Alberta’s approach to clinical AI: evaluate, test, partner and scale

As the chief medical information officer at Health Shared Services (HSS), formerly part of Alberta Health Services (AHS), I represent over 10,000 physicians/prescribers that use our province-wide clinical information system called Connect Care.

The frequency and intensity of calls I hear to “give us AI tools now!” has been ever-increasing over the last few years, so I am intensely aware that my colleagues see AI as a crucial release valve for burnout, and a transformative new way to address the ever-growing volume of waiting patients who need our help.

I work as part of a large digital health team that serves over 130,000 Connect Care users across Alberta. With our provincial scope and standardized system, we have a unique opportunity to scale clinical AI solutions rapidly.

However, while we want to move quickly to give our clinical teams the AI tools they want, we recognize there are inherent risks. Herein lies the dynamic tension with AI – how do we move quickly, but still proactively mitigate the potential negative impacts at a provincial scale?
How do our governance, policy, procurement, and implementation processes keep up with the dizzying pace of AI development, in a rapidly shifting marketplace?

Strategic approach: We see AI as a key strategic enabler in our pursuit of the healthcare quintuple aim, but the benefits can only be realized safely if we first inventory and mitigate the inherent risks. To achieve this goal, we established an interprofessional AI Steering Committee which developed a framework of AI opportunities and risk management strategies. Using this framework, we have been refining our strategic approach to AI, which includes the following principles:

• Solution selection based on clinical value: We prioritize AI solutions that are vetted by clinicians and staff as having high value to their work in healthcare (such as reducing administrative burden, improving efficiency, reducing cost, readmissions, length of stay and/or mortality). We assign higher value to tools that help the widest scope of users (across client organizations, scopes of practice, and clinical specialties) and those tools that integrate directly into core clinical system workflows. Success metrics are defined upfront, aiming to verify and quantify the value that drove prioritization.
• Hybrid approach to AI solutions: We employ a hybrid (“build/buy”) AI solution approach, meaning for each need we evaluate capital and operating costs, solution fit, sustainability, workflow integration, and clinical utility when deciding whether to use internal resources and infrastructure to build our own solution, or to procure an externally developed one.
• Human-first approach: We design for AI that is human-centered, meaning understandable technology that works as a co-pilot, not a replacement, for humans. In this approach, AI refers to “augmented intelligence”, rather than artificial intelligence.
• Proactively mitigate risks: Our review of AI-associated risks was conducted by an internal interprofessional team of digital health, clinical, risk management, legal, and privacy experts, with input from industry third parties. Categories of risk we recognize and manage include quality/safety (AI accuracy, hallucinations, omissions, unintended consequences), ethics (bias, discrimination, fairness, surveillance), HR (talent, skillsets, job displacement), technical infrastructure, data security/sovereignty, legal and privacy (legislative requirements, contracting, liability, intellectual property), financial (funding, sustainability, cost of cyberattacks), and environmental (climate change). A key insight we have reached is that not every AI tool carries the same risks. By stratifying the level of risk among different proposed AI solutions, we can identify those that can proceed more quickly, provided appropriate monitoring is in place.
• “Driver’s License” for AI: Users are required to receive training before using AI tools. They must have a basic understanding of how AI works, know the importance of data privacy and patient consent, and be aware that they remain responsible and accountable for all aspects of patient care. Users are required to be the “human in the loop”, ensuring all content and proposed decisions are verified and free of inappropriate inclusions, exclusions, and bias. Simultaneously, our Digital Health leadership and team members are rapidly upskilling in AI to ensure we make optimal governance and development decisions as we work in partnership with our clinical teams.
• Accelerated cadence: To keep pace with the swiftly evolving AI landscape, we are pivoting to a rapid cycle execution approach, starting small and aiming to reach testing in real clinical practice as soon as it is safe and feasible. We start with small, trusted pilot groups for evaluation, iteratively adjusting solutions based on feedback, and gradually expanding scope of implementation as value is defined and risks are mitigated.
• Learning Health System approach: We build evaluation into each of our implementation plans, feeding the results into an iterative process that informs continuous improvement. This includes continual refinement of not only our solutions, but also our processes and overall strategic approach.

Our progress so far: Our earliest AI work started in 2021 when our Intelligent Automation group began using technologies such as process automation to reduce staff burden from repetitive manual tasks. Over the past four years, this team has deployed over 50 solutions that have saved over 275 years of staff time, or over $18M in staff costs.

Examples include automation of repetitive HR and finance back-office processes (job offers, matching staff to postings, purchase orders) and IT processes (desktop software deployment, license optimization). Clinically facing solutions are also in development using AI (such as automated processing of faxed patient referrals).

In 2024 we partnered with physician innovators to begin rollout of an internally designed and built AI scribe product. Today, hundreds of physicians across multiple specialties have signed on to use the solution, with further expansion underway.

Also in 2024, our Data and Analytics team strengthened our AI solution capacity by providing analysts with foundational AI models and tools embedded within Alberta’s enterprise data warehouse.

On the horizon for 2026 is a conversational analytics application, initially focused on Emergency Department records, that will enable business users to query data in plain language and receive context-aware insights and data visualization, significantly reducing traditional analytics turnaround times.

In late 2025, we began working with clinicians to select key in-system AI tools that are low effort/cost to build and have high value for improving clinical efficiency. These tools are being deployed to small pilot groups and by the time you read this, will include generative AI solutions that summarize the care of inpatients (for clinician handover, discharge summaries) as well as outpatients (for more efficient and complete clinic visits).

Another priority area in 2026 is partnerships. The best AI solutions are not developed in a vacuum, but are the product of collaboration between clinicians, innovators, data scientists, analysts, health organizations, universities, researchers, companies, and government. We are currently exploring a model that proposes to integrate these groups, aiming to enable healthcare teams to prioritize the highest value innovations for development with cross-sector expertise.