Big Data Analytics in Medicine: Five examples of how cloud computing is changing the face of healthcare

March 3, 2022

Cloud computing, the on-demand availability of computational resources such as data storage and computing power offers a myriad of opportunities in healthcare. From sophisticated computer models that predict how and where pandemics will spread to patient health information readily available to physicians to make life-saving decisions, healthcare has tapped into this resource unconditionally. 

Big data analytics is the process of detecting trends and correlations in large amounts of unstructured data to improve decision outcomes. This article will focus on five examples of big data analytics used in healthcare today.

1 - Medical imaging diagnostics and patient selection

Big data analytics is making a real impact in diagnostics, which includes imaging and patient selection. In the medical community, it is a well-known fact that the most important step in approaching a pathology is a correct diagnosis. Alongside consultation by a well-trained health care professional, medical imaging plays a vital role in correctly identifying a disease. 

More than 500 million imaging procedures are performed in the U.S. every year according to iDataResearch. That is an overwhelming number of images radiologists must analyze, in a short amount of time. Big data analytics powered by cloud computing platforms can change the way images are read and how diagnostics are given.

Advanced algorithms, developed by analyzing hundreds of thousands of images, identifying hidden patterns in images' pixels, and converting them into numbers, help healthcare professionals establish a diagnosis.

An accurate diagnosis helps doctors and nurses select patients who would benefit most from life-saving procedures and ultimately improve clinical success rates.

2 - Precision medicine in cancer research

Big data analytics may eventually help find a cure for cancer. Thanks to private and government programs such as Cancer Moonshot in the U.S., medical researchers can use large amounts of data collection over decades and from institutions across the globe to analyze treatment plans and recovery rates. Using big data analytics, finding treatments that have the highest probability of success in clinical settings is made possible. 

Examining tumor samples and biopsy reports linked with patient records, in biobanks, medical researchers can use algorithms on a cloud computing platform to predict how mutations and cancer biomarkers interact with treatments and find trends that eventually improve patient outcomes. This is one of the first steps to achieve greater collaboration in cancer research.

Another benefit of big data analytics in healthcare is the availability of data to all medical researchers throughout a worldwide cancer database.  The genetic sequencing of cancerous tissue samples from clinical trial patients is just one example of shared data. 

3 - Telemedicine 

Telemedicine has been around for more than 40 years. However, recently video conferencing, smartphones, and wireless wearable devices have enabled hospitals and public health institutions to take full advantage of telemedicine. 

Telemedicine is primarily used for consultations, diagnostics, and remote patient monitoring.  Health care professionals also use telemedicine for medical education, training, and sharing best practices. 

Additional uses of telemedicine include:

  • telesurgery using high-speed real-time data delivery and precision robots
  • predictive analytics for personalized treatment plans
  • prediction of acute medical events
  • prevent patient deterioration

The image below shows the dramatic impact the COVID-19 pandemic had on telemedicine in the U.S.

Trends in telemedicine – COVID 19 impact

4 - Self-harm and suicide prevention

Almost 1/5 of the world's population will harm themselves during their lifetime, and nearly 800,000 people die of suicide every year. 

Health care institutions with high daily patient traffic can use data analytics to identify at-risk patients and help prevent tragedies before they happen. A 2018 study conducted by Kaiser Permanente Mental Health Research Network used a predictive algorithm on a cloud computing platform to analyze answers from a standard depression questionnaire. The project resulted in identifying, with great accuracy, persons who had an increased risk of a suicide attempt and is an excellent example of how collaboration between health care professionals and high-performance computing companies can help our society fight this dreadful plague that ravages our community.

5 - Predictive analytics to determine drug responses

Immunotherapy drugs have long been hailed as the next frontier in cancer and degenerative disease research. Using machine learning and predictive analytics, researchers in health care and drug development can efficiently identify patients who will get the most benefit from a particular immunotherapy drug. 

Using cloud computing and a machine learning model, Cleveland Clinic researchers used big data and predictive analytics to determine whether immune checkpoint blockade (ICB), a class of immunotherapy drugs, will be effective in patients with cancer. The tool focuses on patient-specific biological and clinical factors to predict patients' responses to the drugs and their survival outcomes. 

Since ICB drugs are not effective in all cancer types, it is essential to match patients to their treatment resulting in increasing success rates and avoiding unnecessary burdens on both the patients and the health care system. 

These examples, to name a few, showcase how high-performance cloud computing and big data analytics are changing the healthcare industry and improving the quality of life. 

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