Medication Reconciliation

The Institute of Medicine (IOM) stated that preventable medication errors are the most common type of errors in healthcare. It is of fundamental significance when building a safer care continuum, as it highlights the reason for continuous and more vigilant medication reconciliation and required effort at all interfaces of care, including community. Without a robust medication reconciliation process, the potential for catastrophic outcomes remains a constant concern. Prevention of medication errors is essential through strategies that are based in evidence of medication reconciliation strategies on medication errors in community

Effectiveness of Teaching Standardized Protocol on Safe Medication Administration Process Upon the Level of Knowledge among Nurses at a Tertiary Care Hospital, Chennai

Strategies to improve medication safety focused on acute care settings. Twenty-six studies and descriptions of quality improvement projects were identified. Strategies used to focus on recommendations to prevent medication errors at various stages, from a nationwide voluntary organization to improve safety of patients and empower education system of nurses and other health care providers in safe practices in health care system and vast growing technology

Clustering of datasets using PSO-K-Means and PCA-K-means

Abstract Cluster analysis plays indispensable role in obtaining knowledge from data, being the first step in data mining and knowledge discovery. The purpose of data clustering is to reveal the data patterns and gain some initial insights regarding data distribution. K-means is one of the widely used partitional clustering algorithms and it is more sensitive to outliers and do not work well with high dimensional data. In this paper, K-means has been integrated with other approaches to overcome the shortcomings hereby improving the accuracy of clustering. In this paper, basic k-means and the combination of k-means with PCA and PSO are applied on various datasets from UCI repository. The experimental results of this paper show that PSO-K-means and PCA-KMeans improves the performance of basic K-means in terms of accuracy and computational time

Artificial intelligence for smart patient care: transforming future of nursing practice

Artificial intelligence (AI) in today’s era has been described as “the new electricity” as it continually transforms today’s world by affecting our way of living in many different spheres. Extensive government programs in most countries and enhanced technology investments thereof are set to rapidly advance AI. Consequently, healthcare teams will be majorly affected by intelligent tools and systems to be launched into healthcare and patient homecare settings. AI represents a variety of functions under an umbrella of terms like machine learning (ML), deep learning, computer vision, natural language processing (NLP) and automated speech recognition (ASR) technologies. Each of these when used individually or in combination has the potential to add intelligence to applications. Understanding of AI in medical field is crucial for nurses. Utilization of AI in nursing will accelerate innovation and fasten up decision making for them thus saving their time and improving patient outcome plus satisfaction with nursing care provided. Of utmost importance while partnering with AI is the requirement for AI to be safe and effective. A major concern for AI practitioners in the current scenario is managing bias. To realize the full potential of AI, stakeholders (AI developers and users) need to be confident about two aspects: (1) reliability and validity of the datasets used and (2) transparency of AI based system. Issues encompassing AI are novel yet complex, and there is still much to be learnt about it. Nursing experience, knowledge, and skills will transit into new ways of thinking and processing information. This will give new roles to nurses-like information integrators, data managers, informatics specialists, health coaches and above all deliverers of compassionate caring-not replaced by AI technologies yet supported by them

Real Time wave forecasting using artificial neural network with varying input parameter

82-87Prediction of significant wave heights (Hs) is of immense importance in ocean and coastal engineering applications. The aim of this study is to predict significant wave height values at buoy locations with the lead time of 3,6,12 and 24 hours using past observations of wind and wave parameters applying Artificial Neural Network. Although there exists a number of wave height estimation models, they do not consider all causative factors without any approximation and consequently their results are more or less a general approximation of the overall dynamic behaviour. Since soft computing techniques are totally data driven, based on the duration of the data availability they can be used for prediction. In the National data buoy program of National institute of Ocean Technology, not all the buoys have wind sensors and wave sensors and so it is attempted to apply neural network algorithms for prediction of wave heights using wind speed only as the input and then using only wave height as the input. The measurement made by the data buoy at DS3 location in Bay of Bengal (12o11’21"N and 90o43’33"E) are considered, for the period 2003-2004. Out of this, the data of period Jan 2003-Dec 2003 was used for training and the data for the period July 2004- Nov 2004 is used for testing. Real time wave forecasting for 3,6,12 and 24 hours were carried out for a month at the location chosen and the results show that the ANN technique proves encouraging for wave forecasting. Performance of ANN for varying inputs have been analysed and the results are discussed

Effectiveness of Competency Training Program on Modified Early Warning System (MEWS) upon the Knowledge of Nurses in Selected Hospitals, Chennai

Background: Adverse events results in unintended harm to the patients including permanent disability and death. MEWS was introduced to identify and document the deteriorating patients in hospital settings. Adequate training and education of nurses will enhance early recognition and response in preventing adverse events. Objective: The objective of the study was to assess the effectiveness of competency training program on MEWS among nurses. Methodology: A Quasi experimental study was conducted among nurses who were working in inpatient units of selected hospitals, Chennai. 140 nurses were selected as participants out of which (n=70) is constituted to experimental group and (n=70) was constituted to control group. Pre-test knowledge was assessed in both groups. Competency training program on MEWS was given to experimental group of nurses and post test was assessed after one month. Results: The mean post-test knowledge scores was significantly higher in experimental group (M=18.2) to that of control group (M=10.6) which shows the effectiveness of competency training program on MEWS with (t=22.29, p<0.001). Conclusion: The present study reveals that Competency training program on MEWS had a significant increase in the knowledge of nurses in the experimental group