Studies in health technology and informatics最新文献

Primary care clinics face a persistent administrative burden, fragmented workflows, and limited interoperability. We present a clinic-level Four-Pillar Vision-Empowered, Connected, Streamlined, Supported-as future-state characteristics of a modern clinic. Using a people-process-technology review of current workflows/roles and a 2024 physician survey (>1,500 respondents) on usability, burden, and value, we offered a Double Diamond approach via a maturity-model framework, a clinic readiness/assessment tool, and tailored playbooks for implementation. Gaps cluster across people (skills/confidence, change fatigue), process (manual referrals, inbox overload), and technology (limited EMR integration, siloed data); survey signals associate better usability with lower reported burnout and higher perceived value, guiding initial use cases (AI-enabled documentation, EMR-integrated eReferral/eConsult, automated recalls). This measured, system-wide pathway supports standards-aligned adoption and moves clinics from silos to synergy-toward interoperable, clinic-ready operations that deliver practical value for clinicians and patients.

Foodborne diseases remain a major cause of illness in Ghana, yet surveillance is fragmented across paper forms and disconnected systems, delaying outbreak detection and response. We propose a stakeholder co-designed, federated surveillance architecture. From 20 interviews with FDA, GHS, EHOs, labs, food FPPVs, PHRs, and IT experts, we derive requirements and designed an HL7 FHIR-enabled architecture with offline reporting, traceability, real-time alerts, and role-based governance to enable earlier outbreak prevention.

Ontario faces persistent diagnostic imaging (DI) challenges, which includes fragmented implementation of Artificial Intelligence (AI) solutions. The Canadian Association of Radiologists (CAR) has emphasized the need for freely available, modality-specific sandboxes to safely evaluate and refine AI tools prior to clinical use. In response, this paper proposes a provincially governed AI Sandbox for Diagnostic Imaging, designed to enable secure testing, validation, and scaling of AI innovations within a privacy-by-design framework. The AI Sandbox integrates six policy domains, such as clinical utility, ethics and privacy, technical design, governance, legal compliance, and sustainability, to guide responsible adoption. Supported by the Ontario Ministry of Health's commitment to trustworthy AI, this initiative aims to foster equitable, transparent, and system-wide innovation in diagnostic imaging.

There are many interoperability initiatives being undertaken in Canada, but there is a lack of data liquidity solutions that can ensure healthcare information is formatted, presented, and utilized by healthcare providers to deliver the best possible care. This scoping review discovers data liquidity solutions and reports specific health system-related outcomes that could potentially be replicated within the context of the Canadian healthcare system.

Adolescence is a pivotal stage, yet Canadian youth face rising mental health challenges within fragmented health, education, and community systems. Current digital tools and records often fail to meet their needs, creating delays and inequities. This study introduces a pragmatic, digitally enabled model that improves coordination and equity by leveraging existing infrastructure. Using a human-centered, multi-method approach, stakeholders shaped the design through interviews, thematic analysis, and a House of Quality framework. The model unifies trusted youth resources into a single-entry point, enables secure school-clinic referrals, and enhances provincial portals via lightweight interoperability. A novel Early Detection Layer complements these functions by integrating micro-screeners, referral-signal extraction, and an orchestrated triage pathway, enabling earlier identification of emerging concerns. Regional pilots prioritize youth consent, privacy, and equitable access. This approach provides a scalable, context-sensitive blueprint for building connected, proactive, and sustainable adolescent mental health ecosystems in federated health systems worldwide.

Canada's health system focuses mainly on acute care, leaving primary care without a standardized, self-administered intake for capturing prevention-related risks like background, lifestyle factors, environmental exposures and social determinants of health. The Personalized Assessment Tool (PAT) project aims to create a standardized intake that collects patient-generated risk information and reuses it across different healthcare settings. PAT Phase 1 examined 43,011 records and narrowed them down through screenings to identify 31 appropriate self-administered instruments across 15 health-related domains for universal intake and targeted follow-up. A dual-index framework evaluated the readiness and suitability of these tools, leading to three outcomes: some instruments were ready for immediate use, others needed to be adapted, and a few required Canadian validation before implementation. By establishing standardized processes for risk assessment, PAT aligns with Canada Health Infoway's vision and operationalizes the FHLIP 2026 theme, promoting personalized prevention in primary care.

