Objective: To compile and synthesize the available literature describing medical cannabis use across various disease states. Data Sources: PubMed, EBSCO, and Google Scholar searches were conducted using MeSH and/or keywords. Study Selection and Data Extraction: Studies were included if they described the use of cannabis-based products and medications in the treatment of a predefined list of disease states in humans and were published in English. The extraction period had no historical limit and spanned through April 2019. Data Synthesis: Evidence was compiled and summarized for the following medical conditions: Alzheimer disease, amyotrophic lateral sclerosis, autism, cancer and cancer-associated adverse effects, seizure disorders, human immunodeficiency virus, inflammatory bowel disease, multiple sclerosis (MS), nausea, pain, posttraumatic stress disorder, and hospice care. Relevance to Patient Care and Clinical Practice: Based on identified data, the most robust evidence suggests that medical cannabis may be effective in the treatment of chemotherapy-induced nausea and vomiting, seizure disorders, MS-related spasticity, and pain (excluding diabetic neuropathy). Overall, the evidence is inconsistent and generally limited by poor quality. The large variation in cannabis-based products evaluated in studies limits the ability to make direct comparisons. Regardless of the product, a gradual dose titration was utilized in most studies. Cannabis-based therapies were typically well tolerated, with the most common adverse effects being dizziness, somnolence, dry mouth, nausea, and euphoria. Conclusions: As more states authorize medical cannabis use, there is an increasing need for high-quality clinical evidence describing its efficacy and safety. This review is intended to serve as a reference for clinicians, so that the risks and realistic benefits of medical cannabis are better understood.

Comprehensive medication management (CMM) is a patient-centered standard of care that ensures a patient’s medications are optimized. The CMM Practice Management Assessment Tool (PMAT) is a tool to assess areas of CMM practice management. The purpose of this project was to assess the state of CMM practice management based on clinical pharmacist perception for two health systems in the state of Utah, and to identify areas of excellence and/or improvement utilizing a novel method for PMAT analysis. The PMAT was distributed to all primary care-focused ambulatory care pharmacists employed by University of Utah Health (U of U Health) and Intermountain Healthcare (Intermountain). Ordinal responses were assigned to three possible categories of CMM support (High, Indifferent, and Low). Ten surveys were completed from U of U Health, and nine were completed from Intermountain. Thirty-two of the 86 survey questions resulted in a high level of support, and 25 questions resulted in a low level of support from the majority of respondents. Statistically significant differences between the institutions were found for 18 questions. The utilization of the PMAT within two Utah health systems highlighted areas of excellence and improvement and demonstrates a unique method for analysis of PMAT results.

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This study assesses the level of agreement on medication therapy problem (MTP) identification and classification between primary care, ambulatory care pharmacists within a health-system that recently implemented system-wide pharmacist provision of comprehensive medication management (CMM) services. Twenty standardized case vignettes were created and distributed to pharmacists who reviewed each case and identified and categorized MTPs. Outcomes include the number of MTPs identified, identification (yes/no) of specific MTPs within each case (e.g., need for a statin), and Pharmacy Quality Alliance (PQA) category used when classifying MTPs. The level of agreement on MTP identification/categorization was measured using intraclass correlation coefficient (ICC) and interpreted using the Landis and Koch interpretation scale. “Moderate agreement” was observed for the number of MTPs identified by pharmacists (ICC equal to 0.45; 95% confidence interval [CI]: 0.31 to 0.65). In approximately one-half of opportunities, the pharmacists agreed perfectly on the number of MTPs; in approximately one-third of opportunities, the number of MTPs identified varied by 1; and approximately one-tenth of the time, the number of MTPs varied by 2. In regard to the MTP identification (yes/no) and categorization, percent agreement was ≥73% across all MTPs. The results support the need for further training and education and provide the information necessary to target specific disease states.