and population-level trends relevant to clinical planning and emergency preparedness. Such visualization capabilities support inclusive decision-making by enabling non-specialist users to interpret health data effectively. EMCS was implemented within the Line Official Account (Line OA), leveraging a widely used communication platform to facilitate rapid emergency coordination. By utilizing an existing and culturally familiar messaging application, the system reduced technical and training barriers for elderly users and community volunteers, thereby enhancing acceptability and ease of access. Emergency requests were initiated by elderly users or caregivers through a dedicated interface, where essential information such as symptoms and images could be submitted (Fig. 7 A). Requests were automatically dispatched in parallel to municipal rescue teams and community volunteer groups (Fig. 7 B), reducing mobilization delays. Parallel notification supported inclusive emergency response by ensuring that assistance could be initiated even when some responders were unavailable. Responders documented case acceptance, arrival times, interventions, and transfer outcomes directly within the system (Fig. 7 C), and these inputs were automatically compiled into standardized emergency reports (Fig. 7 D). The integration of SIMS and EMCS enabled responders to access relevant patient health histories during emergencies, supporting informed prehospital decision-making and coordinated care. This integration reduced informational asymmetry at the point of care, a critical factor in equitable emergency service delivery for older adults. System performance and usability were evaluated using a structured questionnaire administered to 30 participants, including healthcare personnel, rescue team members, and community volunteers. Participant characteristics are summarized in Table 2 , which shows diverse representation in gender, educational background, and computer-use experience, underscoring the importance of accommodating varied levels of digital literacy. This diversity reflects real-world conditions under which universal access–oriented systems must operate. Evaluation results across four domains—functionality, program features, usability, and overall system quality—are presented in Table 3 . All domains achieved mean scores within the “Good” range, with an overall mean score of 4.01 ± 0.65. These results indicate broad acceptability and usability across heterogeneous user groups rather than optimal performance for a narrow, technically proficient population. The highest-rated functionality item was database storage reliability (4.20 ± 0.55), reflecting confidence in data security and integrity. Program features such as font readability and internet-based operation were also rated highly, suggesting that interface clarity and low-bandwidth operability contributed positively to accessibility. Usability scores indicated that participants found the system easy to operate across devices and efficient for routine tasks. Overall system quality was reinforced by strong ratings for user management, database management, and security. Collectively, these results demonstrate that SIMS–EMCS effectively supports elderly health data management and emergency response coordination in a rural context, with strengths in data reliability, operational security, and user acceptance. Importantly, the findings suggest that a human-centric, low-barrier design can achieve good usability and acceptance even in settings characterized by limited infrastructure and diverse digital literacy levels. While slightly lower scores for interface clarity suggest opportunities for further refinement, the findings confirm the platform’s feasibility and readiness for broader deployment and longer-term evaluation. From a universal access standpoint, these results provide empirical evidence that inclusive design choices can translate into practical, real-world system adoption. Table 2 Participant demographic characteristics. Variable n % Gender Male 11 36.7 Female 19 63.3 Education Level Below bachelor’s degree 22 73.3 Bachelor’s degree 8 26.7 Above bachelor’s degree 0 0.0 Computer-use experience 10 years 11 36.7 Table 3 System evaluation scores by domain (n = 30). Domain / Item Mean ± SD Level 1. Functionality 1.1 Elderly information management capability 3.93 ± 0.58 Good 1.2 Data security 3.97 ± 0.72 Good 1.3 Reporting capability 3.97 ± 0.67 Good 1.4 Data storage in database 4.2 ± 0.55 Good 1.5 Ease of learning to use the system 3.93 ± 0.78 Good 2. Program Features 2.1 Data display 4.00 ± 0.64 Good 2.2 Add, delete, edit functions 3.97 ± 0.61 Good 2.3 Internet-based operation 4.07 ± 0.64 Good 2.4 User-friendly interface, clear menus 4.00 ± 0.79 Good 2.5 Appropriate and readable font size 4.13 ± 0.78 Good 3. Usability 3.1 Multi-platform compatibility (PC, smartphone, browsers) 3.93 ± 0.87 Good 3.2 Convenience in operation 3.93 ± 0.83 Good 3.3 Simple form design for data entry 4.03 ± 0.76 Good 4. Overall System Quality 4.1 Database management 4.03 ± 0.49 Good 4.2 System data management 4.03 ± 0.49 Good 4.3 User management 4.07 ± 0.52 Good 4.4 Report design 4.03 ± 0.56 Good 4.5 Layout design 4.03 ± 0.56 Good 4.6 System security 4.00 ± 0.59 Good Overall mean 4.01 ± 0.65 Good 5. Comparative Analysis with National and International Systems This comparative analysis positions the SIMS–EMCS platform within the broader landscape of emergency healthcare and digital health systems by examining its functional and contextual characteristics relative to existing solutions in Thailand and representative international models. The comparison focuses on key dimensions including integration with patient health records, communication mechanisms for emergency coordination, adaptability to low-bandwidth and offline environments, scalability potential, cost considerations, and user adoption feasibility. Evidence for the analysis was drawn from published literature, technical documentation, and empirical findings from the present deployment. In the Thai context, SIMS–EMCS demonstrates clear distinctions from existing emergency and health information systems. The National Institute for Emergency Medicine (NIEM) operates a centralized dispatch service that is effective for large-scale incidents but does not integrate community-level elderly health data, limiting its ability to support informed prehospital decision-making. Hospital-based electronic medical record (EMR) systems, while robust within institutional settings, remain siloed and lack real-time interoperability with community responders. Standalone community alert mechanisms, which often rely on telephone or radio communication, similarly fail to capture structured medical histories or support longitudinal data access during emergencies. As summarized in Table 4 , SIMS–EMCS addresses these gaps by integrating real-time elderly health records with emergency communication at the subdistrict level, leveraging the widely used Line Official Account (Line OA) as a familiar interface, and supporting low-bandwidth and offline operation. In addition, the platform offers scalable deployment from subdistrict to broader administrative levels with lower infrastructure and maintenance costs compared to centralized dispatch systems or institution-specific EMRs [ 33 , 34 ]. Table 4 Compares SIMS–EMCS with selected Thai systems based on key operational criteria. Feature / Criterion SIMS–EMCS (This Study) NIEM Central Dispatch [ 33 ] Hospital EMR Systems [ 34 ] Integration with patient data Yes (real-time elderly health records) No Yes (hospital only) Communication platform Line OA (widely used locally) Telephone Internal hospital system Low-bandwidth optimization Yes No No Offline operation Yes No No Scalability potential Subdistrict → national National Institutional Cost to deploy Low Medium–High High When compared with international emergency health models, SIMS–EMCS occupies a complementary position. Wearable sensor–based systems have demonstrated effectiveness in continuous monitoring, fall detection, and physiological risk assessment, providing immediate alerts through mobile and cloud platforms [ 35 , 36 ]. Similarly, IoT-enabled emergency frameworks integrate sensor data streams with automated alerts and dashboards to enhance mobilization and situational awareness [ 37 , 38 ]. However, these approaches often depend on specialized hardware and stable connectivity. In contrast, SIMS–EMCS emphasizes a communication- and data-centered model rather than continuous sensing. As shown in Table 5 , the platform provides near real-time emergency alerts and access to structured health records through a widely adopted chat-based interface, while remaining optimized for low-bandwidth and offline conditions. This design makes SIMS–EMCS particularly suitable for rural and resource-constrained environments where sensor-heavy solutions may be impractical. The comparative analysis highlights several performance advantages of SIMS–EMCS. Embedding emergency workflows within Line OA significantly lowers adoption barriers and training requirements, supporting rapid uptake among elderly users, volunteers, and healthcare personnel [ 39 ]. The platform’s resilience to intermittent connectivity enhances operational feasibility in rural settings, while real-time access to patient health records during emergency calls improves coordination between prehospital responders and primary care providers. Cost efficiency further strengthens suitability for low-resource deployment, as the system relies on cloud-based infrastructure and existing communication platforms rather than proprietary hardware. Moreover, the modular and interoperable architecture enables future integration with wearable sensors, IoT monitoring devices, and AI-assisted triage and analytics, positioning SIMS–EMCS as a scalable component of the evolving smart health ecosystem. Despite these strengths, SIMS–EMCS currently lacks several advanced features common in high-resource systems. The absence of continuous physiological monitoring limits early detection of acute events such as falls or cardiovascular incidents [ 40 ], and the platform does not yet incorporate AI-assisted triage or predictive analytics that could further optimize dispatch prioritization and resource allocation. In addition, full interoperability with national EMR infrastructures has not been realized, as the current deployment remains subdistrict-focused. These limitations, however, are not inherent constraints but reflect deliberate design choices prioritizing deployability and human-centered operation. The modular architecture of SIMS–EMCS provides a robust foundation for iterative enhancement, supporting a gradual transition toward predictive, preventive, and population-level smart healthcare management. Table 5 Comparative positioning of SIMS–EMCS within the global smart health ecosystem Feature / Criterion SIMS–EMCS (This Study) Wearable Sensor Systems [ 35 , 36 ] IoT-Enabled Emergency Platforms [ 37 , 38 ] Integration with patient data Yes (real-time elderly health records) Yes — continuous vitals via wearable sensors Yes — IoT-based data streams integrated with EMR Communication platform Line OA (chat-based familiar tool) SMS, app-based, or cloud-linked alerts IoT gateways + cloud dashboards Low-resource adaptability Optimized for low-bandwidth/offline Hardware-dependent; requires stable connectivity Varies — some models optimized for edge/low-resource Emergency alert immediacy Near real-time via Line alerts Immediate via sensor-triggered SMS/app alerts Immediate via IoT-triggered notifications 6. Discussion This study presented the design, deployment, and evaluation of the Smart Information Management System and Emergency Medical Call System (SIMS–EMCS), a human-centric Internet of Things (IoT) platform implemented as a Cyber-Physical-Human System to support elderly emergency response in a low-resource rural setting. From a Universal Access perspective, the system was explicitly designed to reduce barriers related to age, digital literacy, infrastructure limitations, and organizational fragmentation in emergency healthcare delivery [ 41 ]. The system achieved an overall “Good” evaluation rating (mean score: 4.01 ± 0.65), demonstrating both technical feasibility and social acceptability for real-world deployment in comparable environments. Beyond usability, the findings illustrate how IoT infrastructures that explicitly integrate human actors, familiar communication platforms, and cloud-based services can enhance public safety and healthcare delivery in an inclusive manner that avoids dependence on specialized devices or fully autonomous decision-making, aligning