Behavioral Economics Core

Applying research ideas to improve patient and community outcomes

The Behavioral Economics (BE) Core advises on behavioral economics, implementation sciences, and cost effectiveness analyses to translate research findings into clinical practice.

Behavioral Economics
Behavioral economics explores how people’s actions and biases influence their decisions.  Tailoring decision environments can encourage, or “nudge”, people towards making wiser and healthier choices without taking away their freedom of choice. 

Implementation Science
Implementation science studies the optimal logistics of delivering a given intervention and advances strategies that facilitate the uptake of research-based interventions or best practices. In this way, implementation science helps translate ideas into outcomes by identifying, addressing, and overcoming system barriers. 

 Cost-Effectiveness Analyses
Cost-effectiveness analyses aim to categorize the relationship between healthcare costs and health outcomes of an intervention or idea.

 The Behavioral Economics Core is supported by the Center for Health Outcomes and Population Research (NIGMS CoBRE P20GM121341).

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Questions we can help to answers:

  • How can I operationalize results from my study in a real-world or clinical setting? 
  • How can I maximize patient/participant enrollment and participation?
  • How should I communicate with patients/participants? 
  • How can I nudge patients/participants to make healthier decisions without removing their freedom of choice? 
  • How can I measure the cost-effectiveness of my intervention? 


Lang C, Schaap R, Sang H, Weber TL, Weber GA. (in press). Description and Evaluation of a Multilevel Intervention for Improving HPV Vaccination Rates in a Rural South Dakota Clinic. South Dakota Medicine.

Weber TL, Selya A, Wakschlag LS, Dierker L, Rose JS, Hedeker D, Mermelstein RJ. (2021). The effect of maternal smoking on offspring smoking is unrelated to heritable personality traits or initial subjective experiences. Nicotine and Tobacco Research. PMID: 33912956

Selya A, Johnson EL, Weber TL, Russo J, Stansbury C, Anshutz D, Griese E, Hsu B. (2020). Smoking is associated with a higher risk of unplanned medical visits among adult patients with diabetes, using retrospective electronic medical record data from 2014 to 2016. BMC Health Serv Res. May 6;20(1):383. doi: 10.1186/s12913-020-05277-4. PMID: 32375742; PMCID: PMC7204008.

Selya, A.S., Anshutz, D, Griese, E, Weber, TL, Hsu, B, Ward, C. (2021). Predicting unplanned medical visits among patients with diabetes: Translation from machine learning to clinical implementation. BMC Medical Informatics and Decision Making, Mar 31;21(1):111. PMID: 33789660, PMCID: PMC8011134

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