With the recent rise in opioid overdose and death1<br/><br/>, chronic opioid therapy (COT) programs using<br/>Center of Disease Control (CDC) guidelines have been implemented across the United States8<br/>.<br/>Primary care clinicians at Mayo Clinic initiated a COT program in September of 2017, during the<br/>use of Cerner Electronic Health Record (EHR) system. Study metrics included provider<br/>satisfaction and perceptions regarding opioid prescription. Mayo Clinic transitioned its EHR<br/>system from Cerner to Epic in October 2018. This study aims to understand if provider perceptions<br/>about COT changed after the EHR transition and the reasons underlying those perceptions.

Background: Creation and reuse of reliable clinical code sets could accelerate the use of EHR data for research. To support that vision, there is an imperative need for methodologically. driven, transparent and automatic approaches to create error-free clinical code sets. Objectives: Propose and evaluate an automatic, generalizable, and knowledge-based approach that uses as starting point a correct and complete knowledge base of ingredients (e.g., the US Drug Enforcement Administration Controlled Substance repository list includes fentanyl as an opioid) to create medication code sets (e.g., Abstral is an opioid medication with fentanyl as ingredient). Methods: Algorithms were written to convert lists of ingredients into medication code sets, where all the medications are codified in the RxNorm terminology, are active medications and have at least one ingredient from the ingredient list. Generalizability and accuracy of the methods was demonstrated by applying them to the discovery of opioid and anti-depressant medications. Results: Errors (39 (1.73%) and 13 (6.28%)), obsolete drugs (172 (7.61%) and 0 (0%)) and missing medications (1,587 (41.26%) and 1,456 (87.55%)) were found in publicly available opioid and antidepressant medication code sets, respectively. Conclusion: The proposed knowledge-based algorithms to discover correct, complete, and up to date ingredient-based medication code sets proved to be accurate and reusable. The resulting algorithms and code sets have been made publicly available for others to use.

This project is an investigation of the gene by environment (GxE) interactions’ effect on substance use outcomes among refugee communities. Substance use disorders (SUDs) are a major public health concern, affecting individuals and communities worldwide. The etiology of SUDs is complex, involving a combination of genetic, environmental, and social factors. In recent years, there has been growing interest in the role of gene by environment interactions in the development of SUDs, particularly in vulnerable populations such as refugees. Refugee populations are exposed to a range of environmental stressors that may interact with genetic factors to increase their risk of SUDs. However, a number of studies describe a “refugee paradox,” where despite having been exposed to risk factors that can lead to SUDs, they are less likely to develop SUDs. Understanding these gene by environment interactions in refugee communities is crucial for not only understanding this phenomenon, but developing effective prevention and treatment strategies for this population. This thesis aims to investigate the gene by environment interactions underlying substance use in refugee communities and to analyze different methods for gene by environment analyses, ultimately determining which method is best suited for this population.