Artificial intelligence is revolutionizing almost all industries at the moment and it is beginning to radically change the way people work and live. According to Gartner, one of the world’s leading research and advisory companies, the enterprise adoption of AI has grown 270% over the past four years and in 2018 alone, AI adoption rates have tripled.
Big companies like Google are also putting AI under the spotlight by launching initiatives such as the AI Global Impact Challenge. The tech giant pledged $25 million to entice nonprofits, universities, and other organizations to work on AI projects that will help combat issues like crisis relief, environmental conservation or sex trafficking.
But, the healthcare industry is probably one of the biggest recipients and adopters of AI technology. Healthcare facilities are now leaning towards revamping their legacy tech and paperwork filing infrastructures to deliver better services to patients. Because AI is empowering machines to think like humans, it provides relief to healthcare practitioners overwhelmed with the number of patients they need to look after, diagnose and treat.
Medical mobile apps (for automating hospital administration, booking appointments, tracking personal health data and self-health management) powered by AI are being launched every day and there’s a promising growth in the healthcare market waiting to be exploited by app developers and business owners. Here are the ways AI is transforming the healthcare industry:
Helping doctors make better decisions
AI allows the collection of big data and it can make sense which of the information collected are clinically relevant. By implementing AI systems, the data gathered can be turned into statistics that can help physicians and doctors make better decisions. Not only that, but AI can also provide predictions about potential health risks so they can be prevented even before they develop into a health problem.
From early detection, tracking symptoms and improving the accuracy of diagnosis, AI proves to be effective and efficient. Repetitive and uncomplicated tasks like analysis of CT scans and mammograms can be done using AI systems. A recent article published by Wired UK revealed that an AI system developed by researchers at the Houston Methodist Research Institute in Texas was able to provide 99% accuracy in diagnosing breast cancer risks even when patients are not exhibiting the symptoms of the disease.
In reality, it’s not only doctors who are empowered by AI. AI also provides patients customized treatments and personalized approach to healthcare, further reducing the cost of post-treatment complications.
Improving patient outcomes
We’re living in a value-based care environment, which means that the more people talk about the superior care they received from specific healthcare facilities, the more patients will come knocking at those facilities’ doors to get treatment.
This is where AI comes into the picture. Healthcare facilities are now able to provide superior care using systems and apps that act as personal health assistants. These health assistants have the ability to provide medication alerts and human-like interactions even when the patients’ attending physician is not available.
Some of the most prominent healthcare trends are specialist matching services, electronic health records, and telehealth. All of these trends are patient-outcome centered, which means that these technologies are aimed at improving patients’ experience in getting the healthcare services they are paying for and reducing the time they have to allot for filling in forms, finding specialists or traveling for doctors’ appointments.
Healthcare companies and facilities are subject to increasing pressure to provide superior healthcare but technologies that will allow them to do so are often expensive or inefficient. AI, on the other hand, requires significant investments upfront but in the long run, is cost-effective. Because AI has learning abilities, systems that cater to repetitive admin tasks don’t need to be re-programmed on a regular basis.
AI also has the ability to tag information and synchronize them with patients’ medical profiles, reducing possibilities of fraud and additional expense from both the patients’ and doctors’ sides. Other causes of healthcare expenditure waste such as care delivery failures, over-treatment, and improper care delivery can be prevented by AI.
Reducing hospital error
According to a study from Johns Hopkins, more than 250,000 people die each year in the US due to medical errors which are highly preventable. Medical errors are the third-leading cause of death after heart disease and cancer and some studies even present death rates as high as 440,000 per year.
Because AI has the ability to self-correct, it can spot errors in medical charts and notify practitioners and medical staff about the errors found. Unlike EHRs, third-party AI tools have the capability to look at the data in an in-depth manner, perform an analysis that could lead to the uncovering of potential health risks such as drug interactions or allergies, and create summaries that help practitioners make sense of patient data as they review them.
AI can also process data faster. While it may take a while for humans to interpret handwritten notes and images, AI can organize the data in just a matter of seconds or minutes, depending on the volume of data the system needs to take in. Currently, there’s a need for EHR vendors to commit and support AI-based initiatives in order to ensure that the data they have is made readily available to the AI system.
More efficient clinical trials and drug development
Just recently, a start-up supported by the University of Toronto AI programmed a supercomputer with an algorithm that simulates and analyzes millions of potential medicines to predict their effectiveness against Ebola. This clinical trial saved the healthcare industry millions of dollars that would have been spent on costly physical tests. The trial also produced a medicine that saved thousands of lives.
Although there has been a lot of criticisms concerning patient privacy and the ethics of data ownership, we can’t deny that the adoption rates for AI prove that healthcare organizations are willing to take the risk if it means that they can finally bridge the gap between healthcare practitioners and patients.
Many experts are predicting that in the next few years, AI will enable operational efficiency at scale by minimizing healthcare inefficiencies and streamlining cost-effective health ecosystems.