One of the most obvious developments that have taken place in the world is in the field of medical science. Radiology has allowed medical professionals to pinpoint the causes of symptoms of a patient. Reconstructive surgery has enabled breast cancer survivors to have the choice of rebuilding the look and shape of their breasts.
Living-donor donations is now a reality. Robots are being used for hip replacements, kidney transplants and gallbladder removals. Human beings, in general, are living longer and healthier lives. All these jobs were unthinkable fifty years ago. But today? The global healthcare industry is booming.
According to a recent research, it is expected to achieve a growth rate of 4.82% this year. Fascinating, isn’t it? Yes. But here’s a downside: consumers of the healthcare sector have become more demanding than ever.
You must think a person would treat retail and healthcare differently. Unfortunately, it isn’t so. For a consumer, having a pleasant experience at the doctor’s is the same as getting the best value of money spent on furniture.
Outpatient satisfaction level is slumping
It’s happening at a fast pace, and a Deloitte report confirms this. The reason for the dissatisfaction levels are many; including:
- Access to knowledge on the web
- Awareness of choices
- Increasing expectation for value
- A strong desire for a more collaborative approach to get treated
Naturally, this little change in the attitude demands for a change in the way consumers want to be treated. The healthcare sector should understand their evolving requirements and make use of technology in such a way that it is easier for them interact with their consumers.
This brings us to an important segment of this article.
Using machine learning in healthcare
Have you ever heard about the LACE Index?
Developed in Ontario in 2004, it identifies patients that are at a risk for readmission or death within 30 days of discharge from the hospital. The calculation is made on the basis of length of stay of the patient, concurring diseases, acuity of admission and ED visits in the last six months.
Widely accepted as a quality of care barometer, the LACE index is based on the theory of machine learning. It helps predict the future of a patient based on his past health records and enables medical professionals to decide the treatment accordingly.
Basically, the indicator fills the gap in the patient data and allows the doctors to allocate resources on time to reduce the mortality rate. This is one solid example on how machine learning is changing the face of healthcare.
Applications of machine learning in healthcare
Studies have indicated medical technologies such as Internet of Medical Things (IoMT), Artifical Intelligence (AI), Big Data analytics and robotics breakthroughin 2018 in the healthcare sector. Adding to that thought, let us get an insight into four areas of medicine which are evolving due to machine learning:
Every human body is different. How a person reacts to a season, food item or a medicine is completely different from others. When such is the case, why isn’t it possible to optimize the selection of treatment options based on patient’s medical data?
Predictive analytics helps medical professionals calculate patient risk based on his symptoms and family history. In the next ten years, there will be an increased use of micro bio-sensors and devices with more sophisticated capabilities of measuring health.
This will pave way for more real-time patient data that can be used to facilitate R&D in oncology.
2) Radiology and radiotherapy
Do you know it is highly possible that radiologists may get replaced by cyborgs in 20 years? While that’s still far away, Google’s DeepMind Health has partnered with University College London Hospital to develop machine learning algorithms that are capable of detecting differences between healthy and cancerous tissues.
The team is working to boost the segmentation process without causing any harm to healthy structures and to increase accuracy in radiotherapy planning. An MRI scan can take up to four hours. With machine learning this timeline could get cut into half or more.
3) Candidates for clinical trials
As of today, the filters applied to identify a candidate for clinical trials is limited. This means, the person is not always the best fit for the study. With predictive analytics, collecting information like social media behaviour, doctor visits, genetic data to better gauge the patient is now possible. Experfy works on projects like optimization of patient recruitment for clinical trials
Machine learning can also be used to monitor biological and other signs of harm to participants during the trial run. With these two technologies in the picture, medical professionals can find a sample that will help increase experiment efficiency, reduce costs and reduce the trial period.
4) Drug discovery
From initial screening of drug compounds to calculating success rates based on physiological factors, machine learning has given birth to methodologies like next-generation sequencing and precision medicine that can ensure a medicine has the right impact on the patient.
How, you ask? Machine learning enables medical professionals to focus on the development of algorithms to understand disease processes and design efficacious treatments like Type 2 diabetes. Microsoft’s Project Hanover and the Knight Cancer Institute are currently using machine learning to develop an AI that can help personalize drug combinations to cure Leukemia.
As machine learning and predictive analytics gradually come into play in the field of medical science, it is quite clear that oncology is going to benefit the most in the coming years and treating people would be easier, more cost-effective than ever. What are your thoughts on this?