Predictive Analytics: Taking the Measure of Data to Optimize Outcomes

Using predictive analytics in healthcare

Digital health transformation has fundamentally altered the way organizations use data to unify disparate medical functions and deliver optimal outcomes for individual patients and the health enterprise. 

For an organization to achieve this goal, each interconnected component of its healthcare ecosystem (governance and workforce, interoperability, predictive analytics, and person-enabled health) must align with and enable every other component.

Ensuring that each component achieves its potential requires that outcomes for each are measured in a standardized, repeatable way. In the third of a four-part series, we look at measures of effectiveness for predictive analytics. 

Explore the Four Components of Digital Health Transformation


By using data to extrapolate outcomes, predictive analytics equips healthcare organizations with knowledge and real-world insights that can help make informed decisions about individuals, health teams and health system leaders. 

It also leverages health system data, digital tools and population data to inform care delivery and operations—creating personalized healthcare, risk prediction to optimize outcomes and the proactive tracking of population health to support health and wellness.

This advanced form of analytics is measured within the following parameters:

  1. Personalized analytics aggregate individual health and wellness data from multiple sources, such as personal digital tools, mobile devices and wearables. These sources provide progressive genomic and biometric data to support individuals and their provider teams to track progress toward health and wellness goals. Personalized analytics connects people to health teams by reporting outcomes, side effects, adverse events and progress toward health goals.
  2. Predictive analytics track and trace outcomes across the journey of care for every individual patient, to identify outcomes that work best for every individual and the conditions necessary to achieve best outcomes. It also tracks outcomes at program and population levels, reporting risk of harm or poor outcomes to inform quality and safety strategies, and proactively alert clinician teams and individuals about strategies to keep populations healthy.
  3. Operational analytics employ data to track the performance of health systems including supply chain, clinical and financial, and document adverse events. More specifically, they use digital tools and dashboards to track operational outcomes such as efficiency, productivity, quality, safety, access, equity and cost. Operational analytics include real-time dashboards for use by leaders and decision-makers to assess value, system learning, and workforce and financial sustainability. Publicly reported aggregate performance outcomes inform individuals, manufacturers, suppliers, governments and funders. Meanwhile, analytic tools track, monitor and measure value-based outcomes to inform health system performance strategy.

Benefits of Measurement

Predictive analytics represent the point at which data management progresses from reporting on past and current conditions to assessing future outcomes, forecasting their probability and identifying factors that could change them. Documentation is important in any clinical or business setting to maintain health standards and learn from past events, but the real power of digital health transformation lies in its ability to shape next steps that will optimize the health of the individual, the population and the organization that serves them.

When an organization sets out to forecast the future with a high level of accuracy, standards for measurement are essential. Consistent precision behind analytics helps ensure consistent accuracy of the events they predict. 

Along with the other three components of the ecosystem, this information provides healthcare organizations a framework to judge how effective they have been in achieving frictionless, system-wide support for the needs of the people in their care. 

The next post in our series on measuring digital health transformation will explore person-enabled health.

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