The healthcare industry has benefited from many groundbreaking innovations over the past decade. The industry has learned to embrace adoption of new technologies to deliver healthcare services that are best for their patients. Understanding the scale in which artificial intelligence plays in these new technologies to analyze, predict and advise is critical to understand how AI is becoming a force to drive digital transformation in the healthcare industry.
Artificial Intelligence innovations in the healthcare industry are cost and time saving, enabling the healthcare industry to reach that next big breakthrough. Here we discuss how AI can help address major challenges within the healthcare industry.
How AI Solves Healthcare Industry Challenges
The global healthcare market reached a value of nearly $8.4 trillion in 2018 and by 2022 it is expected to be $11.9 Trillion"
Data Management and Intelligence Made Easy 
In healthcare, medical data sourcing increases ten fold every year and the industry is failing to cope with the rate at which 1`medical data (patients records, prescriptions, MHR, clinical research data, data from health-based personal tech) is generated. Astonishingly, despite owning such a vast amount of data, organizations are failing to store, organize, and extract intelligence out of it.
AI-based algorithms driven by big data capacities can target and segment data, and then monitor, analyze, and share insights into respective processes. This way one can make informed decisions about diseases and patients to drive better patient care.
Connecting the Dots - Predicting Diseases and Treatments
There has been tremendous growth of AI-powered tools that have the ability to find disease just before a patient is symptomatic, treat it early and achieve a higher survival rate with far less patient suffering. With technology for diagnostics and imaging tools like MRIs, CT and PET scans, algorithms can be trained to accurately measure symptoms, faster than humans. The cognitive abilities of the algorithms analyze real-time data, assess thousands of data points covering the type and extent of disease, characterizes the scale of disease and suggest treatments.
To understand the real-time implication, let us consider Novartis collaboration with IBM Watson health to assist physicians in choosing the right therapy and provide appropriate recommendations for breast cancer to improve patient treatment outcomes. To provide appropriate solutions regarding breast cancer, they deployed AI algorithms to study real-world cancer data and then cognitive algorithms studied the data to predict treatment by simulating complex drug interfaces. Novartis was able to provide quality treatment with better patient experiences.
Promote Research Capabilities at Improved Visibility
Applying another compelling AI capacity, deep reinforced learning, provides a deep learning-based modeling technique that studies and optimizes chemical reactions of a drug. This reduces the trial and error process. Optimizing the chemical quantities and compositions based on a positive feedback method upon analyzing the outcomes.
Researchers can conduct AI-based modeling at every stage: absorption, distribution, metabolism, and excretion or toxicity predictions. Algorithms like reinforcement learning algorithms can identify, discover and validate novel drugs, updating contemporary medical practices and advancements. This data can help doctors and patients see implications of a drug or particular chemical combination on a health issue.
The New Intelligent Healthcare System
The Majority of healthcare providers say their patients are digitally empowered and are proactively researching diseases associated with their symptoms and even going as far as researching treatments and drugs. Google research reveals that one out of twenty searches is about healthcare. So, patients are not as willing to get medical assistance by visiting doctors in person. To offer initial support, some organizations are offering telemedicine or web-based support, where patients can communicate with medical experts digitally.
In this case, to offer services with ease and accuracy having an AI-powered mobile app and integrated chatbots can enable patients to get information by sharing their symptoms. The AI Chatbot can analyse symptoms and provide trusted knowledgebase articles and even escalate to schedule an appointment or request emergency services.
Wearable health devices like fitness watches etc. collect a lot of data about a person on a constant basis. If these devices integrate with healthcare systems, predictive intelligence can study the patient’s behavior and health data.
Chatbot and mobile applications make it easy for doctors to get information about clinical research, drug information, patient data and prescriptions without spending a lot of time searching through data. Doctors can pull up medical records on any device, facilitating quicker response times to queries for patients. On the other side, patients can communicate with their doctor or digital care team from wherever they are through chatbots or mobile apps. This improves patient engagement, empowerment and satisfaction, thereby resulting in 100 percent value-based care.
