Data Science Applications In The Healthcare Industry

In the new age of technology, data science is rapidly climbing up the ranks with every passing day, offering integrations across all industries. Healthcare is an extremely crucial industry and has been around for centuries. In the past, however, healthcare largely relied solely on medical practitioners to operate. Now, data science has transformed the healthcare industry in numerous ways that improve efficiency, effectiveness, quality, and safety.

Generally, commonly used medical imaging techniques include X-Rays, mammograms, Magnetic Resonance Imaging (MRI), sonography, and CT scans. The purpose of such imaging is to gain a photographic representation of the human body, which doctors then visually examine to look for certain anomalies or unusual signs that might indicate diseases. However, with the integration of data science tools to medical imaging techniques, these processes can be greatly improved. This is achieved through applying the deep learning model, including the implementation of both supervised and unsupervised learning algorithms.

Just like in any other industry, positive customer experience is a top priority in the healthcare sector as well. Due to the intensive nature of this particular industry, it is all the more crucial for medical organisations to ensure top-notch customer service to be able to gain a favourable feedback from the patients. Conventionally, each patient is designated one particular doctor or healthcare specialist who sees to their care personally. However, in large hospitals with a generous number of patients, constant care can be difficult to delegate, and businesses may suffer from a shortage of workforce. Fortunately, data science offers a very practical solution to this problem.

Data science can be used to develop AI-generated apps that interact with the patient in a comprehensive way, asking them sequential questions that allow the patient to report their symptoms to the app. Disease predictive modelling is implemented for this kind of usage when it comes to data science; what hospitals obtain as a result is a virtual assistant that offers many uses, for example:

-Remind the patient to take medicines on time
-Predict initial diagnosis based on patient symptom input
-Advise on precautionary measures based on patient symptom input
-Suggest numerous treatment options

With the recent COVID-19 pandemic, the importance of efficient drug discovery has sky-rocketed, and it has become an even higher priority. But with the help of data science implementation, pharmaceutical companies can also benefit: say goodbye to lengthy, time-consuming, expensive, and labour-demanding processes that go into the development of a single vaccine. Instead, pharmaceutical companies have come to rely on data science and machine learning algorithms to help substantially. Data from patient records such as metadata and mutation profiles are run through the algorithm, which helps optimise the data and provide insights to the company, which are then utilised by the data scientist to obtain statistical relationships between certain attributes by running the data through models developed specifically for this.

Millions and millions of data entries can now be processed rapidly, in the matter of months or weeks, instead of years. This greatly aids facilitation of drug medical research by making incredible use of historical databases.

In addition to this, deep learning algorithms can again be used to derive the probability of a certain infection or disease being developed in a biological system, whereas other algorithms can provide simulations of drug reaction within a human body.
Predictive analysis has the potential to play an extremely crucial role in the healthcare industry. So, with respect to the healthcare industry, predictive analysis can effectively detect patterns of symptoms, and provide accurate predictions of diseases. This is especially important because it allows healthcare facilities to precisely develop disease prevention strategies and allows them to advise their patients accordingly. The most prominent advantages of predictive analysis to healthcare are:

-Quicker documentation of data
-Helps contain chronic disease
-Predicts patient condition
-Predicts demand for certain medications
-Helps in resource management
-Helps in labour management (doctor availability, schedules, etc)

Genomics involves the study of sets of DNA, including the structure, mapping, evolution, and more. This is a significant role of the healthcare industry because genomics research enables high-level and effective treatment options. The issue with genomics in the past was that the millions upon millions of DNA sets present in a body were nearly impossible to process and analyse in any meaningful way. However, now with data science tools such as SQL, MapReduce, Galaxy, and Bioconductor, researchers are able to process the large data sets in no time, compute them, and conduct analysis compressively.

These data science tools provide insights into the response of DNA cells to a particular medicine before it is administered to a patient, so that doctors can perform the treatment efficiently. The useful knowledge regarding genetic structure aids them to make effective strategies to cure diseases.

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