Facilitating evidence-based practice: frequency analysis of audit, feedback, and implementation strategies across Taiwan

ABSTRACT Background: Audit and feedback are established strategies for improving clinical practice, yet evidence gaps remain regarding the mechanisms that link feedback to behavior change. Aim: This study examined implementation projects conducted in Taiwan that used the JBI Model of Evidence-based Healthcare and the JBI 7-phase implementation process framework. The objective was to identify common barriers, enablers, and strategies across diverse health care settings. Methods: Fifteen Taiwanese implementation studies (2020–2025) were included and evaluated using frequency analysis and central tendency measures. Data extraction was piloted and papers were then coded for audit criteria, barriers, enablers, and implementation strategies, with reference to underlying theories, models, and frameworks. Results: Across studies, projects were nurse-led and multidisciplinary, with most applying combined internal–external facilitation models. Process-based audit criteria predominated. Multi-modal implementation strategies aligned with the COM-B behavior change model were frequent, with more enablers than barriers reported. All projects used at least one theory, model, or framework—most commonly the JBI Model of Evidence-based Healthcare and the JBI 7-phase implementation process framework. Improvements in compliance were consistently observed but varied by topic and context. Conclusions and relevance to Taiwan: Successful implementation depended on structured facilitation, leadership engagement, and active team participation. Key challenges reflected organizational hierarchy, communication patterns, and workload. Embedding structured audit and feedback cycles within institutional governance and professional education could strengthen evidence-informed practice nationwide. Future work will develop a culturally attuned implementation toolbox to guide sustained, system-wide improvement in Taiwan. Spanish abstract: https://links.lww.com/IJEBH/A665

Authors

Institutions

Publication Details

Journal
International Journal of Evidence-Based Healthcare
Published
2026-08-18
DOI
https://doi.org/10.1097/xeb.0000000000000640
Primary Topic
Health Policy Implementation Science
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Facilitating evidence-based practice: frequency analysis of audit, feedback, and implementation strategies across Taiwan

Ching-Chuan Liang, Jin-Hung Lin, Craig Lockwood, Y.-H. Cheng et al.
International Journal of Evidence-Based Healthcare
Health Policy Implementation Science
article

Facilitating evidence-based practice: frequency analysis of audit, feedback, and implementation strategies across Taiwan

Ching-Chuan Liang, Jin-Hung Lin, Craig Lockwood, Y.-H. Cheng, Meei‐Ling Gau, Shu‐Chuan Chang, Cheng-Yu Chang, Jui Yuan Su, Yueh-Yin Fang, Ya-Wen Lee, Pei-Fang Chia, Pei-Fan Mu, Shwu-Feng Tsay
article en

Abstract

ABSTRACT Background: Audit and feedback are established strategies for improving clinical practice, yet evidence gaps remain regarding the mechanisms that link feedback to behavior change. Aim: This study examined implementation projects conducted in Taiwan that used the JBI Model of Evidence-based Healthcare and the JBI 7-phase implementation process framework. The objective was to identify common barriers, enablers, and strategies across diverse health care settings. Methods: Fifteen Taiwanese implementation studies (2020–2025) were included and evaluated using frequency analysis and central tendency measures. Data extraction was piloted and papers were then coded for audit criteria, barriers, enablers, and implementation strategies, with reference to underlying theories, models, and frameworks. Results: Across studies, projects were nurse-led and multidisciplinary, with most applying combined internal–external facilitation models. Process-based audit criteria predominated. Multi-modal implementation strategies aligned with the COM-B behavior change model were frequent, with more enablers than barriers reported. All projects used at least one theory, model, or framework—most commonly the JBI Model of Evidence-based Healthcare and the JBI 7-phase implementation process framework. Improvements in compliance were consistently observed but varied by topic and context. Conclusions and relevance to Taiwan: Successful implementation depended on structured facilitation, leadership engagement, and active team participation. Key challenges reflected organizational hierarchy, communication patterns, and workload. Embedding structured audit and feedback cycles within institutional governance and professional education could strengthen evidence-informed practice nationwide. Future work will develop a culturally attuned implementation toolbox to guide sustained, system-wide improvement in Taiwan. Spanish abstract: https://links.lww.com/IJEBH/A665

International Journal of Evidence-Based Healthcare
National Yang Ming Chiao Tung University (TW), National Chung Hsing University (TW), National Taiwan University (TW), Tzu Chi University (TW), National Taipei University of Nursing and Health Science (TW), Tzu Chi Foundation (TW), Taipei Veterans General Hospital (TW), National Yang Ming University Hospital (TW), Cheng Hsin General Hospital (TW), SA Health (AU), Ministry of Health and Welfare (TW), Pingtung Christian Hospital (TW), Changhua Christian Hospital (TW), The University of Adelaide (AU), National Defense Medical Center (TW), Hungkuang University (TW)
Openalex Percentile: Top 5%
Health Policy Implementation Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.