FHLIP CONFERENCE

People, rights, and public voice

• Person-Centred AI Design and Co-Design
  Focuses on how AI can be designed with patients, caregivers, clinicians, and communities to ensure technologies reflect real human needs and values.
• Patient and Public Involvement in AI Evaluation
  Explores how patients and the public can meaningfully contribute to assessing the safety, usefulness, and acceptability of AI in health and social systems.
• Equity, Accessibility, and Bias in AI
  Examines how AI may reduce or worsen disparities, and how inclusive design and evaluation can support fairer outcomes across diverse populations.
• Indigenous Data Sovereignty and Indigenous-Led AI Governance
  Considers how AI development and deployment should respect Indigenous rights, knowledge systems, governance models, and control over data.

Governance, evidence, and infrastructure

• Privacy, Consent, and Trust in the Age of AI
  Addresses ethical and legal questions around personal data use, informed consent, confidentiality, and public trust in AI-enabled systems.
• Cybersecurity and Safe Health Data Stewardship
  Investigates how organizations can protect sensitive data and maintain secure, responsible data environments for AI applications.
• Data Quality, Interoperability, and Digital Infrastructure
  Looks at the foundational role of high-quality, representative, and shareable data systems in supporting safe and effective AI use.
• Transparency, Explainability, and Communicating AI Use
  Explores how AI outputs and decision processes can be made more understandable to clinicians, patients, leaders, and the public.
• Evidence Generation, Validation, and Post-Implementation Monitoring
  Focuses on how AI tools should be tested, validated, monitored, and re-evaluated across their lifecycle in real-world settings.
• Governance, Regulation, and Accountability for AI
  Examines the policies, standards, regulatory models, and accountability structures needed to ensure responsible AI adoption.
• Generative AI and Large Language Models in Health and Public Systems
  Considers the opportunities and risks of tools such as chatbots, documentation assistants, and language models in care, education, and administration.

Care delivery, public health, and trust

• Treatment Personalization and Shared Decision-Making
  Investigates how AI can support more tailored care while preserving patient autonomy, clinician judgment, and collaborative decision-making.
• Remote Monitoring, Virtual Care, and Community-Based AI
  Examines the role of AI in extending care beyond traditional settings through remote monitoring, home care, and digitally enabled community services.
• AI in Public Health, Prevention, and Health System Planning
  Focuses on how AI can support surveillance, prevention, planning, resource allocation, and broader system-level decision-making.
• Misinformation, Digital Health Literacy, and Public Trust
  Explores how societies can address AI-driven misinformation and strengthen the capacity of people to critically engage with health information.

Workforce, implementation, and sustainability

• AI Literacy, Education, and Workforce Readiness
  Considers the knowledge, skills, and competencies needed for students, faculty, clinicians, leaders, and the public to engage with AI responsibly.
• Human-AI Teaming and the Future of Professional Roles
  Examines how AI changes decision-making, professional identity, responsibility, and collaboration across sectors.
• Implementation Science and Scaling AI Beyond Pilots
  Focuses on the practical, organizational, and policy factors that influence whether AI moves successfully from experimentation to sustainable use.
• Environmental Sustainability and the Societal Cost of AI
  Explores the energy, infrastructure, and broader societal impacts of AI, and what responsible innovation should mean in that context.
• Advocacy, Justice, and Human-Centred AI Policy
  Connects the conference theme directly to the role of advocacy in shaping AI policy toward human rights, justice, inclusion, and public benefit.

Submission Instructions

Please ensure that your submission aligns with at least one conference theme and clearly identifies the action, policy change, implementation decision, governance approach, or practice improvement that your work supports.

Key Dates and Peer Review

NOTES:

• Each submission will undergo peer review by an expert panel. Notifications of acceptance will be sent by October 31, 2026.
• If your paper or extended abstract is selected for presentation, you must confirm your participation in the conference within one week of notification and submit the final revised version by November 30, 2026.
• Accepted submissions will only be included in the conference program and considered for publication if at least one author confirms attendance and presents the work at the conference.
• Submissions not selected for oral presentation may be offered an opportunity to present as posters.

FHLIP 2027, themed “From Algorithms to Advocacy: AI for a Human-Centred World,” focuses on advancing responsible, equitable, and human-centred uses of artificial intelligence in healthcare.

Submissions are invited from researchers, practitioners, policymakers, clinicians, health leaders, designers, technologists, patient advocates, students, and other contributors working at the intersection of AI, health leadership, informatics, and policy.

Submitted papers and extended abstracts will undergo peer review. Submissions should align with the conference themes and translate research findings, implementation experience, policy analysis, technical work, or practice-based learning into clear recommendations for action.

Selected papers may have the opportunity to submit revised versions for consideration in the peer-reviewed journal Studies in Health Technology and Informatics.

Call Overview

Artificial intelligence is rapidly entering healthcare. It can summarize, predict, triage, personalize, automate, and guide. But technical capability is not enough.

AI must be connected to the real pressures facing healthcare: limited attention, scarce resources, workforce strain, rising costs, uneven access, fragmented data, and growing public expectations. Administrative burden is consuming clinician time that should be spent with patients. Primary care systems must meet new expectations for accessibility, continuity, team-based care, and formal patient attachment while operating under financial and workforce constraints.

As AI moves from pilots into operational environments, a critical gap remains: translating algorithms, evidence, and insights into practice, policy, and advocacy that meaningfully improve health outcomes.

The central question is: How do we turn algorithms into advocacy for better health systems?

The FHLIP 2027 Conference invites researchers, practitioners, policymakers, clinicians, health leaders, designers, technologists, patient advocates, community leaders, students, and trainees to submit work that explores the journey from algorithms to advocacy – how knowledge about AI is generated, interpreted, communicated, and applied to shape decisions, services, and systems that are fair, effective, and people-centred.

