7 Applications of Machine Learning in Healthcare
7 Applications of Machine Learning in Healthcare
With the increased data in the healthcare industry, it is only natural that we came up with new and more efficient ways to handle it. From electronic health records and integration to interoperability between different systems, healthcare struggled not only with storing this data but also with proper analysis. That is when machine learning and artificial intelligence came into play.
Ml is a subset of AI – a branch that deals with analyzing algorithms and trying to emulate how humans learn. Essentially, it improves accuracy through repetition, making it possible to make predictions based on previously spotted patterns. Through data, the algorithms uncover insights that can later be used for improved decision-making in any business or clinical process. Let’s take a closer look at seven applications of healthcare machine learning.
Healthcare is not one-size-fits-all. Not every patient responds the same way to every treatment, and it’s the clinician’s job to determine not only possible therapies but also the best one. Precision medicine refers to the delivery of patient-specific therapies to individuals. Due to the population-wide model of drug production, medicine is developed to reach optimal results for most people, defining the parameters. But that leaves the possibility of individuals being outside the margins.
Machine learning in healthcare addresses this issue. By analyzing genomic datasets, it could recommend multiple treatment options, offer several approaches to a patient’s therapy, and assist doctors in clinical decision-making. It can analyze the patient’s history and determine the treatment to which the patient will best respond.
Prediction of Infectious Disease
There is a lot of big data in healthcare. From protected patient information to complete medical histories, healthcare is full of information ready to be used. Cybercriminals know this, which is why healthcare has been the most targeted sector for the past twelve years. Machine learning for healthcare can analyze all this data to develop predictions that could benefit humanity.
Aside from protecting us from online threats, it can also recognize patterns and clusters and potentially determine the trajectory of an infectious disease like COVID-19. While numerous factors contribute to the development and spread of disease, the cognitive capacities of artificial intelligence can adequately assess and recommend the best options in the future, potentially preventing another worldwide pandemic.
Doctors can recognize cancer in MRI images. Through years of education, they have acquired the skills of identifying malignant tumors and starting the treatment upon detection. However, detection often comes too late, so we needed better tools to scan the already digital images. And machine learning seems like the perfect solution.
From X-rays to individual cells, doctors can use this technology to spot anomalies in images even invisible to the naked eye. AI can thus help find cancer in its earliest stages and determine the stage of the tumor or whether the treatment is working. Naturally, the results are only as good as the input. One of the challenges with implementing this technology is that it is only as good as the data it has. Therefore, while this technology won’t be able to replace doctors anytime soon, healthcare machine learning can be a valuable asset to the medical community.
Developing a new drug is usually a long and costly process of trial and error. In fact, 90% of medication doesn’t get past human and animal testing. By that time, countless hours and resources had already been spent. There is a better way.
By feeding an algorithm accurate data and running experiments with robots, machine learning in medicine can go through a large pool of blood samples and tissue to determine the proteins that can help develop a drug. It can test whether a disease reacts to a particular medication and whether there is remission without disrupting healthy cells. In other words, this entire process becomes cheaper, faster, and more efficient.
Diagnostic Clinical Decision Support
Clinical decision support tools are a trademark of contemporary medicine. Machine learning algorithms of CDS can analyze large volumes of data and suggest future treatment while also flagging anomalies and potential therapies. However, much like any program, the output is only as good as the input. We can make a notable example of Watson, an IBM support tool in clinical oncology that, after wrong data input, made incorrect and unsafe recommendations. That is why using proper data when utilizing this technology is essential.
Machine learning for healthcare has already proven its worth with healthy data sets: we use it to predict kidney disease, cancer, and remission of a specific condition. In other words, it is up to us to feed these support tools proper data to use them safely and efficiently.
Detection of Prescription Errors
Each year, between 7 000 and 9 000 people die from a medication error. These errors usually come from incomplete EHR integration, a lousy interface, or a doctor choosing the wrong medication from the drop-down menu.
Healthcare machine learning can identify and compare prescription patterns. If a doctor accidentally chooses a medication that doesn’t match those previously prescribed, this technology can flag it and send it back for review. A clinician can address and rectify the potential mistake, effectively saving lives.
We have already established that this technology’s worth lies in its ability to analyze large chunks of data. That means we can apply it in other segments of the healthcare system. Specifically, it can automate some of the non-health-related administrative processes.
Machine learning in healthcare can identify payment anomalies and suspicious activities. In the vast and constantly-growing healthcare landscape, organizations are burdened by patient data and information beyond their clinical histories. AI can analyze professional, facility, and pharmacy claims and potentially stop criminal activity. By flagging suspicious information, it can prevent security risks and protect your medical organization.
Vicert leverages its decades of experience in the healthcare industry to develop solutions specific to your needs. Book a call with us to find out how we can design and develop software to improve your organization’s workflow and protect it from outside threats.