Turbocharging a Car Won't Help if the Engine is Faulty: Addressing Fundamental Issues in Healthcare Before Implementing AI

Imagine you have an old, run-down car with a faulty engine that's leaking oil. But instead of addressing the issue at hand, you turbocharge the engine, hoping to boost its performance. 

Does the car perform better? No, in fact, the issues are exacerbated. The problem is with the engine itself; it needs to be fixed or replaced before you can even think about turbocharging it. 

There is a similar situation happening within healthcare.

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Enthusiasm for implementing advanced technologies like artificial intelligence (AI) is on the rise, and there's no doubt that these technologies hold immense potential. 

However, the efficacy of these innovative technologies depends intrinsically on the effectiveness of the foundation—the engine of healthcare.

But what is the foundation of healthcare? It is the nuanced dynamics between linked clinical and operational workflows and processes that are the bedrock on which healthcare delivery lies. 

It is the structure that healthcare professionals follow in order to provide effective and efficient care to patients. These workflows and processes encompass all aspects of patient care, from patient admission and diagnosis to treatment and discharge. They are the backbone of healthcare, designed to help reduce fragmentation and medical errors, enhance patient safety, improve communication among healthcare providers, and foster efficiency.

So before we can successfully incorporate AI or similar advancements, which are often claimed to revolutionise healthcare, we need to tackle the primary issues within the underlying processes of healthcare delivery. 

Ignoring this step could not only lead to additional problems, but also introduce another layer of complexity to an already faulty engine, making subsequent corrections even more challenging.

This is because the effectiveness of turbocharging directly hinges on the quality of the underlying engine.

If the engine—the clinical and operational workflows—is inefficient or flawed, attempting to turbocharge with complexity will cause problems. At best, it will automate and perpetuate the inefficiencies and flaws. At worst, it will cause the engine to fail altogether, which, in the case of healthcare, creates clinical and patient safety risks.

Implementing advanced technology, such as complex machine learning models and AI automation, has the potential to significantly improve healthcare operations and outcomes. But it must be complemented by an equally efficient operational and clinical workflow that is supported by the right technological solutions.

Only when the engine is repaired can you expect to reap the benefits of a turbo; likewise, only when the clinical processes are robust can you expect to reap the benefits of complex automation.

Once the foundation is strong, technologies like AI can come into play to augment said workflows by, for example, automating repetitive tasks, scheduling appointments based on clinically triaged urgency, communicating outcomes, and monitoring patient progress.

But the foundation must be prioritised before anything else. We need to optimise and improve the operational and clinical workflows to build a healthcare system that is efficient, effective, and ready to fully harness the power of new technology. 

That’s the key to truly making a difference in healthcare delivery.