December 9, 2024

Kathlyn Phan

12th Grade

Fountain Valley High School

In recent years, data analytics have played a vital role in pushing innovation in the healthcare industry by providing insight into medical research, disease likelihood, and treatment effectiveness. Data analytic tools can process and analyze massive amounts of medical information in a short amount of time, providing fast and accurate results. Doctors, researchers, and scientists can advance medicine, improve treatment effectiveness, and personalized patient care–precision medicine–by utilizing data science. 

Powerful computer systems that can quickly scan and analyze multiple large databases are often referred to as “big data.” These datasets can be derived from clinical trial results, patient medical records, and genetic information. Using its pattern recognition capabilities, big data can accurately predict disease likelihood in patients for early diagnosis. For ultrasound and MRI scans, these highly intelligent computer systems can point out anomalies and possible concerns in the patient’s body. This includes tumors, inflections, and disease spread. Complicated and possibly terminal diseases like cancer and diabetes can be combatted through early diagnosis. Treating these diseases in their early stages can optimize treatment effectiveness while minimizing its cost. With early detection and diagnosis, patients can reach out to their healthcare providers and receive cost-effective treatment because these diseases are much more complicated and expensive to treat once they reach a terminal state. Thanks to big data, sick patients have more access to affordable healthcare and have a higher chance of survival. 

Big data also presents new opportunities in medical research by allowing scientists to study the spread of disease. Prior to big data technology, scientists were limited to relying on smaller studies with less information to base their research on. Data science has made the analysis of patient records, genetic sequences, and other health data from all over the world much more accessible. With more data to base their medical findings on, scientists can draw more connections and observe more trends in disease spread to better understand how to treat them. 

A real-world application of this would be in cancer research. Cancer research heavily relies on the patient’s DNA and genomic data. Early methods of cancer detection consist of physical exams and imaging tests. However, the problem with these traditional methods is that they sometimes detect cancer only after it has spread throughout the body. With data science, scientists can identify biomarkers in medical tests, which are substances in the body that indicate the presence of cancer. Using the information from one patient’s disease or tumor, machine learning algorithms can compare that data with thousands of other patients’ data to target personal needs and build a better treatment plan. Data science in cancer research can advance the field of clinical oncology. 

Along with medical research, big data can be used to discover and develop new drugs for treatment. Developing new medicine is a very complex and costly process that often takes scientists years because they have to ensure the drug’s safety and have enough funding for their intensive testing. Failure rates for new drug development can be as high as 90%. Big data can help accelerate this complex process by identifying potential new drugs faster and analyzing their chemical compounds. Machine learning algorithms can test thousands of potential drugs by analyzing their chemical components and how they interact with disease to reduce toxicity. Although data science can’t entirely ensure a new drug’s safety, it can narrow down the candidates for further research and development. 

Big data can help revolutionize medicine by providing powerful data analytic tools for researchers and accurate medical diagnoses for doctors. By traversing thousands of patients’ data, its machine learning algorithm outputs new methods for early disease detection, personalized treatments, and drug discovery. As big data continues to evolve, scientists, doctors, and patients can rely on its advanced medical capabilities.