Studies have shown that Big Data predictive analytics has the potential to save over 25% of annual healthcare costs. 

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As health spending in the US has been experiencing substantial growth in the past couple of years, it’s no wonder that many health institutions are considering adopting machine learning and Big Data into their systems.

And how do they intend to do it? Well, mostly through personalized patient care and improved health risk assessment.

If you want to learn more about how Big Data can benefit patient care and lead to the optimization of healthcare services, we’ve got you covered in the rest of this article.

Reliable Healthcare and Discovery of New Treatments 

There are several notable ways in which Big Data can help improve patient healthcare today and in the future. 

First, Big Data enables the creation of large healthcare databases. 

The more information medical experts have about the health status of their patients, the better the odds that they’ll make safer and higher-quality clinical decisions. 

Moreover, large amounts of historical and real-time health data provide comprehensive insight into the patient’s treatment and medication history. 

Thanks to predictive analytics, doctors all over the globe will be able to write more reliable future prescriptions. 

Aside from that, Big Data can be useful for quantitative analyses. 

For example, Near Infrared Spectroscopies require large databases of NIR spectra to identify, classify, and control the quality of certain products and their properties. spectrometers are based on the Beer Lambert Law, which states that more concentrated solutions absorb more light than dilute solutions do. 

Lastly, machine learning algorithms also help understand how certain diseases and pathogens work. 

This way, Big Data allows for the initiation of new clinical trials that help discover new, cost-effective drugs and therapies.

Improved Health Risk Assessment and Preventative Care 

According to research, certain communities and groups are at higher risk for specific health conditions and diseases.

Taking that into consideration, depending on the geographic area where the patient is coming from, the patient’s genetics and environmental factors should play a role in choosing suitable healthcare services. 

In other words, Big Data makes space for improved health risk assessment and, therefore, the education of the population about the steps that they can take in order to prevent or effectively manage their health issues. 

A perfect example of this is the use of Big Data analytics in attempts to prevent and control the spread of mass diseases such as COVID-19. 

There are many other examples of Big Data being useful in devising better treatment plans and figuring out optimal doses of medications. 

One of them has helped medical experts understand that redheads require up to 20% more anesthesia to be sedated. 

Due to these findings, redheads all across the world can rejoice and let go of worries that they’ll wake up and experience excruciating pain in the middle of complex surgeries.

Early Detection of Diseases, Reduced Medical Errors and Safer Handling of Sensitive Data 

The majority of hospitals in the US have adopted Electronic Health Records (EHR) in an attempt to improve the performance of their organization and their patient care. 

These records contain extensive data on patients’ demographics, vitals, previous diagnoses, medication and treatment plans, allergies, immunization dates, various test results, and much more. 

So, what makes electronic health records so valuable to the healthcare system?

Well, apart from being extremely helpful to patients with disabilities in reading test results, refilling prescriptions and overall accessing healthcare systems, EHRs are also useful for personalizing patient care.

By accessing a patient’s medical history, medical experts can minimize test duplications, reduce the number of unnecessary prescriptions, and create targeted therapies for their patients. 

Moreover, access to such a large healthcare database reduces medical errors in diagnosis and treatment.

Finally, by analyzing and identifying patterns from CTI, PET scan and MRI images, Big Data can help with the early detection of diseases. 

It should be noted that EHRs are HIPPA compliant and follow strict laws regarding storing and sharing personal data. This ensures that all of your sensitive health-related data is private and secure.

Reduced Healthcare Costs  

A medical professional that has access to extensive and relevant data regarding the patient’s health can devise more effective treatments with fewer physical hospital visits.

Why is this important? Because fewer hospitalizations translate into fewer unnecessary services.

Big Data analytics also allows more accurate predictions of increased and decreased in-patient visitations. 

This enables health institutions to improve their day-to-day hospital operations and allocate their resources more effectively, depending on the patient influx. 

Finally, reduction of costs and resources used for patient care is a great way for health institutions and patients to spend less money on collective and individual healthcare. 

Creation of Personalized Healthcare Services 

Each patient has a unique medical history, health risks and exposure to environmental factors. 

That’s why having a personalized approach to patient care can be beneficial for meeting the individual health goals of every patient. 

Wearables with biometric sensors, whether intended for fitness or diagnostic purposes, help collect patients’ biomarkers of health, including their temperature, heart rate, blood pressure, blood sugar levels, and many others.

This sort of Big Data not only improves the diagnostics and treatment plans of health institutions but also helps predict and cut down on the cost of patient care. 

Improved Accessibility of Remote Healthcare Services/Telemedicine/Treatment from Afar 

Telecommunication technologies have come a long way, enabling the remote diagnosis and treatment of patients in rural areas, without access to developed medical infrastructure. 

Telemedicine has led to an increase of:

  • In-home virtual health assistants
  • Health assessments in the form of different symptom checkers
  • Remote medical consultations
  • Remote patient monitoring
  • Robot-assisted surgery and
  • Overall improved healthcare services

Without unnecessary paperwork and hours-long waiting lines, patients can now receive quality healthcare from the comfort of their homes through automated systems relying on Big Data.

Conclusion 

The COVID-19 pandemic has reminded us all of how important prevention and forecasting of medical outcomes are. 

With the help of machine learning and Big Data analytics, health institutions across the globe can now make better medical decisions and provide cost-effective treatments to their patients, all while making healthcare services more accessible. 

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