How Data Science Technology Is Used in Healthcare

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Data Science has become an essential field of almost every industry. Healthcare is one of the sectors that has improved the medical services and research to a new height. Nowadays, doctors and medical staff use advanced medical equipment that generates and stores a large amount of data. Reportedly, around 1.2 billion clinical files are created on an annual level in the US only. About 80% of the data is unstructured and raw, and in the form of notes written by staff, images, scans and other files. 

Other than healthcare, industries like marketing and gaming have been successfully working with data algorithms for improving their working routines. Online casino gaming is using personalised offers and recommendations based on a player’s preferences and style of gaming. So, for example, if you played mystery-themed games inspired by the secrets of Egypt, game reviews like this Book of Dead slot review will be recommended for you to check out. This way, markets become more engaged and familiar with their audience while having the chance to grow even more. 

Handling big data and correctly classifying it became a tiring task for doctors and scientists. This is where Data Science provides opportunities for the healthcare industries to organise and analyse data in an efficient way. 

Let’s go through different use cases of Data Science in the healthcare industry.

Medical Image Analysis 

IBM, the well-known global company for providing technology solutions, concluded that medical images contain around 90% of the medical data. Doctors use the medical imaging technique to visualise the interior parts of the body effectively. Also, the visuals help to diagnose and treat any disorder or disease. The insights from these images make a significant difference in how the patient is treated. 

Some of the imaging techniques are the MRI, X-ray, Mammography etc. Various methods are used to handle the discrepancies in the resolution and modality of these images. Several machine learning algorithms can be used to describe the images and then extract the insights properly to give correct diagnose and treatment solution. 

Genetics and Genomics  

Genomic experts are those responsible for identifying the DNA sequences and then analyse them to understand the disease. Do you wonder how Data Science can contribute to DNA segmentation? The aim here is to provide personalised treatment for every patient. Moreover, it can analyse the impact of the DNA on the patient’s health and predict how the prescribed medicines will affect the patient. The genetic data can be integrated with other medical data to find the relation between genetics, disease and patient’s response to the medicine. Technologies that serve for this purpose are: 

Map Reduce: provides the mapping of the genetic sequences that reduce the time required for data processing. 

SQL, Python: helps in fetching the genomic data, file manipulation and computations involved in the process. 

The research is still going on in this field, and there are still areas left for exploring.

Drug discovery

The discovery and implementation of a new drug is a complex process estimated at around $2.6 billion. It takes around ten years to take medicine from the lab to the market. 

Pharmacy is using Data Science to simplify and shorten the number of processes and testing involved. Researchers are using various Machine Learning algorithms and mathematical models to predict how drugs will affect the human body. 

The purpose of computational drug discovery is to develop computer models equivalent to biological networks. This will further help in predicting the future outcomes of the drugs more accurately and efficiently. It helps in choosing the experiments that should be performed and predicting the possible side effects. 

Virtual assistance and customer support for patients 

The healthcare industry is striving to improve the clinical processes to the extent that it removes the necessity for the patients to meet the doctor in person for general questions. Mobile apps and AI-powered chatbots can work in various cases. You describe your symptoms and tell your doubts. Then, the chatbot will answer on your medical status by linking the symptoms you provided. These apps can also remind you of taking your medicine on time, do the regular health check-up etc. Medical care applications make use of ML algorithms that use natural language processing to analyse the customer’s data and provide a personalised experience. 

Data Management and Governance 

The time when all data was managed in handwritten or printed registers is long gone. Data Science helps to convert all the paperwork in a promising digital form by using machine learning algorithms. It also makes sure that all the data is readily available to the people involved in healthcare at any point in time. The past and present medical data of a patient once stored at a single place will help the doctors to understand the patient and make better decisions. The different algorithms help to extract essential insights from the currently available data of the patient. The data is then compared to those records stored in the database to identify the best possible treatment for the patient

Disease Prevention and Predictive Analysis 

One of the most significant advantages that Data Science provided in healthcare is providing the possibility to predict and prevent events. That can take place during an entire treatment process in advance. Many severe diseases can be cured if identified at the right time. 

Predicting the diseases and risks in the treatment will help in figuring out better prevention plans. Predictive models are built with the help of data such as the patient’s history, doctor’s notes, genetic research details etc. These models find the correlations, interconnections of symptoms, search for similar cases and analyse the impact of biological factors. Such predictive algorithms can help to save the life of a person by detecting the disease at the right time. It also helps the doctors to take various decisions involved in the treatment more precisely. 

Conclusion

From predicting treatment outcomes to curing diseases and making patient care more effective-data science has proven to be a valuable contribution to the future of the industry. Following the abovementioned segments, health innovation is driven by three main factors:

  • Tech advancements 
  • Growth of digital consumerism
  • Need to fight increasing and unnecessary costs 

The number of healthcare institutions making data-driven decisions increases rapidly. In 2019, over 60% of healthcare execs said they’ve been using predictive analytics and successfully implementing it in the working routine. Back in 2015, only 15% of hospitals and medical centres deployed data science solutions. This shows that the number grows at a steady pace, and makes healthcare more efficient, accessible and personalised. 

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