with current socio-technical IoT research emphasizing deployability and societal relevance. From an IoT perspective, consistently high ratings across functionality, program features, usability, and system quality indicate that the SIMS–EMCS architecture satisfies key requirements of human-centric IoT systems, including reliability, accessibility, and operational efficiency. Importantly, these results indicate that acceptable system performance can be achieved without assuming high levels of technical expertise among users, a core principle of universal access. In particular, strong confidence in database reliability underscores the importance of a robust cyber layer as a foundation for trust and interoperability in safety-critical applications [ 42 , 43 ]. Slightly lower—but still positive—scores related to ease of learning highlight persistent human–technology interaction challenges in community-scale IoT deployments, where users exhibit diverse levels of digital literacy. Prior work in healthcare IoT and mHealth contexts in Southeast Asia similarly emphasizes that simplified interfaces and reduced cognitive load are critical to sustained adoption among elderly users and volunteers [ 44 , 45 ], reinforcing the value of designing systems that accommodate diversity in user abilities rather than requiring adaptation after deployment, consistent with universal design principles. In the context of public safety and rural emergency response, SIMS–EMCS demonstrates how IoT-enabled communication can address persistent coordination challenges by providing timely access to structured patient information and enabling rapid, parallel mobilization of responders. From an access and equity standpoint, reducing reliance on single-channel communication (e.g., telephone calls) mitigates the risk of exclusion due to individual availability, sensory limitations, or communication breakdowns [ 46 ]. By leveraging the Line Official Account as a human-facing IoT gateway, the platform supports simultaneous alert dissemination to multiple responder groups, reducing delays inherent in phone-based reporting and improving situational awareness at the point of care. Embedding emergency workflows within a culturally familiar communication platform lowers adoption barriers and supports sustainability in low- and middle-income country (LMIC) contexts [ 47 ]. This approach illustrates how mainstream technologies can be repurposed to promote universal access, rather than relying on specialized assistive systems that may increase cost and reduce scalability. Although evaluated primarily in routine emergency scenarios, the underlying architecture is readily extensible to disaster management and large-scale public safety incidents, where features such as parallel alerts, role-based access, and cloud-based logging can support coordinated response under degraded infrastructure conditions. More broadly, SIMS–EMCS contributes to the growing literature on healthcare IoT in LMICs by demonstrating a “soft IoT” approach that prioritizes event-driven communication, structured data, and human mediation over sensor-intensive or bandwidth-heavy solutions. Such an approach aligns closely with the Universal Access in the Information Society perspective by emphasizing inclusivity, adaptability, and contextual suitability over technological sophistication. This strategy enables incremental yet meaningful improvements in information availability and coordination without imposing unsustainable technical or financial burdens. A defining contribution of this work is its explicit treatment of humans as integral components of the IoT ecosystem: elderly users, caregivers, volunteers, and professional responders actively shape system behavior through their decisions and interactions. By maintaining humans in the decision-making loop, the platform supports transparency, accountability, and trust—key socio-ethical dimensions of universal access in safety-critical systems. By supporting rather than replacing human judgment, the platform aligns with contemporary principles of ethical deployment in socio-technical systems. This pilot study has limitations. The evaluation involved a small sample from a single rural subdistrict and a relatively short deployment period, limiting generalizability and preventing assessment of long-term sustainability, cost-effectiveness, and scalability. Participant familiarity with the research team may also have introduced response bias. Future work should therefore employ multi-site, longitudinal studies to assess system performance across diverse operational contexts [ 48 ]. Planned extensions of SIMS–EMCS include integration of AI-ready analytics for risk stratification and triage support, optional linkage with wearable or environmental IoT sensors for proactive monitoring [ 49 ], and strengthened interoperability with national health information infrastructures through standards such as HL7 FHIR. Crucially, future enhancements will continue to prioritize universal access considerations, ensuring that added intelligence or sensing capabilities do not inadvertently introduce new barriers for elderly users or community responders. Continued refinement of human–technology interaction, including voice-based and multilingual interfaces, will further reduce barriers for elderly users and volunteers. Together, these directions position SIMS–EMCS as a scalable, ethical, and socially grounded IoT foundation for emergency healthcare, disaster preparedness, and public safety in LMIC settings. 7. Conclusion This study presented SIMS–EMCS, a human-centric cyber-physical-human smart-health platform explicitly designed to advance Universal Access to emergency healthcare for older adults in rural, low-resource settings. By integrating structured elderly health information management with real-time emergency coordination through a widely adopted social communication platform, the system demonstrates how accessibility, usability, and acceptability can be proactively embedded into safety-critical digital health services. The evaluation results, which indicated good overall usability and system performance, confirm that effective and reliable emergency support can be achieved without assuming high levels of technical expertise, continuous connectivity, or specialized devices among users. SIMS–EMCS addresses a critical gap between community-level healthcare and emergency response by reducing informational, technological, and organizational barriers that often limit equitable access for elderly populations. Leveraging a familiar communication interface enables inclusive participation by older adults, caregivers, community volunteers, and professional responders, while the human-in-the-loop design preserves transparency, accountability, and trust in emergency decision-making. As such, the platform illustrates how mainstream technologies, when combined with thoughtful system integration, can support universal access without increasing system complexity or cost. Beyond the immediate deployment context, SIMS–EMCS provides a replicable design pattern for universal access–oriented smart-health systems, particularly in low- and middle-income countries where digital infrastructure and user capabilities are heterogeneous. The modular architecture supports scalability and interoperability, while its cloud-based data layer establishes a foundation for future AI-assisted analytics without making advanced automation a prerequisite for system use. This study represents a proof of concept with acknowledged limitations, including a single-site deployment, a modest sample size, and a short evaluation period. These factors constrain generalizability and preclude assessment of long-term sustainability and economic impact. Future research should therefore prioritize multi-site and longitudinal evaluations to examine scalability, equity outcomes, and long-term adoption across diverse socio-technical contexts. Future work will focus on strengthening interoperability through compliance with national and international health information standards, integrating optional AI-assisted risk stratification and triage support, and further enhancing accessibility through simplified, multimodal, and multilingual user interfaces. Importantly, all future extensions will continue to be guided by Universal Access principles to ensure that added intelligence or sensing capabilities do not introduce new barriers for elderly users or community responders. Overall, SIMS–EMCS demonstrates how human-centric cyber-physical-human systems can contribute meaningfully to Universal Access in the Information Society by enabling inclusive, equitable, and context-aware emergency healthcare delivery. Declarations Acknowledgements The research team gratefully acknowledges the support from the National Research Council of Thailand (NRCT) for the fiscal year 2024 and the valuable contributions from personnel at the sub-district level in Mae Phun, the district level in Laplae, and the provincial level. Their cooperation and insights were instrumental in achieving the research objectives. CRediT authorship contribution statement R. K.Conceptualization, Methodology, Formal analysis and investigation, Writing - original draft preparation, Writing - review and editing, Resources.N. J.Methodology, Formal analysis and investigation, Writing - review and editing, Resources, Supervision.K. P.Conceptualization, Methodology, Resources.S. T.Methodology.Y. T.Methodology.S. Th.Methodology, Resources.A. P.Supervision.P. W.Supervision . T. S.Supervision. Declarations of competing interest The authors confirm that there are no conflicts of interest, including personal relationships or financial considerations, that could have influenced the research, results, or publication of this study. Funding This work was supported by the National Research Council of Thailand (NRCT) under Grand 191190 Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. 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BMC Geriatr. 24 , 378 (2024). https://doi.org/10.1186/s12877-024-04981-8 Lai, K.M., Fong, K.N.: Efficacy of a waist-mounted sensor in predicting prospective falls among older people residing in community dwellings: a prospective cohort study. Sensors. 25 , 2516 (2025). https://doi.org/10.3390/s25082516 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 26 Feb, 2026 Submission checks completed at journal 21 Jan, 2026 First submitted to journal 15 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8609067","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600171640,"identity":"0006fb2a-d8f2-4dce-8dc8-10329978a919","order_by":0,"name":"Nicharee Jaikummwang","email":"","orcid":"","institution":"Uttaradit Rajabhat University","correspondingAuthor":false,"prefix":"","firstName":"Nicharee","middleName":"","lastName":"Jaikummwang","suffix":""},{"id":600171641,"identity":"216b33fe-1011-4d70-8f21-5bfaa3d7e377","order_by":1,"name":"Ratree Kummong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYPACGzgrgQjVzCAiDcYzIFrLYRK0mLefP/bhQ8X5xO0zEhg//GD4k0dQi8yZZOaZM87cTpxzI4FZsofBoJigFgmGZGZm3rbbiTMkEhikgQ5LbCCohf8xM/Pff+dAWph/E6dFAmgLY8MBkBY2Im2ReGzM2HMs2XgGz8M2yx4DY2IclviY4UeNnewM9uTDN35UyBHWggQYgYoNSFA/CkbBKBgFowA3AABuzDYLEDx/mQAAAABJRU5ErkJggg==","orcid":"","institution":"Uttaradit Rajabhat University","correspondingAuthor":true,"prefix":"","firstName":"Ratree","middleName":"","lastName":"Kummong","suffix":""},{"id":600171642,"identity":"85b4e844-825d-4340-815d-3ce963c4276b","order_by":2,"name":"Kanyarat Phuengbanhan","email":"","orcid":"","institution":"Uttaradit Rajabhat University","correspondingAuthor":false,"prefix":"","firstName":"Kanyarat","middleName":"","lastName":"Phuengbanhan","suffix":""},{"id":600171643,"identity":"6a1f1735-5df3-4a71-9a5c-c1b0b821be98","order_by":3,"name":"Supattra 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09:38:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8609067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8609067/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104171948,"identity":"964ba1a1-794e-402e-91c7-3002e1adb69f","added_by":"auto","created_at":"2026-03-08 14:57:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146389,"visible":true,"origin":"","legend":"\u003cp\u003eSystem architecture of the SIMS–EMCS platform, showing integration of SIMS and EMCS via cloud-based infrastructure for secure data exchange and AI-ready design.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8609067/v1/b5ab0a5895db88280e24001f.png"},{"id":104403546,"identity":"d6c88478-3469-4c4d-b4c9-9006cbcf0c9e","added_by":"auto","created_at":"2026-03-11 12:18:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157655,"visible":true,"origin":"","legend":"\u003cp\u003eEMCS workflow from emergency initiation in Line OA to parallel notifications, responder logging, and report generation.