Data science in healthcare industry uses modern technologies and techniques to analyse vast amounts of data to find hidden patterns, acquire essential information, and make decisions for businesses. Data science uses sophisticated machine learning techniques to develop forecasting model. One of those technologies whose applications are permeating and transforming every industry is data science. Healthcare, IT, media and entertainment, banking and finance, education, and e-commerce industry are some of the areas where data science is applicable now days.

Why We Use Data Science in Healthcare Industry?

Every human body produces 2 terabytes of data each day, claims a research. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. We now have more sophisticated technology, including Data Science, to handle such a massive volume of data. It makes use of collected data to track the health of patients.

The most significant of them is the competitive need for valuable information in the health market, which makes data science essential to healthcare today. Consumers may receive better-quality healthcare with the aid of the correct data collection procedures. To make educated decisions on the health conditions of their patients, everyone from doctors to health insurance companies to institutions depends on the collection of factual data and its precise analysis.

With the assistance of data science in healthcare, diseases may now be anticipated as early as possible, and that too remotely with cutting-edge appliances powered by machine learning. In order for doctors to develop therapies, mobile applications and smart gadgets continuously gather data regarding heartbeat rates, blood pressure, sugar levels, and other metrics.

Applications and Use Cases of Data Science in Healthcare

• Drug Research

Numerous bodily problems are periodically appearing in the expanding global population. Poor diet, psychological issues, environmental toxins, and physical illness could all contribute to this. Finding treatments or vaccinations for the diseases quickly has grown to be a problem for medical research institutions. It can take millions of test cases for researchers to comprehend the features of the causal agent to develop a formula for a drug. The researchers must do additional experiments on the formula after discovering it.

In the past, it took 10–12 years to examine the data of the millions of test cases stated above. It’s much simpler now thanks to a variety of Data Science applications in the healthcare industry. It may take weeks or months to process the data from millions of test cases. It aids in assessing the drug’s effectiveness through data analysis. As a result, successfully tested vaccination or medication can be made available within less than a year. With the use of data science and machine learning, this is all achievable. Both have completely changed the way all pharmaceutical industry conducts research and development.

How Data Science is used in the field of Genetics in Healthcare.

• Virtual assistance

Data scientists are already designing chatbots and AI platforms to assist people learn more about their health by entering specific health information about themselves and receiving a precise diagnosis. These sites also offer health insurance plans and better lifestyle advice to their users.

Additionally, people with Alzheimer’s, anxiety, sadness, and other psychiatric problems have found virtual aid to be helpful. As a result of the application’s regular reminders to take the necessary actions, these patients’ treatment becomes more effective. This includes taking the right medications, exercising, and eating the right things.

• Tracking Patient Health

Data scientists have created wearable devices for public health that allow clinicians to collect almost majority of this data, including heart rate, sleep patterns, blood glucose, stress levels, and even brain activity. Doctors are able to find and monitor disorders like cardiac or respiratory diseases with the assistance of data science tools and machine learning algorithms.

• Predictive Analytics in Healthcare

A predictive analytical model makes the use of previous data, analysing the information for patterns, and makes precise forecasts. Anything from a patient’s blood pressure and body temperature to sugar level could be included in the data.

Data science predictive models correlate and link every data point to symptoms, routines, and diseases. This makes it possible to determine the stage of a disease, the degree of harm, and the best course of action.

• Diagnostics 

Applications of data science in healthcare can facilitate and accelerate diagnosis, a crucial component of medical services. In addition to helping with early health issue diagnosis, patient data analysis also enables the creation of medical heatmaps that show the demographic trends in disease.

Learn how utilizing Data Science can improve Healthcare future

Fundamentally, there are four basic elements that are causing the healthcare industry to advance rapidly:

• Development of technology
• Digitalization
• Need for reducing treatment costs and duration
• Need for handling large population

To get the intended result, data science has already started targeting all of these. Since data science is already benefiting society, its use in the future will undoubtedly prove to be even more helpful. It will raise the bar for the healthcare sector. Patients will benefit from more individualised care and excellent therapies, while doctors will receive a lot of support.

Data Science Life-cycle

The data science lifecycle includes a range of roles, tools, and processes, which provide analysts with useful and meaningful insights. Here are the steps in a data science project:

• Data ingestion
• Data storage and data processing
• Data analysis
• Communicate

Data ingestion

The data collection phase starts with gathering structured and unstructured data from all relevant sources using a bunch of techniques. The procedure involves entering data by hand, online scraping, and real-time data streaming from machinery and technologies. Unstructured data sources like log files, video, music, photos, the Internet of Things, social media, and more can also be used to gather structured data, such as consumer data.

Data storage and data processing

Using ETL (extract, transform, load) jobs or other data integration tools, this stage involves cleaning, transforming, and merging the data. Prior to being loaded into a data warehouse, data lake, or other storage, this data preparation is vital for enhancing data quality.

Data analysis

Next, data scientists analyze the information in an exploratory manner to look for biases, trends, ranges, and patterns of values. This data analytics examination enables the formulation of assumptions for a/b testing. It also helps analysts to assess whether the data may be used for modelling in predictive analytics, machine learning and deep learning.

Communicate

You can display insights as reports and other graphical representations to help business analysts and other decision-makers better understand the discoveries and how they’re going to affect the organisation. Data scientists can produce visualisations utilising features built into computer languages for data science, such as R or Python, in addition to employing specialised visualisation tools.

Also check: How Data Science is influencing Banking and Finance Industry