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Why Machine Learning Could Lead to Profits For Healthcare

Machine learning and artificial intelligence are in vogue right now–likely the most popular technology terms on the whole of the Internet.  The applications of these technologies have been predicted to revolutionize almost anything, from what we eat to how we show to how we process massive amounts of data in the Enterprise.

Healthcare is no exception to this rule, with physicians detecting cancer with deep learning and hospitals finding new efficiencies by using the technology, but there is a deeper need driving the adoption of machine learning technology in healthcare: the need for speed.

No, this is not an intentional, cheezy reference to a 1986 cult-classic film, it’s a real need, brought on by the Amazon problem: consumers (who are also patients) need things faster and more accurately than ever before. On a less “problem” driven note, so do health care professionals. Solving highly complex healthcare workflows and science-based challenges means that the more tasks we can transfer with confidence to computers, the better.

One still has to go to medical school or nursing school. One still has to receive ACLS certification and practice in blood draws. But the more leeway healthcare professionals and institutions have to move things online, likely the better, and it’s going to make healthcare better.

 

Preventative Care

Ever heard that adage “get a second opinion?” All too often today, that second opinion comes at either great cost, or is an iffy factoid absorbed from the Internet. When it comes to emerging healthcare issues on an individual basis, having accurate, actionable, preventative care information is crucial.

Machine learning and empower medical professionals to understand with extreme specificity the exact medical needs of a patient.

Let’s say you have a patient who comes in with high blood pressure, cellular stress and modest obesity. A machine learning algorithm can take these and all other patient data into account and create a heatmap of data that can inform a physician of their likelihood for future problems if these trends continue. This could eventually expand as deeply as familial genetic information, providing a much, much deeper patient picture than was ever possible before.

This makes preventative care more effective, and that lead to tremendous prevention of pain and suffering as well as cost savings. It simply makes sense as a broad-based application.

 

Deep Care and Research

Charges by the likes of Joe Biden and others to do big things in healthcare, such as solving cancer, are common these days. Revolutions like these are on the scale of antibiotics and vaccinations, and are considerably more complex and difficult than almost any other medical challenge tackled to date.

Machine learning is the only way forward. There is too much genetic and other data to compile or program by traditional means. Machine learning models can not just cut workloads for researchers and “deep care” physicians like oncologists down dramatically, it actually drives results that aren’t possible through human analysis.

Take clinical trials for example. Essential to the development of new drugs and treatments, there has been a century-long problem of matching trial participants with trial treatments. Machine learning can mine healthcare databases on both the drug and patient side to make this match so seamless. There is so much of this data that there is no way a given physician or trial group could hope to be more effective that a computer at this process–and it matters a lot in terms of results and outcomes.

No, we won’t solve cancer in a day this way, and we likely can’t promise remarkable results only through the application of these new technologies–but there is great confidence that they will make a difference.

 

At the Hospital

Hospitals are technology powered marvels, and machine learning is just the type of efficiency driver that they tend to adopt. From monitoring individual patient beds to streamlining management and billing, applications of machine learning could introduce cost savings and efficiencies that everyone could benefit from.

This is particularly important as we move away from the individual clinician model of medicine to group- and hospital-based physician care. How fantastic would it be to have even a scheduling system that understands patients and can help prioritize appointment setting and flow of information between departments and specialties.

The application of machine learning makes sense in so many ways. It will be interesting and exciting to see the applications and reap the benefits.

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How Technology Will Continue Changing the Stock Market of Healthcare

Ever since the discovery of fire by ancient humans, technology has been continually evolving. That evolution continues to this day. However, while the technological developments of hundreds of years ago were a much more gradual process of continual progress, today the progress is occurring in leaps and bounds that are certain to transform society much faster than the discoveries of the past.

Of these new technological developments, perhaps the most important regards healthcare technology. With new and improved healthcare tech, the lives of patients are sure to improve and lengthen greatly. With that benefit for patients, the stock market is also certain to see transformations as new technologies raise the profile of new companies and public offerings. With that in mind, below are some of the ways healthcare tech will bring about this profound change.

 

Robotic Surgeons

One of the sources for some of the biggest innovations in healthcare that will soon be upon us will be the wider application of robots. Robots are poised to transform a number of different industries in which human beings will be replaced by automated machines that can perform the same kind of work. While we usually associate automation with things like manufacturing and shipping, automation will soon impact the healthcare industry as well.

Surgery requires the steady hand of surgeons with years of training to pull off successfully. Despite the skill of surgeons, human error is still a possibility. When the stakes are high, those errors can result in death.

However, in the near future, many routine as well as highly complex surgeries may be completed by robots. In fact, this transition is already taking place. A robot manufactured by Intuitive Surgical was able to perform a soft tissue surgery with more precision than a human being could.

 

Nanobots

While you are probably very familiar with the term robot, you may not be as familiar with the nanobot. A nanobot is a robot or other autonomous machine that is built on a very miniature scale. In specific, nanobots are usually thought of as being less than 10 micrometers in size. How big is a micrometer? A micrometer is one thousand times smaller than one millimeter. This is a size smaller than what can be seen with the human eye.

As technology moves towards the creation of more advanced nanobots and nanomachines, the benefit for patients is clear. In fact Bar-Ilan University in Israel has already begun trial tests for using nanobots as a strategy for fighting cancer. These nanobots are built to detect cancer cells in the human body and apply treatment directly to them. It is hoped this means of treating cancer will be more successful and result in far less debilitating side effects than chemotherapy.

 

Picture Archiving and Communication Systems

Another significant development has been the introduction of new and improved PACS. PACS is an acronym that stands for Picture Archive and Communication System. A PACS system is what a healthcare enterprise like a hospital uses to create and manage medical imagery. This includes things like X-Rays, MRIs, CAT scans, ultrasounds and more.

In the past, such medical images existed as physical film. They had to be stored in huge bulky filing cabinets. Simply retrieving them for use by medical professionals was a chore in itself. Transferring them to doctors in other locations was even more difficult and required fax machines or the US Postal Service. Today, PACS is much more high tech and creates medical images as extremely high resolution image files. These files can be distributed almost immediately through the use of cloud networks. This makes such medical images more readily available to medical professionals when they need them.

Technology is evolving at a pace unseen before. As medical technology improves, so will the lives of patients all over the world. The healthcare industry is changing, and companies and the stock market will have to adjust accordingly.

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