We use cookies to keep our website secure, personalize your experience and for web analytics purposes. Read our Privacy Policy to learn more. By clicking Accept, you agree to our use of cookies.
With the ever-growing healthcare costs in the US, minimizing resources while staying on top of quality service and patient care has never been so relevant. In this regard, the role of artificial intelligence and machine learning cannot be overlooked.
But what are the main reasons ML is gaining momentum nowadays, apart from costs?
In this article, we will observe four main ways in which machine learning can impact the healthcare industry and provide four real business use cases of it.
Machine Learning is a comprehensive subject area within artificial intelligence that includes several types of technologies.
Physical robots are the first thing that may come to one’s mind while discussing the role of artificial intelligence in daily human operations. Though it may sound alarming, physical robots are becoming the best friends of medical workers when it comes to complex surgeries and other medical procedures requiring extreme accuracy of movements.
Neural networks mimic the neural network structure of our brain. Thus, in healthcare, artificial neural networks can imitate the mental process of a human while making a diagnosis. The logic of ANN is built around deep learning, i.e., the capabilities of artificial neural networks to learn from a massive amount of data.
Robotic Process Automation (RPA) is a technology based on the capabilities of a machine to imitate human behavior while performing mundane, routine tasks. Automating such tasks as data entry, transfer, and categorization, significantly reduces the time of medical workers for more value-added tasks.
Natural Language Processing deals with the capability of machines to perceive, analyze and generate human language. In healthcare, this feature can be applied to extracting and analyzing patient data from a doctor’s notes or dedicated medical software.
It is hard to imagine the healthcare industry moving at such a fast pace without the technological advances that it is actively leveraging. Artificial intelligence and machine learning are one of major drivers of this industry. And here are four reasons why.
According to the Agency of Healthcare Research and Quality, around 10% of patients die because of misdiagnosis, and over 17% experience complications.
Machine learning is an excellent tool for improving the diagnosis process. By using it, doctors can better analyze reporting and medical data. By studying similar patient cases, machine learning tools can help conduct more precise diagnostics and predict a specific disease.
Use case:
A UK-based study published in February 2021 found that machine learning can classify common child brain tumors. The research, which involved 117 patients, used diffusion-weighted imaging and software that generates images from the resulting data to analyze images from twelve different hospitals produced by eighteen scanners under the supervision of radiologists and expert scientists in pediatric neuroimaging.
The findings show AI tools can successfully be used for enhanced tumor characterization and treatment.
By automating mundane activities, such as filling out patients’ data and transferring data from one healthcare system to another one, machine learning can save humans’ efforts, time and reduce medical services’ costs.
Use case:
Machine learning is an invaluable tool for healthcare service providers when it comes to revenue cycle management. Here is how it can help cut costs in this field:
These and many other actions performed by ML tools can speed up cash flow, improve the appeal responses rate and impact a healthcare organization’s revenue.
Machine learning can impact the overall patient care process improvement by tracking a patient’s state and providing recommendations on further treatment based on the data collected.
Use case:
Some machine learning tools help in the personalization of treatment—the combination of predictive analytics and individual health yields enhanced disease assessment.
IBM Watson Oncology, a cognitive computing decision support system, processes patient medical history to generate multiple treatment options by not limiting to a specific list of diagnoses.
Machine learning and deep learning stand first in line when it comes to inventions in healthcare. They are valuable tools in analyzing massive data and are widely used in developing and manufacturing new drugs and medicaments.
Use case:
ML is a common tool used in drug manufacturing. DEM AI tool accelerates the manufacturing process by:
Read more about ML and AI-based drug development and production practices.
We’ve observed four main reasons machine learning is at the forefront of the AI movement in the healthcare industry and provided a brief overview of 4 relevant use cases of this technology.
The worldwide practice of embracing intelligent solutions when it comes to human lives instead of perceiving technological breakthroughs as something distant and dangerous has already played its role in the rapidly evolving healthcare environment.
Kanda has 17 years of experience working with healthcare organizations. Being a go-to development partner for enterprises, mid-sized companies, and innovative eHealth startups, we combine expertise in health-related technologies with in-depth domain knowledge and a deep background in cybersecurity, HIPAA-compliant development, EMR, Medical Analytics, Reporting, and mHealth. We’ve always looked beyond the hype of AI and Machine Learning and strived to translate these technological advancements into quantifiable and customized solutions to customers’ problems.
Talk to our experts today and learn how machine learning can transform your healthcare operations.
