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7 minutes read

Utilizing Big Data Analytics in Healthcare

By Robert Kazmi
big data analytics in healthcare
By Robert Kazmi
7 minutes read

Big data analytics has been adopted in most industries and the healthcare industry is no different. Big data analytics in healthcare settings is used to not only improve patient outcomes but also improve daily operations for healthcare organizations and systems. 

Despite the capabilities and benefits of big data analytics in healthcare applications, many healthcare institutions still don’t fully understand or utilize the power of big data analytics. 

Healthcare providers collect massive amounts of data from various sources, not just health records. Therefore, using big data analytics in healthcare settings can improve healthcare services immensely. 

This post will take a closer look at big data analytics in healthcare. We will explain what it is and the variety of ways big data analytics can be used in the healthcare sector. 

What Is Big Data Analytics in Healthcare? 

Big data in healthcare refers to the immense volume of data created and collected by adopting digital technologies like electronic health records (EHR). Big data in healthcare also refers to the data related to operations, from payroll to inventory management. 

Big data analytics in healthcare has several positive benefits for medical institutions. Healthcare management quickly gets complicated, but with the power of big data analytics, the complex data sets of the medical industry can be distilled through statistical analysis to provide data-driven decisions for administration professionals and doctors to act on. 

Big data in healthcare will only continue to grow as more healthcare data and clinical data are collected by increasingly robust tools and systems. As a result, the focus for healthcare facilities or a health system is no longer on the immense volume of big data but on managing big data most effectively and efficiently. 

Big data analytics tools can help a healthcare system process and analyze raw data from a wealth of data sources to improve strategic planning, resource management, and, most importantly, health outcomes for patients and communities. 

Real-World Uses of Big Data Analytics in Healthcare 

It is easy to see the value of big data analytics in healthcare in an abstract sense, but what are the real-world applications of this technology? There are several ways big data analysis is being utilized by healthcare providers and hospital information systems, including the following: 

  • Electronic health records (EHRs)
  • Real-time alerts 
  • Service planning 
  • Predictive analytics for improved care
  • Medical imaging 
  • Tracking public health 
  • Staffing and personnel management 

Electronic Health Records (EHRs)

EHRs are the most significant and widespread source of healthcare data. As a result, big data analytics in healthcare is often tasked with analyzing and managing patient medical data. Before EHRs, patient data was fragmented across the healthcare providers they saw. 

Now with EHRs, healthcare providers can review all health data related to their patients and use healthcare analytics to track prescriptions and potential interactions, trigger automatic alerts for new testing and lab results, and so much more. 

The healthcare analytics made possible through the data sharing of EHRs improves patient care and can also help drive operational cost savings with fewer office visits and lab tests. 

When healthcare providers have a full overview of a patient’s medical history and additional insights powered by big data analytics, preventive medicine can be more effective and produce better outcomes for patients and providers.

Real-Time Alerts 

Real-time alerting is one of the most impressive facets of big data analytics in healthcare. In hospital and clinic settings, big data analytics can analyze data from medical records on the spot to give providers additional resources when making critical treatment decisions. 

In addition, real-time alerting can be combined with the capabilities of wearable technology to provide vital healthcare data to doctors even when patients are outside the practice. For example, doctors can intervene if a patient’s heart rate or blood pressure rises to alarming rates. 

Real-time alerting and predictive analysis can even be used to monitor broad swaths of the population. For example, smart inhalers can track asthma trends on an individual and population level to give providers insight into local trends and conditions, which will help them develop more effective treatment plans. 

Service Planning 

Big data analytics in healthcare can be effectively used to improve the care delivery strategy in a healthcare system, practice, or location. For example, through big data analytics capabilities, providers can compare the availability of medical services in a location versus the healthcare demand of the population. 

By analyzing such data, practitioners can offer more services aligned with the needs of the local population and improve patient satisfaction. This is another example of how big data analytics in healthcare can effectively improve patient care through data mining and processing. 

Predictive Analytics for Improved Care 

We already know how effectively big data analytics can improve business performance and strategies. Big data analytics in healthcare can be used to improve care delivery by empowering doctors to make split-second data-driven care decisions

For example, big data analytics in healthcare can identify which patients are most at risk for developing conditions like diabetes and heart disease. The sooner providers can identify these patients, the quicker they can develop treatment plans to combat or even prevent these conditions from happening. 

Medical Imaging 

Medical imaging is a vital part of healthcare, but it is time-consuming for radiologists and requires a lot of storage space. Making medical imaging more efficient is another application of big data analytics in healthcare

Big data analytics tools can quickly analyze hundreds and thousands of images to find trends and patterns. In addition, these tools can take the unstructured data of medical images and convert them into structured data outputs that are easy for doctors to read and understand. 

Big data analytics not only assists with diagnosis, but in some cases, these tools might be able to spot anomalies and other areas of potential concern before physicians can. Thanks to Machine Learning, the more these big data analytics tools get used, the better they recognize normal and abnormal images. 

Tracking Public Health 

The COVID-19 pandemic illustrated the importance and value of tracking public health. Big data healthcare analytics can help public health officials identify and stem disease outbreaks before they wreak havoc

Tracking public health involves analyzing immense data sets of health and behavioral information. Big data analytics is uniquely situated to handle the data processing requirements of public health organizations. 

Staffing and Personnel Management 

In healthcare, staffing and personnel management are vital to the quality of patient care. Big data analytics can help providers optimize their staffing by forecasting demand. When staffing is at optimal levels, patient care improves. 

When staffing is not handled effectively, there is employee burnout, poor patient care, and wasted resources. Big data analytics can help healthcare organizations through staffing needs to ensure the right number of employees are always in the building. 

Final Thoughts 

Big data analytics in healthcare is not necessarily a new innovation. Still, more providers are beginning to understand how data analytics can help them provide better care to patients and optimize their internal operations.

To learn more about big data analytics in healthcare, contact an experienced MedTech development partner like Koombea

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