Despite a slow and unsteady start at the beginning of the decade, the healthcare industry is gradually advancing for the best due to machine learning. Precisely, the industry is now more comfortable with the idea that embracing big data is the only feasible way to realize financial and clinical success in the future. Prior to the evolution of machine learning, the healthcare system was a mess. There was no diversity in the system’s infrastructure, especially when it came to healthcare provision. However, in a move to transform private practices, hospitals, and other healthcare facilities, a new innovative field of science and technology came about referred to as machine learning (ML).


In its simplest term, machine learning defines the extraction of knowledge from data that leads to providing intelligence. It focuses on establishing algorithms and software of a machine’s past experiences. Machine learning is of two kinds: supervised and unsupervised. With supervised learning, the aim is that you have some data and an outcome of interest. In this case, the interest in learning dwells on how the data is related to the results. Alternatively, with unsupervised machine learning, there is no target or outcome in mind. Therefore, the machine learning model is able to sort and separate the data into categories of choice. Contrary, supervised learning has specific groups that the data must fit into.

The role of machine learning in healthcare


Before ML, a stack of papers and clusters of pencils were a welcoming site to almost every healthcare setting. Today and in the future ML’s basic use will entail data analysis. A patient’s bulk information of X-ray results, blood samples, DNA sequences, current medications and medical history will all be easy to manage and retrieve. In this case, machine learning will effectively apply to patient diagnosis and treatment procedures like:


•    Predicting mortality and length of life remaining using physiological patient vitals and other equipment like blood test results.
•    Machine learning models will assist physicians to diagnose patients with a rare disease or predict hard outcome tests. A practical example may involve the use of electronic health records to forecast heart failures.
•    Determining the most effective medication dosage by minimizing healthcare costs for both patients and healthcare providers.
•    In more advanced settings machine learning models could be developed for robotic surgeries in order to increase the probability of successful surgical outcomes.

 

Challenges and Controversies


Although machine learning bears great promises, it is far from clear how it will change health and health care in the short to mid-term. Currently, the biggest challenge for policy makers and industry executives lies in deciding when and how to invest in machine learning to optimize organizational efficiency and effectiveness in the most economical manner. Adapting artificial intelligence that is associated with machine learning in the healthcare system is not easy. It is difficult shifting from the old system of pen and papers for those who have been in the industry for decades. Additionally, many healthcare facilities lack the motivation and incentive to allocate their budget in adequate research, resources and staff for machine learning models. Starting from the global leader in health care industry, United States, much effort is needed to boost machine learning adoption, in order to start a ripple effect.


Similar to the rise of most new technologies, machine learning also brings about a heated debate on ethics. Programming machines to think for themselves might end up being risky if the machines get out of control. Nevertheless, advancements in machine learning may reach a point where human physicians are no longer required. This would in return affect the economy and ultimately destroy patients’ experience in hospitals.


Undoubtedly, machine learning is the future. Refined automation of data collection and replacement of jobs in all industries besides healthcare is inevitable. In as much as machine learning may take away jobs from physicians, it is imperative to continue changing the quality of healthcare for it is more beneficial for the future.