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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). Patients seeking care or requesting a product for a minor ailments considered in the study (dermatological problems, gastrointestinal disturbance, pain and upper respiratory tract related symptoms) were included. The intervention consisted of a standardised pharmacist-patient consultation guided by a web-based program using co-developed management protocols and patients' educational material. Patients were followed up by phone ten days later. Primary clinical outcomes were appropriate medical referral and modification of direct product request. Secondary outcomes were symptom resolution and reconsultation rates., Results: A total of 808 patients (323 MAS and 485 UC) were recruited in 27 pharmacies of 21 municipalities. Patients visiting MAS pharmacies had higher odds for being referred to a physician (OR = 2.343, CI95% = [1.146-4.792]) and higher reconsultation rates (OR = 1.833, CI95% = [1.151-2.919]) compared to UC. No significant differences between groups were observed for modification of direct product request and symptom resolution., Conclusions: The use of management protocols through the MAS strengthened the identification of referral criteria such as red flags in patients suffering minor ailments. These patients with symptoms of minor ailments possibly due to more severe illness were to be referred and evaluated by physicians. Results reinforce that MAS increases safety for those patients consulting in community pharmacy for minor ailments., Trial Registration: Trial registration number: ISRCTN17235323. Retrospectively registered 07/05/2021, https://www.isrctn.com/ISRCTN17235323., Competing Interests: The authors have declared that no competing interests exist.

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 Background Minor ailments are “self-limiting conditions which may be diagnosed and managed without a medical intervention”. A cluster randomised controlled trial (cRCT) was designed to evaluate the clinical, humanistic and economic outcomes of a Minor Ailment Service (MAS) in community pharmacy (CP) compared with usual care (UC). Methods The cRCT was conducted for 6 months from December 2017. The pharmacist-patient intervention consisted of a standardised face-to-face consultation on a web-based program using co-developed protocols, pharmacists’ training, practice change facilitators and patients’ educational material. Patients requesting a non-prescription medication (direct product request) or presenting minor ailments received MAS or UC and were followed-up by telephone 10-days after the consultation. The primary economic outcomes were incremental cost-utility ratio (ICUR) of the service and health related quality of life (HRQoL). Total costs included health system, CPs and patient direct costs: health professionals’ consultation time, medication costs, pharmacists’ training costs, investment of the pharmacy and consultation costs within the 10 days following the initial consultation. The HRQoL was obtained using the EuroQoL 5D-5L at the time of the consultation and at 10-days follow up. A sensitivity analysis was carried out using bootstrapping. There were two sub-group analyses undertaken, for symptom presentation and direct product requests, to evaluate possible differences. Results A total of 808 patients (323 MAS and 485 UC) were recruited in 27 CPs with 42 pharmacists (20 MAS and 22 UC). 64.7% (n = 523) of patients responded to follow-up after their consultation in CP. MAS patients gained an additional 0.0003 QALYs (p = 0.053). When considering only MAS patients presenting with symptoms, the ICUR was 24,733€/QALY with a 47.4% probability of cost-effectiveness (willingness to pay of 25,000€/QALY). Although when considering patients presenting for a direct product request, MAS was the dominant strategy with a 93.69% probability of cost-effectiveness. Conclusions Expanding community pharmacists’ scope through MAS may benefit health systems. To be fully cost effective, MAS should not only include consultations arising from symptom presentation but also include an oversight of self-selected products by patients. MAS increase patient safety through the appropriate use of non-prescription medication and through the direct referral of patients to GP. Trial registration ISRCTN, ISRCTN17235323 . Registered 07/05/2021 - Retrospectively registered

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]