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  • Rod Morgan, LSSMBB, Head of Faculty, RPM-Academy

The Convergence of AI and Healthcare: A Paradigm Shift in Canada's Healthcare System

Updated: Jul 4, 2023


Welcome to the blog where we explore the fascinating interplay between rapidly emerging artificial intelligence (AI) and our struggling healthcare system in Canada. In this article, we delve into the concept of the Kuhn cycle(1.) and how it relates to the need for a paradigm shift in the design and delivery of healthcare. (Image source: https://www.simplypsychology.org/kuhn-paradigm.html)


Capacity and Capability

I recall during one of our many memorable chats with Dr. Mikel J. Harry, principal architect of Six Sigma and founder of Six Sigma Academy. On the topic of continuous improvement, Mikel told me, “Rod… You can’t Six Sigma your way out of a bad strategy!” And that stuck with me. I have borrowed upon and reused that statement many times over the years and lately even more often when sharing my thoughts and opinions on Canada’s ailing healthcare system.


It comes down to capability. If the healthcare system was a “truck” that was designed to go 100 kilometres per hour (43.5 miles per hour for my American friends) and we observed that over a period of time, it was only travelling at an average of 85 kilometres per hour, we’d typically launch an improvement project, identify the constraints impeding the progress of the vehicle, make improvements, and… voila! The truck is now travelling at an average of 95 kilometres per hour… it is doing what it is capable of doing. What it was designed to do.


But… what happens if, over time, the target for performance has changed? That the same truck now needs to travel at 150 kilometres per hour? And yet, it was never designed to be able to do that. Enter Dr. Mike… “You can’t (Lean) Six Sigma your way out of a bad model, Morgan!” No amount of “kaizen” and continuous improvement is going to solve Canada’s healthcare crisis. The issue is with the design and processes that have evolved to enable that obsolete model. As such, it is the reimagining and redesigning of the healthcare system that first must be addressed so that a truly capable system may emerge to serve Canadians. After all, “Universal Healthcare” means nothing if Canadians cannot access it “just in time”.


It is with that in mind that we respectfully discuss the challenges posed by model drift, model crisis, and model revolution, ultimately advocating for the adoption of a "next available" model to revolutionize the Canadian healthcare system.


The Kuhn Cycle and Paradigm Shifts

Thomas Samuel Kuhn's seminal work, "The Structure of Scientific Revolutions," introduced the concept of the Kuhn cycle, which describes the process by which scientific disciplines undergo radical shifts in thinking. According to Kuhn, scientific progress is not a smooth and continuous evolution but rather occurs through periods of normal science followed by revolutionary shifts in accepted paradigms.


Similar to the scientific community, the healthcare industry also experiences periods of stability and crisis. We can identify these patterns within the context of healthcare systems, particularly in Canada. The emergence and rapid evolution of artificial intelligence, represented here by ChatGBT (see credits below), highlight the potential for paradigm shifts in healthcare.


Model Drift: A Cause for Concern

Model drift refers to the gradual deviation of a model's predictions from the actual outcomes it aims to predict. In healthcare, model drift can manifest as outdated treatment guidelines, inadequate resource allocation, and limited or adverse patient outcomes. Despite the best intentions, the Canadian healthcare system has struggled to keep up with the evolving needs of its population, leading to inefficiencies and disparities.


Model Crisis: A Catalyst for Change

Model crisis occurs when the existing frameworks and approaches fail to address new challenges effectively. In the context of Canadian healthcare, long wait times, accessibility issues, and unsustainable costs are examples of a model crisis. These challenges demand a transformative response to safeguard the well-being of Canadians.


Model Revolution: The Need for Innovation

To address the model crisis, a model revolution becomes necessary. This revolution involves reimagining the healthcare system with a fresh perspective and embracing innovative solutions. Artificial intelligence presents an unprecedented opportunity to redefine healthcare by augmenting decision-making, streamlining processes, and improving patient outcomes.


A key consideration for a new model for healthcare has to be the elimination of batching which hinders efficiency, patient flow, and generates huge waste in terms of time, resources, and associated cost. One common and highly visible example of this detrimental practice is where patients or healthcare providers are grouped together together based on rigid scheduling patterns… Patients batched to care-providers and care-providers assigned (batched) to exam rooms. This practice is anathema to flow.


To eliminate batching, healthcare systems must embrace a more agile and patient-centered approach, moving away from rigid scheduling patterns. This aligns with the concept of the "next available" model, which emphasizes flexibility and responsiveness to patient needs: “Next available care-provider takes next available patient to next available room.” In Lean, this approach would be described as a "pull" versus the "push" that is typical of the existing model.


The Next Available Model: A Path Forward

In light of the paradigm shift occurring at the intersection of AI and healthcare, the adoption of a "next available" model is one of the imperatives for the redesign of the Canadian healthcare system. This model entails harnessing the power of existing and emerging technologies to enhance efficiency, optimize resource allocation, and prioritize patient-centric care.


The next available model may encompass several key elements, including:

  • Intelligent Decision Support Systems: AI-powered tools that provide healthcare professionals with real-time insights, evidence-based recommendations, and personalized treatment options, ultimately improving diagnostic accuracy and treatment outcomes.

  • Telehealth and Remote Monitoring: Leveraging telecommunication technologies to facilitate virtual consultations, remote patient monitoring, and telemedicine, enabling better access to healthcare services, especially for rural and underserved communities.

  • Data-Driven Healthcare: Harnessing big data analytics and machine learning algorithms to extract meaningful insights from vast amounts of healthcare data, enabling proactive disease prevention, early detection, and personalized interventions.

  • Collaborative Ecosystem: Fostering collaborations between healthcare providers, researchers, technology developers, and policymakers to facilitate the seamless integration of AI-driven solutions, ensuring ethical considerations, privacy, and security are upheld.

In Conclusion

As we witness the convergence of artificial intelligence and healthcare, we find ourselves on the precipice of a paradigm shift in the Canadian healthcare system. By embracing the "next available" model, we can harness the power of AI to overcome the challenges of model drift, crisis, and revolutionize healthcare delivery.


Article credits: Rod Morgan, LSSMBB, Head of Faculty, RPM-Academy in collaboration with Open AI ChatGBT.


1. To learn more about the Kuhn Model, visit: https://plato.stanford.edu/entries/thomas-kuhn/

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