Opioid misuse and addiction affect public health as well as social and economic welfare. In response to this national crisis, various states in the United States have promulgated wide-ranging policies. These policies vary substantially and nominally across states and over time. The variations in the key–components and design details come in the way of identifying the most effective policy components and the best way to implement them.
The absence of standardized nomenclature, to classify state-level opioid policies, stands in the way of understanding their effectiveness. The different classification approaches employed result in different empirical findings. Consequently, the ability to identify the reason for the effectiveness of a particular policy and optimal implementation strategies is jeopardized.
When behavioral scientists encountered the challenges associated with the lack of standardized nomenclature, they developed the Behavior Change Technique (BCT) taxonomy. The taxonomy improved scientific methods for specifying key components of behavior-change interventions—thereby facilitating replication, research synthesis, and real-world implementation. Similarly, the shared structure and standardized vocabulary of taxonomy can help in categorizing and evaluating heterogeneous state-level opioid policies.
Developing a taxonomy for state-level opioid policies would yield multiple benefits. Categorizing policies at various levels would help researchers describe the policies, examine the differences, and synthesize the evidence to enhance their effectiveness. Furthermore, the precise and consistent policy classifications in research literature facilitated by the taxonomy would enable policymakers to make informed policy decisions targeting outcomes in specific contexts.
Recent advances in computer science can optimize the value of the taxonomy for opioid policies further by facilitating the development of an ontology. The ontology will form the foundation of an Artificial Intelligence (AI) knowledge system that can automatically annotate reports and anticipate effect sizes for particular policies and components. In addition, it could provide a user-interface for queries from researchers and practitioners and facilitate the synthesis of state-level opioid policy evidence and the uptake of accessible evidence by policymakers.
Developing a state-level opioid policy taxonomy will not happen overnight. Over time, developing and employing such a taxonomy, however, can facilitate the design and implementation of more effective state-level policies—an essential tool in addressing the opioid crisis.
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