Population health analytics: combatting challenges

Population health analytics: combatting challenges

The efficiency of any population health program relies on the ability of caregivers and stakeholders to leverage population data

With the increasing adoption of data analytics, providers, health plans and accountable care organizations are quickly transitioning toward a coordinated, integrated and value-based care delivery ecosystem. We are seeing a general shift toward preventive care, along with a growing demand for accountable and coordinated care. With the Department of Health and Human Services’ recent decision to tie 90 percent of Medicare payments to value based models by 2018, we can expect organizations to move even faster over the next few years toward streamlining their population health management processes.

Population health analytics is now a strategic imperative
Since 2008, PHM processes and related reforms have been guided by the Institute for Healthcare Improvement’s ‘Triple Aim’ – improving the health of populations, improving experience of care and reducing per capita costs of health care. In 2015, IHI published a new report describing three core components that organizations need to execute to pursue the ‘Triple Aim’:

  1. Creating the right foundation for population health management, includes identifying relevant populations, creating a strong governance mechanism and articulating purpose.
  2. Managing services at scale for a population, includes defining subpopulations, analyzing care outcomes, designing or redesigning services based on changing needs, and even leveraging community organizations (e.g. local fire department)  for capacity expansion of care coordination.
  3. Establishing a learning system to drive and sustain the work over time, using population level measures, applying analytics for iterative testing and continuous improvements and understanding individual healthcare needs.

Much of what IHI discusses in the new report has analytics at its core, making population health analytics tools and technologies a strategic imperative for organizations that want to achieve the Triple Aim. Advanced analytics tools and technologies, such as big data and predictive analytics will play an important role in the near future, helping providers drive effective patient engagement and collaboration across care settings.

Deploying an effective population health analytics solution for providers
Population health analytics is being increasingly used as tool for preventive care and overall wellness management rather than reactive localized care. According to recent reports, a few early adopters are using analytics tools for risk stratification, targeted outreach, care plans, performance benchmarking, planning interventions and leveraging registries for surveillance.

An effective PHA solution must be able to perform a number of key tasks for the providers, including:

  • Aggregating data across the continuum of care. This includes clinical applications, claims systems, administrative systems, health information exchange, remote monitoring devices, consumer mobile applications, biometric sensors, etc.
  • Tracking, aggregating and analyzing a vast spectrum of clinical and financial data, following a patient’s journey from prognosis, prevention and treatment to maintenance and wellness management.
  • Measuring performance scores and analyzing clinical outcomes to help enhance quality, cost and efficiency of care delivered at both an individual and population level.
  • Applying risk stratification algorithms to patients in a given population to derive better and more targeted health management programs.
  • Delivering information to care team members and decision makers when and where they need it.
  • Assessing cost and quality metrics of population health programs to deliver return on investment projections and scores.

Key adoption challenges
Advanced PHA solutions are still in the early stages of adoption, with most organizations largely relying on descriptive analytics executed monthly. Predictive analytics is on a rise and recent technologies are equipped with tools to facilitate predictive analytics. In the near future, we believe tools and technologies will support predictive as well as prescriptive analytics. A small percentage of organizations have analytics operations that have a seamless integration of clinical, financial and operational data across the organization. Some of the key challenges for adopting these analytics programs include:

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