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HOW HEALTHCARE IS USING AI TODAY?


Ever since John McCarthy coined this idea about 6 decades ago, artificial intelligence (AI) is fast being regarded as the go to technology for augmenting engagement levels at the touch of a button.

Apple Siri, Amazon Alexa, Google Assistant and Microsoft Cortana are just a few examples of voice-powered personal assistants that help you interact with a platform or a device to get your routine tasks done.

From these virtual assistants to self-driving vehicles, AI & machine learning is slowly but steadily making their way into our lives.

WHY AI WILL HAVE PROFOUND IMPACT?

Are you being told that AI is the future? Might be true. But there are innovative start-ups that uses matured AI aspects practically today as well.

All healthcare providers should be getting used to this term now (if they’re not already), because AI will have a profound impact on their business decisions in the days to come.

While it might seem ridiculously simple, you can actually apply this concept or principle in much more powerful and productive ways in many scenarios in a typical care coordination environment as well and place them where people can experience them publicly.

For long, we have been witnessing computers converting speech into text, but now the communication between a man and machine has become more interactive and increasingly automated.

WE LEARN FROM EXAMPLES & REPETITIVE PRACTICE

Voice AI can be a disruptive technology that could help both healthcare providers and patients to produce positive healthcare outcomes in a safe and effective manner through interactive sessions. For example Cooey's Maya voice engine and integration with Amazon Alexa are being used to assist patients and enabling them to carry out routines healthcare tasks.

It enables you to accomplish more and eliminates tedious, repetitive tasks and leaves you with more time for important tasks.

It’s more impactful and is remarkably effective in a wide variety of problem solving situations such as communication, collaboration and critical thinking.

We learn from examples and repetitive practice! The same concept is applied here as well. When you expose a computing system to examples of various behaviours that you wanted to have, it’s going to learn from those examples.

Speech recognition technology enabled the validation and translation of a spoken language into text by computers and was able to solve quite a number of problems for us. Computer vision is another area where deep learning has made significant progress in recent years, viz., image classification. Here, the raw input is fed in the form of pixels of an image that get analysed before it gives out the desired output.

In other words, it’s like that you are showing a picture to a computer and then tell the computer that the picture has the properties of a particular object, person, or a condition and tell that it’s categorized as this or that, and a model gets created.

Once the model is ready, the computer is fed with more and more complex representation of such examples with layers and layers of colours, edges, blobs, etc.

And then, essentially you get to a point where you show the computer an image and ask what it is and you get an answer, and if it’s wrong, you make required adjustments and try out again. This learning process continues till the computer identifies the right answer that you are looking for and you are done.

Yes, it does take a while before you could automate this entire process.

Thus, computer visual algorithm utilizes the training data (a library of correctly-tagged images) as reference when you input an image to get an output that you care about.

THE CARE COORDINATION FRAMEWORK

Healthcare enterprises can be greatly benefited from this technology. Voice AI not only helps gather patient information faster, easier and improve population health & patient outcomes, it removes unnecessary costs as well.
MyCooey’s Robust AI Care Coordination Framework is designed to receive data input both in the form of voice (Maya & Alexa) and images and can be interfaced to an existing EHR or a practice management system.

The image processing unit in MyCooey now has been trained with tagged-image library of eye, skin, and wound and has the ability to predict desired results.

This can go a long way in streamlining the digitization of patient information, diagnoses, and symptoms, and save care providers’ time and energy in various care coordination settings.

While the ideal solution still seems like a far way off, the future of this tool holds immense potential to empower the caregivers, especially when challenged with tasks in remote care coordination.

It’s for us to acknowledge that these tools can be put to good use, as the AI is only as good as the trained data that we give it.

From empowering patients to independently seek support for their medical concerns to allowing caregivers/physicians access patient’s health data, these virtual assistants are poised to rapidly change the way healthcare is delivered.

If you are in the business of long term care like senior care, home care, or chronic care it is very important for you to have right care co-ordination solution with AI implemented so that in future you are not left behind. At Cooey, we will never let you down when it comes to innovative technology.

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