Decisions in Medicine: How Data Science is Revolutionizing Health Care

Decisions in Medicine: How Data Science is Revolutionizing Health Care

By Aditi Joshi

Many of us who have read ‘Freakonomics’ by Levitt and Dubner (or seen the film), were amazed by the patterns, trends, and knowledge that could be inferred from large volumes of data. For instance, the relationship between the legalization of abortion in the 1970’s and reduced crime in the early 90’s is an interesting case-study from Freakonomics that has been discussed and debated over the last ten years.

Data science refers to knowledge discovery from large volumes of structured or unstructured data. Through advancements in computing, and thanks to the Internet, data science continues to influence the health care sector. But how exactly can it help us be healthier? An oversimplified answer would be through continuous observation. It is estimated that wearable devices such as fitness trackers or smart watches will add up to 2.5 quintillion bytes of data per day. Upon analysis, this data can potentially provide us with deeper insights into the factors influencing our health such as our diet, movement, sleep, medications, behavioral patterns, etc.

An interesting study analyzing Facebook “Likes”, a seemingly simple digital behavior, proved a ‘Like’ to be a good predictor of diseases including obesity, lifestyle behaviors, and mortality. Data science provides us with insight into our behavioral patterns that, in turn, encourages us to modify health risk behaviors. Research has shown that patients feel empowered when they have the right knowledge, skills, and attitude to influence their lifestyle, and data science has consistently contributed towards this empowerment.

Furthermore, data science helps medical professionals to make accurate decisions by providing more information about diseases. For instance, the clinical features of Alzheimer’s disease are similar to frontotemporal dementia, which can often lead to a misdiagnosis. The analysis of the clinical and pathological features of patients, from a nationwide database at the National Alzheimer’s Coordinating Center (NACC), has enabled doctors to give a more accurate diagnosis to their patients. Further, as observed in genetic research, our ability to analyze large datasets related to genomics has led us to identify genes associated with metabolic disorders such as diabetes, which has opened up the possibility of personalized or ‘precision medicine’.

The Centers for Disease Control and Prevention (CDC) now uses ‘big data’ to monitor influenza outbreaks in the US. Based on flu activity reported via ‘apps’ in real time, the CDC is able to anticipate the flu season. Consequently, it now employs prevention measures such as generating awareness among people, estimating the quantity of vaccine supplies, and providing the required medicines. With help from data scientists, policy makers and medical professionals are able to help in preventing disease outbreaks and epidemics. To sum up, data science enables us to spend healthcare dollars in an effective manner.

On the flip side, the process of data-driven decision making is one that requires continued effort (in data collection and analysis) and, if mismanaged, can be time-consuming and expensive. For instance, supposing that the managing authorities of a medical clinic want to offer wellness programs to low-income families, but all that they have is demographic data. Leveraging data science for decision making may require additional efforts and investments to define the standards for low income in that region and to collect the relevant data.

Despite the challenges, data science is helping us to make better decisions regarding our health. Data scientists and clinical scientists are now increasingly collaborating to advance the science of healthcare. With further advances in the computing technologies and trained personnel, medicine will continue to evolve based on data driven decisions.


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