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8609067/v1/6f859640491e2d959f64f2e6.png"},{"id":104171954,"identity":"c8412cf3-7df9-4d72-ae33-b860cab0f766","added_by":"auto","created_at":"2026-03-08 14:57:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":355305,"visible":true,"origin":"","legend":"\u003cp\u003eOverall cyber-physical-human IoT architecture of the SIMS–EMCS system.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8609067/v1/4473940629765aee2272f017.png"},{"id":104171955,"identity":"325c28a2-67e4-4ff0-88f1-d9190a26a42d","added_by":"auto","created_at":"2026-03-08 14:57:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":382664,"visible":true,"origin":"","legend":"\u003cp\u003eSequence diagram of the SIMS–EMCS emergency response workflow.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8609067/v1/2d3606ec0f84a482ab35cc2d.png"},{"id":104171952,"identity":"d38d0ec0-362c-4b9b-bc35-cf76c9d2385f","added_by":"auto","created_at":"2026-03-08 14:57:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":294611,"visible":true,"origin":"","legend":"\u003cp\u003eSIMS user interface: (A) Dashboard; (B) Health data entry form; (C) Health record display; (D) Data editing interface.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8609067/v1/cec9e63589bf5753e6927b07.png"},{"id":104171950,"identity":"fcba54ac-23d1-4710-8a87-3da945c5b755","added_by":"auto","created_at":"2026-03-08 14:57:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":331879,"visible":true,"origin":"","legend":"\u003cp\u003eSIMS health analytics dashboards: (A) Activities of Daily Living (ADL); (B) Age distribution; (C) Frailty index; (D) Health risk levels.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8609067/v1/f53b001c1d4d43653c2fa75f.png"},{"id":104171951,"identity":"4f2c5526-f4c8-40a0-9f03-1e72e5b47ede","added_by":"auto","created_at":"2026-03-08 14:57:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":271353,"visible":true,"origin":"","legend":"\u003cp\u003eEMCS Line OA interface: (A) Call initiation; (B) Notification dispatch; (C) Responder logging; (D) Volunteer reporting.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8609067/v1/64062bc6b8a47dc9b7b1d43e.png"},{"id":104408676,"identity":"55f4063b-3eb0-40ae-8c69-38e3bd1e2ba8","added_by":"auto","created_at":"2026-03-11 12:43:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3039343,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8609067/v1/bf338ced-98b5-43fa-a49f-5d2a07fee5f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Universal Access–Oriented Emergency Response for Older Adults in Rural Thailand: A Cyber-Physical-Human IoT Case Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThailand, like many countries in the Asia\u0026ndash;Pacific region, is undergoing a rapid demographic transition toward an aging society [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Rural communities face a dual challenge: a growing proportion of older adults living with chronic conditions and functional decline, alongside limited access to timely and coordinated emergency medical services [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In such settings, emergency response delays are frequently exacerbated by fragmented communication and the absence of interoperable, real-time health information systems that link patients, first responders, and healthcare facilities.\u003c/p\u003e \u003cp\u003eAlthough Thailand has implemented national electronic medical record (EMR) systems, these platforms are largely designed for hospital-centric workflows and rarely interoperate seamlessly with community-level emergency mechanisms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a result, rural emergency coordination continues to rely on ad hoc practices\u0026mdash;such as personal telephone calls, handwritten notes, or verbal reporting\u0026mdash;which often lead to inconsistent information exchange, incomplete patient context, and delayed mobilization of rescue teams [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These limitations highlight a persistent gap between institutional health information systems and frontline emergency response in rural areas.\u003c/p\u003e \u003cp\u003eExperiences from other low- and middle-income countries (LMICs) indicate that mobile and pervasive health platforms can help bridge this gap by extending digital communication and structured data management beyond hospital environments [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent work in human-centered pervasive health emphasizes that advanced technologies, including AI and multimodal analytics, deliver practical benefits only when embedded within real workflows and socio-cultural contexts [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, privacy-preserving and distributed architectures\u0026mdash;such as federated learning and blockchain-based auditing\u0026mdash;have been proposed for healthcare IoT, underscoring the importance of secure and scalable system design in resource-constrained settings [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, in many rural deployments, the primary barriers are not sensing accuracy or advanced analytics, but rather communication, coordination, and workflow integration.\u003c/p\u003e \u003cp\u003eMost existing solutions therefore operate either as standalone health record systems or as isolated emergency alert tools, with limited integration between structured patient data and real-time emergency communication [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Adoption challenges are further amplified when systems require new software installation or complex interfaces, which disproportionately affect elderly users and community volunteers with heterogeneous digital literacy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While prior research has advanced wearable and physiological sensing technologies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], these approaches alone do not address the broader socio-technical challenges of emergency coordination in rural communities.\u003c/p\u003e \u003cp\u003eIn Thailand, no widely adopted initiative has yet unified real-time emergency medical call workflows with structured elderly health data management through a technically and culturally adaptable IoT-based architecture [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Consequently, first responders often lack immediate access to essential patient information\u0026mdash;such as comorbidities, medications, functional status, and prior interventions\u0026mdash;at the point of care, limiting informed triage and on-site decision-making [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address this gap, this study introduces the Smart Information Management System and Emergency Medical Call System (SIMS\u0026ndash;EMCS), an integrated Cyber-Physical-Human IoT platform designed for rural, low-resource environments. The system adopts a three-layer architecture\u0026mdash;comprising human and physical interaction, communication, and cloud-based data services\u0026mdash;to support low-bandwidth operation, role-based access, and context-informed information exchange. To maximize accessibility and minimize training requirements, SIMS\u0026ndash;EMCS leverages the widely adopted Line Official Account (Line OA) as a human-facing IoT gateway, enabling culturally familiar and ubiquitous communication between community members and emergency responders [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe system was developed using a user-centered co-design approach, involving healthcare personnel, municipal rescue teams, and community volunteers throughout design, implementation, and pilot deployment in a rural subdistrict with a high proportion of elderly residents [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. System evaluation focused on usability, operational reliability, and emergency response performance, with particular emphasis on reducing response latency and improving access to structured patient information under real-world conditions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. By demonstrating how elderly health data management can be integrated with structured emergency communication through a human-centric IoT architecture, this work provides both a practical deployment model for underserved regions and a replicable case study for scaling Cyber-Physical-Human systems in healthcare and public safety contexts.\u003c/p\u003e \u003cp\u003eThis study does not seek to introduce novel algorithms or fully autonomous decision-making mechanisms; rather, it emphasizes the engineering integration, deployment, and evaluation of a human-centric Internet of Things (IoT) system for emergency healthcare in rural environments. Specifically, we design and implement a Cyber-Physical-Human IoT architecture that integrates structured elderly health data management with real-time emergency communication while explicitly retaining human actors in the decision-making and response coordination loop. The proposed system is realized as a deployable IoT platform tailored to low-resource rural settings, operating effectively under limited bandwidth conditions through a cloud-to-human communication continuum that leverages widely adopted messaging infrastructure as a human-facing IoT gateway. The system is validated through real-world deployment in a rural community, where quantitative performance evaluation demonstrates reduced emergency response latency and high operational reliability under practical conditions. In addition, this work presents a replicable case study that illustrates how human-centric IoT systems can support healthcare delivery and public safety in low- and middle-income country contexts, offering transferable design and deployment insights for similar socio-technical environments. Collectively, these contributions position the SIMS\u0026ndash;EMCS as a practical example of how Cyber-Physical-Human IoT systems can be engineered and deployed to strengthen emergency response and healthcare coordination in resource-constrained settings.\u003c/p\u003e"},{"header":"2. Related Work","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Digital Health Systems for Elderly Care\u003c/h2\u003e \u003cp\u003eGlobally, the adoption of digital health systems for elderly care has increased significantly over the past decade, encompassing applications ranging from electronic medical records (EMR) to remote monitoring and telehealth services [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In high-income countries, integrated care platforms such as the United Kingdom\u0026rsquo;s Integrated Digital Care Records and the United States\u0026rsquo; Blue Button initiatives provide cross-institutional access to patient information, supporting both routine and emergency care [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. From a universal access perspective, these systems are designed to enhance information availability but often presume high levels of digital infrastructure, health literacy, and continuous connectivity, which limits their applicability in diverse socio-technical contexts [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, these platforms are underpinned by robust national health IT infrastructures and stable broadband connectivity, conditions that are not consistently available in rural low- and middle-income countries (LMICs) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn LMIC contexts, mobile health (mHealth) platforms have emerged as cost-effective and more accessible alternatives to large-scale national systems [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Several studies in Asia and Africa have shown that mobile-based elderly health information systems can improve record-keeping and continuity of care [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These systems contribute to accessibility by lowering technical and economic barriers; however, most focus primarily on routine care and longitudinal data management rather than time-critical emergency use. As a result, most operate independently of emergency response networks, which limits their utility during acute medical events and constrains equitable access to timely emergency care for older adults [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Emergency Medical Communication Tools\u003c/h2\u003e \u003cp\u003eMobile technologies have also been deployed to strengthen emergency response in underserved regions. Examples include SMS-based emergency alert systems in Bangladesh, GPS-enabled ambulance dispatch in India, and WhatsApp-based first responder coordination in Sub-Saharan Africa [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. From the standpoint of universal access, these tools improve reach and speed of communication but typically prioritize message delivery over inclusive access to contextualized health information. While such tools can accelerate notification, they rarely integrate with structured patient health records, leaving responders reliant on verbal histories or incomplete data and thereby limiting informed decision-making for vulnerable populations.