Purpose of the Conference

FHLIP 2027 aims to:

• Advance knowledge translation between AI research, clinical practice, health system operations, and policy.
• Share practical insights that inform real-world decision-making and care delivery.
• Bridge disciplinary, professional, and sectoral boundaries in healthcare.
• Support responsible adoption of AI through shared learning, dialogue, and evidence-based advocacy.

Papers and extended abstracts should go beyond describing AI systems to demonstrate how insights are operationalized, communicated, or scaled to influence outcomes for patients, providers, organizations, and populations.

Submissions are invited across, but are not limited to, the following themes:

From Algorithms to Health Outcomes

• How is knowledge about AI performance translated into improved patient outcomes and health system value?
• What evidence has influenced clinical decisions, care redesign, or policy shifts?
• How do we measure whether AI is actually making care better, not just different?

Clinical Adoption and Workflow Integration

• How are AI insights communicated to support clinical judgment and accountability?
• What knowledge is required for clinicians to effectively oversee AI-supported decisions?
• How can AI reduce administrative burden rather than add to it?
• How can AI be incorporated into practice without increasing fragmentation or cognitive load?

Access, Equity, and Community-Centred Care

• How are insights about bias, access, or harm translated into system design or clinical practice?
• What approaches enable patients and communities to shape AI decisions that affect them?
• How can AI improve access to care, especially for underserved populations?
• How can AI support patient rostering, attachment, and continuity requirements?

Financing, Procurement, and Sustainability

• How should AI-enabled care be financed, procured, evaluated, and sustained?
• What payment and reimbursement models support responsible AI adoption?
• How do we build business cases that balance innovation with affordability and equity?

Governance, Policy, and Regulation

• How are ethical principles translated into governance frameworks, controls, and everyday workflows?
• What practical tools help health organizations apply responsible AI guidance?
• How should policymakers, regulators, and health leaders share accountability?

Transparency, Explainability, and Trust

• How can complex AI decisions be explained in ways that are meaningful to clinicians and patients?
• What knowledge translation practices support trust, contestability, and informed consent?
• How do we make AI legible to those who must use it or live with its consequences?

Agentic AI with Guardrails

• How is knowledge about risk, autonomy, and system behaviour translated into clinical guardrails?
• What lessons are emerging from real-world use of AI agents in healthcare settings?
• When should AI act independently, and when must humans remain in the loop?

Patient-Facing AI and Self-Management

• How can patients use AI to achieve their own health goals?
• What knowledge translation is needed to support health literacy and shared decision-making?
• How do we design AI that empowers rather than replaces human agency?

Population Health, Prevention, and Public Health AI

• How can AI support earlier intervention, risk stratification, and prevention?
• What knowledge about population patterns should inform resource allocation and program design?
• How do we translate predictive insights into equitable public health action?

Digital Foundations: Data Infrastructure, Interoperability, and Model Monitoring

• How can healthcare organizations translate data quality, interoperability, and model performance requirements into operational practice?
• What infrastructure, standards, and informatics capabilities are needed to safely deploy and monitor AI at scale?
• How do we build the digital foundations—data pipelines, integration, governance, and monitoring—not just the algorithms?

Types of Submissions Welcome

All submissions should include an advocacy angle. They should argue for action, change, adoption, adaptation, disinvestment, or a different path forward. We want work that translates findings into clear recommendations: here is what we should do, or here is what we should stop doing.

Submissions Welcome

We welcome:

• Research papers, including empirical studies, scoping reviews, realist reviews, and systematic reviews with clear recommendations for action.
• Case studies demonstrating what worked, what failed, and what should happen next.
• Policy analyses arguing for regulatory action, governance reform, or resource reallocation.
• Guidelines, frameworks, and decision-making criteria for AI adoption, governance, or evaluation.
• Comparative analyses, including jurisdictional comparisons, international benchmarking, gap analyses, or vendor/model comparisons.
• Implementation reports making the case for spreading, adapting, or abandoning an approach.
• Quality improvement projects advocating for scale, redesign, or disinvestment.
• Practice-based reflections from frontline clinicians, community organizations, or Indigenous health contexts that argue for operational changes.
• Technical reports on data quality, interoperability, or model monitoring that call for standards, investment, or vendor accountability.
• Workshops and panels designed to build consensus for specific actions.
• Student papers and emerging scholar perspectives.

Submissions should clearly articulate what was learned, how it was translated, and what action you are advocating for.

Methodological Expectations

We value practical learning over academic novelty, but all submissions must meet basic standards of methodological credibility:

We will not publish work that makes unsupported claims, ignores contradictory evidence, or conflates correlation with causation without acknowledgment. We welcome honest accounts of what happened, why it matters, and what should happen next, even when the methods are observational, the sample is small, or the evidence is experiential.

Evaluation Criteria

Submissions will be evaluated on the following:

Audience Relevance

FHLIP 2027 is a multidisciplinary forum for those working at the intersection of AI, health leadership, informatics, and policy. Submissions should be accessible and relevant to a diverse audience, including policymakers, public servants, health system leaders, clinicians, informaticians, researchers, patient advocates, community leaders, students, trainees, and emerging scholars.

Closing Invitation

Thank you for considering the FHLIP Conference 2027 as a platform to showcase the policy, leadership, implementation, and advocacy implications of your research in health informatics, digital health, and AI. FHLIP 2027 invites you to help turn algorithms into advocacy—and advocacy into healthcare that is more accessible, sustainable, equitable, and human-centred.

Questions may be directed to: ihpme.fhlip.conference@utoronto.ca

We appreciate your valuable contributions and look forward to reviewing your submission.