Identification of high-risk patients for referral through machine learning assisting the decision making to manage minor ailments in community pharmacies.

Data analysis techniques such as machine learning have been used for assisting in triage and the diagnosis of health problems. Nevertheless, it has not been used yet to assist community pharmacists with services such as the Minor Ailment Services These services have been implemented to reduce the burden of primary care consultations in general medical practitioners (GPs) and to allow a better utilization of community pharmacists' skills. However, there is a need to refer high-risk patients to GPs.

Strengthening patients' triage in community pharmacies: A cluster randomised controlled trial to evaluate the clinical impact of a minor ailment service.

Background: Self-perceived minor ailments might conceal other health conditions if patients are not appropriately assisted by health care professionals. The aim of the study was to evaluate the patient-related outcomes of a community pharmacy Minor Ailment Service (MAS) compared to usual pharmacist care (UC).

Methods: A cluster randomised controlled trial was conducted over six months in community pharmacy in the province of Valencia (Spain).

Medicines use review service in community pharmacies in Spain: REVISA project.

Background Community pharmacy services play an important role in controlling some factors related to medicine use and patients can benefit from these services to improve the adherence and knowledge of their medications, besides to reduce medicine-related problems. Objective The aim of the REVISA project is to carry out a study on preliminary implementation of the medicines use review service in Spanish community pharmacies. Setting Sixty-four community pharmacies from all regions of Spain.