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State Medicaid agencies, managed care and other healthcare payers are good at collecting data. Demographics, co-morbidities, vital statistics, medications, assessments, previous interventions — it is all in the data set. But despite data being acquired at virtually every touchpoint of the continuum of care, little of it becomes actionable information that can be used to create better outcomes.

This raises the question of how to ingest all the data and make sense of it in a way that can prevent future problems and influence better health outcomes for the individual member and the entire population. One way to analyze data to provide meaningful, actionable information is by implementing population health management strategies in a way that brings the data to life.

Telligen’s approach to population health management provides an analytic path for population health management by identifying actionable gaps in care among segments of a population, providing evidence-based intervention strategies, and monitoring of quality, cost, and utilization outcomes.

The solution deploys analytics across phases of the population health management framework to provide an intuitive interface for population health practitioners to inform decision making.


The Assess phase explores the drivers of health outcomes for the population. Dashboards that visualize the data helps payers and their stakeholders to get a better understanding of what’s there. An approach emphasizing data visualization makes it easier to understand point-in-time comparisons, as well as trends across time. This provides a pathway to understanding the population which can identify patterns and exceptions or even potential gaps in care.

The population can be stratified by demographics, such as gender, age, race and ethnicity, preferred language, level of rurality, and location, or program enrollment. Selected chronic disease conditions within the population are identified and can be filtered by demographic attributes.


In the Focus phase, analysts take a deeper dive into known areas of opportunity for improving health outcomes. Focus areas may center on a subpopulation (i.e., maternal and child health), a health condition (i.e., diabetes, behavioral health), or health care utilization (i.e., ED visits, inpatient visits, readmissions, pharmacy). Greater details of chronic disease conditions are provided during the Focus phase with metrics of utilization of services by condition, and the ability to explore more than one chronic disease condition at a time; for instance, the impact of depression on diabetes.


Predictive analytics can then identify specific members who are at the highest risk of future adverse outcomes. Chronic conditions and prior utilization of health care services are analyzed to estimate the risk level of individuals over the next twelve months. Results can be filtered by demographics, location, or other factors.


Any gaps in care identified through the first phases of the analysis can be connected to evidence-based interventions or mitigation strategies. For instance, if the data shows that a particular sub-population of members have high rates of emergency department visits or inpatient stays, evidence-based strategies or current programs or services that may be underutilized can then be used to address gaps.


A robust population health management approach includes tools for tracking process and outcome measures across the population. Process and outcome measures are tailored to programs implemented in the Intervene phase. Quality measures are monitored over time and can inform continuous quality improvement activities based on past performance and comparisons to benchmarks.

Data In Action

At Telligen, we realize that quality of data is the key to ensuring valid and reliable outputs.  A rigorous process to audit the data inputs can ensure the quality of the data used to produce the dashboards and reports that assist our clients in managing the health of their diverse populations.

Using a population health management framework to move from data collection to actionable information requires collaboration between payers, clinicians and other stakeholders, data analysts and operational experts; always keeping in mind that better outcomes for members is the goal.