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5 Benefits of Big Data Analytics in Telemedicine

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The healthcare industry benefited from big data analytics because it enabled better and faster decision-making, modeling and prediction of future outcomes, and greater healthcare intelligence. Here are 5 benefits of big data analytics in telemedicine.
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According to the CDC, telemedicine is the use of electronic information and telecommunication technology to deliver the care a patient needs without face-to-face interaction. Traditionally, telemedicine has been utilized to help patients in rural areas for years due to distance and transportation difficulties. However, with the rapid expansion of wireless connectivity and advancements in technology resources, telemedicine is becoming a more common medical tool for treatment, monitoring, and management of patients’ health in a more efficient and cost-effective way.

Furthermore, with the outbreak of the COVID-19 pandemic, many physicians resorted to the use of telemedicine to allow patients to adhere to quarantine and travel restrictions while receiving continuous care.

Big Data and Data Analytics in Healthcare?

In the healthcare industry, “big data” refers to the vast amount of data generated by utilizing advanced technology in collecting patients’ data to aid in the delivery of care.

Big data analytics, on the other hand, is a term that refers to the practice of utilizing advanced analytical techniques to process large volumes of data at high velocity and a diverse range of data types, including structured, semi-structured, and unstructured data from a variety of sources and in sizes ranging from terabytes to zettabytes.

The healthcare industry benefited from big data analytics because it enabled better and faster decision-making, modeling and prediction of future outcomes, and greater healthcare intelligence. Here are 5 benefits of big data analytics in telemedicine.

5 Benefits of Big Data Analytics in Telemedicine:

1. Early identification and prevention of Communicable Diseases

Communicable diseases, like COVID-19, if not detected early and prevented from spreading, may put the whole world on lockdown. However, with the use of big data analytics, communicable diseases can be prevented and identified at their earliest. The application of deep learning algorithms to healthcare related data is vital to studying the patterns and trends of infectious diseases and their spread.

Big data searches and queries from the internet are used to better analyze and forecast the trends of communicable diseases. Also, from this data, infectious diseases may be identified and contained early. The moment the disease-infected areas are identified, telemedicine sets in. These areas may be quarantined to prevent the further spread of the disease. Also, with the use of telemedicine and remote patient monitoring (RPM), patients who are infected with the disease may receive continuous medication without endangering the lives of healthcare providers.

2. Better Diagnosis

Traditionally, patients’ health data is gathered through routine check-ups, patients’ visits to medical facilities, and what the patients tell their providers. While this is a good foundation, healthcare providers may be left with insufficient or inaccurate health information to correctly diagnose their patients, which will eventually lead to injury or worse, death. In fact, the NCBI reports that the overall rate of misdiagnosis is roughly 10% to 15%, resulting in 40,000 to 80,000 deaths or injuries annually.

Today, with the use of telemedicine and analytical algorithms, healthcare providers need not rely on what their patients tell them but rather on the data collected by their wearables. Since telemedicine allows continuous collection of data, healthcare providers are given sufficient data to better diagnose and understand their patients.

3. Post-treatment monitoring and medication

Traditionally, the best care is delivered in the hospital or in a medical facility. As such, when patients are discharged from the hospital, they are less likely to receive the same quality of care they received in the hospital. This sometimes results in unwarranted hospital re-admission. However, this scenario is no longer true due to the advancement of technology applied to healthcare.

When patients are discharged from the hospital, they will receive the same or better quality of care through telemedicine. Patients get to enjoy remaining in the comfort of their homes while being monitored remotely. Patients’ data are continuously collected using wearables or medical devices. And, with the use of data analytics, information regarding the condition of the patients is generated helping healthcare providers deliver better post-treatment monitoring and medication.

Furthermore, data obtained via the patients’ wearables and medical devices are analyzed using analytics techniques to determine the most effective treatment for a certain patient as patients may have similar illnesses but may need a different approach of treatment.

4. Electronic Health Records (EHR) on the Cloud

Traditionally, patients’ information is written on papers and is stored in cabinets, creating a bottleneck to accessing them. However, with the growing number of patients and the demand for bigger and more accessible storage, some providers have resorted to the cloud. With the cloud, patients’ data can be accessed remotely.

Furthermore, the EHR powered by cloud data analytics enables providers to deliver timely information and customize reports. Providers can also discover patterns and trends, such as why patients taking a certain medication are experiencing high blood pressure. As such, providers can modify their patients’ medications immediately. With this, providers do not need to go to the hospital and look at their patients’ files to discover that something might be wrong, but rather they can access that data anytime and anywhere.

Also, secure access to the cloud eliminates geographic location as a factor in obtaining the best treatment possible. It benefits healthcare providers as it enables more efficient scheduling of appointments, hence boosting the effectiveness of care. Cloud storage is both a forerunner to and a facilitator of the advent of big data.

5. Predictive Analytics

One of the greatest contributions of big data in telemedicine is the early detection of potential health concerns before they deteriorate into worsening diseases. This has become a reality with the development of the Internet of Medical Things (IoMT), which enables the collection of real-time patient data via wearables and other health monitoring devices. Data obtained from these wearables is utilized to do predictive analytics on possible future outcomes. Acquiring data from multiple sources is critical for identifying people who are at high risk of developing chronic diseases early in the disease’s progression.

Healthcare analytics provide new information to health providers, which is processed and validated by skilled data scientists, allowing them to make more accurate diagnoses and recommend the best treatment regimens. The future is more promising, since predictive and prescriptive analytics enable the prediction and monitoring of disease rather than only its detection. The data evaluated can come from claims data, electronic medical records (EMRs), and even real-time health sources such as wearables and mobile apps that track vital signs, providing a more complete picture of the patient’s health condition.


The combination of big data analytics and telemedicine gives providers data-driven insights to help them provide better treatment to patients. It promotes value-based health care delivery while also lowering costs. Apart from the aforementioned benefits, big data analytics has enabled the development of numerous new possibilities in the field of medicine that were previously deemed unattainable. The latest advancements in telemedicine are merely the tip of the iceberg, since there is a vast world of potential yet to be discovered in the field of telemedicine.

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