Big Data Analytics and Data Science- How it is Aiding Healthcare

In healthcare, the demand for big data is at an all-time high right now. It also provides the benefit of solving existing problems, especially when used alongside other techniques like mining, statistics, modeling, machine learning and artificial intelligence allows healthcare professionals to perform predictive analysis. The predictive analysis enables healthcare professionals to detect emerging issues in patients and allows them to take action preemptively, leading to better patient outcomes.

Let’s look a some of the ways in which big data is transforming the healthcare sector:

1.Improving Diagnosis for Prediction and Prevention of Illnesses
Big data technologies in healthcare can be used to track a vast amount of indicative and causative factors of diseases and report the outcomes to doctors. This enables doctors to perform tests that can confirm the presence of the suspected medical condition and start the appropriate treatment.

This helps to improve healthcare overall, by predicting which patients are at a higher risk and ensuring early intervention before the condition gets worse, thereby decreasing costs to a great extent.

For instance, big data can be used to identify patients with a high chance of developing cancer. By analyzing large datasets from Electronic Health Records (EHR), reports from MRI and CT scans, as well as genetic data such as lab values and patient’s medical history, analytics can single out data points and patterns that characterize a typical cancer patient.

Doctors can further investigate this information to identify high-risk patients and monitor them for early signs of contracting cancer. This allows doctors to be proactive and help patients avoid the worse effects of cancer, as it is easier to treat, the sooner it is identified.

2.Expediting Clinical Research to Develop New Treatments
Clinical trials enable the development and testing of innovative new treatments including medicines and procedures. These procedures need their researchers to manage and assess a huge number of test subjects, their individual EHR information, medical histories, their lifestyle, allergies, genetic details and any other data that can be relevant to the study in question.

Manually keeping track of all this data is practically impossible, let alone analyzing it to detect hidden patterns and insights. Data analytics in healthcare can be deployed to analyze such data and detect patterns and insights that can aid in judging the effectiveness of various medicines with greater accuracy.

Researchers and analysts can run through the data from the analytics report to confirm and test their hypothesis before attesting to the effectiveness of a drug or a medical procedure. As a result, new treatment procedures can be commercialized faster, benefiting both the healthcare professionals and their patients.

3.Enabling Real-time Care with the Help of IoT
The Internet of Things (IoT) has enabled doctors and healthcare providers to employ wearables and other bio-sensing devices in order to monitor patient health after surgeries and other invasive treatments. Such wearable devices are able to constantly gather data on key health parameters of the wearer after they have been discharged from hospitals and record the same wearer’s health records.

Any irregularities in the health parameters that suggest a potential health complication can be reported to both the relevant doctors and patients in real-time. Patients and doctors can quickly schedule consultations to probe further and detect any signs of relapse or the onset of potential complications by receiving their health alerts in real-time. This allows them to mitigate any potential negative side effects of surgeries and treatments.

4.Placing importance on Value-based Care by Reducing the Chances of Error
One of the leading causes of death and a core issue that every healthcare institution needs to focus on is medical error. Healthcare data analytics can be utilized to negate any human error occurring in-patient care, whether it is occurring at the doctor’s table or the back office. It can analyze the prescribed medication and validate its effectiveness with user data to flag potential discrepancies or errors which can be life-saving.

In conclusion
On top of the precision and certainty provided by big data analytics, it also provides healthcare institutions with another key benefit — time. Interpreting analytics reports, which tend to be in the form of massive quantitative tables, take a lot of time and effort to understand and interpret.

If handled without data scientists and analysts, converting these figures into medical references may not be a straightforward process for healthcare professionals.

As a result, it is crucial to deliver the analytics reports in a language that doctors can understand. To combat this, healthcare institutions can leverage automated report writing with Natural Language Generation (NLG) which uses AI technology to convert big data into simplified text summaries.

Healthcare professionals can easily understand the analytics reports and make decisions faster using these text reports. As a result, it allows them to treat more patients while minimizing the average waiting time between two patients. It also leads to expediting treatments and potentially saving countless more lives.