The fields of healthcare and finance may not have a lot in common. But in recent times, the lack of data science and technology has disrupted both these fields.
The United States alone produces about 1.2 billion clinical documents giving life scientists and doctors a sea of data to base their research. Plus, wearable tech’s widespread adoption has made high volumes of health-related information accessible. This massive data volume opens up new avenues for better and more informed healthcare.
To achieve a deeper understanding of the human body, data scientists and machine learning experts worldwide are collecting, structuring, processing, and making sense of an enormous amount of data. Plus, the data science domain has the best potential to revolutionize the health care sector as it can speed-up new drug formulation and help in better diagnosis. Hence, there is a growing demand for health care professionals who know data science applications. Sensing the trend, academic institutes like INSOFE India And MIT now are infusing data science skills in courses like MS in Biomedical Engineering. If you are still unfamiliar with the role of data science in the healthcare industry, here we talk about four areas where data science has revolutionized the healthcare industry.
Medical image analysis
In medical imaging, doctors create a visual representation of the body for clinical analysis and medical intervention. It is highly beneficial as it allows a non-invasive way for doctors to look inside the human body or model organs before a procedure. Medical imaging requires accurate images with subsequent meticulous interpretation. Data analysis refines image analysis by enhancing the resolution, modality difference, and
image size. Supervised and unsupervised learning makes medical imaging hassle-free. The computational capabilities process images at better speed and provide more accuracy.
Scientists collect and analyze health data to find symptoms and identify diseases. Similarly, doctors can track the clinical course of the patients with a confirmed diagnosis. Personalized treatment and informed care using technology can significantly reduce the death rate and result in predictable medical outcomes.
Soon we will see an end to ‘one size fits all’ treatments. Precision medicine will replace those treatments by opening up opportunities for personalized and more effective treatments. Instead of treating a patient for lung cancer, doctors will define each specific symptom of the disease, the patient’s condition, medical history, and genetic information to customize the treatment accordingly to increase the chances for positive outcomes.
The start-up Oncora Medical is an example of using data science to help physicians make informed treatment. The start-up uses historic data from multiple cancer treatment centers and patients’ individual EHR information to give personalized treatment recommendations.
Traditionally, the drug discovery process took 12 years and cost around $2.6 billion, with a single formula passing through a million testing procedures until it got approved. In some cases, the formula gets rejected even after investing plenty of time, effort, and money. However, the advent of data science has shortened the process and made it much more efficient. Machine learning adds steps for each component’s initial screening and predicts the success rates using various biological factors. The algorithms can predict the response and reaction of a specific compound with the body.
Hospitals run on a tight budget and go through complex operational problems, like the number of staff to assign at certain hours to maximize efficiency, manage hospital beds to meet patient demand, and enhance utilization in the operating room.
Analytics software helps to streamline emergency room operations. It also ensures each admitted patient goes through the most efficient order of operations. Emory University Hospital cut wait time by 75 percent by using data science to predict the demand for various lab test types.
Business intelligence is also useful for streamlining billing, identifying patients at risk of late payments or financial difficulties, and coordinating with insurance departments.