Using Data to Prevent Addiction and Support Recovery | Resultant

The Role of Data in Preventing Addiction and Enabling Recovery

The impact of the opioid crisis within the United States is well known and highly publicized. The cost—to individuals, families, and the economy—is unsustainable. Resultant’s Data-Driven Addiction Prevention and Recovery (DDAPR) holistic methodology works to meet this complex public health crisis using a multi-pronged approach.

DDAPR consultants analyze state, federal, and community data from macro and micro viewpoints to identify key levers for preventing citizens from becoming addicted to drugs and aiding in positive outcomes as they recover.

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What's Included

Record Linkage as a Data Driver

The key differentiator for the DDAPR project is the longitudinal database that captures data and events from multiple source systems. These disparate observations are then linked in order to generate a unified picture of an individual’s actions and interactions with state programs.

Resultant's Unique Methodology

Resultant has developed a unique methodology that combines a custom record linkage system with refined processes to probabilistically link large-scale datasets, resulting in highly accurate individual correlations, which enable preventative interventions.

Flexible and Scalable

Our proprietary data-source and identifier agnostic solution is completely flexible and scalable to any number of data sources and field arrays. In practice, the system is remarkably robust yielding approximately 97% accuracy.

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Key Facts

  • Record linkage is the gasoline that drives the engine of data-driven approaches to solving public health issues.
  • Government agencies, funding mechanisms, and programs organize around common problems and themes; unfortunately, society’s most complex problems defy compartmentalization.
  • Understanding the entire universe of characteristics that drive unwanted behavior and outcomes is critical.
  • Resultant’s record linkage solutions bridge the technical silos that naturally occur, and give analysts dependable, robust datasets to find answers to our most serious problems.

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