Use of Artificial Intelligence tools in supporting decision-making in hospital management DOI Creative Commons

Maurício Martins Alves,

Joana Seringa,

Tatiana Silvestre

et al.

BMC Health Services Research, Journal Year: 2024, Volume and Issue: 24(1)

Published: Oct. 25, 2024

The use of Artificial Intelligence (AI) tools in hospital management holds potential for enhancing decision-making processes. This study investigates the current state management, explores benefits AI integration, and examines managers' perceptions as a decision-support tool. A descriptive exploratory was conducted using qualitative approach. Data were collected through semi-structured interviews with 15 managers from various departments institutions. transcribed, anonymized, analyzed thematic coding to identify key themes patterns responses. Hospital highlighted inefficiencies processes, often characterized by poor communication, isolated decision-making, limited data access. traditional like spreadsheet applications business intelligence systems remains prevalent, but there is clear need more advanced, integrated solutions. Managers expressed both optimism skepticism about AI, acknowledging its improve efficiency while raising concerns privacy, ethical issues, loss human empathy. identified challenges, including variability technical skills, fragmentation, resistance change. emphasized importance robust infrastructure adequate training ensure successful integration. reveals complex landscape where are balanced significant challenges concerns. Effective integration requires addressing technical, ethical, cultural focus on maintaining elements decision-making. seen powerful tool support, not replace, judgment promising improvements efficiency, accessibility, analytical capacity. Preparing healthcare institutions necessary providing specialized crucial maximizing mitigating associated risks.

Language: Английский

Facilitators and Barriers of Large Language Model Adoption Among Nursing Students: A Qualitative Descriptive Study DOI Creative Commons

Yingzhuo Ma,

Tong Liu,

Jianwei Qi

et al.

Journal of Advanced Nursing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 4, 2025

ABSTRACT Aim To explore nursing students' perceptions and experiences of using large language models identify the facilitators barriers by applying Theory Planned Behaviour. Design A qualitative descriptive design. Method Between January June 2024, we conducted individual semi‐structured online interviews with 24 students from 13 medical universities across China. Participants were recruited purposive snowball sampling methods. Interviews in Mandarin. Data analysed through directed content analysis. Results Analysis revealed 10 themes according to 3 constructs Behaviour: (a) attitude: perceived value expectations facilitators, while caution posed barriers; (b) subjective norm: media effects role model effectiveness described as whereas organisational pressure exerted universities, research institutions hospitals acted a barrier usage; (c) behavioural control: design free access strong incentives for use, geographic restrictions digital literacy deficiencies key factors hindering adoption. Conclusion This study explored attitudes, norms control regarding use models. The findings provided valuable insights into that hindered or facilitated Implications Profession Through lens this study, have enhanced knowledge journey models, which contributes implementation management these tools education. Impact There is gap literature views influence their usage, addresses. These could provide evidence‐based support nurse educators formulate strategies guidelines. Reporting adheres consolidated criteria reporting (COREQ) checklist. Public Contribution No patient public contribution.

Language: Английский

Citations

1
Towards effective adoption of artificial intelligence in talent acquisition: A mixed method study DOI Creative Commons
Julia Stefanie Roppelt, Andreas Schuster, Nina Sophie Greimel

et al.

International Journal of Information Management, Journal Year: 2025, Volume and Issue: 82, P. 102870 - 102870

Published: Jan. 18, 2025

Language: Английский

Citations

1
Artificial intelligence in talent acquisition: a multiple case study on multi-national corporations DOI
Julia Stefanie Roppelt, Nina Sophie Greimel, Dominik K. Kanbach

et al.

Management Decision, Journal Year: 2024, Volume and Issue: unknown

Published: May 7, 2024

Purpose The aim of this paper is to explore how multi-national corporations (MNCs) can effectively adopt artificial intelligence (AI) into their talent acquisition (TA) practices. While the potential AI address emerging challenges, such as shortages and applicant surges in specific regions, has been anecdotally highlighted, there limited empirical evidence regarding its effective deployment adoption TA. As a result, endeavors develop theoretical model that delineates motives, barriers, procedural steps critical factors aid TA within MNCs. Design/methodology/approach Given scant literature on our research objective, we utilized qualitative methodology, encompassing multiple-case study (consisting 19 cases across seven industries) grounded theory approach. Findings Our proposed framework, termed Framework Effective Adoption , contextualizes success essential for Research limitations/ implications This contributes theory. Practical Additionally, it provides guidance managers seeking implementation strategies, especially face challenges. Originality/value To best authors' knowledge, unparalleled, being both based an expansive dataset spans firms from various regions industries. delves deeply corporations' underlying motives processes concerning

Language: Английский

Citations

7
Determinants of artificial intelligence-assisted diagnostic system adoption intention: A behavioral reasoning theory perspective DOI

Weixia Li,

Wang Jian-guo

Technology in Society, Journal Year: 2024, Volume and Issue: 78, P. 102643 - 102643

Published: June 21, 2024

Language: Английский

Citations

7
Virtual human on social media: Text mining and sentiment analysis DOI
Sihong Li, Jinglong Chen

Technology in Society, Journal Year: 2024, Volume and Issue: 78, P. 102666 - 102666

Published: July 24, 2024

Language: Английский

Citations

5
Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis DOI Creative Commons

Suparna Das,

Hirak Mazumdar, Kamil Reza Khondakar

et al.

