Understanding the safety culture of community pharmacies can help identify areas of strength and those that require improvement. Improvement efforts that focus on staffing, work pressure, and pace in community pharmacies may lead to better safety culture.

Background Older adults are the largest consumers of over the counter (OTC) medications. Of the older adults who are at risk of a major adverse drug event, more than 50% of these events involve an OTC medication. Objective To explore how older adults select and hypothetically use OTC medications and if the selected medications would be considered safe for use. Methods Walking interviews were conducted with 20 community-dwelling older adults in a community pharmacy. Each participant selected an OTC medication for a hypothetical pain and sleep scenario. Data were analyzed for four types of misuse: drug-drug interaction, drug-disease interaction, drug-age interaction, and excess usage. Results At least one instance of potential misuse was found in 95% of participants. For sleep medications, drug-drug interactions and drug-age interactions were more common, affecting 50% and 65% of participants respectively. The most common type of misuse noted in the pain products selected was that of drug-drug interaction, with a total of 39 occurrences, affecting 60% of the participants. Conclusions OTC misuse is common among older adults, and it is important for older adults to seek out resources, such as a pharmacist, to help them make safe OTC decisions.

The language used by physicians and health professionals in prescription directions includes medical jargon and implicit directives and causes much confusion among patients. Human intervention to simplify the language at the pharmacies may introduce additional errors that can lead to potentially severe health outcomes. We propose a novel machine translation-based approach, PharmMT, to automatically and reliably simplify prescription directions into patient-friendly language, thereby significantly reducing pharmacist workload. We evaluate the proposed approach over a dataset consisting of over 530K prescriptions obtained from a large mail-order pharmacy. The end-to-end system achieves a BLEU score of 60.27 against the reference directions generated by pharmacists, a 39.6% relative improvement over the rule-based normalization. Pharmacists judged 94.3% of the simplified directions as usable as-is or with minimal changes. This work demonstrates the feasibility of a machine translation-based tool for simplifying prescription directions in real-life.