Data Science in Healthcare

Data Science and Artificial Intelligence are rapidly growing to occupy all industries across the world today. Plenty of transformations have already been made in the healthcare sector using Artificial Intelligence and it’s principles, yet enormous potential remains hidden. This blog aims to highlight how various underlying concepts of data analytics can be employed in the medicine industry.

Traditionally, healthcare relied on the sole discretion advised by the doctor. A doctor used to come up with a treatment plan based on the patient’s symptoms. As technology advanced, Data Science made it possible to give accurate diagnostic results. Doctors could now see in depth about a disease, analyze and then come to a conclusion about your health. Also, with the advent of various innovative tools and technologies, doctors are able to monitor patients’ conditions from remote locations.

While we are already aware of the various use cases of data science in the healthcare field like medical imaging tests like CT Scans, MRI’s, CAT Scans etc, there’s still a lot to look at.  If you really think about it, you’ll see that the healthcare sector is swimming in data. With the widespread adoption of virtual pharmacies, wearable fitness devices, digital health coaches and more, it is now estimated that 33% of the world’s stored data comes from healthcare industry. And this figure does not include the semi-traditional, low on technology data sources like EMRs- Electronic Medical Records and clinical trials. Now that we are aware that health industry generates massive amounts of data, let us look at how can it be utilized to benefit all parties involved. Healthcare Analytics has the potential to predict outbreaks of an epidemic, reduce costs of treatment, avoid the spread of preventable diseases and more. All of that can be done by implementing Big Data in Healthcare.

Big Data refers to the vast quantities of information created by digitization of everything, that gets consolidated and analyzed by specific technologies. Thus, the application of big data analytics in healthcare pose for numerous benefits and positive outcomes. Like there are KPI’s in all the industries, doctors believe there are Key Performance Indicators in the Healthcare sector too. Doctors wish to understand as much as they can about a patient and his illness in the early stages only. This is because they could pick up warning signs of any serious illness and accordingly treat the patient. The KPI’s required for this come from Big Data. With Big Data in place, it is easier to create comprehensive healthcare reports and convert them into relevant critical insights which can then be used to provide better care. Now that we have covered the importance of Big Data in Healthcare, let’s see its real- world applications –

• Big Data might help in curing Cancer– Various Community Oncology programs are now running worldwide which rely solely on Big Data. It is now possible for doctors to prescribe a drug for a new strain of cancer that they have not seen before. How , you ask? It’s because they have access to enormous amounts of data( Doctor’s notes, prescriptions, cancer researches) which is structured in a way that a doctor can easily get what he is looking for. A doctor from the remote parts of Asia can access information about this new strain of Cancer which was already present in Mount Sinai, New York. Big Data has made it all possible. The biggest adoption came in the form if EHR’s.
• Electronic Health Records– This is by far the most widespread application of Big Data in Healthcare Analytics. The EHR’s can be shared via secure information systems and are available for healthcare providers from both the private and public sectors.
• Increasing Patient Engagement– The data produced by millions of wearable fitness devices by consumers – and hence potential patients, can be coupled with other data to identify potential health risks.
• Predictive Analytics- We now know that data in healthcare can range from anything like patient’s blood glucose levels, blood pressure, body temperature to results of various clinical trials of a new drug. In predictive analytics, this data is analyzed for patterns and correlations. This process helps to identify the symptoms and stage of a disease, the extent of damage and more.
• Enhance Security– Data Breach in hospitals and research organizations can be a major concern as personal data is worth billions on the black market.  The consequences can be severe. Big Data Analytics can help to prevent these security threats by identifying changes in network traffic.

After being informed about all Data Science can do for Heathcare, it’s time to see what Data Scientists can do too. We, at Rubikon Labs work towards implementing all techniques and applications of Data Science for integrating them in healthcare softwares. Our Data Scientists work towards a common goal- making life healthier by helping healthcare providers. Many front line medical workers lost their lives while protecting us in the Covid-19 pandemic. We wish to develop an environment which prevents another outbreak of the pandemic, and we can do so by joining medicine with data science. Reach out to us at Rubikon Labs to know more!