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Description of the problem: Pharmacy students are expected to learn how condition and patient-specific factors influence medication decision-making. Our objective was to create an interactive learning tool that would support students as they learn how individual factors change over-the-counter (OTC) medication recommendations. Description of the innovation: OTC Coach was created to allow student pharmacists to practice making recommendations about OTC medications. First year student pharmacists enrolled in a required self-care therapeutics course were given access to the optional OTC Coach, which included electronic decision algorithms for 10 topics. Student perceptions were collected via an online survey. Critical analysis: Two-thirds of the first-year students enrolled in the self-care therapeutics course activated their OTC Coach account (n=53/79, 67%). Among the students who completed the survey and reported using the tool (n=60/75, 80%), there was agreement that it help them learn the course material (78%), increased their confidence in making appropriate therapeutic recommendations (78%), increased their confidence when answering examination questions (63%), and improved their examination performance (61%). Next steps: Implementing an electronic tool positively supported student learning about OTC medication recommendations, according to student self-report. The tool is being expanded to include an option for students to generate a series of scenarios with randomized patient and condition factors to further allow students to efficiently practice making repeated patient-centered recommendations.

Personalized medicine refers to using individual patient characteristics including phenotype and genotype, to tailor the therapeutic strategy. This approach seeks to challenge the “one-size-fits-all” method to patient management. In the bargain, it reduces adverse drug reactions, improves compliance, and reduces the economic burden of disease management. Traditionally, as extrapolated from usage in oncology, the term applied to using genomic, metabolomic, and epigenomic data in selecting medications; however, even simpler data such as clinical phenotype can aid therapeutic decision making. Autoimmune diseases provide many such data points, owing to the multi-organ nature of clinical manifestations as well as the availability of a wide variety of tests for antibodies as well as cytokines. Rheumatologists already use personalization intuitively, based on various factors such as organ involvement, comorbidities, fertility concerns, and costs. However, in a literature search, few studies look at tailoring treatment regimens to individual characteristics. Building coherent databases can help in better analysis of data and answering locally relevant questions in the future.

Background: Despite clear indications for intervention, therapeutic decision-making for elderly patients with severe symptomatic aortic stenosis (AS) remains a complex issue due to the wide variation in individual risk profiles and the involvement of patients' subjective preferences. We aimed to investigate the reasons leading to the decisions against intervention and the consequences thereof on survival.Methods: Data were derived from the China Elderly Valve Disease (China-DVD) Cohort Study on patients aged ≥60-year-old with severe symptomatic AS consecutively enrolled between September to December 2016. Patients were analyzed according to the initial therapeutic decisions made by consensus between patients and physicians at the time of the index evaluation: intervention group (patients who were evaluated as suitable for intervention and accepted the treatment proposal); patient-refusal group (patients who were evaluated as suitable for intervention but refused due to subjective preferences); physician-deny group (patients who were denied intervention by physicians after evaluation). The least absolute shrinkage and selection operator (LASSO)-penalized logistic regression model was used to identify the factors associated with physicians' decisions against intervention. Twelve-month survival was analyzed using Cox proportional hazards models, with multivariate adjustment using inverse probability weighting (IPW).Results: Among the enrolled 456 elderly patients with severe symptomatic AS, 52 (11.4%) patients refused intervention and 49 (10.7%) patients were denied intervention by their physicians. LASSO-penalized logistic regression model identified that reduced left ventricular ejection fraction and increased EuroSCORE-II were strongly associated with physicians' decisions against intervention. At 12-month follow-up, only 8 (15.4%) patients who initially refused the intervention proposal underwent the subsequent intervention, with an average delay of 195 days. Patients' initial decisions against intervention were significantly associated with 12-month mortality, even after IPW adjustment (Hazard ratio: 2.61; 95% confidence interval: 1.09–6.20; P = 0.031).Conclusions: The decision against intervention was taken in about one-fifth of elderly patients with symptomatic severe AS, half of which were due to patients' subjective preferences. Surgical risk remains the primary concern for physicians when making therapeutic decisions. Elderly patients' initial decisions against intervention have a profound impact on subsequent intervention rates and prognosis, and therefore should be treated as a “risk factor” at the subjective level.Clinical Trial Registration:clinicaltrials.gov/ct2/show/NCT02865798, China elDerly Valve Disease (China-DVD) cohort study (NCT02865798).