AUTHOR=Castor Delivette , Saidu Rakiya , Boa Rosalind , Mbatani Nomonde , Mutsvangwa Tinashe E. M. , Moodley Jennifer , Denny Lynette , Kuhn Louise 

TITLE=Assessment of the implementation context in preparation for a clinical study of machine-learning algorithms to automate the classification of digital cervical images for cervical cancer screening in resource-constrained settings

JOURNAL=Frontiers in Health Services

VOLUME=Volume 2 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2022.1000150

DOI=10.3389/frhs.2022.1000150

ISSN=2813-0146

ABSTRACT=Introduction: We assessed the implementation context and image quality in preparation for a clinical study evaluating the effectiveness of automated visual assessment devices within cervical cancer screening of women living without and with HIV. 

Methods: We developed a semi-structured questionnaire based on three Consolidated Framework for Implementation Research (CFIR) domains; intervention characteristics, inner setting, and process, in Cape Town, South Africa. Between December 1, 2020, and August 6, 2021, we evaluated two devices: MobileODT handheld colposcope; and a commercially-available cell phone (Samsung A21ST). Colposcopists visually inspected cervical images for technical adequacy. Descriptive analyses were tabulated for quantitative variables, and narrative responses were summarized in the text. 

Results: Two colposcpists described the devices as easy to operate, without data loss. The clinical workspace and gynecological workflow were modified to incorporate devices, and manage images. Providers believed either device would likely perform better than cytology under most circumstances unless the squamocolumnar junction (SCJ) was not visible, in which case cytology was expected to be better.  Image quality (N=75) from the MobileODT device and cell phone was comparable in terms of achieving good focus (81% vs. 84%), obtaining visibility of the squamous columnar junction (88% vs. 97%), avoiding occlusion (79% vs. 87%), and detection of lesion and range of lesion includes the upper limit (63% vs. 53%) but differed in taking photographs free of glare (100% vs. 24%). 

Conclusion: Novel application of the CFIR early in the conduct of clinical study including image quality highlight real-world factors about intervention characteristics, inner clinical setting, and workflow process that may affect clinical study findings and ultimate pace translation to clinical practice. The application for the CFIR in this study context highlighted adaptations needed for the framework to better measure digital interventions and characterize early implementation.