Browsing by Author "Gormley, Kevin"

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Fatigue, anxiety, depression and sleep quality in patients undergoing haemodialysis
(2021) Gormley, Kevin
Objective: Patients undergoing haemodialysis may experience troubling symptoms such as fatigue, anxiety, depression and sleep quality, which may affect their quality of life. The main objective of this study is to determine the prevalence of fatigue, anxiety, depression and sleep quality among patients receiving haemodialysis during the coronavirus disease 2019 (COVID-19) pandemic, and to explore the contributing predictors. Methods: A cross-sectional and descriptive correlational design using Qualtrics software was performed. Data were collected using the Functional Assessment of Cancer Therapy-Fatigue (FACT-F), the Hospital Anxiety and Depression Scale (HADS) and the Pittsburgh Sleep Quality Index (PSQI). Logistic regression analyses were used to explore the predictors that were associated with fatigue, anxiety, depression and sleep quality. Results: Of the 123 patients undergoing haemodialysis who participated, 53.7% (n = 66) reported fatigue, 43.9% (n = 54) reported anxiety, 33.3% (n = 41) reported depression and 56.9% (n = 70) reported poor sleep. Fatigue, anxiety and sleep quality (P < .05) were significantly associated with being female, and whether family members or relatives were suspected or confirmed with COVID-19. Logistic regression showed that being within the age group 31–40, having a secondary education level, anxiety, depression and sleep quality were the main predictors affecting the fatigue group. Conclusion: Fatigue, anxiety, depression and sleep quality are significant problems for patients receiving haemodialysis during the COVID-19 pandemic. Appropriate interventions to monitor and reduce fatigue, psychological problems and sleep quality amongst these patients are needed. This can help to strengthen preparations for responding to possible future outbreaks or pandemics of infectious diseases for patients receiving haemodialysis.
Using natural language processing in facilitating pre-hospital telephone triage of emergency calls
(2022-09) Gormley, Kevin; Isaac, Jolly
Introduction: Natural language processing (NLP) is an area of computer science that involves the use of computers to understand human language and semantics (meaning) and to offer consistent and reliable responses. There is good evidence of significant advancement in the use of NLP technology in dealing with acutely ill patients in hospital (such as differential diagnosis assistance, clinical decision-making and treatment options). Further technical development and research into the use of NLP could enable further improvements in the quality of pre-hospital emergency care. The aim of this literature review was to explore the opportunities and potential obstacles in implementing NLP during this phase of emergency care and to question if NLP could contribute towards improving the process of nature of call screening (NoCS) to enable earlier recognition of life-threatening situations during telephone triage of emergency calls. Methods: A systematic search strategy using two electronic databases (CINAHL and MEDLINE) was conducted in December 2021. The PRISMA systematic approach was used to conduct a review of the literature, and selected studies were identified and used to support a critical review of the actual and potential use of NLP for the call-taking phase of emergency care. Results: An initial search offered 204 records: 23 remained after eliminating duplicates and a consideration of title and abstracts. A further 16 full-text articles were deemed ineligible (not related to the subject under investigation), leaving seven included studies. Following a thematic review of these studies two themes emerged, that are considered individually and together: (i) use of NLP for dealing with out-of-hospital cardiac arrest and (ii) responding to increased accuracy of NLP. Conclusions: NLP has the potential to reduce or eliminate human bias during the emergency triage assessment process and contribute towards improving triage accuracy in pre-hospital decision-making and an early identification and categorisation of life-threatening conditions. Evidence to date is mostly linked to cardiac arrest identification; this review proposes that during the call-taking phase NLP should be extended to include further medical emergencies (including fracture/trauma, stroke and ketoacidosis). Further research is indicated to test the reliability of these findings and a proportionate introduction of NLP simultaneous with increased quality and reliability.