Collaboration to reduce antibiotic use and resistance and identify opportunities for improvement and awareness (CARA).

INTRODUCTION: CARA is a five-year Health Research Board (HRB) project. Superbugs cause resistant infections that are difficult to treat and pose a serious threat to human health. Providing tools to explore the prescription of antibiotics by GPs may help identify gaps where improvements can be made. CARA's aim is to combine, link and visualise data on infections, prescribing and other healthcare information. METHODS: The CARA team is creating a dashboard to provide GPs with a tool to visualise their own practice data and compare this with other GPs in Ireland. Anonymous patient data can be uploaded and visualised to show details, current trends and changes in infections and prescribing. The CARA platform will also provide easy options to generate audit reports. RESULTS: After registration, a tool for anonymous data upload will be provided. Through this uploader, data will be used to create instant graphs and overviews as well as comparisons with other GP practices. With selection options, graphical presentations can be further explored or audits generated. Currently, few GPs are involved in the development of the dashboard to ensure it will be efficient. Examples of the dashboard will be shown at the conference. DISCUSSION: The CARA project will provide GPs with a tool to access, analyse and understand their patient data. GPs will have secure accounts accessible through the CARA website to allow easy anonymous data upload in a few steps. The dashboard will show comparisons of their prescribing with other (unknown) practices, identify areas for improvement and conduct audit reports.

Evaluation of different landing pages on behavioural engagement with the CARA dashboard: A user research protocol.

BACKGROUND: CARA set out to develop a data-visualisation platform to facilitate general practitioners to develop a deeper understanding of their patient population, disease management and prescribing through dashboards. To support the continued use and sustainability of the CARA dashboards, dashboard performance and user engagement have to be optimised. User research places people at the centre of the design process and aims to evaluate the needs, behaviours and attitudes of users to inform the design, development and impact of a product. OBJECTIVE: To explore how different initial key messages impact the level of behavioural engagement with a CARA dashboard. METHODS: Participating general practices can upload their practice data for analysis and visualisation in CARA dashboards. Practices will be randomised to one of three different initial landing pages: the full dashboard or one of two key messages: a between comparison (their practice prescribing with the average of all other practices) or within comparison (with practice data of the same month the previous year) with subsequent continuation to the full dashboard. Analysis will determine which of the three landing pages encourages user interaction, as measured by the number of 'clicks', 'viewings' and 'sessions'. Dashboard usage data will be collected through Google analytics. DISCUSSION: This study will provide evidence of behavioural engagement and its metrics during the implementation of the CARA dashboards to optimise and sustain interaction. TRIAL REGISTRATION: ISRCTN32783644 (Registration date: 02/01/2024).

Design and architecture of the CARA infrastructure for visualising and benchmarking patient data from general practice.

OBJECTIVE: Collaborate, Analyse, Research and Audit (CARA) project set out to provide an infrastructure to enable Irish general practitioners (GPs) to use their routinely collected patient management software (PMS) data to better understand their patient population, disease management and prescribing through data dashboards. This paper explains the design and development of the CARA infrastructure. METHODS: The first exemplar dashboard was developed with GPs and focused on antibiotic prescribing to develop and showcase the proposed infrastructure. The data integration process involved extracting, loading and transforming de-identified patient data into data models which connect to the interactive dashboards for GPs to visualise, compare and audit their data. RESULTS: The architecture of the CARA infrastructure includes two main sections: extract, load and transform process (ELT, de-identified patient data into data models) and a Representational State Transfer Application Programming Interface (REST API) (which provides the security barrier between the data models and their visualisation on the CARA dashboard). CARAconnect was created to facilitate the extraction and de-identification of patient data from the practice database. DISCUSSION: The CARA infrastructure allows seamless connectivity with and compatibility with the main PMS in Irish general practice and provides a reproducible template to access and visualise patient data. CARA includes two dashboards, a practice overview and a topic-specific dashboard (example focused on antibiotic prescribing), which includes an audit tool, filters (within practice) and between-practice comparisons. CONCLUSION: CARA supports evidence-based decision-making by providing GPs with valuable insights through interactive data dashboards to optimise patient care, identify potential areas for improvement and benchmark their performance against other practices.Supplementary file 1. Graphical abstract.

