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

Amador-Fernández, Noelia; Benrimoj, Shalom I.; Lima, Antonio Olry de Labry; García-Cárdenas, Victoria; Gastelurrutia, Miguel Ángel; Berger, Jérôme; Baixauli-Fernández, Vicente J.; Climent-Catalá, María Teresa; Colomer-Molina, Vicente; Martínez-Mart́inez, Fernando

doi:10.1371/journal.pone.0275252

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…The outcome variables measured related to the impact of MAS were ( Amador-Fernández et al, 2022 ): • Appropriate medical referral: patient referral by the pharmacist made in accordance with the co-designed protocols. • Modification of direct product request.…”

Section: Methodsmentioning

confidence: 99%

“…Although, studies have reported that patients can be appropriately triaged in community pharmacy to GPs ( Dineen-Griffin et al, 2020 ; Amador-Fernández et al, 2022 ); the use of predictive modelling at the time of the consultation would suggest, although not proven, a contribution to the increase in patient safety, clinical decision-making and governance. Future research should focus on developing artificial intelligence techniques to help clinicians in daily practice, always taking into consideration the legal and ethical aspects.…”

Section: Implications For Research and Practicementioning

confidence: 99%

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

et al. 2023

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Implications For Research and Practicementioning

confidence: 99%

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

et al. 2023

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“…41,42 Considering that approximately one-quarter of this student cohort could not identify warning symptoms in the case of childhood fever, there is a need for interventions to improve the ability of pharmacy students and also pharmacists to recognize red flag referral criteria for patient consultations. The use of a standardized protocol has been reported to have improved pharmacist referrals; 43 however, further research is still needed to identify appropriate strategies in improving the knowledge of Indonesian pharmacy students and possibly pharmacists in identifying conditions that require medical referral and conditions that can be self-medicated.…”

Section: The Types Reasons and Appropriateness Of The Recommendations...mentioning

confidence: 99%

Pharmacy Students’ Over-the-counter Recommendations for Primary Dysmenorrhea and Childhood Fever Cases in an Indonesian University

Brata,

Wibowo,

Natasya

et al. 2024

J. Manaj. dan Pelayanan Farm.

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Background: The quality of pharmacy education is stated in the literature to be one of the factors influencing the quality of self-medication services in pharmacies. However, research describing the ability of pharmacy students to handle self-medication cases is limited.Objectives: This study aims to describe pharmacy students’ recommendations for two vignette cases involving analgesic-antipyretics (i.e., primary dysmenorrhea and childhood fever cases) in an Indonesian university and to identify factors related to the appropriateness of their recommendations.Methods: Apothecary students were asked to provide recommendations and their reasoning for primary dysmenorrhea and childhood fever cases using a structured telephone interview.Results: Of the 86 participants, appropriate recommendations were provided by 86% and 78% for a case of primary dysmenorrhea and for childhood fever respectively. One-quarter of students did not identify referral criteria in the case of childhood fever and thus made inappropriate recommendations. Age and study period were factors significantly related to providing appropriate recommendations, in which students who were younger and completed their study program within 5 to 6 years were significantly able to provide appropriate recommendations compared to students who were older and whose study period exceeded 6 years.Conclusion: A considerable number of apothecary students in an Indonesian university were able to properly manage cases related to analgesic-antipyretic recommendations. Further qualitative research is needed to identify factors underlying the knowledge of Indonesian pharmacy students in identifying major and minor patient presentations.

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“…Medications are paid fully by the patient; therefore, for those consultations resulting in non-pharmacological advice or triage, the service is not remunerated. Protocols have been agreed between community pharmacists and general practitioners for the management of minor ailments, 31 evaluation of the service impact have been made with positive results 32 , 33 and machine learning algorithms have been developed to assist pharmacists when triaging patients with minor ailments. 34 The adherence management service is also defined by Foro AF-FC.…”

Section: Community Pharmacy Services Related To Self-care In Spainmentioning

confidence: 99%

Development of self-care in Spanish community pharmacies

Amador-Fernández,

Gastelurrutia,

García-Cárdenas

2023

Exploratory Research in Clinical and Social Pharmacy

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

Cited by 5 publications

References 24 publications

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

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

Pharmacy Students’ Over-the-counter Recommendations for Primary Dysmenorrhea and Childhood Fever Cases in an Indonesian University

Development of self-care in Spanish community pharmacies

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