One of the most stressful and sometimes painful things to go through is to being seriously sick or injured.  We all expect to go through some aspect of these in our lives, but it is very different when you are down in the depths of the situation.  It is also a challenge within the health care industry and doctors personally because they are doing all they can with all they know to make things better.  In some circumstances, there is very little that can be done, yet in others, having more insight and understanding can make a significant difference.  As such, the health care community has turned to more technology to aid them in their jobs.  This technology is known as machine learning in health care and is just starting to make meaningful inroads.

 

Machine learning, which is closely related to artificial intelligence, but is more of the mechanics, has and is being used in many other industries and businesses around the world.  However, like many technologies and processes, health care hasn’t and sometimes wasn’t able to integrate them into practices due partially to the sheer complexity of data that is handled at a hospital or doctor’s office.  Luckily, advances in technology, programming and computer learning have assisted in this obstacle.  More health care organizations have been able to reap the benefits of computer analysis and are thus able to provide better and more accurate care for their patients.

 

Why is this such a big deal?

Of any other industry or service being utilized every day, health care is probably the one each of us both depend upon and fear the most.  We all hear about all the progress that health care has experienced when it comes to DNA sequencing and editing, wireless instruments and devices, real-time precision care, digital medical records, and micro/nanotechnology.  Most of these and other technologies are to help people live longer, better lives.  These are also helping doctors to do their job more accurately, and we are all helping to contribute by providing information into the system. 

 

There are two specific things that we should understand: firstly, our health care information is not completely our own, and secondly, privacy is still an essential factor in health care.  With that being said, you are being naïve if you believe that your medical records aren’t being used to better care for everyone else.  In a day and age where we sign away most of rights with a click of a mouse, we must also come to an appreciation that data is being gathered and used in ways that we can’t even begin to imagine.  But, our names and other personal information isn’t attached to the data; this is the privacy aspect of the health care data. 

 

What does my information have to do with machine learning?

If you understand the basics about surveys and patterns, you can also recognize that the more people that participate, or the larger the group contributing, the more accurate the results or finding will also be.  This is no different in health care; the more insight gained, both when it comes to negative and positive outcomes, the more accurate the understanding of a situation can be.  Most of us react quite similarly to illnesses and also recuperate relatively comparably to injuries.  There are outliers, and those cases are extremely informative and important to sensing patterns. Altogether, every piece of medical data collected goes to provide a better picture of how care can be offered. 

 

One of the hardest parts of machine learning is programing a computer to learn the patterns, and then be able to further grow in this learning without actually having someone program the information into the system.  Perpetual learning, especially in something as vast and complex as health care is nothing short of a miracle and yet a necessity.  When a computer is able to continually take in health data and turn it into actionable strategies on a case-by-case basis, you will see care improve almost exponentially.  As for right now, it is a slow process that is yielding many helpful processes and is gaining more autonomy with each new step forward.

 

Do I see any of the benefits right now?

When you’ve watched old episodes of Star Trek or other futuristic shows, the medical treatment received seems so beyond anything we could implement today, yet machine learning is facilitating better care by helping doctors practice a standard of right patient, right medication, right dose, right time and right schedule.  Whether the treatment has to do with medication or procedure, most of these “rights” are applicable; ensuring that the right patient is in front of you, that the most accurate information is available, that the most current and correct procedural information is pulled up and that the patient is being subjected to unnecessary testing, actions or medication.  Machine learning systems pool together the patient’s medical record, other relevant materials and present that data to the treating health care professional so as to eliminate errors, duplication and needless actions.

 

The accuracy is diagnoses is also being seen currently, especially when it comes to imaging results.  The human eye and biases always have their flaws, although are still very necessary.  However, coupling machine learning and advances in imaging resolution has brought forward a number of early diagnosed cases of illnesses and diseases that weren’t possible just a few years ago.  This allows for earlier treatment, better outcomes, and more specialized care on an individual basis. 

 

These and many other results have put machine learning in health care at the forefront of many technologically-minded people.  This is only the tip of the iceberg and wonderful possibilities for each of us in the short- and long-term treatments that we will seek out within our lifetimes.  Machine learning has a ways to go before turning over much of our care to it, but these small (and quite significant) steps are just the boost that health care needs to help improve care.

 

___________________________________________________________________________________________

Images courtesy of:
Freedigitalphotos.net/kdshutterman
Freedigitalphotos.net/ratch0013
Freedigitalphotos.net/kromkrathog
Freedigitalphotos.net/patrisyu