New technologies like Machine Learning, Artificial Intelligence, Deep Learning, etc., are revolutionizing every industry, and data science in healthcare is a promising tech advancement helping doctors. With the help of new tech, health organizations can tap into precious insights, get valuable information, and optimize their in-house operations to improve patient care and reduce emergencies.
Data science solutions in the medical sector are essential in helping healthcare professionals harness data analytics to provide better diagnoses to patients. This article reveals the primary applications of data science in the healthcare sector that are transforming the medical industry.
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Why Use Data Science In Healthcare?
Every human body generates around 2 terabytes of data per day with information related to daily activities. The data generated mainly includes various aspects such as brain activity, stress and sugar level, heart rate, and more. Data science is one of the latest technologies that help health experts handle large data amounts and monitor patients’ health using the information.
Integrating modern data science tools in the medical industry allows doctors to predict health symptoms at an early stage. As a result, they can offer immediate care to reduce the chances of negative consequences on patients’ health. Furthermore, the devices built with the data science algorithms store necessary information about patients’ health and help doctors understand their health conditions.
8 Data Science Healthcare Applications
Data science helps streamline healthcare facilities and processes while improving the accuracy of diagnosis. A recent report by Vantage Market Research indicates that the adoption of big data analytics in the healthcare market will reach USD 79.23 billion by 2028. The reason behind the growth of data science in healthcare is the vital use cases and applications of modern technology, like:
One of the most vital use cases of data science in healthcare is medical imaging which helps professionals identify distinct medical conditions in patients. The latest techniques, like X-rays, mammography, CT scans, MRI scans, etc., are advanced tools assisting doctors in visualizing the inner human body parts and finding irregularities and deformities in the scanned images.
Genomics and Genetics
Individuals have different genetic makeup, and data science is helping expert doctors to analyze gene sequences, detect diseases, and tailor-made patient care. The latest deep learning technologies in data science allows experts to integrate multiple data strands with genetic information. Using data science in healthcare helps caregivers identify the correlation between disease and generic parameters to provide better care at a lower rate.
The primary role of healthcare professionals in the medical sector is to create effective drugs for patients and help them live healthier lives. Data science, deep learning, and Machine Learning algorithms are shaping the drug discovery process. The extensive insights obtained from patient metadata and mutation profiles help researchers develop models, design drugs, and boost the success rate of the drug.
The healthcare industry relies heavily on the predictive analytics model that uses historical data to find health patterns and forecast accurate medicine predictions. With predictive analytics models, healthcare professionals find correlations between habits and diseases to predict diagnosis with a high success rate.
Monitoring Patients Health
IoT (Internet of Things) devices powered by data science are gaining immense popularity in improving healthcare facilities. The analytical tools that track temperature, blood pressure, heartbeat, and other medical parameters help doctors to take necessary steps and help patients avoid health risks.
Tracking & Preventing Diseases
Data science algorithms and predictive analytics tools detect chronic diseases early and provide medications before the ailment becomes fatal. Using data science algorithms enables patients to avoid emergencies and reduce the high cost of curing diseases.
The predictive modeling and virtual assistance applications are designed to help patients get insights about their medical condition. Patients can use this platform by entering the symptoms and getting complete information about the possible diseases. Two of the best examples of virtual assistance platforms in healthcare are Woebot (a chatbot developed by Stanford University for patients dealing with depression) and Ada (a Berlin startup that predicts diseases).
Optimize Clinical Performance
Data science in healthcare not only helps in improved patient care but also assists professionals in optimizing clinical performance. Data obtained from disparate sources can effectively be used to optimize clinical staff scheduling, manage supplies, reduce patient wait times, and build efficient healthcare programs.
Inferenz has a certified team of data scientists and analysts who help healthcare businesses integrate the latest tools and technologies. The team worked with a pharmaceutical company from Germany to help doctors implement advanced tools to predict diseases and save patients from emergency care. Read the comprehensive case study here.
Get Ready To Integrate Data Science in Healthcare
The modern world is driven by data, and the healthcare industry can no longer afford to neglect to implement the latest tools like data science. With the aid of data science in healthcare, experts can use scattered information to get a holistic view of a patient’s fitness and improve medical treatment plans.
In addition, the data science tools streamline the in-house operations and reduce caregiving costs that benefit patients and organizations alike. If you are a healthcare organization intending to use data science in healthcare, Inferenz experts can help you implement modern tools based on your specific needs.