Background: Nurses are the largest group of health information technology (HIT) users. As such, nurses' adaptations are critical for HIT implementation success. However, longitudinal approaches to understanding nurses' perceptions of HIT remain underexplored. Previous studies of nurses' perceptions demonstrate that the progress and timing for acceptance of and adaptation to HIT varies.
Objective: This study aimed to explore nurses' experience regarding implementation of HIT over time.
Methods: A phenomenological approach was used for this longitudinal qualitative study to explore nurses' perceptions of HIT implementation over time, focusing on three time points (rounds) at 3, 9, and 18 months after implementation of electronic health records and bar code medication administration. The purposive sample was comprised of clinical nurses who worked on a medical-surgical unit in an academic center.
Results: Major findings were categorized into 7 main themes with 54 subthemes. Nurses reported personal-level and organizational-level factors that facilitated HIT adaptation. We also generated network graphs to illustrate the occurrence of themes. Thematic interconnectivity differed due to nurses' concerns and satisfaction at different time points. Equipment and workflow were the most frequent themes across all three rounds. Nurses were the most dissatisfied approximately 9 months after HIT implementation. Eighteen months after HIT implementation, nurses' perceptions appeared more balanced.
Conclusions: It is recommended that organizations invest in equipment (ie, wireless barcode scanners), refine policies to reflect nursing practice, and improve systems to focus on patient safety. Future research is necessary to confirm patterns of nurses' adaptation to HIT in other samples.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6043728 | PMC |
| http://dx.doi.org/10.2196/medinform.8734 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hematologic Toxicity Profiles and the Impact of Hemoglobin Nadir and Transfusion on Oncologic Outcome in Definitive Radiochemotherapy for Cervical Cancer.
Cancers (Basel)
November 2024
Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
: Definitive radiochemotherapy with concomitant cisplatin 40 mg/m weekly represents the standard of care for locally advanced cervical cancer. Current studies (KEYNOTE-A18 and INTERLACE) are intensifying this regimen at the cost of increased hematologic toxicity. We aimed to evaluate influencing factors on hematotoxicity to ensure the safe application of radiochemotherapy.
View Article and Find Full Text PDFvScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening.
Int J Mol Sci
November 2024
Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA 19111, USA.
Article Synopsis
- The increasing use of on-demand chemical libraries for virtual screening has revealed that traditional methods often lead to inaccurate results, missing many compounds that would interact with target proteins.
- New machine learning tools, like vScreenML, have improved hit-finding rates, but previous versions faced issues with usability and reliance on outdated software.
- vScreenML 2.0 has been developed to overcome these obstacles, featuring a more user-friendly Python implementation and demonstrating superior performance in hit discovery compared to other common virtual screening tools.
Unveiling the Dynamic Mechanisms of Generative AI in English Language Learning: A Hybrid Study Based on fsQCA and System Dynamics.
Behav Sci (Basel)
October 2024
School of Management, Harbin Institute of Technology, Harbin 150001, China.
Article Synopsis
- The rise of generative artificial intelligence (GenAI) is transforming English language education, but its effective integration depends on various factors that are not yet fully understood.
- This study used mixed methods, including qualitative comparative analysis and system dynamics modeling, to examine how GenAI impacts English learning in higher education, analyzing data from 33 classes at the Harbin Institute of Technology.
- The research identified key conditions for successful GenAI integration and created an integrative framework that emphasizes the interactions between learners, technology, teaching methods, and context, aiming to enhance responsible and effective use of GenAI in language education.
DrugRepPT: a deep pretraining and fine-tuning framework for drug repositioning based on drug's expression perturbation and treatment effectiveness.
Bioinformatics
November 2024
Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China.
Motivation: Drug repositioning (DR), identifying novel indications for approved drugs, is a cost-effective strategy in drug discovery. Despite numerous proposed DR models, integrating network-based features, differential gene expression, and chemical structures for high-performance DR remains challenging.
Results: We propose a comprehensive deep pretraining and fine-tuning framework for DR, termed DrugRepPT.
Improving screening, brief intervention and referral to treatment for unhealthy alcohol use in diverse, low-resourced primary care clinics.
BMC Health Serv Res
November 2024
Oregon Rural Practice-Based Research Network, Oregon Health & Science University, 3181 SW Sam Jackson Park, Road, Portland, OR, 97239, USA.
Background: Implementation of screening brief intervention and referral to treatment (SBIRT) and medication-assisted treatment for alcohol use disorder (MAUD) remains low in primary care. ANTECEDENT (Partnerships to Enhance Alcohol Screening, Treatment, and Intervention) was a practice-facilitator led implementation study to increase SBIRT and MAUD use in diverse primary care clinics.
Methods: From November 2019 - April 2023, we conducted a convergent parallel mixed methods evaluation.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!