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Predictive analytics uses machine learning and artificial intelligence on historical and real-time data to make predictions about future events.

For many years, the healthcare industry has been advancing the promise of predictive analytics to drive decisions that improve patient outcomes, increase operational efficiencies, reduce healthcare spending, and much more. In healthcare, the possibilities for predictive analytics use cases are endless, and everyone from patients to hospitals to insurance providers to product manufacturers stands to benefit.

The keys to predictive analytics success in healthcare lie in identifying and prioritizing the correct use cases, capturing the right data, and applying the best models to uncover meaningful insights.

Predictive Analytics in Healthcare: Use Cases Abound

Predictive analytics has disrupted the healthcare industry and will continue to be a game-changer as use cases expand. Some of the most common benefits of predictive analytics in healthcare include the following:

• Increased accuracy of patient diagnostics and treatment. By mining data from millions of electronic medical records, predictive analytics can identify more patient risk factors than humans can, often leading to more accurate diagnoses for diseases such as congestive heart failure and sepsis.
  
  Early manifestations of congestive heart failure, for example, are easily missed because symptoms mimic other diseases. At more than $43 billion per year, congestive heart failure is the costliest disease to treat in the US. Early diagnosis improves patient outcomes and reduces expensive complications and hospitalizations.
  
  
• Improved operational efficiency of healthcare. Predictive analytics helps identify operational trends such as patient census levels to optimize staffing and prevent bed shortages, diagnostic equipment usage and maintenance, or staff skills and competencies so healthcare facilities run smoothly and efficiently.
  
  
• Improved precision medicine. The availability of millions of patient records means predictive analytics can uncover patients who are the same age, gender, ethnicity, and even have a similar response to a specific medication. This makes it easier to customize medical treatments, practices, or products to individual patients.
  
  
• Reduced costs from fraud, waste, and abuse. Every year, fraud, waste, and abuse cost the US healthcare system more than $234 billion. By analyzing patient data and billing records, healthcare companies are able to identify billing and treatment anomalies, including duplicate claims, medically unnecessary treatments, or providers who prescribe higher rates of tests.

In addition to institutional benefits, predictive analytics directly impact patient care by:

• Reducing inpatient admissions and emergency department visits
• Improving patient participation in care, including adherence to medications and doctor’s visits
• Lowering patient risk through the implementation of more appropriate interventions

Realizing the Promise of Predictive Analytics Using the Cloud

Considering healthcare costs almost $4 trillion per year in the US, the promise of predictive analytics is enormous. Never before have we had access to this much historical and real-time data from so many diverse sources, including electronic medical records, connected monitors and wearable devices, medical imaging, billing records, patient registries, opt-in genome registries, healthcare workflows, pharmacy and health plan claims, government records, and more.

The challenge now is in capturing and harnessing all this data to uncover insights that improve patient care while reducing costs. How do you bring together all these disparate data sources and where do you keep it?

For many, the answer is: the cloud. Cloud infrastructures makes it possible to cost-effectively aggregate and store huge volumes of data in all its various formats, including both structured and unstructured data. Though healthcare organizations once were skeptical of the cloud to meet the industry’s demanding and specific security and compliance needs, many have come to trust the cloud for at least some of their data.

Challenges

Healthcare organizations that are making the leap to cloud infrastructures, including data warehouses and data lakes, to fuel predictive analytics, must overcome key challenges: 

• Integration of Disparate data sources. For predictive analytics use cases to be successful in healthcare – in any industry really – organizations must be able to quickly, securely, and reliably integrate data from many disparate sources both inside and outside the organization and then store and process these massive volumes of data.

Electronic health records (EHRs), just one of the many sources of healthcare data, pose their  own integration challenges. There are dozens of EHR vendors on the market, and the average hospital uses 16 different EHR platforms. Furthermore, 62% of hospitals don’t use patient data external to their EHR “because external providers’ data is simply not available in their EHR systems’ workflow.”

To complicate integration even further, medical and health records are kept separate from financial, purchasing, and HR data. These silos of data make it difficult to build a comprehensive view of patient care, treatment, and cost.

• Cloud technology adoption. Though cloud adoption is growing within the healthcare industry, the growth is constrained by ongoing security and privacy concerns. According to Gartner, “healthcare CIOs are now viewing the cloud as an extension of their internal infrastructure,” which means increasingly, healthcare data environments are hybrid. Predictive analytics applications that use data from on-premises and cloud infrastructures will need to easily and securely connect data from both environments.
  
  
• Changing technology. With history as an indicator, we can count on one thing: technology is always changing. As healthcare data goes to the cloud, organizations and the technology stack they choose will need to be flexible to new data sources, new technologies, and organizational change. Look for a technology stack that is scalable and adaptable to these inevitable adjustments.
  
  
• Security and privacy. Though the major cloud providers are diligent about security, healthcare is a highly regulated industry and organizations need to be vigilant about patient privacy. Data must be secure at all stages of its lifecycle. That means that particularly for hybrid environments, healthcare organizations must ensure security of data stored behind their firewall and data that is in motion between on-premises and cloud infrastructures.

Success Factors for Predictive Analytics

The majority of health system CIOs, CTOs, and Chief Analytics executives believe analytics will only grow in importance in the future. As healthcare costs continue to skyrocket, the need for predictive analytics has never been greater. The following three factors will help ensure the success of predictive analytics for future healthcare use cases:

• Cloud technology to bring any type of data from diverse sources together into a central repository
• Data integration to consolidate data from many diverse systems and to make it consistent, reliable, and available for predictive analytics
• Data literacy to make sure your data-driven organization is aligned and equipped to use all the insights generated from predictive analytics.