How Are Providers Using AI in Healthcare?
AI in healthcare is changing the way providers care for their patients, use health records, identify diseases, and organize their data. Without it, so many opportunities to use data to improve all aspects of healthcare are left wasted.
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Read on to learn more about AI applications in healthcare and how organizations can begin implementing it today.
Applications of AI in the Healthcare Industry
AI has many unique applications in the healthcare industry from organizing the backend to directly helping in patient care plans. Here are a few of the most helpful ways that AI can be beneficial to healthcare providers.
The global market for AI in healthcare is expected to reach $95.65 billion by 2028
Enhanced Disease Detection and Identification
AI’s ability to sift through huge amounts of data can draw links between thousands of different patient’s EHRs to identify patterns of diseases unnoticeable to human eyes. AI helps radiologists and cardiologists to identify insights in clinical imaging where, in a single clinical study, hundreds to thousands of images need to be analyzed and compared to understand the trends that signal specific diseases. This info is then used in future diagnoses.
In addition, machine learning is able to identify diseases in medical imagery, predict disease processes, and even if patients might be depressed or suicidal based on their speech patterns.
More Efficiently Structure Data
Thanks to innovative technologies like connected medical devices and wearable devices that all communicate, hospitals and other providers have more data than ever before. But they can struggle to make sense of it all due to the sheer amount of time required to analyze and organize that data, making it usable in day-to-day medicine.
AI comes in handy here because of its ability to quickly and effectively sift through it all and organize it into structured datasets. Without AI, most of this data would be either left unorganized or unusable, wasting so much relevant information that could easily be turned into life-changing insights for patients now and in the future.
AI helps healthcare providers turn data into a predictive tool to help make more informed medical decisions and build stronger treatment plans based on trends in health history. Machine learning tools can quickly analyze the entire medical history of patients and their families and connect it with a database of symptoms and illnesses to help make predictions on treatment.
Related: Why Does Predictive Analytics in Healthcare Matter?
Using electronic health records (EHRs), chronic diseases can be largely predicted and decisions made to improve patient outcomes.
Improving Quality of Care
AI is being used in a variety of ways to improve patient care through the use of analysis.
For example, when someone calls 911, AI can use voice analysis tools to establish a quick diagnosis as to the severity of a patient’s situation and align verbal and non-verbal cues with symptoms of ailments like cardiac arrest. This information is then passed on to first responders who can use it to become more prepared on their way to the scene.
Additionally, AI can quickly go over and report on medical history, medication use, family history, and more to give responders even more data that can be applied quicker once they arrive.
At hospitals, these insights help doctors more quickly and effectively produce treatment plans based on real data.
How Can Providers Start Implementing AI?
Adopting AI into established processes and systems can seem daunting, especially to organizations that have been relying on legacy systems or human brainpower for so long.
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But, it doesn’t have to be difficult, especially if you get the help of digital innovation experts at a managed service provider (MSP) like Impact where you can get access to the experience and expertise of specialists who’ve done it all before.
Learn more about how big data is helping revolutionize the healthcare industry or check out our webinar all about managing healthcare data where our experts discuss how providers can organize, analyze, and make use of its massive amounts of raw data.