Barriers and facilitators to implementing an automated AI-guided in-vitro diagnostic platform
Abstract
Background: Antimicrobial resistance (AMR) is an increasing public health threat driven by improper antimicrobial use and limited diagnostic capacity. An automated AI-guided whole-genome sequencing (WGS) platform has the potential to improve AMR diagnostics. To our knowledge, this study is the first to examine the organisational factors influencing the adoption of this novel automated platform in hospital settings.
Methods: An explanatory sequential mixed-methods study is being conducted across four European hospitals for a comprehensive understanding of how organisational alignment influences implementation feasibility. In the quantitative phase, an electronic survey assessed participant perceptions on the platform’s impact on diagnostic workflow, barriers and facilitators to implementation, and cost feasibility using 5-point Likert scales. Ongoing semi-structured interviews among eligible healthcare professionals in diagnostics and care will further explain the identified perceptions.
Results: Forty-one healthcare professionals, including molecular microbiologists, biomedical engineers, clinicians, and laboratory technicians from the different hospitals filled the survey. Preliminary quantitative results show that respondents perceived the platform as likely to reduce time to result, supporting improved clinical decision-making. Key barriers to adoption included a limited availability of trained personnel, the absence of a structured implementation process, and resistance to organisational change. Perceived facilitators included strong leadership support, adequate IT support for system integration, and comprehensive staff training.
Conclusions: While automated molecular diagnostic platforms show promise for improving efficiency and clinical decision-making, their successful adoption depends on organisational readiness. These findings highlight the need for coordinated strategies to support effective and sustainable integration into routine practice.
Key messages: • Automated AI-guided WGS platforms show strong potential to reduce time-to-result and improve clinical decision-making in AMR diagnostics across European hospital settings.
• Successful adoption depends on organisational readiness: addressing staff training gaps, structured implementation processes, and leadership support is critical for sustainable integration.
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BibTeX
@article{anyimiah2026barriers,
title = {{Barriers and facilitators to implementing an automated AI-guided in-vitro diagnostic platform}},
author = {P Anyimiah and S Welk and J De Zeeuw},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.1205},
url = {https://doi.org/10.1093/eurpub/ckag169.1205},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Barriers and facilitators to implementing an automated AI-guided in-vitro diagnostic platform
AU - P Anyimiah
AU - S Welk
AU - J De Zeeuw
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.1205
UR - https://doi.org/10.1093/eurpub/ckag169.1205
AB - Background: Antimicrobial resistance (AMR) is an increasing public health threat driven by improper antimicrobial use and limited diagnostic capacity. An automated AI-guided whole-genome sequencing (WGS) platform has the potential to improve AMR diagnostics. To our knowledge, this study is the first to examine the organisational factors influencing the adoption of this novel automated platform in hospital settings. Methods: An explanatory sequential mixed-methods study is being conducted across four European hospitals for a comprehensive understanding of how organisational alignment influences implementation feasibility. In the quantitative phase, an electronic survey assessed participant perceptions on the platform’s impact on diagnostic workflow, barriers and facilitators to implementation, and cost feasibility using 5-point Likert scales. Ongoing semi-structured interviews among eligible healthcare professionals in diagnostics and care will further explain the identified perceptions. Results: Forty-one healthcare professionals, including molecular microbiologists, biomedical engineers, clinicians, and laboratory technicians from the different hospitals filled the survey. Preliminary quantitative results show that respondents perceived the platform as likely to reduce time to result, supporting improved clinical decision-making. Key barriers to adoption included a limited availability of trained personnel, the absence of a structured implementation process, and resistance to organisational change. Perceived facilitators included strong leadership support, adequate IT support for system integration, and comprehensive staff training. Conclusions: While automated molecular diagnostic platforms show promise for improving efficiency and clinical decision-making, their successful adoption depends on organisational readiness. These findings highlight the need for coordinated strategies to support effective and sustainable integration into routine practice. Key messages: • Automated AI-guided WGS platforms show strong potential to reduce time-to-result and improve clinical decision-making in AMR diagnostics across European hospital settings. • Successful adoption depends on organisational readiness: addressing staff training gaps, structured implementation processes, and leadership support is critical for sustainable integration.
SN - 1101-1262
SN - 1464-360X
ER -