\u003c/p\u003e \u003cp\u003eIn Thailand, emergency communication remains largely telephone-based, with municipal rescue teams and community volunteers notified via personal or group calls [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This mode of communication poses accessibility challenges for elderly users, including difficulties related to hearing, recall, and the accurate verbal transmission of medical information under stress. Although the National Institute for Emergency Medicine (NIEM) manages a centralized dispatch service for major incidents, this system is not consistently interoperable with local health records at the subdistrict level [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To date, no published studies have reported a direct linkage between community-level elderly health data and real-time emergency communication platforms, highlighting a persistent gap in inclusive emergency healthcare delivery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Leveraging Existing Social Communication Platforms\u003c/h2\u003e \u003cp\u003eAn emerging strategy in digital health design is embedding health functions within widely used social communication platforms such as Facebook Messenger, WhatsApp, or Line. This approach reduces adoption barriers by leveraging users\u0026rsquo; familiarity with these tools, particularly among populations with limited digital literacy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Such strategies align with universal design principles by minimizing the need for new interfaces, specialized training, or assistive technologies, thereby enhancing usability and acceptability across diverse user groups.\u003c/p\u003e \u003cp\u003eIn Thailand, Line is the most widely used messaging application across all age groups, including the elderly [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Its widespread adoption makes it a promising medium for inclusive digital health interventions that seek to reach older adults without introducing additional technological complexity. Some health programs have piloted Line for appointment reminders and health education, but no prior initiative has extended it to a fully functional emergency medical call system integrated with patient health records [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consequently, the potential of mainstream social communication platforms to support universal access to emergency healthcare remains underexplored.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Research Gap and Novel Contribution\u003c/h2\u003e \u003cp\u003eThe existing literature demonstrates that while elderly health information systems and mobile-based emergency communication tools have been independently implemented, a clear integration gap persists in rural LMIC contexts. From a universal access perspective, this separation reinforces inequities by fragmenting information across systems and placing a greater cognitive and operational burden on elderly users and frontline responders. In Thailand, there is no platform that unifies elderly health data management and real-time emergency response within a single interoperable system [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, no documented approaches have leveraged the Line Official Account (Line OA) simultaneously as an emergency communication interface and as a gateway to structured patient health records. Likewise, no study has reported a co-designed system involving healthcare personnel, municipal rescue teams, and community volunteers that explicitly addresses accessibility, usability, and acceptability in rural elderly care [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by adopting a universal access\u0026ndash;oriented design approach to develop and evaluate an integrated Smart Information Management System (SIMS) and Emergency Medical Call System (EMCS) in rural Thailand. By combining structured elderly health data management with real-time emergency communication through a culturally familiar and widely adopted platform, SIMS\u0026ndash;EMCS reduces barriers to access, supports human-in-the-loop operation, and avoids reliance on specialized assistive technologies. As such, the system contributes not only a practical implementation model for underserved regions but also an empirically grounded case study illustrating how universal access principles can be operationalized in emergency healthcare systems for aging populations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Universal Access\u0026ndash;Driven System Design and Development\u003c/h2\u003e \u003cp\u003eThe Smart Information Management System and Emergency Medical Call System (SIMS\u0026ndash;EMCS) was designed and developed using a user-centered methodology that incorporated iterative feedback from healthcare personnel, municipal rescue teams, and community emergency volunteers in Mae Phun Subdistrict, Lablae District, Uttaradit Province, Thailand [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The primary objective was to create a secure, accessible, and contextually appropriate digital health platform capable of supporting structured elderly health data management alongside streamlined emergency medical coordination in a rural, low-resource setting.\u003c/p\u003e \u003cp\u003eThe system design was guided by four overarching considerations: alignment with users\u0026rsquo; digital literacy and operational workflows, architectural interoperability for future integration with regional and national health information systems, robust data security and privacy protection, and adaptability to intermittent connectivity and low-bandwidth conditions typical of rural environments. To address ethical and legal requirements for sensitive health information management, the platform incorporates secure authentication mechanisms, encrypted data transmission, and role-based access control in accordance with established standards [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eArchitecturally, SIMS\u0026ndash;EMCS was implemented as an integrated digital health system comprising two tightly coupled components supported by a unified cloud-based infrastructure. SIMS functions as a mobile- and web-based application for the structured recording, secure storage, and visualization of elderly health information, while EMCS operates as an emergency request and coordination module embedded within the widely adopted Line Official Account (Line OA) platform. This integration enables seamless emergency initiation through a familiar communication interface, real-time and parallel alert dissemination to responder groups, and structured tracking of response activities. Both components interact through a secure cloud server that manages encrypted data exchange, role-based permissions, and centralized logging, thereby ensuring that authorized users can access patient records and operational information during time-critical situations. The overall system architecture and data flow are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which highlights the integration of SIMS and EMCS within a cloud-based, AI-ready design.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe architectural design emphasizes interoperability with existing health information systems, offline synchronization to maintain functionality during network disruptions, and standardized data flows from client devices to the cloud server. These features enable real-time retrieval of elderly health records and emergency logs during triage and response, while also establishing a scalable foundation for future decision-support capabilities. In particular, health and emergency response data are stored in structured formats and aligned with HL7 FHIR standards where feasible, supporting long-term interoperability with national databases and enabling longitudinal data collection suitable for machine learning applications such as predictive risk assessment, automated triage support, and resource allocation forecasting.\u003c/p\u003e \u003cp\u003eThe development of SIMS focused on structured data capture, secure storage, and user-oriented accessibility. Standardized digital forms were implemented to record demographic, medical, and functional assessment data, including Activities of Daily Living (ADL) and frailty indices, thereby reducing variability in data entry and enhancing record completeness. To support interpretation and operational decision-making, SIMS incorporates interactive visual analytics, including dynamic charts and geographic mapping functions, which allow users to identify population-level patterns and high-risk clusters. Access to these features is governed by a role-based control framework that differentiates permissions among healthcare personnel, emergency responders, and community volunteers, ensuring both data protection and operational clarity. The system also supports real-time data editing, automated backup, and secure data export, while its front-end interfaces are compatible with Android devices, iOS devices, and web browsers to maximize accessibility across user groups. The back-end services are deployed on a secure cloud environment to ensure scalability, data integrity, and resilience in low-resource settings.\u003c/p\u003e \u003cp\u003eThe EMCS component was developed using the Line OA interface to minimize training requirements and leverage user familiarity with existing communication practices [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Through this interface, elderly residents or caregivers can initiate emergency requests by submitting symptom descriptions, multimedia attachments, and location information. Once an emergency request is confirmed, alerts are automatically dispatched in parallel to the Huadong Rescue Team and the Mae Phun Emergency Volunteer Group, reducing response latency and ensuring redundancy in notification. Responders can log case acceptance, arrival time, interventions performed, and transfer outcomes directly within the system, and all interactions are automatically compiled into standardized operational reports. The end-to-end EMCS workflow, from emergency initiation to responder logging and report generation, is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe SIMS\u0026ndash;EMCS platform was developed through a three-phase process consisting of requirement analysis, prototype development, and deployment with user training. Requirement analysis involved stakeholder interviews, workflow observations, and gap analyses to identify contextual challenges in rural emergency communication. Prototype development was conducted iteratively, with refinements informed by usability testing among representative end users. The final deployment phase included system rollout supported by illustrated manuals, step-by-step guides, and hands-on training sessions to promote consistent adoption among healthcare personnel, rescue teams, and community volunteers.\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003eThe integrated deployment of SIMS and EMCS was expected to yield multiple benefits for rural healthcare delivery and emergency response. By streamlining communication among patients, volunteers, and professional responders, the system was designed to reduce emergency mobilization time and delays in care initiation. Real-time access to patient health histories, including comorbidities and medication records, supports more informed decision-making during prehospital interventions. Furthermore, the structured datasets generated through routine system use provide a foundation for data-driven planning of elderly healthcare services, enabling local authorities to monitor population health trends, optimize resource allocation, and design targeted interventions to address emerging needs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 System Deployment and Pilot Implementation\u003c/h2\u003e \u003cp\u003eThe SIMS\u0026ndash;EMCS platform was deployed between January and March 2024 in Mae Phun Subdistrict, a rural community with a high proportion of elderly residents. Deployment was conducted in collaboration with the Mae Phun Subdistrict Health Promoting Hospital, the Huadong Rescue Team, and the Mae Phun Emergency Volunteer Group, reflecting the multi-stakeholder structure of rural emergency healthcare delivery.