Machine learning can also be referred to as cognitive reasoning or artificial intelligence and it is a field of computer science that gives computers the ability to learn without being explicitly programmed. The birth of Machine learning goes back to 1959 and has continued to be improved upon and adapted by different industries including the healthcare industry ever since. Though Artificial Intelligence (AI) and Machine Learning are used interchangeably they are not exactly the same thing. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider  as smart while Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. This definition best describes the use of Machine Learning in Health Care.

Another term in machine learning to take note off is Neural networks. The development of neural networks has been key to teaching computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy and lack of bias. A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain. Essentially it works on a system of probability – based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” – by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future. Machine Learning applications can read text and work out whether the person who wrote it is making a complaint or offering congratulations. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood. In some cases, they can even compose their own music expressing the same themes, or which they know is likely to be appreciated by the admirers of the original piece.

There are several and (some might even say limitless) uses of machine learning There are limitless opportunities for machine learning in healthcare. The introduction and widespread use of machine learning in healthcare will be one of the most important, life-saving technologies ever introduced and will be more than just a fad. The opportunities that machine learning in healthcare offers are virtually limitless for machine learning to improve and accelerate clinical, workflow, and financial outcomes.  

The following are just a few examples of the advantages machine learning brings to healthcare

Reduce readmissions:  Machine learning can reduce readmissions in a targeted, efficient, and patient-centered manner. Clinicians can receive daily guidance as to which patients are most likely to be readmitted and how they might be able to reduce that risk.

Prevent hospital acquired infections (HAIs): Health systems can reduce HAIs, such as central-line associated bloodstream infections (CLABSIs)—40 percent of CLABSI patients die—by predicting which patients with a central line will develop a CLABSI. Clinicians can monitor high risk patients and intervene to reduce that risk by focusing on patient-specific risk factors.

Reduce hospital Length-of-Stay (LOS): Health systems can reduce LOS and improve other outcomes like patient satisfaction by identifying patients that are likely to have an increased LOS and then ensure that best practices are followed.

Predict chronic disease: Machine learning can help hospital systems identify patients with undiagnosed or misdiagnosed chronic disease, predict the likelihood that patients will develop chronic disease, and present patient-specific prevention interventions.

Reduce 1-year mortality: Health systems can reduce 1-year mortality rates by predicting the likelihood of death within one year of discharge and then match patients with appropriate interventions, care providers, and support.

Predict propensity-to-pay: Health systems can determine who needs reminders, who needs financial assistance, and how the likelihood of payment changes over time and after particular events.

Predict no-shows: Health systems can create accurate predictive models to assess, with each scheduled appointment, the risk of a no-show, ultimately improving patient care and the efficient use of resources.