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Background: Health systems worldwide are under pressure. Integration seems a possible solution to improve healthcare systems efficiency. This research aims to gather stakeholders' opinions on integrating community pharmacy and the primary healthcare system and secondly to explore and prioritise interventions for an initial integration plan. Method: Using a constructivist qualitative research approach, a two-phase qualitative study was conducted in the Basque Country, Spain. Thematic analysis using NVivo® was undertaken on data gathered during focus groups and semi-structured interviews (phase 1). During phase 2, a nominal group prioritised potential integration interventions identified in phase 1. Results: The study amalgamated findings from four focus groups and nine interviews, revealing six themes. Stakeholders had a diverse understanding of integration, associating the term mainly with collaboration, communication or cooperation. Community pharmacies were positively perceived; however, their commercial and privately owned nature was of concern. Remuneration methods for pharmacists were controversial, with a suggested shift to service-based remuneration. Information availability and barriers such as interprofessional communication gaps were highlighted. The nominal group prioritised, according to importance and feasibility, bidirectional communication development, coordination in using interprofessional protocols and community pharmacist participation in primary healthcare centre meetings as interventions for integrating community pharmacies and primary healthcare centres. Conclusion: Based on the opinions of stakeholders, three interventions are proposed to initiate the integration process of community pharmacy and primary care. The implementation of these interventions will need to be negotiated with the relevant authorities and evaluated. [ABSTRACT FROM AUTHOR]

Objective of this work was assessing the impact on safety, adherence, use and knowledge (U&K) about medicines and patient's quality of life (QOL), of community pharmacist (CP)-led interventions in a new service focused on AM medicines targeted at three prevalent chronic diseases: diabetes mellitus type 2, chronic obstructive pulmonary disease and cardiovascular disease. Patients and Methods: A prospective interventional cohort study was conducted with a 6-month follow-up in 27 community pharmacies (145 patients). Safety, adherence to treatment, patient U&K and QOL were assessed at follow-up visits (months 0, 3 and 6). Results: The number of detected ADRs was 163 with 41 patients referred to the doctor. At baseline, 24.1% of the patients were non-adherent, mainly due to unintentional causes. After six months and 130 interventions by CPs on adherence, a significant reduction to lower than 5.8% was achieved. The inadequate U&K of medicines also decreased, from 47.6% to 7.9% after 182 interventions. Also, the patient's QOL improved. Conclusion: A new patient-centered pharmacy service provides some evidence on the important role of CP in assisting the proper and safe use of AM medicines, improving patient health outcomes. [ABSTRACT FROM AUTHOR]

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Background: 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. Aim: To develop a predictive model for high-risk patients that need referral assisting community pharmacists' triage through a minor ailment service. Method: An ongoing pragmatic type 3 effectiveness-implementation hybrid study was undertaken at a national level in Spanish community pharmacies since October 2020. Pharmacists recruited patients presenting with minor ailments and followed them 10 days after the consultation. The main outcome measured was appropriate medical referral (in accordance with previously co-designed protocols). Nine machine learning models were tested (three statistical, three black box and three tree models) to assist pharmacists in the detection of high-risk individuals in need of referral. Results: Over 14'000 patients were included in the study. Most patients were female (68.1%). With no previous treatment for the specific minor ailment (68.0%) presented. A percentage of patients had referral criteria (13.8%) however, not all of these patients were referred by the pharmacist to the GP (8.5%). The pharmacists were using their clinical expertise not to refer these patients. The primary prediction model was the radial support vector machine (RSVM) with an accuracy of 0.934 (CI95 = [0.926,0.942]), Cohen's kappa of 0.630, recall equal to 0.975 and an area under the curve of 0.897. Twenty variables (out of 61 evaluated) were included in the model. radial support vector machine could predict 95.2% of the true negatives and 74.8% of the true positives. When evaluating the performance for the 25 patient's profiles most frequent in the study, the model was considered appropriate for 56% of them. Conclusion: A RSVM model was obtained to assist in the differentiation of patients that can be managed in community pharmacy from those who are at risk and should be evaluated by GPs. This tool potentially increases patients' safety by increasing pharmacists' ability to differentiate minor ailments from other medical conditions. [ABSTRACT FROM AUTHOR]