Machine learning is a data analysis approach that automates analytical model building. Using algorithms that iterate based on the data returned to them, machine learning uses software to locate hard to discover information without being explicitly programmed on where to look.
This iterative aspect of machine learning allows for independent adaptation. In other words, the computer and its software program ‘learns’ from previous computations and the patterns that is saw to produce reliable, repeatable decisions and results.
While machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data is now affordable enough to be applied in a variety of ways. This is critical in healthcare, because it has large amounts of varied data. This can provide medical scientists, drug makers, bio-tech companies and healthcare providers with a treasure trove that can be used to derive key insights about how to render better treatment. This is therefore likely to revolutionize the entire healthcare industry. Let’s look at a five areas in which this is likely to have a very large and immediate impact.
- Better use of medical data physicians gather from their patient population, in any form. This includes medical images, doctors’ notes, structured lab tests, and many other data inputs. Machine learning is capable of taking all of this data and using deep learning and image analysis make medical diagnostics faster, more accurate, and more accessible.
- For patients with depression and general anxiety, smartphone technology, data science, and clinical input can be harnessed by machine learning to introduce new and more effective predictive models to create more personalized, affordable ways to deliver mental healthcare. This includes giving patients the chance to analyze their own mood over time, learn coping strategies, and receive additional mental health support as needed.
- Health plans are seeking more ways in which they can work with providers of all kinds to extend the reach of care management services to support patients toward improved experience, care plan adherence and more positive health outcomes. Through machine learning and the use of artificial intelligence, mobile-enabled care management platforms can be made available which give all stakeholders individualized plans, automatically adjusting based on experiences.
- Doctors can make prescription errors from time to time. By using big data analytics and machine learning algorithms it is possible to analyze large scale data contained in Electronic Medical Records (EMRs), to learn automatically how physicians treat patients including the drugs they prescribe. This data can be used to set up alerts to manage the more common risks that are identified.
- Hospital networks and insurance carriers often have hundreds of thousands of patients to deal with every year. Through machine learning, predictive health analytics, which organize and analyze hundreds of millions of data points, allows analysts to start to provide higher quality care to more patients in less time. It does this by providing more accurate insights and predictions related to symptoms, diagnoses, procedures and medications looking at millions of possible combinations that would be impossible for one person to come up with.
As the above 5 examples indicate, as more and more big data is collated and databases of brought together intelligently, machine learning techniques have the potential for a very wide array of new innovations in healthcare that will be transformative for both providers and their patients.
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CEO-RX4 Group-The Business of Healthcare