BACKGROUND: Glycemic goals (hemoglobin A1c less than 7%) are often not achieved in patients with type 2 diabetes despite the availability of many effective treatments and the documented benefits of glycemic control in the reduction of long-term microvascular and macrovascular complications. Several studies have established the important positive effects of pharmacist-led management on achieving glycemic control and other clinical outcomes in patients with diabetes. Diabetes prevalence and mortality are increasing rapidly in Jordan. Nevertheless, clinical pharmacists in Jordan do not typically provide pharmaceutical care; instead, the principal responsibilities of pharmacists in Jordan are dispensing and marketing of medical products to physicians. OBJECTIVE: To assess the primary clinical outcome of glycemic control (A1c) and secondary outcomes, including blood pressure, lipid values, self-reported medication adherence, and self-care activities for patients with type 2 diabetes in an outpatient diabetes ...

Pharmacy Practice is a free full-text journal with a scope on the practice of pharmacy. Pharmacy Practice covers pharmaceutical care, social pharmacy, pharmacy education, process and outcome research, health promotion and education, health informatics, pharmacoepidemiology

Artificial intelligence (AI) is the capacity of machines to perform tasks that ordinarily require human intelligence. AI can be utilized in various pharmaceutical applications with less time and cost. To evaluate community pharmacists’ willingness and attitudes towards the adoption of AI technology at pharmacy settings, and the barriers that hinder AI implementation. This cross-sectional study was conducted among community pharmacists in Jordan using an online-based questionnaire. In addition to socio-demographics, the survey assessed pharmacists’ willingness, attitudes, and barriers to AI adoption in pharmacy. Binary logistic regression was conducted to find the variables that are independently associated with willingness and attitude towards AI implementation. The present study enrolled 401 pharmacist participants. The median age was 30 (29–33) years. Most of the pharmacists were females (66.6%), had bachelor’s degree of pharmacy (56.1%), had low-income (54.6%), and had one to five years of experience (35.9%). The pharmacists showed good willingness and attitude towards AI implementation at pharmacy (n = 401). The most common barriers to AI were lack of AI-related software and hardware (79.2%), the need for human supervision (76.4%), and the high running cost of AI (74.6%). Longer weekly working hours (attitude: OR = 1.072, 95% C.I (1.040–1.104), P < 0.001, willingness: OR = 1.069, 95% Cl. 1.039–1.009, P-value = 0.011), and higher knowledge of AI applications (attitude: OR = 1.697, 95%Cl (1.327–2.170), willingness: OR = 1.790, 95%Cl. (1.396–2.297), P-value < 0.001 for both) were significantly associated with better willingness and attitude towards AI, whereas greater years of experience (OR = 20.859, 95% Cl (5.241–83.017), P-value < 0.001) were associated with higher willingness. In contrast, pharmacists with high income (OR = 0.382, 95% Cl. (0.183–0.795), P-value = 0.010), and those with<10 visitors (OR = 0.172, 95% Cl. (0.035–0.838), P-value = 0.029) or 31–50 visitors daily (OR = 0.392, 95% Cl. (0.162–0.944), P-value = 0.037) had less willingness to adopt AI. Despite the pharmacists' positive willingness and attitudes toward AI, several barriers were identified, highlighting the importance of providing educational and training programs to improve pharmacists' knowledge of AI, as well as ensuring adequate funding support to overcome the issue of AI high operating costs.