Results 131 to 140 of about 32,139 (193)

Improving Digital Transformation Outcomes and Pace: The Role of the Board and C‐Suite in Reducing Transformation Friction

Information Systems Journal, Volume 36, Issue 5, Page 652-660, September 2026.
ABSTRACT Most organisations are confronting a dilemma: the need to embark on digital transformation to be competitive and sustainable versus the inherently high costs and extremely high risks involved. Lack of executive support has long been identified as a leading cause of project failure, including digital transformation projects.
Natalie Smith, Andrew Burton‐Jones
wiley   +1 more source

Nursing informatics and patient safety outcomes in critical care settings: a systematic review. [PDF]

BMC Nurs
Shi Q, Wotherspoon R, Morphet J.
europepmc   +1 more source

Healthcare Professionals' Perceptions of Artificial Intelligence in Healthcare—A Systematic Review of Qualitative Studies

Journal of Advanced Nursing, Volume 82, Issue 9, Page 8304-8343, September 2026.
ABSTRACT Aim To identify the experiences and perceptions of healthcare professionals on artificial intelligence in healthcare. Design Systematic literature review of qualitative studies and meta‐aggregation. Data Sources CINAHL, PubMed, Scopus, Medic and ProQuest were systematically searched on December 9, 2024. Results Twenty‐six studies were included
Huotari Sini, Mikkonen Kristina, Jarva Erika, Suonnansalo Petra, Kaikkonen Assi, Lee Jay Jung Jae, Oikarinen Anne   +6 more
wiley   +1 more source

Nursing Informatics: The Vital Nursing Link Between Technology And Patient Care. [PDF]

HCA Healthc J Med
Hess S, Alper C.
europepmc   +1 more source

Enhancing Learning in Graduate Nursing Education Through a Co‐Designed AI Virtual Tutor: A Mixed‐Methods Evaluation

Journal of Clinical Nursing, Volume 35, Issue 9, Page 3769-3783, September 2026.
ABSTRACT Background Large language model tools are increasingly used in higher education, offering opportunities to support self‐directed learning. In nursing education, course‐specific AI virtual tutors may provide contextualised support while addressing concerns about content accuracy and alignment; yet empirical evidence remains limited.
Charlene H. Chu, Lindsay A. Jibb, Neal MacInnes, Cordelia C. Shan, Shan D. Mohammed, Stephanie M. Nanos, Mary O'Sullivan, Carles Muntaner, Heather Thomson, Kimberley Widger   +9 more
wiley   +1 more source

Age‐stratified effectiveness of a nurse‐led heart failure programme: A population‐based study

Journal of Internal Medicine, Volume 300, Issue 3, Page 345-359, September 2026.
Abstract Aim Old age is associated with poor outcomes in patients with heart failure (HF). This study aimed to assess the impact of age on the effectiveness of a new integrated, transition‐based nurse‐led HF programme in a healthcare area with 209,255 inhabitants.
Blanca Torres‐Cardús, Emili Vela‐Vallespín, David Monterde, Gerard Carot‐Sans, Jordi Piera‐Jiménez, Júlia Folguera, Laia Alcober‐Morte, Raúl Ramos‐Polo, Encarnación Hidalgo‐Quirós, Núria José‐Bazán, Sergi Yun Viladomat, Pedro Moliner, Santiago Jiménez‐Marrero, Alberto Garay Melero, Alexandra Pons Riverola, Lídia Alcoberro Torres, Herminio Morillas, Betlem Salvador‐González, Sílvia Jovells‐Vaqué, Mireia Andrés Villareal, Cristina Enjuanes, Josep Comin‐Colet   +21 more
wiley   +1 more source

Nurses' and Nursing Students' Experiences With Generative Artificial Intelligence in Educational and Clinical Settings: A Scoping Review

Nursing &Health Sciences, Volume 28, Issue 3, September 2026.
ABSTRACT This scoping review follows the Arksey and O'Malley five‐stage framework: (1) identifying research questions, (2) identifying relevant studies, (3) selecting studies, (4) extracting, mapping, and charting data, and (5) collating, summarizing, and reporting findings.
Ming Wei Jeffrey Woo, Adrian Heng Tsai Tan   +1 more
wiley   +1 more source

Models of collaboration and dissemination for nursing informatics innovations in the 21st century. [PDF]

Nurs Outlook, 2019
Wang J, Gephart SM, Mallow J, Bakken S.
europepmc   +1 more source

Multivariable Radiomics Model for Predicting Programmed Death‐Ligand 1 Expression After Neoadjuvant Chemoradiotherapy in Esophageal Squamous Cell Carcinoma

Cancer Medicine, Volume 15, Issue 8, August 2026.
ABSTRACT Background We aimed to develop models for predicting programmed death‐ligand 1 (PD‐L1) expression following neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) using a multifaceted approach that integrates traditional radiomics, deep learning, and machine learning.
Hyun Do Jung, Tae Hoon Lee, Byoung Hyuck Kim, Hye Seung Lee, Jaeman Son, Hwiyoung Kim, Hak Jae Kim   +6 more
wiley   +1 more source