Drug-Related Problems in Neonatal Patients

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.

Neonatal Intensive Care Unit, Medication Errors, Drug-Related Problems, Adverse Drug Event, Drug-Drug Interaction, Clinical Pharmacy, Machine Learning

Score for Neonatal Acute Physiology and Perinatal Extension Score is predictor of mortality in neonates.

It is use first week of life for determined sepsis risk with gestational age, highest maternal antepartum temperature, duration of rupture of membranes, etc.

It could be used on all infants in the neonatal intensive care on a weekly basis by nursing staff to identify those at high risk of poor growth and in need of additional nutrition support during their stay.

It describes a consensus process that led to the development of standard severity criteria for neonatal adverse events. The use of this tool could improve the quality of drug and device safety evaluations and facilitate the conduct of neonatal clinical trials.

This scale uses a series of questions relating to the potential drug interaction to estimate a probability score.

The new algorithm developed using actual patient data is more valid and reliable than the Naranjo algorithm for identifying adverse drug reactions in the neonatal intensive care unit population.

NASA Task Load Index (NASA-TLX) is a widely used, subjective, multidimensional assessment tool that rates perceived workload in order to assess a task, system, or team's effectiveness or other aspects of performance.

Inclusion Criteria: Newborns aged 0-28 days, Consent form taken by the parents to participate in the study, Patients admitted to neonatal intensive care unit or surgical wards Exclusion Criteria: Have a postnatal age greater than 28 days, Patients who will not be given any medication, Patients who took part in any drug research within the last 28 days