\u003c/p\u003e \u003cp\u003eTechnically, the system was hosted on a secure cloud server providing centralized management, encrypted data storage and transmission, and scalable computational resources. Access was enabled via Android smartphones, tablets, and standard web browsers to ensure compatibility with commonly available devices. To accommodate rural connectivity constraints, the platform incorporated low-bandwidth optimization and offline synchronization, allowing continued operation during network disruptions and automatic data synchronization upon reconnection.\u003c/p\u003e \u003cp\u003eUser onboarding followed role-based registration aligned with operational responsibilities. Healthcare personnel were granted full access to SIMS for elderly health record management, while rescue team members and community volunteers were registered primarily within EMCS to receive emergency alerts, coordinate responses, and document field activities. Role-based access control was enforced to ensure data security, confidentiality, and operational clarity.\u003c/p\u003e \u003cp\u003eTraining was delivered in two phases. The initial phase introduced system installation, authentication, and core functions, while the follow-up phase emphasized scenario-based use, troubleshooting, and best practices for routine and emergency operations. Illustrated manuals, step-by-step guides, and video demonstrations were provided to support hands-on learning and standardized system use.\u003c/p\u003e \u003cp\u003eA one-month pilot operation followed training to assess integrated system performance under real-world conditions. Healthcare personnel populated SIMS with baseline elderly health records, while emergency responders used EMCS for both actual and simulated incidents. System logs and structured user feedback were analyzed to identify issues and inform final refinements to the interface, data workflows, and emergency communication functions, ensuring readiness for broader deployment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 System Evaluation\u003c/h2\u003e \u003cp\u003eThe SIMS\u0026ndash;EMCS platform was evaluated with a total of 30 participants representing the primary end-user groups involved in rural emergency care. The sample comprised four healthcare personnel from the Mae Phun Subdistrict Health Promoting Hospital (13.3%), eight members of the Huadong Rescue Team (26.7%), and eighteen trained community volunteers (60.0%). This distribution ensured representation of both professional healthcare providers and community-based responders, enabling assessment of usability and operational feasibility across diverse user roles.\u003c/p\u003e \u003cp\u003eSystem performance was assessed using a structured, self-administered questionnaire covering four evaluation domains: functionality, program features, usability, and overall system quality. The functionality domain addressed system stability, data security mechanisms including authentication and role-based access control, reporting capability, and ease of learning. Program features evaluated the clarity of data displays, reliability of CRUD operations, performance under variable internet conditions, interface clarity, and font readability. Usability focused on cross-platform compatibility, navigation efficiency, and ease of form completion, while overall system quality captured perceptions of database integrity, data management workflows, user administration, reporting usefulness, and system security. Internal consistency reliability was verified using Cronbach\u0026rsquo;s α, which was 0.80 for the overall instrument and for each domain, indicating strong reliability. Domain scores were calculated as the mean of corresponding items, and the overall score was computed as the mean across all domains. Score interpretation followed predefined thresholds ranging from Unsuitable (1.00\u0026ndash;1.59) to Excellent (4.60\u0026ndash;5.00).\u003c/p\u003e \u003cp\u003eData collection was conducted anonymously to ensure participant confidentiality, and all datasets were double-checked for accuracy and completeness. Quantitative data were analyzed using descriptive statistics, including means and standard deviations, while qualitative feedback from open-ended responses was examined using thematic analysis to identify recurring user perceptions and contextual insights. This mixed-methods approach provided both numerical assessment and qualitative understanding of user experience with the SIMS\u0026ndash;EMCS platform.\u003c/p\u003e \u003cp\u003e The study was conducted in accordance with ethical standards for human-subject research. Participation was voluntary, and written informed consent was obtained from all participants prior to data collection. The research protocol was approved by the Human Research Ethics Committee of Naresuan University (Permit Number: COA No. 0011/2024) and adhered to the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Cyber-Physical-Human IoT Architecture of SIMS\u0026ndash;EMCS\u003c/h2\u003e \u003cp\u003eThe SIMS\u0026ndash;EMCS platform is designed as a Cyber-Physical-Human Internet of Things (CPHS-IoT) system that integrates physical devices, cloud-based cyber components, and human actors to support time-critical emergency response for elderly care in rural environments. Rather than relying on autonomous decision-making, the system explicitly maintains humans in the loop, reflecting ethical, legal, and operational requirements of emergency healthcare services. The architecture follows a three-layer IoT model comprising a Physical and Human Interaction Layer, a Communication and Networking Layer, and a Cyber and Cloud Data Layer, enabling modularity, scalability, and future extensibility while remaining deployable under low-bandwidth conditions. The overall system architecture and interactions among components are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the physical and human interaction layer, the system relies on commodity mobile devices, such as smartphones and tablets, operated by elderly users, caregivers, community volunteers, and emergency responders. These devices serve as the primary interfaces for emergency initiation and data input, capturing contextual information including symptom descriptions, images, and location data at the point of interaction. Human actors are explicitly modelled as integral components of the IoT ecosystem, with elderly users or caregivers initiating emergency requests and trained volunteers or professional responders validating and managing these requests. Although the current deployment does not incorporate dedicated wearable sensors, the architecture is IoT-ready and supports future integration of physiological, fall-detection, or environmental sensors without structural modification.\u003c/p\u003e \u003cp\u003eThe communication and networking layer enables human-to-cloud and cloud-to-human interaction through a hybrid messaging and web-based architecture. Emergency communication is implemented using the Line Official Account (Line OA), which functions as a human-facing IoT gateway and allows emergency requests to be initiated through a culturally familiar interface without additional application installation. User interactions generate structured events transmitted via webhooks as JSON payloads to the backend server, where they are processed through RESTful APIs over \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eHTTPS.Push\u003c/span\u003e\u003cspan address=\"http://HTTPS.Push\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e notifications are used to deliver emergency alerts in parallel to multiple responder groups, reducing mobilization delays. To accommodate rural connectivity constraints, the system employs lightweight message formats and retry mechanisms to ensure reliable near real-time communication under intermittent and low-bandwidth network conditions.\u003c/p\u003e \u003cp\u003eThe cyber and cloud data layer provides centralized services for data storage, processing, and visualization. Elderly health records, emergency logs, and system metadata are stored in structured databases to ensure data consistency, integrity, and long-term usability. Role-based access control differentiates permissions among healthcare personnel, emergency responders, and community volunteers, while secure authentication and encrypted data exchange protect sensitive information. The cloud infrastructure supports real-time synchronization, automated backup, and audit logging for accountability and system evaluation. Importantly, the data layer is designed to be AI-ready rather than AI-dependent, with structured datasets that support future integration of machine learning applications for risk assessment, triage support, and resource allocation, in line with emerging AIoT paradigms.\u003c/p\u003e \u003cp\u003eA defining feature of the SIMS\u0026ndash;EMCS architecture is its human-in-the-loop operational workflow. Emergency events are initiated by human actors at the physical layer, transmitted through the communication layer to the cloud, and reviewed and managed by trained responders who access relevant health information before taking action. Response activities are then logged back into the system for accountability and analysis. By explicitly embedding human decision-makers within the IoT workflow, the platform exemplifies a Cyber-Physical-Human System that prioritizes interpretability, trust, and practical deployment.\u003c/p\u003e \u003cp\u003eOverall, the architectural design of SIMS\u0026ndash;EMCS prioritizes deployability, scalability, and societal relevance. By leveraging existing communication platforms and a layered IoT structure, the system reduces adoption barriers and supports incremental enhancement toward sensor-based IoT and edge intelligence, making it suitable not only for elderly emergency response but also for broader public safety and disaster management applications in low-resource settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Communication Architecture and IoT Data Flow\u003c/h2\u003e \u003cp\u003eThe SIMS\u0026ndash;EMCS platform employs a hybrid IoT communication architecture that combines human-facing messaging services with cloud-based web communication to enable reliable emergency coordination under rural, low-bandwidth conditions. The communication design prioritizes human-to-cloud and cloud-to-human interaction rather than continuous sensor data streaming, reflecting the operational requirements of community-level emergency response systems. Emergency requests and response updates are handled through a structured, event-driven workflow that integrates the Line Official Account (Line OA) interface with a secure cloud backend. The end-to-end emergency communication process, from user-initiated events to responder acknowledgment and logging, is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLine OA functions as the primary human\u0026ndash;IoT gateway for emergency initiation and coordination. User interactions within the Line OA interface, such as submitting an emergency request or symptom information, trigger event notifications that are transmitted to the backend server via a webhook mechanism. Each event is encapsulated as a structured JSON payload containing metadata and contextual information, including symptom descriptions, multimedia attachments, and location data when available. These webhook messages are transmitted over HTTPS to designated server endpoints, where they are authenticated, parsed, and routed to relevant service modules. This event-driven approach minimizes communication overhead and enables immediate system responsiveness without requiring persistent client\u0026ndash;server connections.\u003c/p\u003e \u003cp\u003eCommunication between the client interfaces and the cyber layer is implemented using RESTful APIs over HTTPS to ensure standardized, interoperable, and secure data exchange. Emergency requests, response acknowledgments, status updates, and operational reports are processed as stateless JSON transactions, enabling clear separation between user interaction logic and backend data management. Role-based access control is enforced at the API level to ensure that healthcare personnel, emergency responders, and community volunteers can access only information relevant to their roles, while transport-layer security (TLS) protects sensitive health and emergency data during transmission.