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

Abstract

IntroductionWe 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.MethodsWe 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.ResultsTwo colposcopists 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) were 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%).ConclusionNovel application of the CFIR early in the conduct of the clinical study, including assessment of image quality, highlight real-world factors about intervention characteristics, inner clinical setting, and workflow process that may affect both the clinical study findings and ultimate pace of translating to clinical practice. The application and augmentation of the CFIR in this study context highlighted adaptations needed for the framework to better measure factors relevant to implementing digital interventions.

Innovation in healthcare: leadership perceptions about the innovation characteristics of artificial intelligence—a qualitative interview study with healthcare leaders in Sweden

“…Determinant frameworks are used in implementation science to describe different types of influences on implementation that are hypothesised to—or have been found to—impact implementation outcomes. CFIR has previously been used to assess the implementation of AI applications in various settings [ 49 , 50 ]. In this study, we limited its use to eight characteristics in the innovation domain in the CFIR framework [ 9 ] which we found suitable to address aspects of perceptions deemed relevant to the study aim.…”

The Extent of Implementation of Coa Circular No. 2012-001 to Dost-Itdi Employees: An Assessment

“…With this, the assessment of administrative Practice is vital to determine the alignment of the implementation plan to organizational goals and Memorandum. Castor et al (2022) [1] state that reviews in one's approach is one of the methods in identifying the effectiveness of Implementation to the attainment of organizational goals and satisfaction of its target recipient of services offered. With the constant evaluation of Practice, the organization determines the hindrances to effectively exercising rules and regulations and the course of action to take.…”