Aim and Objective
To assess and compare the quality of paper-based and electronic-based health records. The comparison examined three criteria: content, documentation process, and structure.

Background
Nursing documentation is a significant indicator of the quality of patient care delivery. It can be either paper-based or organized within the system known as the Electronic Health Records (EHRs). Nursing documentation must be completed at the highest standards, in order to ensure the safety and quality of health care services. However, the evidence is not clear on which one of the two forms of documentation (paper-based versus EHRs) is more qualified.

Methods
A retrospective, descriptive, comparative design was utilized to address the study's purposes. A convenient number of patients’ records, from two public hospitals, were audited using the Cat-ch-Ing Audit Instrument. The sample size consisted of 434 records for both paper-based health records and EHRs from medical and surgical wards.

Results
EHRs were better than paper-based health records in terms of process and structure. In terms of quantity and quality content, paper-based records were better than EHRs. The study affirmed the poor quality of nursing documentation and lack of nurses’ knowledge and skills in the nursing process and its application in both paper-based and electronic-based systems.

Conclusion
Both forms of documentation revealed drawbacks in terms of content, process, and structure. This study provided important information, which can guide policymakers and administrators in identifying effective strategies aimed at enhancing the quality of nursing documentation.

Relevance to Clinical Practice
Policies and actions to ensure quality nursing documentation at the national level should focus on improving nursing knowledge, competencies, practice in nursing process, enhancing the work environment and nursing workload, as well as strengthening the capacity building of nurses practice to improve the quality of nursing care and patients’ outcomes.

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Quality of nursing documentation: Paper-based health records versus electronic-based health records

Customer’s experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer’s experience. Features were extracted from the tweets using word embedding with Glove dictionary approach and n-gram approach. Further, SVM (support vector machine) and several ANN (artificial neural network) architectures were considered to develop classification model that maps the tweet into positive and negative category. Additionally, convolutional neural network (CNN) were developed to classify the tweets and the results were compared with the most accurate model among SVM and several ANN architectures. It was found that CNN outperformed SVM and ANN models. In the end, association rule mining have been performed on different categories of tweets to map the relationship with sentiment categories. The results show that interesting associations were identified that certainly helps the airline industries to improve their customer’s experience.

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A machine learning approach to analyze customer satisfaction from airline tweets

An algorithm and method for sentiment analysis using the text and emoticon

Telehealth and COVID-19: Using technology to accelerate the curve on access and quality healthcare for citizens in India

Nowadays, a million users use social networking services such as Twitter to tweet their products and services by placing the reviews based on their opinions. Sentiment analysis has emerged to analyze the twitter data automatically. Sentiment classification techniques used to classify US airline tweets based on sentiment polarity due to flight services as positive, negative and neutral connotations done on six different US airlines. To detect sentiment polarity, we explored word embedding models (Word2Vec, Glove) in tweets using deep learning methods. Here, we investigated sentiment analysis using the Recurrent Neural Network (RNN) model along with Long-Short Term Memory networks (LSTMs) units can deal with long term dependencies by introducing memory in a network model for prediction and visualization. The results showed better significant classification accuracy trained 80% for training set and 20% for testing set which shows that our models are reliable for future prediction. To improve this performance, the Bidirectional LSTM Model (Bi-LSTM) is used for further investigation studies.

Sentiment Analysis of US Airlines Tweets Using LSTM/RNN

Social Media has taken the world by surprise at a swift and commendable pace. With the advent of any kind of circumstances may it be related to social, political or current affairs the sentiments of people throughout the world are expressed through their help, making them suitable candidates for sentiment mining. Sentimental analysis becomes highly resourceful for any organization who wants to analyse and enhance their products and services. In the airline industries it is much easier to get feedback from astute data source such as Twitter, for conducting a sentiment analysis on their respective customers. The beneficial factors relating to twitter sentiment analysis cannot be impeded by the consumers who want to know the who's who and what's what in everyday life. In this paper we are classifying sentiment of Twitter messages by exhibiting results of a machine learning algorithm using R and Rapid Miner. The tweets are extracted and pre-processed and then categorizing them in neutral, negative and positive sentiments finally summarising the results as a whole. The Naive Bayes algorithm has been used for classifying the sentiments of recent tweets done on the different airlines.

