Pain management is complex and challenging due to inter-patient variability, including disease pathophysiology and comorbidities (e.g., anxiety, depression, substance use disorder). Appropriate selection of therapy, both opioid and non-opioid) is dependent on multiple patient-specific factors. Pharmacogenomic (PGx) variability in CYP2D6 and CYP2C9 impacts the efficacy and toxicity profile for specific pain medications and may add objectivity to pharmacologic pain management. The purpose of this study is to describe how pharmacogenomics, including screening, counseling, and provision of clinical recommendations, can be integrated into a community practice to optimize pain management. This study is a sub-analysis of patients enrolled in the Tiger Meds Personalized Prescription (TMPP) program within the Auburn University (AU) Pharmaceutical Care Center. The TMPP study population will consist of up to 1000 AU employees or covered dependents enrolled in the self-insured health insurance plan, age 19 years of age or older, taking one of the following medications: amitriptyline, doxepin, imipramine, nortriptyline, citalopram, escitalopram, fluvoxamine, paroxetine, sertraline, venlafaxine, atomoxetine, clopidogrel, tamoxifen, codeine, tramadol, celecoxib, flurbiprofen, ibuprofen, meloxicam, or piroxicam. A chart review of patients enrolled in the TMPP program between October 2020 and October 31, 2021, with a completed PGx screening panel available, will be conducted to obtain CYP2D6 and CYP2C9 genotype information Furthermore, the in-silico analysis will be performed using SwissADME and Quikpro software to validate the findings. All patients are provided a comprehensive PGx medication consult. Patient specific pain management recommendations are provided for the following drug-gene pairs, consistent with evidence-based guidelines: CYP2D6 – codeine, tramadol; CYP2C9 – celecoxib, flurbiprofen, ibuprofen, meloxicam, piroxicam. Primary outcomes include the number of CYP2D6 and CYP2C9 drug-gene and drug-drug-gene interactions identified leading to a recommendation to alter the dosage or administration of opioid and non-opioid pain medications. Data collection will be completed utilizing the Research Electronic Data Capture (REDCap) system. Count and continuous data will be analyzed using measures of central tendency and dispersion. Chi-square analyses will be used to compare categorical outcomes between patients with versus without an actionable PGx-phenotype.