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Filter by:Drug-related problems in newborn babies have been reported with a rate of 4-30%. It is estimated that the higher rates of these problems in hospitalized children under the age of two are related to the variety of drugs used and the differences in the age, weight and diagnosis of the patients. In this context, with the clinical parameters and demographic data obtained in the first 24 hours of the patients hospitalized in the neonatal intensive care unit, machine learning algorithms are used to predict the risks that may arise from possible drug-related problems (prescribing and administration errors, side effects and drug-drug interactions) that may occur during hospitalization. The algorithm, which will be created by modeling with a high number of big data pool, is planned to be transformed into a clinical decision support system software that can be used easily in clinical practice with online and mobile applications. By processing the data of the patients to be included in the model, it is aimed to prevent and manage drug-related problems before they occur, as well as to provide cost-effective medşcation treatment to patients hospitalized in the neonatal intensive care unit, together with a reduction in the risk of drug-related mortality and morbidity.
The hypothesis is that the intervention of an operational clinical pharmacy team (EOPC), targeting both patients and hospital and health care professionals, allows: i) to initiate a therapeutic review during hospitalization, ii) to accompany the patients upon hospital discharge, iii) to maintain, in outpatient care, the drug treatments that have been optimized during hospitalization. The main objective of the study is to demonstrate that the intervention of an EOPC in surgical departments and then in outpatient care makes it possible to maintain, 45 days after the discharge of the patients aged 65 years and over, the chronic outpatient treatments revised and optimized during the hospital stay. The secondary objectives are to measure the impacts of EOPC's intervention on: - unexpected readmissions, emergency use, medical complications and adverse drug reactions; - patient and health professional satisfactions (community pharmacists and physicians); - the costs of drug treatments in ambulatory care.