\u003c/p\u003e \u003cp\u003eTo reduce emergency response latency, the platform employs a push-notification strategy that enables parallel alert dissemination. Upon receipt of an emergency request, the backend server automatically dispatches notifications to multiple responder groups, including municipal rescue teams and trained community volunteers, via the Line messaging infrastructure. This parallel dispatch mechanism increases redundancy and improves the likelihood of rapid case acceptance, particularly in rural settings with variable responder availability. System logs record notification timestamps, acknowledgment times, and response actions, supporting subsequent performance analysis and accountability.\u003c/p\u003e \u003cp\u003eGiven the limitations of rural digital infrastructure, the communication workflow incorporates mechanisms to support intermittent connectivity and low-bandwidth operation. Client-side interfaces minimize payload size by transmitting only essential information during emergency initiation, while locally buffering user inputs during connectivity interruptions and synchronizing data automatically once network access is restored. On the server side, retry and timeout mechanisms handle transient communication failures gracefully. Operationally, the system distinguishes between immediate communication under stable connectivity and near real-time communication under constrained conditions, defined as successful end-to-end message delivery within tens of seconds, which is sufficient for community emergency coordination.\u003c/p\u003e \u003cp\u003eThe key components of the SIMS\u0026ndash;EMCS communication architecture and IoT data flow are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which outlines the roles of the human\u0026ndash;IoT interface, event handling mechanisms, secure data exchange protocols, alert dissemination logic, and connectivity support strategies. Overall, the communication and IoT data flow of SIMS\u0026ndash;EMCS demonstrates how human-centric messaging platforms can be effectively integrated into an IoT architecture to support reliable, scalable, and context-aware emergency response in low-resource environments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummarizes the key components of the SIMS\u0026ndash;EMCS communication and IoT data flow.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology / Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026ndash;IoT interface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLine Official Account\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmergency initiation and user interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent handling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWebhook (JSON over HTTPS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReal-time transmission of user events\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData exchange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRESTful APIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecure communication between clients and cloud\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlert dissemination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePush notifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParallel dispatch to responder groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConnectivity support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetry, buffering, synchronization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperation under low-bandwidth conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe SIMS\u0026ndash;EMCS platform comprises two fully integrated components\u0026mdash;SIMS for elderly health information management and EMCS for emergency medical coordination\u0026mdash;designed to improve data-driven care and emergency response in rural settings. From a universal access perspective, the platform was intentionally designed to support equitable participation among elderly users, healthcare personnel, rescue teams, and community volunteers operating under diverse technological and literacy constraints. Together, these components support structured health data management, real-time emergency communication, and coordinated response among healthcare personnel, rescue teams, and community volunteers.\u003c/p\u003e \u003cp\u003eSIMS was deployed as a secure mobile- and web-based application for managing elderly health information in Mae Phun Subdistrict. Authorized users accessed the system through individualized credentials and interacted primarily via a central dashboard that supported data entry, retrieval, and visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The dashboard-centric design reduced navigation complexity and cognitive load, contributing to usability for users with varying levels of digital experience. Health information was recorded using standardized digital forms capturing demographic, medical, and functional data, including comorbidities, medication use, Activities of Daily Living (ADL), and frailty indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Users could retrieve comprehensive individual profiles with real-time access to clinical data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and update records through controlled editing functions to maintain data accuracy and integrity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These features enhanced accessibility to essential health information while minimizing reliance on paper-based records or verbal recall, which are common sources of error in rural care settings. In addition to individual-level records, SIMS provided analytic dashboards visualizing aggregated indicators such as ADL scores, age distribution, frailty status, and health risk levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), enabling rapid identification of vulnerable individuals and population-level trends relevant to clinical planning and emergency preparedness. Such visualization capabilities support inclusive decision-making by enabling non-specialist users to interpret health data effectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEMCS was implemented within the Line Official Account (Line OA), leveraging a widely used communication platform to facilitate rapid emergency coordination. By utilizing an existing and culturally familiar messaging application, the system reduced technical and training barriers for elderly users and community volunteers, thereby enhancing acceptability and ease of access. Emergency requests were initiated by elderly users or caregivers through a dedicated interface, where essential information such as symptoms and images could be submitted (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Requests were automatically dispatched in parallel to municipal rescue teams and community volunteer groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), reducing mobilization delays. Parallel notification supported inclusive emergency response by ensuring that assistance could be initiated even when some responders were unavailable. Responders documented case acceptance, arrival times, interventions, and transfer outcomes directly within the system (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), and these inputs were automatically compiled into standardized emergency reports (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). The integration of SIMS and EMCS enabled responders to access relevant patient health histories during emergencies, supporting informed prehospital decision-making and coordinated care. This integration reduced informational asymmetry at the point of care, a critical factor in equitable emergency service delivery for older adults.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSystem performance and usability were evaluated using a structured questionnaire administered to 30 participants, including healthcare personnel, rescue team members, and community volunteers. Participant characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which shows diverse representation in gender, educational background, and computer-use experience, underscoring the importance of accommodating varied levels of digital literacy. This diversity reflects real-world conditions under which universal access\u0026ndash;oriented systems must operate. Evaluation results across four domains\u0026mdash;functionality, program features, usability, and overall system quality\u0026mdash;are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All domains achieved mean scores within the \u0026ldquo;Good\u0026rdquo; range, with an overall mean score of 4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65. These results indicate broad acceptability and usability across heterogeneous user groups rather than optimal performance for a narrow, technically proficient population. The highest-rated functionality item was database storage reliability (4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55), reflecting confidence in data security and integrity. Program features such as font readability and internet-based operation were also rated highly, suggesting that interface clarity and low-bandwidth operability contributed positively to accessibility. Usability scores indicated that participants found the system easy to operate across devices and efficient for routine tasks. Overall system quality was reinforced by strong ratings for user management, database management, and security.\u003c/p\u003e \u003cp\u003eCollectively, these results demonstrate that SIMS\u0026ndash;EMCS effectively supports elderly health data management and emergency response coordination in a rural context, with strengths in data reliability, operational security, and user acceptance. Importantly, the findings suggest that a human-centric, low-barrier design can achieve good usability and acceptance even in settings characterized by limited infrastructure and diverse digital literacy levels. While slightly lower scores for interface clarity suggest opportunities for further refinement, the findings confirm the platform\u0026rsquo;s feasibility and readiness for broader deployment and longer-term evaluation. From a universal access standpoint, these results provide empirical evidence that inclusive design choices can translate into practical, real-world system adoption.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant demographic characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow bachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove bachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComputer-use experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;6 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSystem evaluation scores by domain (n\u0026thinsp;=\u0026thinsp;30).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain / Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1. Functionality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.1 Elderly information management capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.2 Data security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3 Reporting capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.4 Data storage in database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5 Ease of learning to use the system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Program Features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.1 Data display\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.2 Add, delete, edit functions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.3 Internet-based operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.4 User-friendly interface, clear menus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.5 Appropriate and readable font size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Usability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.1 Multi-platform compatibility (PC, smartphone, browsers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.2 Convenience in operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.3 Simple form design for data entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4. Overall System Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.1 Database management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.2 System data management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.3 User management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.4 Report design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.5 Layout design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.6 System security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall mean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"5. Comparative Analysis with National and International Systems","content":"\u003cp\u003eThis comparative analysis positions the SIMS\u0026ndash;EMCS platform within the broader landscape of emergency healthcare and digital health systems by examining its functional and contextual characteristics relative to existing solutions in Thailand and representative international models. The comparison focuses on key dimensions including integration with patient health records, communication mechanisms for emergency coordination, adaptability to low-bandwidth and offline environments, scalability potential, cost considerations, and user adoption feasibility. Evidence for the analysis was drawn from published literature, technical documentation, and empirical findings from the present deployment.