Enhancing AD/ADRD Management Through iHelpCare: A Compliant and Culturally Sensitive AI-Driven Digital Healthcare Platform

Development and Validation of MyHTCare: An mHealth Application for Remote Monitoring and Self-Management of Hypertension

(1) Background: Hypertension is a prevalent chronic condition requiring sustained self-management to prevent complications; however, long-term adherence to monitoring, medication, and lifestyle modification remains suboptimal. Mobile health (mHealth) technologies, supported by cloud-based connectivity, offer scalable platforms for structured remote monitoring and patient engagement. This study aimed to design, develop, and validate MyHTCare, a user-centered mHealth application for comprehensive hypertension self-management and connected remote monitoring. (2) Methods: An Agile-based, three-iterative development framework was adopted, incorporating clinical recommendations and inputs from patients, caregivers, and physicians. The application was developed using Flutter and integrated with Cloud Fire store to enable secure cloud-based data storage and real-time synchronization. Core modules included blood pressure tracking, medication reminders, infographic-based lifestyle education, and automated clinical alerts. Content validation involved 10 experts (clinicians and IT professionals) and 30 end users (adults with hypertension or caregivers). Usability was assessed using a pre-tested structured questionnaire, and educational materials were evaluated using the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V). (3) Results: End-user validation demonstrated high usability, with mean scores ranging from 4.4 to 5.0 on a 5-point scale. Educational materials achieved 100% actionability and 92–100% understandability. Expert evaluation showed high ratings across usability domains, with acceptability scores exceeding 70%. (4) Conclusions: MyHTCare demonstrated strong content validity and usability, supporting further clinical evaluation to determine its effectiveness in improving blood pressure control and self-management.

Digital therapeutics and mHealth applications in cardiovascular prevention: strongest evidence in hypertension and emerging perspectives in dyslipidemia

Digital health interventions are increasingly used to support cardiovascular prevention, but their clinical maturity differs across risk factors and types of technology. This narrative review summarizes current evidence and implementation challenges related to digital therapeutics, mobile health applications, telemonitoring systems, and integrated digital health platforms in hypertension, dyslipidemia, and broader cardiometabolic prevention. The strongest evidence currently supports digitally enabled blood pressure management, particularly when home blood pressure monitoring, telemonitoring, behavioral support, medication adherence tools, and clinician-guided treatment adjustment are integrated into routine care. In contrast, digital interventions for dyslipidemia remain less established and are mainly focused on education, lifestyle modification, adherence support, shared decision-making, and long-term risk reduction. Therefore, most lipid-related digital tools should currently be interpreted as supportive mHealth or prevention interventions rather than fully established digital therapeutics. Successful implementation requires more than patient-facing applications. It depends on clinical workflow integration, professional responsibility for data review and telemonitoring alerts, regulatory and reimbursement pathways, data protection, interoperability, equity, and evidence of clinical and economic value. Future studies should distinguish between digital therapeutics, mHealth tools, telemonitoring systems, and digital ecosystems, and should evaluate clinically meaningful endpoints, safety, cost-effectiveness, and long-term sustainability.

[Technology-Driven, Human-Centered Care: Challenges and Opportunities].

A Scalable Interoperable Data Science System for Trauma-Informed Care: Enhancing ACE Interventions with Real-Time Analytics, AI-Powered Insights, and Clinical Decision Support

Adverse Childhood Experiences (ACEs) remain a critical public health challenge, with long-term effects on physical health, mental well-being, and socioeconomic outcomes. Despite ongoing trauma-informed care (TIC) initiatives, existing systems lack integrated, real-time data analytics and accessible decision-support tools for stakeholders. This paper presents the design and development of a scalable, interoperable Data Science Management Application (DSMA) conceptualized for Resilient Georgia to support ACE prevention and TIC implementation. The proposed prototype integrates an interactive visualization dashboard, an AI-powered chatbot for natural-language querying, an ACE risk-tracking tool, and an automated reportgeneration pipeline. The application is developed in a research context, using synthetic and representative datasets to demonstrate the system’s capabilities while preserving data privacy. Preliminary user feedback from domain stakeholders indicates strong perceived usability and relevance, particularly in visualization clarity and natural-language interaction. The system demonstrates the potential to identify regional disparities, improve access to complex data, and support data-driven decision-making. However, the platform has not yet been deployed in real-world settings, and formal quantitative validation of chatbot accuracy, report reliability, and risk assessment performance remains future work. This study contributes a human-centered, AI-enabled framework for scalable public health data systems, providing a foundation for future development, validation, and deployment in traumainformed care environments.

Medicare.AI: Design, Implementation, and Safety Evaluation of an Intelligent Healthcare Management Platform

A reproducible evaluation of safety and access control for Medicare.IO is reported, a transparent case study of how an ambitious student prototype can be transformed into a more testable, safety-aware system.

Passive Digital Marker-enabled identification and engagement for Alzheimer's disease and related dementias care in primary care: the INCLUDE study protocol.

INTRODUCTION More than half of older adults living with Alzheimer's disease and related dementias (ADRD) never receive a formal diagnosis, and when a diagnosis occurs, it is often years after symptom onset. Primary care clinicians are ideally positioned to detect ADRD early; however, current workflows lack scalable tools that support systematic identification and follow-up. The Passive Digital Marker (PDM), a machine learning model that uses structured electronic health record (EHR) data, can identify patients at elevated risk for ADRD without adding burden to clinicians. This protocol outlines a feasibility study to develop and evaluate a patient-informed secure messaging intervention paired with PDM-based risk stratification to enhance patient engagement in cognitive assessment in primary care settings. METHODS AND ANALYSIS This will be a non-randomised pilot study conducted across 12 single health system primary care clinics. The PDM will be applied to EHR data to identify patients aged ≥65 years who are at high risk for ADRD. High-risk patients will receive a co-designed secure message prior to and after upcoming primary care visits encouraging follow-up evaluation with a trained nurse, the Brain Health Navigator (BHN). The primary objectives are to: (1) determine the feasibility of applying the PDM to EHR data across 12 primary care clinics; (2) assess the feasibility of engaging patients identified as positive on the PDM through secure text messaging prior to a primary care encounter and (3) evaluate engagement with the BHN following secure text messaging. Study outcomes will assess the feasibility of implementing the PDM and secure messaging workflow, including identification of high-risk patients using the PDM, message delivery and patient engagement measured through message open rates, completion of cognitive concern questions and appointments scheduled with the BHN. Quantitative data will be analysed using descriptive statistics. ETHICS AND DISSEMINATION This study was deemed exempt as part of enhanced patient care. The findings will be disseminated through peer-reviewed publications, professional conferences, health system reports and public-facing communications. TRIAL REGISTRATION NUMBER NCT07016178.