Big data is all about large datasets. Modern datasets are so large and complex that you need special software to extract any information. The internet is a classic example of big data. In fact, the amount of information online today is so vast that it would be impossible for anyone to see it all within their lifetime, regardless of how many hours a day they spend trying. This issue is exactly why technologies like machine learning and artificial intelligence were developed. Amidst the millions of entries, there are patterns to be found, and useful information to be discovered.
The healthcare industry is similar to the internet. There are countless sources of information such as diseases and their symptoms, how patients respond to treatment, treatment outcomes, and follow-up care. Electronic health records (EHR) are the main storage for this information. Such data is available to healthcare professionals at the push of a button. However, just like other sources of big data, finding the right information takes special skills. You need the ‘right’ kind of help connecting the dots among millions of records and analyzing their content.
Fortunately, we are at the forefront of a big data revolution. This includes the advent and continuous improvement of Artificial Intelligence (AI). AI is simply software that allows computers to search through millions of records, across a variety of platforms worldwide, and to extract data that conform to specific patterns.
Machine Learning is a subset of AI. This technology enables computers to solve complex problems and make accurate predictions using a large set of data. Deep Learning, a specialized form of Machine Learning, uses layered neural networks to simulate the way the human brain makes informed decisions. The mathematical algorithms used by Deep Learning computers can categorize and label pieces of information and identify specific patterns.
The thing about Deep Learning AIs that makes them so unique is that they are not simply following a script. They are learning through repetition and through their mistakes to make better and better decisions about the data. In a nutshell, Deep Learning teaches a computer to put two and two together and become better with time.
Deep Learning is based on layered Neural Networks. Such networks take inspiration from the human nervous system and how it processes information in our brain. The system moves data from one layer to the next and processes it mathematically within each layer. As the information moves between layers, the computer learns more about it. Once the information reaches the final layers, the machine is ready to produce the output. Also, AI software learns how to recognize patterns and gets better at it over time. Eventually, it comes up with insightful new ways to interpret data.
The advent of AI is one of the major breakthroughs in the health sector today. The increasing need to manage hundreds of thousands of health records led to the development of EHRs. Thanks to AIs, EHRs are constantly improving and empowering the health care community.
The use of EHRs is a way to promote excellence in your healthcare practice by increasing your efficiency and lowering your costs. Paired with AI technology, EHRs can assist medical professionals in identifying healthcare trends and risks. This leads to better-informed decisions and more accurate allocation of clinical resources.
Among the many promising capabilities of AI-powered EHRs is the ability to use big data embedded in the medical records to predict clinical outcomes based on past experiences. This provides the opportunity for more informed prognostic decisions. Imagine the peace of mind it would give you to be able to know ahead of time which health problems you may be at risk for, and to be able to plan ahead and take preventative steps to minimize your risk long before you’re ever diagnosed. These are the kind of things you’d expect to see in a science fiction movie, but the development of healthcare AI is moving at a fast pace towards making them possible today.
Predictive analysis leads to better healthcare management. Any practice looking for a way out of the constant uncertainty that pervades healthcare should consider predictive tools as a promising way to model potential outcomes before they occur.
There are other promising AI developments in healthcare; there are chatbots that process natural language, for example. These programs are capable of asking questions to patients the same way a healthcare professional would. The patient’s answers trigger the AI to scan large sets of data and come up with a basic diagnosis. This can save consultation time and overall costs. It may also help provide the patient with resources that would not have been easily available otherwise.
In the field of AI vision technology, computers can process large sets of photographic images to detect skin cancer and other diseases. Similarly, computers can process radiographs and magnetic resonance images (MRI), compare them to large datasets and find potential diagnostic features. In fact, the most promising application of AI technology in healthcare is the analysis of MRI results. A study, published in 2018, showed that computers using Neural Networks achieved 10% higher accuracy than dermatologists in identifying melanoma. This type of technology may significantly lower the cost of MRI scans by reducing the time it takes to process them.
Technological changes are inevitable for the healthcare industry. Leveraging technology is critical to remain relevant and to improve your practice’s workflow, patient satisfaction and elevate your standard of care.
In short, technology is your friend. Learn how to use it and make it available at all levels of your practice. Artificial intelligence is recognized as today’s most credible solution to the increased demands placed upon physicians today.
At John Lynch & Associates, we work daily with healthcare businesses large and small to provide them with training and solutions to enhance their technology needs. Connect with email@example.com to discover how we can assist you with choosing the right system that fits your business model and workflow.
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