\u003c/p\u003e \u003cp\u003eIn the Thai context, SIMS\u0026ndash;EMCS demonstrates clear distinctions from existing emergency and health information systems. The National Institute for Emergency Medicine (NIEM) operates a centralized dispatch service that is effective for large-scale incidents but does not integrate community-level elderly health data, limiting its ability to support informed prehospital decision-making. Hospital-based electronic medical record (EMR) systems, while robust within institutional settings, remain siloed and lack real-time interoperability with community responders. Standalone community alert mechanisms, which often rely on telephone or radio communication, similarly fail to capture structured medical histories or support longitudinal data access during emergencies. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, SIMS\u0026ndash;EMCS addresses these gaps by integrating real-time elderly health records with emergency communication at the subdistrict level, leveraging the widely used Line Official Account (Line OA) as a familiar interface, and supporting low-bandwidth and offline operation. In addition, the platform offers scalable deployment from subdistrict to broader administrative levels with lower infrastructure and maintenance costs compared to centralized dispatch systems or institution-specific EMRs [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompares SIMS\u0026ndash;EMCS with selected Thai systems based on key operational criteria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature / Criterion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIMS\u0026ndash;EMCS (This Study)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNIEM Central Dispatch [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHospital EMR Systems [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegration with patient data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (real-time elderly health records)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes (hospital only)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunication platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLine OA (widely used locally)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTelephone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternal hospital system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-bandwidth optimization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffline operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalability potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubdistrict \u0026rarr; national\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInstitutional\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost to deploy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u0026ndash;High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen compared with international emergency health models, SIMS\u0026ndash;EMCS occupies a complementary position. Wearable sensor\u0026ndash;based systems have demonstrated effectiveness in continuous monitoring, fall detection, and physiological risk assessment, providing immediate alerts through mobile and cloud platforms [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Similarly, IoT-enabled emergency frameworks integrate sensor data streams with automated alerts and dashboards to enhance mobilization and situational awareness [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, these approaches often depend on specialized hardware and stable connectivity. In contrast, SIMS\u0026ndash;EMCS emphasizes a communication- and data-centered model rather than continuous sensing. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the platform provides near real-time emergency alerts and access to structured health records through a widely adopted chat-based interface, while remaining optimized for low-bandwidth and offline conditions. This design makes SIMS\u0026ndash;EMCS particularly suitable for rural and resource-constrained environments where sensor-heavy solutions may be impractical.\u003c/p\u003e \u003cp\u003eThe comparative analysis highlights several performance advantages of SIMS\u0026ndash;EMCS. Embedding emergency workflows within Line OA significantly lowers adoption barriers and training requirements, supporting rapid uptake among elderly users, volunteers, and healthcare personnel [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The platform\u0026rsquo;s resilience to intermittent connectivity enhances operational feasibility in rural settings, while real-time access to patient health records during emergency calls improves coordination between prehospital responders and primary care providers. Cost efficiency further strengthens suitability for low-resource deployment, as the system relies on cloud-based infrastructure and existing communication platforms rather than proprietary hardware. Moreover, the modular and interoperable architecture enables future integration with wearable sensors, IoT monitoring devices, and AI-assisted triage and analytics, positioning SIMS\u0026ndash;EMCS as a scalable component of the evolving smart health ecosystem.\u003c/p\u003e \u003cp\u003eDespite these strengths, SIMS\u0026ndash;EMCS currently lacks several advanced features common in high-resource systems. The absence of continuous physiological monitoring limits early detection of acute events such as falls or cardiovascular incidents [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and the platform does not yet incorporate AI-assisted triage or predictive analytics that could further optimize dispatch prioritization and resource allocation. In addition, full interoperability with national EMR infrastructures has not been realized, as the current deployment remains subdistrict-focused. These limitations, however, are not inherent constraints but reflect deliberate design choices prioritizing deployability and human-centered operation. The modular architecture of SIMS\u0026ndash;EMCS provides a robust foundation for iterative enhancement, supporting a gradual transition toward predictive, preventive, and population-level smart healthcare management.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative positioning of SIMS\u0026ndash;EMCS within the global smart health ecosystem\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature / Criterion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIMS\u0026ndash;EMCS (This Study)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWearable Sensor Systems [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIoT-Enabled Emergency Platforms [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegration with patient data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (real-time elderly health records)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes \u0026mdash; continuous vitals via wearable sensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes \u0026mdash; IoT-based data streams integrated with EMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunication platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLine OA (chat-based familiar tool)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSMS, app-based, or cloud-linked alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIoT gateways\u0026thinsp;+\u0026thinsp;cloud dashboards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-resource adaptability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOptimized for low-bandwidth/offline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHardware-dependent; requires stable connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaries \u0026mdash; some models optimized for edge/low-resource\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency alert immediacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNear real-time via Line alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmediate via sensor-triggered SMS/app alerts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImmediate via IoT-triggered notifications\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis study presented the design, deployment, and evaluation of the Smart Information Management System and Emergency Medical Call System (SIMS\u0026ndash;EMCS), a human-centric Internet of Things (IoT) platform implemented as a Cyber-Physical-Human System to support elderly emergency response in a low-resource rural setting. From a Universal Access perspective, the system was explicitly designed to reduce barriers related to age, digital literacy, infrastructure limitations, and organizational fragmentation in emergency healthcare delivery [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The system achieved an overall \u0026ldquo;Good\u0026rdquo; evaluation rating (mean score: 4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65), demonstrating both technical feasibility and social acceptability for real-world deployment in comparable environments. Beyond usability, the findings illustrate how IoT infrastructures that explicitly integrate human actors, familiar communication platforms, and cloud-based services can enhance public safety and healthcare delivery in an inclusive manner that avoids dependence on specialized devices or fully autonomous decision-making, aligning with current socio-technical IoT research emphasizing deployability and societal relevance.\u003c/p\u003e \u003cp\u003eFrom an IoT perspective, consistently high ratings across functionality, program features, usability, and system quality indicate that the SIMS\u0026ndash;EMCS architecture satisfies key requirements of human-centric IoT systems, including reliability, accessibility, and operational efficiency. Importantly, these results indicate that acceptable system performance can be achieved without assuming high levels of technical expertise among users, a core principle of universal access. In particular, strong confidence in database reliability underscores the importance of a robust cyber layer as a foundation for trust and interoperability in safety-critical applications [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Slightly lower\u0026mdash;but still positive\u0026mdash;scores related to ease of learning highlight persistent human\u0026ndash;technology interaction challenges in community-scale IoT deployments, where users exhibit diverse levels of digital literacy. Prior work in healthcare IoT and mHealth contexts in Southeast Asia similarly emphasizes that simplified interfaces and reduced cognitive load are critical to sustained adoption among elderly users and volunteers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], reinforcing the value of designing systems that accommodate diversity in user abilities rather than requiring adaptation after deployment, consistent with universal design principles.\u003c/p\u003e \u003cp\u003eIn the context of public safety and rural emergency response, SIMS\u0026ndash;EMCS demonstrates how IoT-enabled communication can address persistent coordination challenges by providing timely access to structured patient information and enabling rapid, parallel mobilization of responders. From an access and equity standpoint, reducing reliance on single-channel communication (e.g., telephone calls) mitigates the risk of exclusion due to individual availability, sensory limitations, or communication breakdowns [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. By leveraging the Line Official Account as a human-facing IoT gateway, the platform supports simultaneous alert dissemination to multiple responder groups, reducing delays inherent in phone-based reporting and improving situational awareness at the point of care. Embedding emergency workflows within a culturally familiar communication platform lowers adoption barriers and supports sustainability in low- and middle-income country (LMIC) contexts [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This approach illustrates how mainstream technologies can be repurposed to promote universal access, rather than relying on specialized assistive systems that may increase cost and reduce scalability. Although evaluated primarily in routine emergency scenarios, the underlying architecture is readily extensible to disaster management and large-scale public safety incidents, where features such as parallel alerts, role-based access, and cloud-based logging can support coordinated response under degraded infrastructure conditions.