Determinant characteristics in EEG signal based on bursts amplitude segmentation for predicting pathological outcomes of a premature newborn, with validation using ANN

International audienceEEG signal contains some specific patterns that predict neuro-developmental impairments of a premature newborn. Extracting these patterns from a set of EEG records provides a dataset to be used in machine learning in order to implement an intelligent classification system that predict prognosis of the baby. In a previous work we proved that inter-burst intervals (IBI) found in the EEG records predicts abnormal outcomes of the premature. A bibliographic study on the amplitude of an EEG signal, with the annotations of the neuro-pediatricians, showed that low amplitudes in EEG signal are strongly correlated with an abnormal prognosis of the premature, similar to that of IBI. According to these hypotheses, we present in this paper, a segmentation methodology on the amplitude of bursts intervals of EEG signal into 3 segments: low, medium and high, in addition to the inter-burst intervals. We create a new algorithm that detects 6 important parameters in each interval of these 4 segments. After applying this new methodology, we obtain a new classified dataset that contains 24 parameters extracted from these 4 segments to obtain with gestational age of the preterm and the day of recording 26 input attributes and one output which is the class (normal, sick or risky). Finally we validate the pertinence of these attributes using artificial neural network

Challenges of implementing a standardized process for discharge summaries (5 years experience)

Introduction: Discharge summaries are essential documents to provide a long-lasting record of a patient's visit to a hospital. It provides an effective method of communication between various hospital services and primary care providers.We conducted a study recently in KFSH&RC recommending that every admitted patient to the Pediatric Department must have a discharge summary initiated as soon as possible within the first five days of hospitalization and to be updated periodically until its completion on the patient's discharge day. Results of this study showed that most of the patients received their discharge summaries within the time limit as recommended by the JCIA standard. Objectives: The aim of this paper is to present our department's experience in regard to the difficulties, challenges, and outcomes of the adopted work flow for discharge summaries over a period of five years. Methods: The residents have been instructed to initiate the discharge summaries as soon as possible within the first five days of hospitalization for every patient admitted under the Department of Pediatrics regardless of the expected discharge date. Afterward, it will be the responsibility of the attending junior and senior residents to update the summaries on regular basis as long as the patient under their care. They should transfer the updated summary to the coming resident that will take over the medical care until the discharge day when the most recent update will be forwarded to the attending consultant for final review and signature. Results: Between 2011 and 2016, a significant drop in the number of delinquent records was noted. From 1131 delinquent records at the end of the fourth quarter of 2011, the number has fallen to 15 in the fourth quarter of 2016. Furthermore, compliance to JCIA documentation standards showed sustained improvement since the initiation of the project. The department used to score around 50% in the discharge documentation compliance rate which has improved to be maintained around 80%s in average. Conclusions: Every new project concerning the quality of patient care provided in any institution is expected to face multiple challenges and difficulties. Proper identifications of the challenges, standardize approach for solutions, sustainability of quality monitoring for an improvement projects can maintain the success for any new project