Healthcare Using Machine Learning is the process of automating processes in the healthcare system using machines that can learn from themselves and apply knowledge to deliver more accurate results at a faster pace. Of course, machine learning is a lot more complex in definition and in use, but this definition simplifies what Healthcare Using Machine Learning embodies at its core.
However, machine learning lends itself to some processes better than others. Algorithms can provide immediate benefit to disciplines with processes that are reproducible or standardized. Also, those with large image datasets, such as radiology, cardiology, and pathology, are strong candidates. Machine learning can be trained to look at images, identify abnormalities, and point to areas that need attention, thus improving the accuracy of doctors or nurses at grander scales. Machine learning can offer an objective opinion to improve efficiency, reliability, and accuracy. Below are some of the ways machine learning can benefit healthcare organizations
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.
Health systems can determine who needs reminders, who needs financial assistance, and how the likelihood of payment changes over time and after particular events.
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.
The industry continues to build and experiment with complex neural networks, machine learning systems, and even question-answering engines like Watson because they are fun and interesting, and they improve our understanding of larger artificial intelligence pursuits. And certainly, all of these AI-adjacent technologies have the potential to dramatically alter any industry. Given how likely AI-based start-ups are to secure venture funding, it’s easy to imagine that AI is making sweeping, revolutionary changes in business and people’s lives. But with so many AI projects focused on relatively innocuous applications like photo-identification or Black Friday insights and even still coming up short, it shows this technology still has a long way to go before some of these benefits listed above can be reaped.
In the far future, machine learning will be truly intelligent and able to operate without context but that future is really far away. Even though some people argue that it will come sooner rather than later, most of it is just being sensationalized. But for now, regardless of what we are led to think by the media’s more apocalyptic interpretations of machine learning and artificial intelligence, existing technologies are not nearly advanced enough to master simple healthcare tasks on their own, let alone pose existential threats to humanity.