Agenda - Day 1

Gain insight into today’s sessions so you can get the most out of your conference experience.

What does it take to not just manage data, but turn it into a strategic advantage for AI innovation and policy leadership? At VHA Home HealthCare, significant investments in data governance and infrastructure have created the richest and most accurate homecare dataset in Canada—over 1 million high-integrity data points. Walk away with strategic insights and technical know how to:

• Develop your own data cleansing capabilities that deliver quality data
• Build predictive models for hospital readmission risk in homecare patients
• Experiment with AI tools like chatbots and language translation with confidence
• Anticipate and model patterns of absenteeism to inform operational planning

Discover how quality date and robust governance isn’t just compliance—it’s the launchpad for innovation.

The first wave of AI in healthcare was defined by experimentation and proof-of-concept pilots. Today, we’re entering a new era — AI 2.0 — where the focus is shifting from isolated innovation to sustainable, integrated clinical tools that enhance outcomes, reduce burden, and scale impact. Source practical tips to:

• Transition from pilot projects to enterprise-scale implementation.
• Align AI development with clinical governance, safety, and usability standards.
• Integrate AI tools into EHRs and care pathways to reduce friction and enhance adoption.
• Overcome pilot fatigue, ensure clinical buy-in, and achieve measurable improvements.

Improve patient care by positioning AI 2.0 not as a future vision, but as a practical engine for change today.

As healthcare organizations strive to become more data-driven, modern data and AI platforms are being used to dismantle silos, integrate multimodal patient data, and enforce stringent privacy controls. These platforms are not just about storage — they are about reducing latency, enabling real-time insights, and activating AI/ML to support better decisions across clinical and operational functions. Develop a blueprint to:

• Integrate multimodal patient data, including imaging, text, and structured clinical data, into a unified platform.
• Deploy GenAI, large language models, and machine learning workloads to extract timely insights.
• Operationalize AI-driven insights to improve patient experience, outcomes, and organizational efficiency.

Increase your team’s ability to transform data into intelligence — and intelligence into impact.

As demand for data-driven decision-making grows, so does the need for scalable, reliable data science infrastructure. Evolve your data science teams, tooling, and pipelines to deliver timely insights that support operational and strategic goals. Source your plan of action by:

• Communicating more effectively with data science teams to maximize the impact of models and insights on business strategy.
• Prioritizing innovation and quality over manual processes.
• Collaborating with data scientists to create a seamless and efficient pipeline that drives success.

Optimize the path from insight to sustained action with data science pipelines that reduce friction from experimentation to deployment.

Canada doesn’t have a single health system — it has twelve. Each province and territory governs its own healthcare delivery, creating structural fragmentation that challenges national coordination, interoperability, and innovation. Now is the time to invest in provincial and regional data strategies that respect jurisdictional autonomy while enabling pan- Canadian insight, performance measurement, and AI readiness. Master the success factors to:

• Develop decentralized governance that enables localized data strategies.
• Align data priorities across ministries, health authorities, and care providers.
• Enable interoperability and federated data models without centralization.
• Implement data leadership strategies that drive national digital health and AI initiatives.
• Ebed robust privacy safeguards from design to deployment.

Advance patient outcomes with a strategy that bridges Canada’s health data divide.

GenAI is accelerating the need for data unification within organizations. This innovative process unites various data-driven functions — such as enterprise integration, reporting, AI development, and data science — around a shared core of information, despite differing objectives. Central to this strategy is the implementation of a Logical Data Fabric, which is critical for transcending simple centralization. Walk away with an action plan on:

• Enabling enterprise-wide consistent data management practices that are standardized yet flexible enough to meet the specific needs of different departments.
• Fostering cross-functional collaboration by providing a common platform where data from various business functions can be integrated and analyzed cohesively.
• Scaling effectively to accommodate increasing data volumes and complexity, ensuring that data management remains robust, agile, and capable of supporting both current and future business demands.

Perfect your organization’s ability to unify data to enable smarter decisions and greater business impact.

Review the key solutions and takeaways from today’s sessions. Source a summary of action points to implement in your work. Discuss tomorrow’s highlights!