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Successful AI Implementation in Healthcare

Adoption, System Integration

What defines a successful artificial intelligence (AI) implementation? Is it the extraordinary innovation that powers it? The remarkable promises that sit behind it? The answer is ‘yes’ and ‘all of the above’, but only if it delivers on the promises and the potential of the technology to improve medical practitioner wellbeing, enhance performance and transform patient care. A great AI implementation isn’t solely defined by its technology prowess or the name that sits behind it but rather by the on-the-ground feedback from those who use it. 

There are AI implementations in healthcare today that should be enough for anyone to just pause for thought, and to feel quite a bit better about what the future may hold. There are equally inventive platforms and solutions that are evolving within healthcare and introducing better ways of working – reducing burnout and changing physician and patient life for the better. However, technology is only as good as the way it’s implemented and the value it delivers over both the long and the short term. 

A Roadmap to AI Implementation

AI implementation isn’t plug-and-play, particularly in healthcare. It requires foresight, careful consideration and a fairly lengthy checklist. Aidoc, developer of the always-on and intelligent AI platform for radiology departments, has also developed a comprehensive roadmap that outlines precisely what factors have to be addressed in any AI implementation to ensure that it is a success.  

The first step is to find the right technology partner. Not all AI is created equal and an AI implementation in healthcare is very likely to be a long-term partnership which means that a trusted partner with a solid track record is the first, best step. You want to work with a vendor that has a good reputation and a product that is capable of evolving with your healthcare system over time. In radiology, AI needs to be capable of helping the radiologist make faster decisions and pull out critical information at speed. You want an AI solution that offers a significant breadth and depth of pathologies and modalities, that has its own clearly defined roadmap, and that has a future-proof strategy. This will directly influence its relevance. The future-forward capabilities of any AI vendor are a critical factor, especially for larger healthcare groups that require a partner that can keep them relevant and manage multiple areas and locations. 

Another important factor to consider in the roadmap is regulatory approval. These are mandatory for any clinical usage and are a good mark of a reliable solution. Look at working with a partner that has regulatory approval from multiple global bodies and that is continuing to expand these in the future. Regulatory approval in itself has its own roadmap, Aidoc prides itself in having FDA clearance and CE marks for 6 different pathologies within its portfolio of solutions. 

Regulatory approval alone is not sufficient, this should be further supported by a platform that’s easy to use. There is little point investing time and money into an AI implementation that nobody can use. Maximize user adoption by using a platform that can integrate with existing systems, doesn’t require an extensive learning curve, and immediately introduces seamless quality to the user experience. Don’t force medical practitioners with insanely busy schedules to master entirely new interfaces before they can get results. 

Finally, a solid AI implementation roadmap should include a straightforward deployment model. It needs to be scalable, adhere to industry standards, be easy to use, and straightforward to implement. Aidoc can be set up in 24 hours and that’s a solid benchmark for rapid and efficient deployment. Evan Kaminer, MD, Director of Radiology and President of the Medical Staff at Montefiore Nyack Hospital shared that “the integration of Aidoc was fast and seamless, and the results were witnessed almost immediately. In one month alone, Aidoc flagged 77 patients with acute intracranial hemorrhage.”

Examples of Successful AI Implementation

While Aidoc has a multitude of successful AI implementations, there are other companies that have also stepped up to create remarkable AI solutions that have made a difference to healthcare and society.

Beth Israel Deaconess Medical Center uses AI to diagnose blood diseases at an early stage using AI-enhanced microscopes. The solution used thousands of blood images to teach the AI what to look for and since then the machine can predict issues with an accuracy of nearly 95%.  In another instance, one driven by the pandemic, an AI implementation is capable of determining whether or not someone is wearing a mask. It allows for tighter controls over health and safety in potentially crowded spaces. Another example of this is the use of AI in assisted living facilities or retirement homes where AI can detect heart rates, can detect falls, and can also detect changes in blood pressure. Saving lives one AI moment at a time.

AI implementations can also be found in medical diagnostics, drug discovery, clinical trials, pain management, atmospheric regulation, security management, and drug controls. It can be used to reduce the risks of theft of drugs while improving patient access to the right drugs while shifting the goal posts of treatment. It can also support the practitioner by using a blend of AI, automation and machine learning to take over basic administrative tasks and reduce the burden on the medical professional. One area where AI implementation is really starting to shine is in clinical decision support – this is where Aidoc provides immense value and where practitioners can see an immediate benefit. AI never sleeps, never stops so it can accurately detect issues or catch problems faster and it can provide incredibly valuable support to the tired human brain. It most will never replace the human, but it can offer up an extra pair of eyes where they are needed the most. 

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