Automation and Artificial Intelligence (AI) are very closely related, but they aren’t always the same. Automation is the process of outsourcing tasks to technology with the goal of reducing the need for human intervention.
For example, when a patient signs in for their doctor's appointment, an automated system can log them into the electronic medical record without requiring a human to manually enter their information. This is a simple automation process that doesn't necessarily involve ‘artificial intelligence’.
AI, on the other hand, is about making technology capable of performing increasingly human-like tasks. AI systems can learn from data to make intelligent decisions. For instance, with AI, we can now decipher ancient languages, a task that requires an incredible level of intelligence. But again, this doesn’t necessarily fit the definition of automation.
The real power lies in combining Automation and AI - known as Intelligent Automation (IA). This is all about leveraging increasingly capable technology to outsource increasingly complex tasks with the ultimate goal of reducing the need for human intervention. We’re still learning what is possible with Intelligent Automation, and the capabilities are growing by the day, but there are many risks we need to be careful of. The rest of this post explores what we’ve been learning about IA in the healthcare sector.
How could Intelligent Automation work in Healthcare?
We argue that Intelligent Automation has the potential to revolutionise healthcare in two main ways.
First, it expands the scope of what can be automated. Until now, traditional automation has been limited to highly logical processes - if data is entered here, send it there; if the data is this value, do this action. This type of automation is great for manual tasks like data entry, triggering template emails, or scheduling appointments, but not for anything that requires a more complex thought process.
With AI, we can start to automate even the most complex tasks. For example, diagnosing a medical condition based on a picture of cells is something that previously only a highly trained medical person could do. But with enough data, we can teach machines to do this too (and with potentially higher accuracy). A recent example showed how AI can diagnose critical newborn conditions faster and more efficiently than humans.
Second, Intelligent Automation makes technology increasingly adaptable to work around us, saving time and effort. With simple automation technology, a doctor might have to input very specific conditions or codes into a system to trigger an automated step to alert another department. Now, technology could, in theory, transcribe a patient meeting, intelligently understand the situation, and alert the department without the need for any additional steps from the doctor.
As AI develops, we will likely see increasingly complex Intelligent Automation workflows that incorporate both AI and standard automations. The goal is to take the heat off workers and make it easier to interact with technology, ultimately improving patient outcomes and reducing the burden on healthcare systems.
It’s not all sunshine and rainbows
While the potential of Intelligent Automation in healthcare is exciting, it's important to be aware of the risks and challenges that come with it.
Hallucinations: AI can get things wrong, and the impacts of incorrect decisions are amplified when downstream actions in a process depend on them. Instead of relying solely on AI, we should use 'human-in-the-loop' systems, where AI outputs are checked by humans before proceeding. This approach can still significantly reduce the time taken to carry out tasks while ensuring accuracy.
Bias: AI learns from historic data, and where there is historic data, there is often bias. If we're not careful, AI systems can perpetuate or even amplify these biases, leading to worse outcomes for certain individuals and marginalised groups. It's crucial that we actively work to identify and mitigate these biases when developing and deploying AI systems in healthcare.
Explainability: When errors occur, we should be able to understand why. However, AI systems are often 'black boxes', meaning it's difficult to interpret how they arrive at their decisions. This lack of explainability can be a major issue in healthcare, where understanding the reasoning behind a decision is often critical. Significant work is being done in this area, but we're not there yet.
Privacy and Security: AI systems require data, but where there is data, there need to be privacy and security considerations. This is especially important when it comes to sensitive health records. As we develop Intelligent Automation systems for healthcare, we need to put robust data protection measures in place and ensure compliance with relevant regulations.
While these challenges are significant, they're not insurmountable. By being aware of these risks and actively working to address them, we can harness the power of Intelligent Automation in healthcare in a responsible and beneficial way.
What’s next?
The risks of Intelligent Automation in healthcare are significant, and we must proceed thoughtfully and responsibly. In the near future, we can expect experimentation and pilot projects with 'human-in-the-loop' systems. Widespread adoption will require addressing challenges like bias, explainability, privacy, and security, as well as collaboration between stakeholders. The journey is complex, but the potential for a more efficient, effective, and equitable healthcare system is worth pursuing.
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