BMEMat, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 28, 2024

Abstract Machine learning (ML) and nanotechnology interfacing are exploring opportunities for cancer treatment strategies. To improve therapy, this article investigates the synergistic combination of Graphene Oxide (GO)‐based devices with ML techniques. The production techniques functionalization tactics used to modify physicochemical characteristics GO specific drug delivery explained at outset investigation. is a great option treating because its natural biocompatibility capacity absorb medicinal chemicals. Then, complicated biological data analyzed using algorithms, which make it possible identify best medicine formulations individualized plans depending on each patient's particular characteristics. study also looks optimizing predicting interactions between carriers cells ML. Predictive modeling helps ensure effective payload release therapeutic efficacy in design customized systems. Furthermore, tracking outcomes real time made by permit adaptive modifications therapy regimens. By medication doses settings, not only decreases adverse effects but enhances accuracy.

Language: Английский

Citations

5
Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation DOI
Jie Chen,

Alice Shijia Yan

Medical Care, Journal Year: 2025, Volume and Issue: 63(3), P. 227 - 233

Published: Jan. 3, 2025

Objective: To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly relation to neighborhood deprivation. Background: AI/ML-assisted care coordination has potential reduce health disparities, but there a lack of empirical evidence on AI’s impact equity. Methods: We used linked datasets from 2022 American Hospital Association Annual Survey 2023 Information Technology Supplement. The data were further Area Deprivation Index (ADI) for each hospital’s service area. State fixed-effect regressions employed. A decomposition model was also quantify predictors implementation, comparing hospitals higher versus lower ADI areas. Results: Hospitals serving most vulnerable areas (ADI Q4) significantly less likely apply ML or other predictive models (coef = −0.10, P 0.01) provided fewer AI/ML-related workforce applications -0.40, 0.01), compared with those least Decomposition results showed that our specifications explained 79% between Q4 Q1–Q3. In addition, Accountable Care Organization affiliation accounted 12%–25% differences utilization various measures. Conclusions: underuse economically disadvantaged rural areas, management electronic record suggests these communities may not fully benefit advancements AI-enabled care. Our indicate value-based payment could be strategically support AI integration.

Language: Английский

Citations

0
Artificial intelligence technologies and entrepreneurship: a hybrid literature review DOI Creative Commons
Sebastián Uriarte, Hugo Baier-Fuentes, Jorge Espinoza Benavides

et al.

Review of Managerial Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 24, 2025

Language: Английский

Citations

0
Assessing the disconnect between student interest and education in artificial intelligence in medicine in Saudi Arabia DOI Creative Commons
Abeer F. Almarzouki, Atalay Alem,

Faris Shrourou

et al.

BMC Medical Education, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 30, 2025

Although artificial intelligence (AI) has gained increasing attention for its potential future impact on clinical practice, medical education struggled to stay ahead of the developing technology. The question whether is fully preparing trainees adapt changes from AI technology in practice remains unanswered, and influence students' career preferences unclear. Understanding gap between interest knowledge may help inform curriculum structure. A total 354 students were surveyed investigate their of, exposure to, role health care. Students questioned about anticipated specialties preferences. Most (65%) interested medicine, but only 23% had received formal based reliable scientific resources. Despite willingness learn, 20.1% reported that school offered resources enabling them explore use medicine. They relied mainly informal information sources, including social media, few understood fundamental concepts or could cite clinically relevant research. who cited more primary sources (rather than online media) exhibited significantly higher self-reported understanding context Interestingly, courses levels skepticism regarding less eager learn it. Radiology pathology perceived be fields most strongly affected by AI. overall choice specialty was not impacted Formal seems inadequate despite enthusiasm concerning application such practice. Medical curricula should evolve promote structured, evidence-based literacy enable understand applications

Language: Английский

Citations

0
Algorithmic emergence? Epistemic in/justice in AI-directed transformations of healthcare DOI Creative Commons

Imo Emah,

SJ Bennett

Frontiers in Sociology, Journal Year: 2025, Volume and Issue: 10

Published: Feb. 7, 2025

Moves toward integration of Artificial Intelligence (AI), particularly deep learning and generative AI-based technologies, into the domains healthcare public health have recently intensified, with a growing body literature tackling ethico-political implications this. This paper considers interwoven epistemic, sociopolitical technical ramifications healthcare-AI entanglements, examining how AI materialities shape emergence particular modes organization, governance roles, reflecting on to embed participatory engagement within these entanglements. We discuss socio-technical entanglements between Evidence-Based Medicine (EBM) for equitable development AI. applications invariably center medical knowledge practice that are amenable computational workings. This, in turn, intensifies prioritization furthers assumptions which support AI, move decontextualizes qualitative nuances complexities while simultaneously advancing infrastructure domains. sketch material ideological reconfiguration is being shaped by embedding assemblages real-world contexts. then consider this, might be best employed healthcare, tackle algorithmic injustices become reproduced assemblages.

Language: Английский

Citations

0