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 of which artificial intelligence plays in these new technologies to analyze, predict and advise is critical to understand the pivotal force AI is becoming to achieve digital transformation in the healthcare industry.
AI innovations in the healthcare industry are cost saving, time saving and, most importantly, life saving, enabling the healthcare industry to reach that next big breakthrough. Here we discuss how various artificial intelligence capabilities are solving major healthcare challenges.
Health Care Industry Challenges that AI Solves
- Access to health information is limited. Physicians and patience can't access this information on many devices.
- Identifying, discovering and validating of novel drugs process lacks transparency.
- Legacy, business and engagement models.
- Updating contemporary medical practices on a timely basis is difficult.
- Reaching patients on digital platforms is challenging.
- No proper mechanisms to manage Electronic Drug Records (EDR) and Electronic Medical Records (EMR).
- Not able to serve on-demand service needs without interruption.
- No robust security to enterprise systems and patient data.
How These Challenges are Impacting Healthcare
- During emergencies, manual, time consuming processes can be inaccurate, which can cause injury or even loss of life.
- Inconsistencies in data and data organization can lead to wrong decisions in disease prediction.
- Inconsistencies in process workflows across the organization leads to confusion and missed information.
- Delay in information delivery to agents/MHPs can be critical.
- Lost patient trust and satisfaction.
- Service malfunction in case of emergency services can cost the loss of life.
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 failing is to cope with the rate at which the 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 inadequate in storing, organizing and extracting intelligence out of it.
AI-based algorithms driven by big data capacities can target and segment the data, and then conduct robust monitoring, generate advanced analytics, and share deeper insights into respective processes. This way one can gain deeper transparency and make informed decisions about diseases and patients to drive better patient care.
Connecting the Dots - Predict Disease and Solutions
The best use of AI is to compile, decipher, record and execute decisions based on large amounts and a variety of metrics.
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 and its expected outcomes.
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 study the data to predict treatment by simulating complex drug interferences. 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 as a part of a particular therapy. This facilitates the researchers in the healthcare industry to reduce trial-and-error inherently related to research and development activities. Optimizing the chemical quantities and compositions based on a positive feedback method upon analyzing the resulting outcome. These platforms are ultimately providing not just granular enhancements in time savings.
This way, researchers can conduct AI-based modeling at every stage: absorption, distribution, metabolism, and excretion or toxicity predictions. Use AI algorithms like reinforcement learning algorithms to identify, discover and validate novel drugs. This way timely updating contemporary medical practices and advancements. This data, when fed to the AI-based disease diagnosis system, can help doctors and patients to see the possible implication of the drug or particular chemical combination on a particular health issue.
AI-Powered Intelligent Healthcare System
The Majority of MHPs asserts that their patients are digitally empowered and are proactively checking out numerous online sources to gain knowledge about the disease associated with the symptoms they have and also associated drug and its functionality issues. Google research reveals that one out of twenty searches is about healthcare and got associated with a renowned clinic to prepare a database of health data. So, customers are no more just willing to get medical assistance by visiting doctors in person. To offer initial support, some organizations are offering telemedicine or web-based support, where people spend time getting information digitally. Despite doing all these findings the accurate result is a distinct dream.
In this case, to offer services with ease and accuracy having an AI-powered mobile app and integrated chatbots can enable patients to get services by just sharing their symptoms. The AI chatbot studies the disease from the given data and provides knowledge articles to the patient and can easily manage their doctor appointments.
Wearable health devices (like fitness watches, pendants and so on) source a lot of data about a person on a constant basis. If these devices integrate with healthcare systems, the predictive intelligence capability studies the patient’s behavior or health data and makes intelligent notifications to keep patients in a preventive approach to disease.
Chatbot and mobile applications make it easy even for MHPs and doctors to get information about clinical research, drug information, patient data and prescriptions without having to go through rigorous search in the web portals. Doctors can pull up medical records on any device, facilitating quicker response times to queries for patients and medical professionals. This decreases the resolution time and helps them stay up-to-date on research information. On the other side, patients can communicate with their doctor or digital care team from wherever they are through chatbot or mobile app, without having to wait or set an appointment. This improves patient engagement, empowerment and satisfaction, thereby resulting in 100 percent value-based care.