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 is 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, and the secret to machine learning 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 machine learning 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.
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 gain more clarity on AI adoption, download our free eBook, The Enterprise Guide to Kicking Off the AI Adoption Process.
Keep up with Redapt
- Business Transformation
- Cloud Native
- Artificial Intelligence (AI)
- Modern Datacenter
- Advanced Analytics
- IoT and Edge
- Emerging Tech
- Systems Integration
- Google Cloud
- Hybrid Cloud Implementation
- Google Anthos
- Machine Learning
- Microsoft Azure
- Application Development
- Application Modernization
- Business Resilience
- Managed Services