Healthcare

How can machine learning optimize COVID-19 admission decisions?

The ongoing COVID-19 pandemic has highlighted existing gaps in the healthcare system, with many areas with room for improvement brought to the forefront of the medical community’s attention. Fortunately, machine learning technologies offer a unique solution to these issues – something we’ll explore in this blog post.


With any highly contagious airborne virus, there’s always the inevitable reality that some individuals who contract the disease will require hospitalization. Knowing which patients to admit isn’t always black and white, with myriad factors influencing the decision-making process – and considering the current pressure healthcare systems are under, it’s not unlikely that mistakes will be made. It goes without saying that physicians and care providers will be under substantial stress in any public health emergency – and the COVID-19 pandemic is no exception. For some, this might not have any major consequences, but for others, these decisions could have life-altering repercussions.

Fortunately, this is where machine learning steps in. By introducing the power of data analytics and artificial intelligence into clinical decision support systems, data analysts will be able to better determine which patients ought to receive certain kinds of support and deliver these valuable insights to healthcare practitioners accordingly. For example, based on critical information such as a person’s age, weight, disease history and other key factors, state-of-the-art algorithms will be able to determine the appropriate course of treatment – including whether or not hospitalization is a necessity on a case-by-case basis.

Of course, the issue of ethics arises once again in this context. Ensuring full patient consent and confidentiality is a key factor in deciding whether or not to use something as sensitive and private as personal medical information, and appropriate steps must be taken in order to make sure these data are stored safely and securely on a need-to-know basis, with only explicitly relevant individuals given access. 

It goes without saying that the question of whether or not this information ought to be used at all gets brought up a lot in conversations surrounding this topic. Whilst patients naturally should have a right and expectation of privacy, these data exist anyway, and providing the individuals in question give full consent for their anonymized data to be analyzed, it might as well be used in order to derive potentially life-saving insights for themselves and for others.

Taking chances with the lives of patients is something no care provider wants to do, and it’s a high-risk endeavor that can have a huge impact on the quality of life of their patient cohort. The implementation of machine learning technologies is growing traction across all aspects of the medical sector, and the decision-making process is no different.

Healthcare providers are often forced to make difficult choices based on the evidence they have – and it’s completely for these to be made in stressful, time-sensitive, and high-risk situations. Minimizing any possible room for human error is a big first move in providing better quality of care for those who matter most – the patient themselves – and machine learning takes us one step closer to ensuring individuals in need of medical attention receive the appropriate support.

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