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Implementation of a breast cancer screening prediction algorithm: a knowledge management approach

The Warwickshire, Solihull and Coventry Breast Screening Unit (‘Unit’) is one of the largest screening services in the UK. In collaboration with the Unit, an Artificial Intelligence Attendance (AI-ATT) algorithm was recently developed within Biomedical Computing Research Group (BIOCORE), for the prediction of attendance (starting from the second episode) to the Breast Screening Programme (BSP). This research aims to integrate a new algorithm (entitled “Java-based Attendance prediction by Artificial Intelligence for Breast Screening (JAABS)” within this Unit in order to streamline and automate the invitation process and thus increase screening attendance. The work deploys a hybrid research methodology which incorporates both the qualitative and quantitative paradigms. Efforts had focussed on the formulation of a BSP messaging protocol. The protocol processes the uptake dataset from the second episode upwards and uses it as the input for the prediction process. The non-attendee list produced by the AI-ATT algorithm is segregated into their respective GP groups. Using the proposed framework, a message transmission process will be initiated to send the information to the GPs’ network. Following proper authentication and acceptance, the information is updated on the GP’s database. This can instigate the deployment of measures to improve attendance and potentially result in a further reduction in morbidity and mortality rates due to breast cancer. Knowledge Management (KM) tools and techniques were used to effectively manage the transfer of knowledge between the Unit, GPs and eligible women within a screening episode. In concurrence with the UK government’s “e-Government Interoperability Framework” (e-GIF) policy, and in projecting the importance of the Electronic Data Interchange (EDI) within the BSP structure, this project addresses the limitations of the existing communication path between the BSP and primary care providers. The deliverables of the project include an investigation and evaluation of the suitability of HL7 and XML standards for the proposed messaging protocol, a comparison of the proposed standards with EDIFACT-based protocols to highlight salient features. As part of the research, questionnaires were designed and distributed to evaluate the GPs’ role in reducing the BSP non-attendance. A comprehensive triangulation-based validation of the two algorithms (AI-ATT and JAABS) was also featured as part of the research. The JAABS algorithm proposed includes a new attribute (associated to the distance travelled by the women for a mammogram) as a predicting variable to increase the accuracy of prediction. Based on these evaluations and validations, technical reports were produced and communicated to all stakeholders. Implementation of a breast cancer screening prediction algorithm: a knowledge management approach A PhD project funded by the NHS Cancer Screening Programmes Carried out by the Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Coventry University (www.coventry.ac.uk/biocore/)