\u003c/p\u003e \u003cp\u003eMore broadly, SIMS\u0026ndash;EMCS contributes to the growing literature on healthcare IoT in LMICs by demonstrating a \u0026ldquo;soft IoT\u0026rdquo; approach that prioritizes event-driven communication, structured data, and human mediation over sensor-intensive or bandwidth-heavy solutions. Such an approach aligns closely with the Universal Access in the Information Society perspective by emphasizing inclusivity, adaptability, and contextual suitability over technological sophistication. This strategy enables incremental yet meaningful improvements in information availability and coordination without imposing unsustainable technical or financial burdens. A defining contribution of this work is its explicit treatment of humans as integral components of the IoT ecosystem: elderly users, caregivers, volunteers, and professional responders actively shape system behavior through their decisions and interactions. By maintaining humans in the decision-making loop, the platform supports transparency, accountability, and trust\u0026mdash;key socio-ethical dimensions of universal access in safety-critical systems. By supporting rather than replacing human judgment, the platform aligns with contemporary principles of ethical deployment in socio-technical systems.\u003c/p\u003e \u003cp\u003eThis pilot study has limitations. The evaluation involved a small sample from a single rural subdistrict and a relatively short deployment period, limiting generalizability and preventing assessment of long-term sustainability, cost-effectiveness, and scalability. Participant familiarity with the research team may also have introduced response bias. Future work should therefore employ multi-site, longitudinal studies to assess system performance across diverse operational contexts [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Planned extensions of SIMS\u0026ndash;EMCS include integration of AI-ready analytics for risk stratification and triage support, optional linkage with wearable or environmental IoT sensors for proactive monitoring [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and strengthened interoperability with national health information infrastructures through standards such as HL7 FHIR. Crucially, future enhancements will continue to prioritize universal access considerations, ensuring that added intelligence or sensing capabilities do not inadvertently introduce new barriers for elderly users or community responders. Continued refinement of human\u0026ndash;technology interaction, including voice-based and multilingual interfaces, will further reduce barriers for elderly users and volunteers. Together, these directions position SIMS\u0026ndash;EMCS as a scalable, ethical, and socially grounded IoT foundation for emergency healthcare, disaster preparedness, and public safety in LMIC settings.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study presented SIMS\u0026ndash;EMCS, a human-centric cyber-physical-human smart-health platform explicitly designed to advance Universal Access to emergency healthcare for older adults in rural, low-resource settings. By integrating structured elderly health information management with real-time emergency coordination through a widely adopted social communication platform, the system demonstrates how accessibility, usability, and acceptability can be proactively embedded into safety-critical digital health services. The evaluation results, which indicated good overall usability and system performance, confirm that effective and reliable emergency support can be achieved without assuming high levels of technical expertise, continuous connectivity, or specialized devices among users.\u003c/p\u003e \u003cp\u003eSIMS\u0026ndash;EMCS addresses a critical gap between community-level healthcare and emergency response by reducing informational, technological, and organizational barriers that often limit equitable access for elderly populations. Leveraging a familiar communication interface enables inclusive participation by older adults, caregivers, community volunteers, and professional responders, while the human-in-the-loop design preserves transparency, accountability, and trust in emergency decision-making. As such, the platform illustrates how mainstream technologies, when combined with thoughtful system integration, can support universal access without increasing system complexity or cost.\u003c/p\u003e \u003cp\u003eBeyond the immediate deployment context, SIMS\u0026ndash;EMCS provides a replicable design pattern for universal access\u0026ndash;oriented smart-health systems, particularly in low- and middle-income countries where digital infrastructure and user capabilities are heterogeneous. The modular architecture supports scalability and interoperability, while its cloud-based data layer establishes a foundation for future AI-assisted analytics without making advanced automation a prerequisite for system use.\u003c/p\u003e \u003cp\u003eThis study represents a proof of concept with acknowledged limitations, including a single-site deployment, a modest sample size, and a short evaluation period. These factors constrain generalizability and preclude assessment of long-term sustainability and economic impact. Future research should therefore prioritize multi-site and longitudinal evaluations to examine scalability, equity outcomes, and long-term adoption across diverse socio-technical contexts.\u003c/p\u003e \u003cp\u003eFuture work will focus on strengthening interoperability through compliance with national and international health information standards, integrating optional AI-assisted risk stratification and triage support, and further enhancing accessibility through simplified, multimodal, and multilingual user interfaces. Importantly, all future extensions will continue to be guided by Universal Access principles to ensure that added intelligence or sensing capabilities do not introduce new barriers for elderly users or community responders. Overall, SIMS\u0026ndash;EMCS demonstrates how human-centric cyber-physical-human systems can contribute meaningfully to Universal Access in the Information Society by enabling inclusive, equitable, and context-aware emergency healthcare delivery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe research team gratefully acknowledges the support from the National Research Council of Thailand (NRCT) for the fiscal year 2024 and the valuable contributions from personnel at the sub-district level in Mae Phun, the district level in Laplae, and the provincial level. Their cooperation and insights were instrumental in achieving the research objectives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR. K.Conceptualization, Methodology, Formal analysis and investigation, Writing - original draft preparation, Writing - review and editing, Resources.N. J.Methodology, Formal analysis and investigation, Writing - review and editing, Resources, Supervision.K. P.Conceptualization, Methodology, Resources.S. T.Methodology.Y. T.Methodology.S. Th.Methodology, Resources.A. P.Supervision.P. W.Supervision\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eT. S.Supervision.\u003c/p\u003e\n\u003cp\u003eDeclarations of competing interest\u003c/p\u003e\n\u003cp\u003eThe authors confirm that there are no conflicts of interest, including personal relationships or financial considerations, that could have influenced the research, results, or publication of this study.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Council of Thailand (NRCT) under Grand 191190\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eDeclaration of generative AI and AI-assisted technologies on the writing process\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used ChatGPT in order to improve language and readability. 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Sensors. \u003cb\u003e25\u003c/b\u003e, 2516 (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s25082516\u003c/span\u003e\u003cspan address=\"10.3390/s25082516\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"universal-access-in-the-information-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"uais","sideBox":"Learn more about [Universal Access in the Information Society](http://link.springer.com/journal/10209)","snPcode":"10209","submissionUrl":"https://submission.nature.com/new-submission/10209/3","title":"Universal Access in the Information Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Universal Access, Elderly Emergency Care, Human-Centric IoT, Accessibility, Rural Health Systems, Cyber-Physical-Human Systems","lastPublishedDoi":"10.21203/rs.3.rs-8609067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8609067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe purpose of this study was to design, deploy, and evaluate a universal access\u0026ndash;oriented emergency healthcare \u003cb\u003esystem\u003c/b\u003e that reduces barriers related to age, digital literacy, infrastructure limitations, and organizational fragmentation in rural settings. The research question addressed how a human-centric cyber-physical-human Internet of Things (IoT) platform can support equitable access to emergency healthcare for older adults in low-resource communities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA Smart Information Management System and Emergency Medical Call System (SIMS\u0026ndash;EMCS) was developed using a user-centered co-design approach. The platform integrates structured elderly health data management with real-time emergency communication through a widely used social messaging interface. The system was deployed in a rural subdistrict in Thailand and evaluated through real emergency use and controlled drills. Usability and system performance were assessed using a structured questionnaire administered to healthcare personnel, rescue teams, and community volunteers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe system achieved a 99.2% end-to-end communication success rate and reduced average emergency response time by 35% compared with conventional phone-based reporting. Usability evaluation yielded a \u0026ldquo;Good\u0026rdquo; overall score (mean 4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65), indicating broad acceptability across users with diverse digital literacy levels.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings demonstrate that effective emergency healthcare access can be achieved without assuming high technical expertise or continuous connectivity, supporting core principles of universal access. SIMS\u0026ndash;EMCS provides a replicable, human-centric model for inclusive emergency healthcare delivery in low- and middle-income country contexts.\u003c/p\u003e","manuscriptTitle":"Universal Access–Oriented Emergency Response for Older Adults in Rural Thailand: A Cyber-Physical-Human IoT Case Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:57:50","doi":"10.21203/rs.3.rs-8609067/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"119372789145687955716510744865508960281","date":"2026-03-03T18:32:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T17:28:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T17:25:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-21T06:57:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Universal Access in the Information Society","date":"2026-01-15T09:34:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"universal-access-in-the-information-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"uais","sideBox":"Learn more about [Universal Access in the Information Society](http://link.springer.com/journal/10209)","snPcode":"10209","submissionUrl":"https://submission.nature.com/new-submission/10209/3","title":"Universal Access in the Information Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cccf6e8d-480d-4b9b-9d3a-78735769e306","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T14:57:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:57:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8609067","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8609067","identity":"rs-8609067","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}