Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

Objective: The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital. Methods: A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I-III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility. Results: The study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53-100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243-4132). Median time for the system to run a query and return results was 15.5 s (range 7.2-37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen's kappa 0.70-1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%. Discussion and Conclusion: The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment

Implementation of health promotion programmes in schools: an approach to understand the influence of contextual factors on the process?

International audienceBackground: Implementing complex and multi-level public health programmes is challenging in school settings. Discrepancies between expected and actual programme outcomes are often reported. Such discrepancies are due to complex interactions between contextual factors. Contextual factors relate to the setting, the community, in which implementation occurs, the stakeholders involved, and the characteristics of the programme itself. This work uses realist evaluation to understand how contextual factors influence the implementation process, to result in variable programme outcomes. This study focuses on identifying contextual factors, pinpointing combinations of contextual factors, and understanding interactions and effects of such factors and combinations on programme outcomes on different levels of the implementation process. Methods: Schools which had participated in a school-based health promotion programme between 2012 and 2015 were included. Two sets of qualitative data were collected: semi-structured interviews with school staff and programme coordinators; and written documents about the actions implemented in a selection of four schools. Quantitative data included 1553 questionnaires targeting pupils aged 8 to 11 in 14 schools to describe the different school contexts. Results: The comparison between what was expected from the programme (programme theory) and the outcomes identified in the field data, showed that some of the mechanisms expected to support the implementation of the programme, did not operate as anticipated (e.g. inclusion of training, initiation by decision-maker). Key factors which influenced the implementation process included, amongst other factors, the mode of introduction of the programme, home/school relationship, leadership of the management team, and the level of delegated power. Five types of interactions between contextual factors were put forward: enabling, hindering, neutral, counterbalancing and moderating effects. Recurrent combinations of factors were identified. Implementation was more challenging in vulnerable schools where school climate was poor