Is Artificial Intelligence in Healthcare Delivering Its Promise?

The healthcare sector is under pressure. So much pressure that there is no room in the operational process to test and implement innovative technologies or smart ideas. That’s while logistics and facilities would benefit a lot from a little process improvement.

Artificial intelligence is not the end of human anxiety

We are all a little tired in healthcare. Well-meaning pilots are often well-intentioned, especially in the field of artificial intelligence, but are realized by pushing technology or as a stand-alone project. Operating outside the context of actual operations, up to 40% of all initiatives fail. When looking at solutions that really make problems disappear into the background, the thinking should be based on the value it brings to the target group.

We are convinced, and we also have proven evidence, that artificial intelligence, also known as artificial intelligence, has real added value for employees in the healthcare sector. And we’re not talking about replacing people with machines, because that’s totally out of the question. Healthcare workers have creativity, compassion, and empathy, something a robot will never be able to replace.

Healthcare logistics and process optimization are areas where AI can be used very effectively. Routine tasks for planners and logistics personnel can be eliminated, freeing up more time for other actions.

Practical examples

During the webinar on April 14, Rob van Zoest will take you into the world of artificial intelligence in healthcare. And not a heavy theory, but a Web Talk Where real life examples are discussed. Does artificial intelligence live up to its promise? Or is it a matter of endless trial and error? We will discuss the first successful applications of AI in healthcare, with our current focus on the following two main topics:

1. Improving machines that produce Baxter rollers. How can you optimally set up such a device? How can you increase the current rate of improvement and how can you increase the percentage of automation?
2. Optimizing the supply chain in a company that supplies medical devices and medicines. How can we anticipate demand based on historical data or even comparison to other products and trends, through deep learning? How do we make sure that the AI ​​model is built in a way that provides usable and understandable outputs to the end user?

AI is preferred over machine learning in these cases, because you can define parameters with AI. For example, by entering parameters in an AI model (inability to offer/lots of stock/expected crowds), you prevent shortages and surpluses.

What is the next step in artificial intelligence?

In addition to healthcare logistics and process improvement, AI could also offer a solution to healthcare understaffing in the coming years. Models can be developed for intelligent scheduling, resulting in a better match between (intensity) of care request and caregiver. This will allow healthcare departments or organizations to create space at the staffing level, providing more breathing space. Another opportunity for healthcare is the improvement or regulation of (semi) planable environments, such as bed occupancy, operating room occupancy, and MRI occupancy.

Artificial intelligence comes at its own best. By addressing a small, specific problem, a model can be used in a very purposeful way. This allows you to create added value when needed, and is much more efficient and feasible than optimizing a very broad process with AI.

By solving a particular problem, people see the added value in their work process and then the accelerator is expected to come to the workplace on its own. At a certain point, people will start to think more broadly about more or more different applications of AI, so that point-solving can act as a catalyst.

Why a webinar on artificial intelligence in healthcare?

It is imperative that healthcare organizations embrace artificial intelligence. We do not have time to quietly develop and implement, but it is an urgent matter. If competitors or partners have already taken the lead in the future, it will not benefit the chain of sponsorship organization cooperation.

It is no longer an obscure theoretical formula, but rather a practical application. A way to move forward in the process and build on the successes. We are now really at a point where we can offer certainty. Security in the process and safety in the workplace.

Not only is the data platform performed randomly or the data is analyzed somewhere, but there is a concrete solution to a specific problem in the workplace. You want to take advantage of the history that already exists; Is history good enough to train an AI model? Only then will we take action if all the signals are green and the implementation appears to be successful.

Best of all, this skeptical thought (does this work for us?) is easy to persuade. We can create a ‘digital twin’, where you can experience exactly how you are adding value within your organization via simulation.

Registration for the webinar on April 14

Do you want to know how you can improve your healthcare organization, healthcare department or logistics process? Come back to your workplace with clear ideas and tangible solutions. Apply now In this webinar on April 14th.