12 Sep 2022
Importance Of Data Science In The Health Care Sector
Works at Ekeeda
Data Science is rapidly growing in various fields and has nearly penetrated all industries, sectors, and processes across the globe. How can the healthcare industry be left out of the data science storm? Yes! In this blog, we will discuss and understand how data science is transforming the healthcare world. We will understand the various underlying concepts of data science being utilized in medicines, pharmacy, and biotechnologies. Medicines and healthcare tools are the two most important elements to cure human lives. Traditionally, medicines totally relied on clear prescriptions by doctors. For instance, doctors would suggest suitable treatments and medicines based on a patient’s ailments. But, it was never full proof and at the end of the day, even doctors are human so chances of errors were quite evident. But with the advancements of technologies, computers, and in particular, data science techniques, it is now possible to suggest accurate treatment or diagnostic measures for individuals with a forecast of cure rate.
About HealthCare Sector
India's healthcare sector was worth about $280 billion in 2020 and it was estimated to reach up to $372 billion dollars by 2022. The country's healthcare market is the one of the largest sectors in terms of revenue & employment. The health care industry is growing at a rapid pace.
Along with the growth, comes voluminous amount of data that has to be processed and come up with valuable insights for to benefit the humankind and make operations more smooth and automated. Thus, data science plays a critical role in it.
There are several fields in health care that make efficient use of data science techniques such as: Medical Scan, Drugs, Genetics, Diagnosis, Virtual Assistance, etc.
Surgeries Our blog will take you through a few of the fields where data science is used quite evident. But before that, let us inform the health care industry in a need of skilled data scientists that will help them meet the technological advancement and overcome obstacles.
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Let’s understand how data science is used in health sectors –
Data Science In X-Ray & Scanner
The primary and main use of data science in the health care industry is for medical images. There are various image techniques such as X-Ray, CT Scan, MRI Scan, etc. All these techniques will help get a clear picture of the human body parts. Traditionally, doctors use to inspect these images and find the issues. But, it was often difficult to find the microscopic problems and thus, doctors couldn’t suggest a full proof and accurate diagnosis for the patient.
But with the introduction of deep learning technologies in data science, it’s possible to find such microscopic deformities in medical images. With the help of image segmentation, it is possible to search for the microscope defects present in the scanned images.
Also, it’s used in image processing techniques like image recognition with support vector machines, image enhancing or edge detection, etc.
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Data Science In Genetics
Genomics is the study of the sequence and analysis of genomes. A genome basically consists of DNA and all the genes of the organisms. Due to the compilation of the HGP – Human Genome Project, the research has been advancing at a rapid pace and entered in the areas of BIG data and data science. Before this, companies would spend a lot of time and money analyzing the gene sequence. This was an expensive, time-consuming and tedious process. But with the advanced data science tools, it’s just so easy to derive quick insights from the human genes in lesser time and cost.
Research scientists mainly analyze the genomic trends and search for irregularities and defects in them. They will find connections between genetics & the health of the patient.
Data Scientists will use data science techniques to analyze the genetic sequence and come up with correlations between parameters within & the diseases. Research in the genetics field involved getting the right drug that provides deep insights into the way the drug will react to certain genetic issues. A recent field that combines data science and genetics is known as Bioinformatics.
There are several data science tools like Galaxy, SQL, etc. that process the genetic data and reduce the time and cost it takes to process the sequences. SQL is a relational database language that helps to perform querying & retrieve data from genetic databases. Galaxy is again an open-source and GUI-based biomedical research application that performs various operations on genomes. The research is always carried out in computational biology and bioinformatics. But there is still a lot of ocean that remains undiscovered. There are advanced fields that are still being researched for benefit of mankind such as predicting genetic risks, gene expressions, etc.
Drug invention is a highly expensive and complicated field. Pharmaceutical companies heavily rely on data science techniques and tools to solve problems and discover impeccable drugs for people. To invent a drug is an expensive, time-consuming, and complex process that involves heavy spending on testing and trying. Data Science and Machine learning algorithms have revolutionized the process and offer deep insights into optimizing and increase the success rate of predictions. Pharmaceutical companies use insights from patient information such as mutation profiles & patient metadata. It helps the researchers to develop models and find statistical relationships between the medical attributes.
Thus, companies are able to design drugs that address patient health-related problems. Deep learning can find the probability of the development of disease in humans.
The data science algorithms will also help to simulate the drugs in the human body. With the advancements in data science, it is now possible to improve the collection of historical data and assist in the drug invention process. With the combination of genetics & drug databases, it is now possible to develop new medicinal innovations. Also, data scientists can analyze & test the chemical compounds for different cells, genetic changes, etc. With the use of ML algorithms, skilled data scientist researchers can develop models to calculate the prediction from the given variables.