Building on previous work, we posed a practical challenge: can we bridge the silo between social-media engagement and clinical care without importing platform risks? We present a minimal viable design for a Canadian primary care intervention that adapts familiar social media features and aligns them to Self-Determination Theory (SDT). Feeds support autonomy with user-controlled topics, pacing, and plain-language "why you saw this" rationales. Timelines support competence through short lessons, tiny practice steps, light data entry, and brief coach feedback. Groups support relatedness via moderated, goal-aligned peer exchanges. At population scale, a predictive service classifies patients as at-risk for diabetes within an 8-year horizon. Clinicians then enroll eligible patients by placing a preventive-care prescription in the electronic record. Clinicians receive a monthly patient population-level report that lists actionable cohorts, aggregate SDT signals, safety metrics, and equity views. The existing literature supports mapping of social-media features to health apps and several authors have successfully implemented these features in various clinical settings. To adapt these insights for the Canadian context, we consulted a variety of stakeholders (N=13) who validated and refined the design and produced measurable acceptance criteria for implementation. These included managing privacy and governance, misinformation controls, engagement ethics, platform independence, peer matching, prediction, and reporting. Next steps are architecture finalization, broader co-design, and a single-system feasibility pilot.

Artificial intelligence (AI) is increasingly integrated into clinical workflows worldwide, yet in Canada its adoption remains fragmented, unevenly distributed, and siloed within acute care and diagnostic imaging. To address the absence of a pan-Canadian view, Digital Health Canada's CHIEF Executive Forum convened the AI in Action Working Group, a national collaborative created to advance shared learning, interoperability, and responsible clinical adoption of AI. As Stage One of this initiative, the Working Group conducted the first structured environmental scan of clinical AI deployments across Canada to create a national baseline and inform future interoperable scaling. Using publicly available sources and a standardized taxonomy, 152 initiatives were identified across provinces and territories. For each entry, data elements (including venue, function, technology type, deployment stage, partnerships, and outcomes) were independently verified using a common coding framework. This taxonomy enabled semantic and analytic interoperability by allowing consistent comparison across jurisdictions and maturity levels. Four signals emerged: (1) workflow-embedded applications show the strongest adoption; (2) equity and interoperability gaps persist, with primary care, long-term care, community health, and Indigenous/remote settings underrepresented; (3) evidence reporting is minimal, hindering evaluative and organizational interoperability; and (4) large language models and robotics represent emerging clinical frontiers. This national scan demonstrates that Canada's clinical AI landscape is diverse but constrained by gaps in semantic, workflow, organizational, and evaluative interoperability. By establishing a shared national taxonomy and baseline dataset, this work creates foundational infrastructure to support coordinated, equitable, and interoperable adoption.

Ontario's health system holds one of the richest and most diverse datasets in the world, yet barriers to access and fragmented governance limit its potential to drive innovation and improve outcomes. This project explores how Ontario can responsibly navigate health data privatization to support research and private-sector collaboration while maintaining public trust. The project engages key stakeholders to identify enablers and risks in developing a unified framework for responsible data access. Grounded in the foundational principles of the Ontario Health Data Council, the work examines current data-sharing pathways, legal constraints under PHIPA, governance gaps, and comparative international models such as the UK's Trusted Research Environments and Finland's Findata authority. The project aims to produce actionable recommendations for a policy framework that balances innovation with ethical stewardship, enabling privacy-protected access to data that advances patient care, population health, and economic growth. Through this research, Ontario's health data can be responsibly unlocked to foster public-private partnerships, accelerate discovery, and strengthen its position as a leader in digital health and life sciences.