Over the next two years, legal access to marijuana in the State of Montana will continue to grow. Based on trends from other states, legalization will result in more patients using cannabis/cannabinoids for medical and recreational purposes. This presentation will briefly review the efficacy of cannabis/cannabinoids in treating various medical conditions. Management of side effects, such as acute intoxication and hyperemesis cannabinoid syndrome, will be discussed. Finally, current knowledge regarding drug interactions with different cannabis formulations will be surveyed.

Disclosures:

1.     Criteria for successful completion: Watch the recording in its entirety and mark lessons as complete. A certificate will be provided and available for download after the final part of the online course is marked as complete.

2.    There are no relevant financial relationships with ineligible companies for those with the ability to control the content of this activity.

3.     Montana Nurses Association is accredited with distinction as a provider of nursing continuing professional development by the American Nurses Credentialing Center’s Commission on Accreditation.

4.     This activity is available for contact hours through 12/31/2023.

Contact hours awarded upon successful completion: 1, including 1 Rx

Questions? Email them to [email protected]


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Staci Hemmer is an assistant professor of pharmacy practice at the University of Montana. She is a preceptor for fourth-year pharmacy students completing their hospital/inpatient APPE rotations at Community Medical Center in Missoula, MT. Staci also teaches multiple topics in the didactic pharmacy curriculum including pharmacokinetics, therapeutics, sterile compounding, and other advanced laboratory topics. Staci graduated from the Doctor of Pharmacy program at the University of Montana in 2017. Before joining the pharmacy practice faculty at UM, she completed a PG-Y1 pharmacy practice residency at Community Medical Center. Staci’s professional interests include pharmacokinetic modeling with Bayesian estimation and transitions-of-care.