Background: The absence of collaboration between health professionals is known to influence prescriptions' quality, also disadvantaging elderly frail patients' polytherapies.Objectives: This study aims to improve the adherence to medications of elderly patients suffering from multiple diseases through interpersonal continuing medical education (CME). The CME was organized for general practitioners (GPs) by hospital pharmacists (HPs) from a Territorial Pharmaceutical Centre of Piedmont, in collaboration with pharmacists from the Drug Science and Technology Department of the University of Turin, to enhance awareness on the management of chronic therapies and de-prescription. Methods: Pharmacists set face-to-face lessons for GPs between April 2018 and November 2018, while therapies' reconciliation and delivery of the Illustrated Therapy Schedules (ITS) lasted until September 2019. Polytherapies were evaluated by pharmacists and GPs in terms of appropriateness (number of potentially inappropriate prescriptions -PIPs according to 2019 Beers Criteria) and number of drug-drug interactions (DDIs), using a clinical decision support system (CDSS -NavFarma©) to help health professionals dealing with the process of review, reconciliation and individuation of possible adverse reactions. Results: From the CME organization it emerged that the collaboration between health professionals supported by a CDSS could improve the quality of elderly patients polytherapies. Two-hundred fifteen patients were enrolled by GPs; patients included were agedresults reported as average (sd) -76.4 (6.3), mostly men (54.9%), number of daily medications per patient was 8.1(2.4); 2.1(1.8) DDIs per patient were individuated, 12.9% of which were solved thanks to the CME. Average number of PIPs found was 2.5 (1.4) per patient. Conclusions: The CME represented a proactive approach by HPs to the management of elderly patients' polytherapies. Moreover, clinicians' engagement is a mean to enhance quality, safety, professionalism and communication in health processes.

Medication adherence represents a complex and multifaceted process. Standardized terminology is essential to enable a reproducible process in various languages. The study’s aim was to translate and adapt the original Ascertaining Barriers for Compliance (ABC) Taxonomy on medication adherence, first proposed in 2012, into Italian language. The study was carried out according to the Preferred Methods for Translation of the ABC Taxonomy for Medication Adherence adopted by the ESPACOMP. Key steps included: (1) a systematic literature review using PubMed and Embase according to the PRISMA Guidelines to identify published Italian terms and definitions, and Italian adherence experts; (2) a forward translation of terms and definitions; (3) panelists’ selection; (4) a three-round Delphi survey. From the systematic review, 19 studies allowed detection of 4 terms, 4 definitions and 767 Italian experts. To these, Italian ESPACOMP members and experts though snowball sampling were added. The identified Italian adherence experts received the Delphi questionnaire. The Italian ABC Taxonomy was achieved after three rounds of Delphi survey by reaching at least a moderate consensus on unambiguous naming and definition of medication adherence-related terms. The Taxonomy is intended to be used in research, academic, and professional fields in order to harmonize adherence terminology and avoid confusion in comparing research findings.

ObjectiveClinical decision support systems (CDSSs) can reduce medical errors increasing drug prescription appropriateness. Deepening knowledge of existing CDSSs could increase their use by healthcare professionals in different settings (ie, hospitals, pharmacies, health research centres) of clinical practice. This review aims to identify the characteristics common to effective studies conducted with CDSSs.Materials and methodsThe article sources were Scopus, PubMed, Ovid MEDLINE and Web of Science, queried between January 2017 and January 2022. Inclusion criteria were prospective and retrospective studies that reported original research on CDSSs for clinical practice support; studies should describe a measurable comparison of the intervention or observation conducted with and without the CDSS; article language Italian or English. Reviews and studies with CDSSs used exclusively by patients were excluded. A Microsoft Excel spreadsheet was prepared to extract and summarise data from the included articles.ResultsThe search resulted in the identification of 2424 articles. After title and abstract screening, 136 studies remained, 42 of which were included for final evaluation. Most of the studies included rule-based CDSSs that are integrated into existing databases with the main purpose of managing disease-related problems. The majority of the selected studies (25 studies; 59.5%) were successful in supporting clinical practice, with most being pre–post intervention studies and involving the presence of a pharmacist.Discussion and conclusionA number of characteristics have been identified that may help the design of studies feasible to demonstrate the effectiveness of CDSSs. Further studies are needed to encourage CDSS use.

In a Drug Prescription Network (DPN), each drug is represented as a node and two drugs co-prescribed to the same patient are represented as an edge linking the nodes. The use of DPNs is a novel approach that has been proposed as a means to study the complexity of drug prescription. The aim of this study is to demonstrate the analytical power of the DPN-based approach when it is applied to the analysis of administrative data. Drug prescription data that were collected at a local health unit (ASL TO4, Regione Piemonte, Italy), over a 12-month period (July 2018–June 2019), were used to create several DPNs that correspond to the five levels of the Anatomical Therapeutic Chemical classification system. A total of 5,431,335 drugs prescribed to 361,574 patients (age 0–100 years; 54.7% females) were analysed. As indicated by our results, the DPNs were dense networks, with giant components that contain all nodes. The disassortative mixing of node degrees was observed, which implies that non-random connectivity exists in the networks. Network-based methods have proven to be a flexible and efficient approach to the analysis of administrative data on drug prescription.

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