According to Axios, in 2018 alone, Americans spent an estimated $3.65 trillion on healthcare. For some context, that’s more than the total GDP of Brazil.
With costs continually increasing, healthcare providers—from hospitals to doctors’ offices to insurance companies—are always searching for ways to provide better services at a lower price tag.
Enter predictive analytics.
Built upon an ocean of incoming data, predictive analytics can unlock unforeseen and unexpected information that providers and other players in the healthcare industry can apply to their business models.
In fact, in many ways, healthcare is already at the forefront of predictive analytics. And that’s due to Fast Healthcare Interoperability Resources, or FHIR.
What is FHIR?
A project of the organization HL7, FHIR is a standard for the capturing and storing of healthcare data. It’s a set of agreed-upon rules for medical data that is utilized by everything from insurance companies to EKG monitors to Apple Watches.
The types of data FHIR standardizes are staggering. It includes:
- Patient vital signs
- Insurance claims
- Appointment dates
- Medication usage
- Emergency care
- All other healthcare information
This information is collected, placed within a framework, and made available to approved parties.
With FHIR, data scientists have a single source for creating data lakes of specific data that can be mined for insight—all with the governance and rules in place to limit what information is accessed by individual parties to preserve privacy.
A predictive playground
A key component of predictive analytics is machine learning (ML), and the secret to ML is the ability to run models on a wide range of data sources.
FHIR gives data scientists a large playground in which to run models efficiently, then feed the outcomes of those models back into the data stream—a learning loop that can reveal new insights and even raise flags when appropriate.
For insurance providers, running ML through FHIR can gauge and flag potential fraud. For researchers, it can predict things such as regions likely to be hit by a particular strand of the flu or where unhealthy pollutants will increase. And for your average physician, it can help predict how a patient’s lifestyle could affect their health in the coming years.
ML during the pandemic
Over the past year, healthcare providers and government agencies increasingly utilized ML to combat COVID-19.
By creating models to help track the spread of the virus in communities, their organizations were able to take big steps toward:
- Anticipating where larger-scale outbreaks were likely to occur
- Improving contact tracing efforts
- Track the evolution of misinformation about COVID-19 via social media data streams
While these were important capabilities for battling the pandemic, the real benefits of ML will likely be revealed if another major outbreak of a virus occurs because unlike the pandemic of 1918, health agencies and researchers now have more than enough data in which to apply sophisticated ML models going forward.
In other words, the world will be able to better react if another pandemic occurs by utilizing data science in more extensive and creative ways.
We’ve only just begun
In many ways, the healthcare industry is still just scratching the surface when it comes to the power of predictive analytics.
FHIR has already provided the most advanced healthcare record in history, and it’s only going to get more robust as time goes on. Similarly, the models run by various enterprises and organizations in the healthcare industry will only become smarter and more efficient.
While not every industry can produce and utilize something as groundbreaking as FHIR, predictive analytics can still provide essential insights into data. All that’s needed is enough data and the models to run on it.
To learn more about predictive analytics, artificial intelligence, and ML, check out our free resource on Advanced Analytics.
