Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10144 - 10144

Published: Nov. 6, 2024

The integration of artificial intelligence (AI) in healthcare management marks a significant advance technological innovation, promising transformative effects on processes, patient care, and the efficacy emergency responses. scientific novelty study lies its integrated approach, combining systematic review predictive algorithms to provide comprehensive understanding AI’s role improving across different contexts. Covering period between 2019 2023, which includes global challenges posed by COVID-19 pandemic, this research investigates operational, strategic, response implications AI adoption sector. It further examines how impact varies temporal geographical addresses two main objectives: explore influences domains, identify variations based Utilizing an we compared various prediction algorithms, including logistic regression, interpreted results through SHAP (SHapley Additive exPlanations) analysis. findings reveal five key thematic areas: enhancing quality assurance, resource management, security, pandemic. highlights positive influence operational efficiency strategic decision making, while also identifying related data privacy, ethical considerations, need for ongoing integration. These insights opportunities targeted interventions optimize current future landscapes. In conclusion, work contributes deeper provides policymakers, professionals, researchers, offering roadmap addressing both

Language: Английский

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Analysis of CQC Ratings of Care Home Business Performance in England: Implications for Quality Improvement

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

According to the most recent data provided by Office for National Statistics (ONS) in 2023, there are 372,035 residents care homes England. Many of these experience a low quality life due poor service delivered those facilities. The Care Quality Commission (CQC) strives regulate health and social business country promote well-being homes. CQC measures services different England ranging from inadequate outstanding, depending on performance domains (safety, caring, effective, responsive, well-led). However, regions, home ownership types vary for-profit, third-sector, or public types. It is therefore paramount investigate relationships between location types, closures. This study investigatesthe relationship regions evaluates various local authorities also further investigates type closure England, A descriptive design was adopted study, using database active their ratings up August 2024. study's findings revealed significant region Notably, exceptionally high-quality Northeast reflects an outstanding positive impact (compared other England) sector. Also, service. Most very effective; but many do not perform well terms safety leadership. proportion highly rated within each highest type, followed lowest for-profit type. Furthermore, closure. this reveal that higher closed involuntarily compared third-sector types; voluntarily Finally, number involuntary closures overall suggest domains; while, high voluntary with ratings, suggests did close domains. Instead, reasons such might be attributed factors. Recommendations future studies were made study.

Language: Английский

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Research on the Evolutionary Game of Performance Assessment of Urban Underground Comprehensive Pipeline Gallery PPP Project

ICCREM 2021, Journal Year: 2025, Volume and Issue: unknown, P. 964 - 975

Published: March 26, 2025

Language: Английский

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Machine Learning for Evaluating Hospital Mobility: An Italian Case Study

Published: April 1, 2024

This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, clinical variables impact patient perceptions influencing their healthcare choices. Through analysis mobility trends with learning, significant differences between were uncovered, highlighting influence regional context quality. integration SHAP (SHapley Additive exPlanations) values our provided deeper insights model, elucidating specific contribution each variable to incorporation underscores study's commitment employing advanced, explainable AI techniques enhance interpretability fairness evaluations. choice elucidated quality perception, offering essential for optimizing resource distribution underscoring importance data-driven strategies foster more equitable, efficient, patient-centred systems. Contributing understanding dynamics within context, research paves way further investigations enhancing accessibility leveraging a tool improving services efficiency in diverse settings.

Language: Английский

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Machine Learning for Evaluating Hospital Mobility: An Italian Case Study

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 6016 - 6016

Published: July 10, 2024

This study delves into hospital mobility within the Italian regions of Apulia and Emilia-Romagna, interpreting it as an indicator perceived service quality. Utilizing logistic regression alongside other machine learning techniques, we analyze impact structural, operational, clinical variables on patient perceptions quality, thus influencing their healthcare choices. The analysis trends has uncovered significant regional differences, emphasizing how context shapes To further enhance analysis, SHAP (SHapley Additive exPlanations) values have been integrated model. These quantify specific contributions each variable to quality service, significantly improving interpretability fairness evaluations. A methodological innovation this is use these scores weights in data envelopment (DEA), facilitating a comparative efficiency facilities that both weighted normative. combination SHAP-weighted DEA provides deeper understanding dynamics offers essential insights for optimizing distribution resources. approach underscores importance data-driven strategies develop more equitable, efficient, patient-centered systems. research contributes promotes investigations accessibility leveraging tool increase services across diverse settings. findings are pivotal policymakers system managers aiming reduce disparities promote responsive personalized service.

Language: Английский

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Enhancing Healthcare Efficiency in Iran: A Comprehensive Analysis of Health-Oriented APIs Using Machine Learning Techniques

InfoScience Trends, Journal Year: 2024, Volume and Issue: 1(3), P. 1 - 33

Published: Sept. 14, 2024

Language: Английский

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An efficient cardiovascular disease prediction model through AI-driven IoT technology

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 183, P. 109330 - 109330

Published: Oct. 28, 2024

Language: Английский

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Efficiency of Primary Health Services in the Greek Public Sector: Evidence from Bootstrapped DEA/FDH Estimators

Healthcare, Journal Year: 2024, Volume and Issue: 12(22), P. 2230 - 2230

Published: Nov. 8, 2024

Strengthening primary healthcare (PHC) is vital for enhancing efficiency and improving access, clinical outcomes, population well-being. The World Health Organization emphasizes the role of effective PHC in reducing costs boosting productivity. With growing demands limited resources, efficient management critical.

Language: Английский

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