PurposeThis study aimed to identify the barriers and facilitators related to the implementation of radical innovations in secondary healthcare.Design/methodology/approachA systematic review was conducted and presented in accordance with a PRISMA flowchart. The databases PubMed and Web of Science were searched for original publications in English between the 1st of January 2010 and 6th of November 2020. The level of radicalness was determined based on five characteristics of radical innovations. The level of evidence was classified according to the level of evidence scale of the University of Oxford. The Consolidated Framework for Implementation Research was used as a framework to classify the barriers and facilitators.FindingsBased on the inclusion and exclusion criteria, nine publications were included, concerning six technological, two organizational and one treatment innovation. The main barriers for radical innovation implementation in secondary healthcare were lack of human, material and financial resources, and lack of integration and organizational readiness. The main facilitators included a supportive culture, sufficient training, education and knowledge, and recognition of the expected added value.Originality/valueTo our knowledge, this is the first systematic review examining the barriers and facilitators of radical innovation implementation in secondary healthcare. To ease radical innovation implementation, alternative performance systems may be helpful, including the following prerequisites: (1) Money, (2) Added value, (3) Timely knowledge and integration, (4) Culture, and (5) Human resources (MATCH). This study highlights the need for more high-level evidence studies in this area.
ObjectivesTo operationalise fairness in the adoption of medical artificial intelligence (AI) algorithms in terms of access to computational resources, the proposed approach is based on a two-dimensional (2D) convolutional neural networks (CNN), which provides a faster, cheaper and accurate-enough detection of early Alzheimer’s disease (AD) and mild cognitive impairment (MCI), without the need for use of large training data sets or costly high-performance computing (HPC) infrastructures.MethodsThe standardised Alzheimer’s Disease Neuroimaging Initiative (ADNI) data sets are used for the proposed model, with additional skull stripping, using the Brain Extraction Tool V.2approach. The 2D CNN architecture is based on LeNet-5, the Leaky Rectified Linear Unit activation function and a Sigmoid function were used, and batch normalisation was added after every convolutional layer to stabilise the learning process. The model was optimised by manually tuning all its hyperparameters.ResultsThe model was evaluated in terms of accuracy, recall, precision and f1-score. The results demonstrate that the model predicted MCI with an accuracy of 0.735, passing the random guessing baseline of 0.521 and predicted AD with an accuracy of 0.837, passing the random guessing baseline of 0.536.DiscussionThe proposed approach can assist clinicians in the early diagnosis of AD and MCI, with high-enough accuracy, based on relatively smaller data sets, and without the need of HPC infrastructures. Such an approach can alleviate disparities and operationalise fairness in the adoption of medical algorithms.ConclusionMedical AI algorithms should not be focused solely on accuracy but should also be evaluated with respect to how they might impact disparities and operationalise fairness in their adoption.
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