Healthcare is one of the most demanding fields in terms of data management. Not only do physicians need to diagnose and treat patients, but they also need to make sense of the data presented to them. One problem doctors face is that there are too many pieces of information to make sense of, and often they end up relying on their intuition rather than cold, complex data. Such a scenario can lead to mistakes or missed opportunities for treatment. However, with the help of data science, healthcare providers can use different techniques to process all this information and come up with better diagnoses and treatments. The benefits of data science in healthcare are endless!
Data-driven decisions
The age of information has left physicians with an overwhelming amount of data. The problem is that they have to make sense of this data to make sound decisions about their patients’ treatments.
Data science can help healthcare providers process all these pieces of information and come up with better diagnoses and treatments. One study found that when oncologists used IBM Watson, it enabled them to diagnose lung cancer more accurately than doctors working alone.
Paired with AI, data science assisted in predicting the risk of a patient’s mortality or the severity of their disease. The knowledge presents physicians with a valuable tool for determining the best course of treatment for individual patients.
Doctors are becoming more data-driven as they rely on soft sciences like statistics, math, machine learning, and pattern recognition rather than intuition to make patient treatment decisions.
Data-driven public health initiatives
Data science has the power to improve public health initiatives. Take the CDC’s National Violent Death Reporting System (NVDRS). Data scientists partnered with the CDC to develop a system to assess different data points and identify trends in violent deaths, contributing to more informed prevention strategies.
Additionally, data science helps analyze large datasets of all sorts of diseases. For instance, researchers use machine learning algorithms to study genomic mutations in cancerous cells that new drugs might target.
For example, IBM Watson uses data analytics and artificial intelligence to create new cancer treatments by studying genomic mutations in cancerous cells. This powerful technology has been able to identify potential therapies that humans have missed for decades.
Disease tracking – COVID-19
One of the many advantages of data science in healthcare is tracking and monitoring diseases. For example, it’s possible to detect when an infection spreads by looking at social media posts – COVID-19 as an example. Data science can identify patterns in the way people talk about their symptoms, allowing researchers to identify trends for certain diseases.
A considerable benefit of data science in healthcare is that it can help improve the effectiveness of vaccinations. Data scientists can develop optimal vaccination schedules by examining trends in disease outbreaks.
Data science can also help doctors make better predictions about patients’ mental health. When doctors cannot diagnose someone with bipolar disorder or depression, they can rely on data analysis showing patterns for these illnesses over time. The analysis results allow them to come up with more accurate diagnoses and give people the treatment they need, making happier patients!
Use in Radiography
When it comes to data, radiologists are faced with many pieces to put together. To process the data, they sometimes rely on their intuition and judgement to make decisions about diagnosis and treatment. Using instincts can lead to mistakes or missed opportunities for treatment.
Data science can help radiologists make better decisions by processing this information fast and accurately. One way that data science can be beneficial in radiography is for segmentation purposes. Segmentation is the process of dividing images into areas that are homogeneous in appearance. For example, if an image is too dark, one might segment it into two pieces: the foreground and the background.
Data scientists use various tools to segment images based on content such as texture, colour, and contrast. When radiology technicians use these different techniques, they can see things that aren’t visible with their human eyes, such as cancers or other abnormalities, before presenting themselves as symptoms later in life.
Conclusion
Data science is an emerging field, and its benefits for healthcare are still under discovery. As more and more data becomes available, we will make better and more informed decisions about public health and disease prevention.
Be sure to keep an eye out for emerging data science developments in the healthcare field.
Next week I will discuss the application of data science in finance.
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