How Data Science Boosts Healthcare: You Should Know How?

No wonder how data science leverage diverse fields, including finance, healthcare, education, banking, e-commerce and many more. Data science continuously provides support to healthcare to align with new algorithms of machine learning and data science.

The healthcare sector is producing a huge number of data. Almost 30% of the world’s data comes from healthcare industry. Some low-tech data sources such as clinical trials, electronic medical records (EMRs) and care management data help predate data analysis.

Although a huge number of data is available from healthcare sector but most of them are used to improve access to healthcare, drug development and patient care. In the U.S, only 3% of data scientists serve healthcare industry.

COVID-19 Pandemic Tracking:

When COVID-19 spread worldwide and ended as a severe lockdown in almost all countries, no governments have a system to trace the patient. Even the U.S failed to launch a national contact tracing system. They only have a way to survey to predict coronavirus hotspot location and the rate of transmission.

In pandemic situation, a website was launched to track coronavirus patient’s records: the website Rt. Live was created by the co-creator of Instagram, Mike Krieger and Kevin Systrom.

The website pulls data from the COVID tracking project and provides user authentic information and metrics about coronavirus spread.

Improvement in Clinical Trial Research:

With the help of machine learning algorithms, a visible advancement has been seen. Healthcare companies can extract relevant EMR details to simple physician prescriptions. In this way, companies or hospitals can get all information about patients very easily and conveniently.  Even they can collect data about those patients who are on a clinical trial with a single click.

Advancement in the Diagnosis Process:

With the help of data science and machine learning, a doctor can access patient’s data. With just a single click, the doctor enables to diagnose and treat on behalf of patient history. Doctors and physicians use the drug record to understand the drug’s particular feature and how a particular genetic feature reacts against the drug. Moreover, they now better know how a new drug can impact patients’ health—these electronic records fueling healthcare and bring a revolution in this sector.

Advance Drug Development:

Data science and machine learning help a lot in discovering drugs. The process of drug development requires research and a lot of hard work. The highly complicated process involves many steps to discover a drug. Before testing a drug in the market, companies need billion of testing, time and money. To officially launch a medicine in the market, a company requires almost 12 years for testing and research.

Data science helps to shorten the process, from step one to the last step, by predicting the compound features against specific genetic features to the screening and success rate of medicine.

Improved Patient Care:

 With the help of data science and machine learning, doctors and healthcare staff enable to serve their patients better. Now it is possible to predict actionable interventions from a patient’s history or medical records. Now, the healthcare sector leverage all sub-sectors; hence, improved patient care has been seen.

Accurate Epidemic Prediction:

With machine learning and data science algorithms predicting and monitoring is being easy. Epidemic outbakes can be forecast based on the information collected from the net, satellite and social platforms. This is another breakthrough step towards better healthcare.

Medical Image Analysis and Diagnosis:

Data science serves healthcare efficiently. A great example is the medical image analysis and diagnosis sector. A doctor can diagnose diseases by some key procedures such as CT scan, X-rays, MRIs, and ultrasound to detect artery stenosis, detecting tumors, organ delineation.

With the advancement of computing technologies, machines and image recognition tools, the diagnosis process has changed into an easy and straightforward way.

Hence, what are you thinking about?

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