The Effectiveness of Interactive Dashboards to Optimise Antibiotic Prescribing in Primary Care: A Systematic Review.

Governments and healthcare organisations collect data on antibiotic prescribing (AP) for surveillance. This data can support tools for visualisations and feedback to GPs using dashboards that may prompt a change in prescribing behaviour. The objective of this systematic review was to assess the effectiveness of interactive dashboards to optimise AP in primary care. Six electronic databases were searched for relevant studies up to August 2022. A narrative synthesis of findings was conducted to evaluate the intervention processes and results. Two independent reviewers assessed the relevance, risk of bias and quality of the evidence. A total of ten studies were included (eight RCTs and two non-RCTs). Overall, seven studies showed a slight reduction in AP. However, this reduction in AP when offering a dashboard may not in itself result in reductions but only when combined with educational components, public commitment or behavioural strategies. Only one study recorded dashboard engagement and showed a difference of 10% (95% CI 5% to 15%) between intervention and control. None of the studies reported on the development, pilot or implementation of dashboards or the involvement of stakeholders in design and testing. Interactive dashboards may reduce AP in primary care but most likely only when combined with other educational or behavioural intervention strategies.

Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review.

Introduction: The use of antibiotics leads to antibiotic resistance (ABR). Different methods have been used to predict and control ABR. In recent years, artificial intelligence (AI) has been explored to improve antibiotic (AB) prescribing, and thereby control and reduce ABR. This review explores whether the use of AI can improve antibiotic prescribing for human patients. Methods: Observational studies that use AI to improve antibiotic prescribing were retrieved for this review. There were no restrictions on the time, setting or language. References of the included studies were checked for additional eligible studies. Two independent authors screened the studies for inclusion and assessed the risk of bias of the included studies using the National Institute of Health (NIH) Quality Assessment Tool for observational cohort studies. Results: Out of 3692 records, fifteen studies were eligible for full-text screening. Five studies were included in this review, and a narrative synthesis was carried out to assess their findings. All of the studies used supervised machine learning (ML) models as a subfield of AI, such as logistic regression, random forest, gradient boosting decision trees, support vector machines and K-nearest neighbours. Each study showed a positive contribution of ML in improving antibiotic prescribing, either by reducing antibiotic prescriptions or predicting inappropriate prescriptions. However, none of the studies reported the engagement of AB prescribers in developing their ML models, nor their feedback on the user-friendliness and reliability of the models in different healthcare settings. Conclusion: The use of ML methods may improve antibiotic prescribing in both primary and secondary settings. None of the studies evaluated the implementation process of their models in clinical practices. Prospero Registration: (CRD42022329049).

Initial symptoms and three months follow-up after acute COVID-19 in outpatients: An international prospective cohort study.

BACKGROUND: Most studies on long-term follow-up of patients with COVID-19 focused on hospitalised patients. No prospective study with structured follow-up has been performed in non-hospitalised patients with COVID-19. OBJECTIVES: To assess long-COVID and post-COVID (WHO definition: symptomatic at least 12 weeks), describe lingering symptoms, their impact on daily activities, and general practice visits and explore risk factors for symptom duration in outpatients. METHODS: A prospective study of adult outpatients with confirmed SARS-CoV-2 infection and symptoms consistent with COVID-19 in 11 European countries, recruited during 2020 and 2021 from primary care and the community. Structured follow-up by phone interviews (symptom rating, symptom impact on daily activities and general practice visits) was performed at weeks 2, 4, 8, and 12 by study personnel. Data was analysed descriptively by using correlation matrixes and Cox regression. RESULTS: Of 270 enrolled patients, 52% developed long-COVID and 32% post-COVID-syndrome. When only considering the presence of moderate or (very) severe symptoms at weeks 8 and 12, these percentages were 28% and 18%, respectively. Fatigue was the most often reported symptom during follow-up. The impact of lingering symptoms was most evident in sports and household activities. About half (53%) had at least one general practice contact during follow-up. Obese patients took twice as long to return to usual health (HR: 0.5, 95%CI: 0.3-0.8); no other risk profile could predict lingering symptoms. CONCLUSION: Long-COVID and post-COVID are also common in outpatients. In 32%, it takes more than 12 weeks to return to usual health.