Be a Step Ahead of Diseases with Data Analytics

Many industries have been using data analytics to make predictions about future events and use them to their advantage. It’s time for the healthcare industry to now utilize the innovative as well as the productive technology.

FREMONT, CA: Hospitals and healthcare systems are shifting toward value-based care models, which are perched to increase the use of predictive data analytics to make available improved and more comprehensive population health management services to patients. The technology, if mingled with the traditional way to care, can play an essential role in developing health outcomes for patients as well as help hospitals cut down on readmission penalties.

Ground-Breaking System:

The technology behind predictive data analytics tends to deliver life-changing diagnostic information for healthcare providers by using refined algorithms. Built with AI and ML tools, the algorithms generated can examine millions of healthcare claims and electronic medical records. Predictive models can confirm existing diagnoses and categorize the patients who were left undetected or misdiagnosed. To further help doctors detect diseases well, improve treatment plans, and rectify misdiagnosis, the technology generates provider-friendly reports from the big data black boxes.

Identifying Patients with Autoimmune Disease:

The capability to find and foretell autoimmune diseases through data can be a life-saver as they are challenging to detect early because of the non-specific symptoms. 

Traditionally, the patients with autoimmune diseases had to wait for their symptoms to kick-in before a definitive diagnosis was made, and the treatment began. It can take as long as three to five years to treat an autoimmune disease like multiple sclerosis using the traditional methods, including spinal taps, MRIs, and other tests along with possibilities of misdiagnosis. The expenditure of giving healthcare for patients with an autoimmune disease tends to see an increase even before a definitive diagnosis and then increases swiftly in case of any adverse events, or even relapses or flares. The consequences can be solved by employing predictive analytics tools that can uncover patterns within large datasets that can help to facilitate an early diagnosis with improved patient health and reduced costs.

Futuristic Approach:

The healthcare industry is on the verge of harnessing the supremacy and commitment of predictive analytics, and hospitals and health systems will play an imperative role. The technologies are likely to be critical in deriving meaningful information from data to monitor patients and find new ways of collecting, processing, and analyzing the data. The new approaches provide improved data analytics platforms, which are capable of analyzing multiple datasets by uncovering patterns and helping in predicting the outcomes.