Predictive Analysis With Data Science
Predictive analysis plays a crucial role in the healthcare sector. It’s one of the popular topics in health analysis. Predictive analytics will use data, learn from it, find patterns and come up with accurate predictions from it. It will determine various correlations and association of symptoms, finds patient habits, and diseases and then come up with some meaningful predictions. It helps in improving patient health, helping in disease management, and increasing the efficiency of supply chain management and logistics related to pharmacy. People's health management has become a very important topic and it's quite popular during predictive analytics. It’s a data-driven approach that focuses on preventing disease which is commonly prevalent in the environment.
Through data science, doctors, and medical practitioners can predict the patient’s ailing health conditions, provide preventive measures and go with early treatment. This will help in reducing the risk of further worsening the health of patients. Also, Predictive analytics help in monitoring and assisting the smooth functioning of logistic supply in hospitals & pharmaceutical departments.
Monitor Patient Health Condition
Data Science plays a key role in times of Machine Learning, AI, and IoT (Internet Of Things). These IoT devices that now come as wearable devices like smart gadgets, track heartbeat, BP, sugar, temperature, and other medical parameters have data that is collected and later analyzed with the help of data science. Through data analytical tools, doctors, and surgeons are able to track the patient’s daily health cycle, blood pressure, calorie intake, sugar levels, or other health-related activities. Other than these monitoring sensors, doctors even monitor patients’ health through home devices. For patients with chronic illness, there are various systems that track patients’ movements, monitor physical parameters, and analyze the patterns available in the patient’s data.
It makes the use of real-time analysis to predict whether the patient will face any health-related issues based on the present conditions. Furthermore, it will also help doctors to take necessary decisions to help patients who are in pain, distress, and ailing health conditions.
Track & Prevent Pandemics
Pandemics are no less than shocking waves in lives; that will mentally break you down. Thanks to the advanced data science techniques our system is now able to effectively monitor any haphazard. Data Science plays a pivotal role to monitor patients’ health and suggest necessary steps to be taken in order to prevent potential diseases or health threats. Data Scientists will use powerful predictive analytical tools to detect chronic illness at an early level.
61 million people vaccinated against yellow fever in Africa (2018)
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In prior days, due to lack of analysis techniques, there used to be instances where due to negligibility the diseases would not be caught at the earlier stages.
This was a big threat to patients’ health and economic crisis. As the disease would grow, the costs of medical equipment and medicines would also increase. Thus, today data science plays a crucial role in optimizing the money spent on healthcare.
There are several instances where data science AI machine learning has played an important role to predict diseases or rather epidemics at an early stage. Data Science Researchers have developed a platform that can diagnose coronavirus, H1N1 flu, and swine flues at earlier stages and prevent turning it into a pandemic.
Offer Virtual Assistance
Through disease predictive models, data scientists are able to develop a comprehensive virtual platform that will give complete assistance to the patients. With the help of AI platforms, patients will be able to enter their symptoms into the input and get exact insights into the various diseases they are dealing with.
Some ways in which Virtual Assistance can help doctors:
Focus On Patients
Provide Better Services
Save On Training
Access Mobile Services
High Level Of Flexibility
Also, patients who suffer from issues like depression, anxiety, and diseases like Alzheimer’s, and Parkinson's can use virtual applications to help them inS their daily tasks. Some popular examples of virtual assistants are Dr. Lal PathLabs Virtual Assistant – which helps the customers track COVID-related information, locate the nearest center, and track reports. Octopus Tech Virtual Assistance For Doctors – It’s capable of handling the clinic’s daily administrative tasks at a lesser cost and in-house strength. Mfine - is an AI platform for hospitals capable of track more than 1,200 common diseases based on hundreds of health parameters & more.
Thus, you must have now got an idea on how data science is efficiently used in the health care sector worldwide.
So, this is how data science techniques are used in the field of the healthcare sector.
On A Concluding Note –
Data Science has many applications and uses in the healthcare industry. It’s heavily utilized in medicines and the healthcare sector to improve, and monitor the health of patients, prevent diseases, and predictive early diseases, suggest accurate diagnosis, and for a lot of health-related things.
With the advancements in medical image analysis, it is possible for doctors, and surgeons to see the microscopic issues which are otherwise hard to guess. Thus, data science has revolutionized the way healthcare and the medical industry is handled and benefits human mankind. Data Science is the need of the hour and a rapidly growing field in the world. Data Scientists are in high demand in nearly every industry, and there are plenty of jobs available out there in the market. You need to strike the right chord to enjoy an outstanding data science career.
Sign up with the Ekeeda Data Science course online and take a headstart for an outstanding career in the healthcare industry now!
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