Presentation Type
Poster
Faculty Mentor’s Full Name
Donna Beall
Faculty Mentor’s Department
College of Health - Skaggs School of Pharmacy
Abstract / Artist's Statement
- Purpose – The management of drug interactions in healthcare is a large concern for pharmacists and other healthcare professionals, especially for patients requiring large amounts of medications. Healthcare professionals and patients alike utilize drug-interaction software programs (DISPs) frequently to manage medications and make clinical decisions. There is a large discrepancy between DISPs in terms of detecting clinically significant drug interactions. Notably, cannabis does not flag as an interaction as reliably as pharmaceutical drugs on many DISPs. With rising cannabis use in the United States, patients deserve to make decisions on how to safely use cannabis with their prescription drugs which may depend on the accuracy of DISPs.
- Methods – First, I made a literature review table of 30 interactions between cannabis and pharmaceutical drugs (18 true interactions, 12 false). Next, I entered each interaction pair into eight individuals DISPs and noted if it was captured or not. After this, I calculated the inter-rater reliability between the DISPs to assess how much agreement there was between them. I then calculated the specificity, sensitivity, and accuracy of each DISP.
- Significance – Overall, DISPs did not have a high “agreeance” on what was detected as a true drug interaction with cannabis; Fleiss kappa was very low at k=0.216 (95% CI = 0.148, 0.284), where scores range from -1 to 1 with “1.000” being high agreement. Average accuracy of detecting a drug interaction with cannabis was somewhat low (76.2%), where DISPs tailored for healthcare professionals far outperformed DISPs tailored for the layperson. There is a notable disparity between the sensitivity of DISPs with the lowest score being 44.4% and the highest being 100%. A lower sensitivity equates to missing clinically important interactions which may result in negative health outcomes. Healthcare professionals and patients should recognize the importance of drug interactions with cannabis and should utilize DISPs that most accurately reflect drug interactions with cannabis.
Category
Life Sciences
Assessing the accuracy and deficits of popular drug-interaction software programs in detecting interactions between cannabis and pharmaceutical drugs
UC South Ballroom
- Purpose – The management of drug interactions in healthcare is a large concern for pharmacists and other healthcare professionals, especially for patients requiring large amounts of medications. Healthcare professionals and patients alike utilize drug-interaction software programs (DISPs) frequently to manage medications and make clinical decisions. There is a large discrepancy between DISPs in terms of detecting clinically significant drug interactions. Notably, cannabis does not flag as an interaction as reliably as pharmaceutical drugs on many DISPs. With rising cannabis use in the United States, patients deserve to make decisions on how to safely use cannabis with their prescription drugs which may depend on the accuracy of DISPs.
- Methods – First, I made a literature review table of 30 interactions between cannabis and pharmaceutical drugs (18 true interactions, 12 false). Next, I entered each interaction pair into eight individuals DISPs and noted if it was captured or not. After this, I calculated the inter-rater reliability between the DISPs to assess how much agreement there was between them. I then calculated the specificity, sensitivity, and accuracy of each DISP.
- Significance – Overall, DISPs did not have a high “agreeance” on what was detected as a true drug interaction with cannabis; Fleiss kappa was very low at k=0.216 (95% CI = 0.148, 0.284), where scores range from -1 to 1 with “1.000” being high agreement. Average accuracy of detecting a drug interaction with cannabis was somewhat low (76.2%), where DISPs tailored for healthcare professionals far outperformed DISPs tailored for the layperson. There is a notable disparity between the sensitivity of DISPs with the lowest score being 44.4% and the highest being 100%. A lower sensitivity equates to missing clinically important interactions which may result in negative health outcomes. Healthcare professionals and patients should recognize the importance of drug interactions with cannabis and should utilize DISPs that most accurately reflect drug interactions with cannabis.