https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042067/pdf

Sentimental Analysis for Airline Twitter data

Electronic Health Record has potential to improve patient care by managing patient's medical and personal information efficiently and effectively. It is easy to maintain patient information electronically compared to paper based records. Many studies have been done in other countries to study the effective use of Electronic Health Record, but a small number of studies exist in Indian situation. This study is a footstep in this route. This study has been done to know the use of electronic health records among nurses in private medium sized hospitals of Tamil Nadu, India. The objective of the study is to explore the use of Electronic Health Records and barriers in using it among nurses. This study also analyzes the factors affecting nurses to adopt electronic health record. Only a third of the nurses (33%) use electronic health record. Lack of training is the major hindrance in use electronic health record among nurses.

/pdf/factors-affecting-the-adoption-of-electronic-health-records-1usjofn432.pdf

Factors Affecting the Adoption of Electronic Health Records by Nurses

The aim of the study was to assess the comfort level and frequency in the use of information technology (IT) among nurses and analyze the relationship between various factors governing their use. A survey was conducted during 2013-14 at select Indian Hospitals of Tamil Nadu State. Correlation analysis and ANOVA test were used to analyze the data. The nurses working in the medium sized hospitals in the State are the study population. A random sample of 600 nurses was selected for the study. Gender, qualification and age level were analyzed to found the influence on use of computer hardware, communication and administrative related activities using computers. Effective and majority use of computers for information processing activities, communication, planning and policy development activities, finance, administration, education and research can lead healthcare organization to run their processes efficiently. Hospitals can provide appropriate computer training programs and computers access to nurses to realize the benefits from IT. Healthcare professionals may encourage nurses to use IT and computers at their work place by providing them attractive incentives. DOI: 10.5901/mjss.2015.v6n4s2p658

/pdf/use-of-information-technology-by-nurses-in-private-hospitals-ib8lgtbyey.pdf

Use of Information Technology by Nurses in Private Hospitals in the State of Tamil Nadu in India

Computational intelligence innovation and the use of computers have changed the entire healthcare delivery system. Nurses are the leading crew of healthcare organization. But, these nurses are either lacking in computer usage or automated analysis generated by computers. Therefore, it motivates to study the use of computers and information technology by nurses in Indian healthcare system. Further, it is essential to identify the chief factors where these nurses are lacking while using computers and information technology. This will help the management to take necessary measure to train them and make the healthcare industry more productive in perception with usage of computer and information technology. To this end, data has collected from nurses in hospitals in the state of Tamilnadu, India. Data collection is not beneficial unless it is analyzed and meaningful information obtained from it. In this paper, we hybridize rough set and formal concept analysis to arrive at chief factors affecting the decisions. Rough set is used to analyze the data and to generate rules. These generated rules further passed into formal concept analysis to identify the chief characteristics affecting the decisions. This in turn help the organization to provide adequate training to the nurses and the healthcare system will move further to the next stage.

Computational intelligence techniques for efficient delivery of healthcare

Software development is a sequential process where the allied steps in the development lifecycle involve planning and modularization, requirement engineering, analysis of product viability, profits estimation, strategic decision making, maintenance strategies etc. Often, most of these phases are pretty complex and thereby, extremely difficult to handle solely through human intervention, mainly due to the size of the project, the number of factors to be taken into consideration at each modular level and the rapidly changing external environment. In this paper we aim to provide an intuition on using Artificial Intelligence (AI) in the different phases of the software development lifecycle. Our paper focuses on a specific software development example for clarity and precision, but most of the techniques are highly general and scalable to any software development process.

/pdf/use-of-artificial-intelligence-in-automation-of-sequential-2ud7e4ilry.pdf

Brijendra Singh

Papers

Sentimental Analysis for Airline Twitter data

Factors Affecting the Adoption of Electronic Health Records by Nurses

Use of Information Technology by Nurses in Private Hospitals in the State of Tamil Nadu in India

Computational intelligence techniques for efficient delivery of healthcare

Use of artificial intelligence in automation of sequential steps of software development / production