{"title":"Information Systems Facilitate the UK Ambulance Service Transition towards an Omni-Channel Service","authors":"Alan Slater","doi":"10.30564/jeis.v6i2.6454","DOIUrl":"https://doi.org/10.30564/jeis.v6i2.6454","url":null,"abstract":"This paper represents a description of those ‘hidden’ policies supported by information systems that currently influence the direction of change within the UK ambulance service. When an ambulance service displays poor response times it may have reached a situation where demand exceeds supply regularly, then using ‘critical thinking’ the management should reconstruct their approach and focus exclusively on patient needs and the sources of appropriate help. The UK ambulance service has developed three strategic approaches; firstly, a single-channel strategy employing ambulance service staff only (‘hear and treat, or ‘see and treat’); secondly, a multi-channel strategy involving other NHS services (from call to on–scene then accident and emergency or direct to a hospital ward); and thirdly, a new omni-channel strategy where the patient is directed on a pathway from the ambulance service to a combination of other NHS or voluntary services. The key element in developing an omni-channel strategy is a secondary triage system, supported by a new database, which ensures patients are directed to a specialist service that could provide them with the most immediate help to meet their needs. Some of these services to patients (for example, the Falls team) are provided by the ambulance service directly. Patients are directed to alternative services, identified in the database, this system allows the ambulance service to bypass accident and emergency and monitor the patient’s pathway from the initial call to the outcome. Initial small projects indicate that the omni-channel system reduces duplication of effort, improves system productivity, reduces cost and shortens patients call to outcome time.","PeriodicalId":510369,"journal":{"name":"Journal of Electronic & Information Systems","volume":"155 1‐5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141686706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adeyinka Orelaja, Chidubem Ejiofor, Samuel Sarpong, Success Imakuh, Christian Bassey, Iheanyichukwu Opara, Josiah Nii Armah Tettey, Omolola Akinola
{"title":"Attribute-specific Cyberbullying Detection Using Artificial Intelligence","authors":"Adeyinka Orelaja, Chidubem Ejiofor, Samuel Sarpong, Success Imakuh, Christian Bassey, Iheanyichukwu Opara, Josiah Nii Armah Tettey, Omolola Akinola","doi":"10.30564/jeis.v6i1.6206","DOIUrl":"https://doi.org/10.30564/jeis.v6i1.6206","url":null,"abstract":"Cyberbullying, a pervasive issue in the digital age, poses threats to individuals’ well-being across various attributes such as religion, age, ethnicity, and gender. This research employs artificial intelligence to detect cyberbullying instances in Twitter data, utilizing both traditional and deep learning models. The study repurposes the Sentiment140 dataset, originally intended for sentiment analysis, for the nuanced task of cyberbullying detection. Ethical considerations guide the dataset transformation process, ensuring responsible AI development. The Naive Bayes algorithm demonstrates commendable precision, recall, and accuracy, showcasing its efficacy. The Bi-LSTM model, leveraging deep learning capabilities, exhibits nuanced cyberbullying detection. The study also underscores limitations, emphasizing the need for refined models and diverse datasets.","PeriodicalId":510369,"journal":{"name":"Journal of Electronic & Information Systems","volume":"46 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}