Information Sharing and Privacy Protection of Electronic Nursing Record Management System

The traditional centralized storage of traditional electronic medical records (EMRs) faces problems like data leakage, data loss, and EMR misplacement. The current protection measures for patients’ privacy in EMRs cannot withstand the fast-developing password cracking technologies and frequency cyberattacks. This paper intends to innovate the information sharing and privacy protection of electronic nursing records (ENRs) management system. Specifically, the signature interception technology was introduced to EMRs, the different phases of certificateless signature interception scheme were depicted, and the validation procedures of the scheme were designed. Then, the six phases of ENR information sharing protocol based on alliance blockchain were described in detail. Finally, an end-to-end memory neural network was constructed for ENR classification. The proposed management scheme was proved effective through experiments.

Studying Medical Records Management in the Public Healthcare Sector of South Africa Using Multi-Method

This chapter reflects on the lesson learnt from the application of multi-methods in a quantitative study that was conducted to study patient record management in the public healthcare sector. In this study, a questionnaire was the main data collection tool, which was supported by interviews, observations, and document/system analysis data. In conducting the study, triangulation of multi-methods data was performed at different stages of the study. Currently there is no clear framework in social science research about the application of multi-method, mono-method, and mixed method research, which the study intends to clear. The study revealed that quantitative data need to be augmented with some narrative/qualitative data to make an empirical conclusion and recommendations because alone, it may not be completely reliable. Triangulation of multi-methods eliminates bias and closes some gaps where data leave some questions unanswered. The study provides a framework to guide on research method based on methods ingredients.

Securing Medical Record Using Blockchain

Patients leave data in random order across different organizations loose simple access to historical data as they lose contact from a developer, as the developer, not the client, often retains first general ship to overcome this problem, they use leveraging block chain technology to create a revolutionary decentralised record management system for emrs to overcome this problem. Keywords: blockchain, medical record, IPFS, EMRS, secure, cryptography

Record Management System with Document Control

The primary purpose of this study was to provide a new way of keeping and retrieving documents in a digital form available in the Records Office and a computerized leave management system modified for the employees of Northern Iloilo Polytechnic State College Estancia, Iloilo. Specifically, this paper sought to design and develop the Record Management System with Document Control and evaluated its level of usability and performance as perceived by the target users. A total of 165 respondents of the said institution participated in the study which includes the five experts for School Year 2016-2017. The data were gathered through a survey questionnaire that primarily solicited feedbacks from respondents using the International Standard Organization/International Electrotechnical Commission 9126 Model. Descriptive research design was employed to describe the observations of the respondents based on the set objectives. The results revealed that the functionality of the system product, the level of usability as well as its performance were all interpreted as “Very Good”. This significant result implied that the respondents were impressed by the system features of the developed system in a convenient way.

Implementation of a Blockchain System Using Improved Elliptic Curve Cryptography Algorithm for the Performance Assessment of the Students in the E-Learning Platform

Blockchain technology allows for the decentralized creation of a propagated record of digital events, in which third parties do not control information and associated transactions. This methodology was initially developed for value transmission. Still, it now has a broad array of utilization in various industries, including health, banking, the internet of things, and several others. With its numerous added benefits, a blockchain-based learning management system is a commonly utilized methodology at academic institutes, and more specifically during and after the COVID-19 period. It also presents several potentials for decentralized, interoperable record management in the academic system in education. Integrity, authenticity, and peer-executed smart contracts (SC) are some of the qualities of a blockchain that could introduce a new degree of safety, trustworthiness, and openness to e-learning. This research proposes a unique encryption technique for implementing a blockchain system in an e-learning (EL) environment to promote transparency in assessment procedures. Our methodology may automate evaluations and provide credentials. We built it to be analytical and content-neutral in order to demonstrate the advantages of a blockchain back-end to end-users, including student and faculty members particularly during this COVID-19 era. This article explains the employment of blockchain and SC in e-learning. To improve the trust in the assessment, we propose a novel improved elliptic curve cryptography algorithm (IECCA) for data encryption and decryption. The performance of the suggested method is examined by comparing it with various existing algorithms of encryption. The evaluation of the behaviour of the presented method demonstrates that the technique shall enhance trust in online educational systems, assessment processes, educational history, and credentials.

Radio frequency identification (RFID) and database- based attendance management system

Effective record and management of students and staff attendance of academic and non academic activities/events are vital for the smooth functioning of the educational system. This is still a difficult task in most institutions, particularly in Nigeria with a large number of staff and students attending different academic and non-academic functions. This is even more difficult to manage when the traditional method of paper and pen is used to record attendance, prone to errors, and in most cases lack integrity due to manual handling of the record. Electronics and Information Communications Technologies (ICT) can be deployed to help mitigate these problems and improve reliability, ease, speed, efficiency, effectiveness, and integrity of recording and managing attendance. In this work, we used Radio Frequency Identification (RFID) and database management to provide an alternative solution that addresses issues of objects (humans and non-humans) authentication, authorization, and record management with high accuracy, reliability, and integrity using RFID-Arduino technology. The system works by reading staff’s and/or student’s details stored in a unique RF tag wirelessly through theRF reader and then matched and stored the record in the system’s database. Attendancemarking was achieved by matching the scanned ID with the database record. The system was successfully implemented and tested with 10 students and staff participants with the feature of exporting the records into excel format for statistical analysis and performance evaluation.

Heart Failure Detection Using Quantum-Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.