Electronic Health Records

The #aware.hiv Europe study is a real-world, multicenter, stepped-wedge cluster randomized, effectiveness-implementation trial designed to evaluate whether the introduction of dedicated HIV teams in hospitals can improve HIV testing rates among patients presenting with HIV indicator conditions across ten European countries. Study Design: The study employs a stepped-wedge design, whereby clusters of hospitals transition sequentially from a control phase (routine care) to an intervention phase. All patient data are collected retrospectively from routine care, while prospective data are gathered at the healthcare professional level. The project spans four years and involves hospitals from the Netherlands, Belgium, United Kingdom, Germany, Spain, France, Italy, Romania, Poland, and Ukraine. This design allows for comparison of HIV testing rates and related outcomes before and after the implementation across different settings and time points. Intervention: The core intervention involves the establishment of hospital-based HIV teams. Each team is led by an HIV specialist and supported by nurses and data collectors. Their responsibilities include: Identification and Surveillance: Screening routine electronic health records for HIV indicator conditions using predefined ICD-10 codes and verifying cases that warrant HIV testing. Audit \& Feedback: Providing targeted recommendations to treating physicians when an HIV test is indicated but has not been performed, thereby prompting action. Education \& Training: Delivering training sessions to healthcare professionals to improve their knowledge and attitudes towards HIV testing, prevention, and care. Enabling Environment: Implementing digital solutions and other support mechanisms to streamline testing processes, reduce stigma, and enhance overall guideline adherence. Linkage to prevention: Improving linkage to the locally available preventive services. The intervention is intended to integrate seamlessly into routine hospital care, thereby reinforcing existing guidelines while addressing the current diagnostic testing gap. Endpoints and Outcome Measures: Primary Endpoint: The change in HIV testing rate among patients diagnosed with HIV indicator conditions before and after the implementation of HIV teams. Key Secondary Endpoints: The change in the incidence of new HIV diagnoses among patients with HIV indicator conditions. Variations in HIV testing rates across different countries, medical specialties, and types of indicator conditions, as well as over time. Assessment of the cascade of HIV diagnosis, including the proportion of patients identified with an indicator condition, the offer and acceptance of HIV testing, and documented reasons for non-testing. Evaluation of the cascade of HIV care and prevention, including linkage to HIV care, achievement of viral suppression, and referral and uptake of preventive services. Changes in healthcare professionals' knowledge, attitudes, and levels of stigma towards HIV. Implementation outcomes such as fidelity of HIV team activities, resource utilization, cost-effectiveness, and sustainability of the intervention. Analysis of contextual factors, barriers, and facilitators impacting the implementation process, using established frameworks like CFIR and RE-AIM. Impact: By introducing HIV teams and systematically monitoring their effect on HIV testing practices, the study aims to enhance early HIV diagnosis and improve patient outcomes. The findings will contribute to evidence-based guidelines and may promote the adoption of similar interventions across European healthcare settings, ultimately reducing HIV-associated morbidity, mortality, and transmission rates. This project not only addresses a critical diagnostic gap in HIV care but also provides valuable insights into the effective implementation of complex interventions in routine clinical practice.

Cardiovascular diseases are the leading cause of mortality from treatable conditions in the European Union and the second from preventable causes, with a standardized mortality rate of 257.8 deaths per 100,000 inhabitants. In 2022, more than 1.11 million deaths in individuals under 75 years could have been avoided. Atrial fibrillation (AF) and major adverse cardiovascular events (MACE) are highly prevalent in the elderly and generate substantial healthcare costs. AF significantly increases the risk of MACE and is projected to rise markedly in the coming decades. In Europe, AF prevalence is expected to increase 2.5-fold over the next 50 years, with a lifetime risk of 1 in 3-5 individuals after age 55. AF-related strokes are projected to increase by 34%, and ischemic strokes in individuals over 80 are expected to triple between 2016 and 2060. Additionally, a 27% increase is anticipated among stroke survivors who subsequently develop AF or related conditions. AF substantially impacts morbidity, mortality, and disease progression, and early detection and treatment are crucial to prevent severe outcomes. European action plans (2018-2030) and the 2024 ESC/ESO guidelines emphasize early detection and management of AF in primary care. Although several AF prediction models exist, their integration into clinical practice remains challenging. AF represents a clinical continuum, with thrombotic risk present even before arrhythmia onset. High-risk patients for AF also show a high incidence of MACE, defined as a composite of myocardial infarction, stroke, systemic embolic events, and cardiovascular death. The proposed strategy involves developing and clinically validating an Artificial Intelligence (AI) model to improve early thrombotic risk prediction in patients at high risk of AF, using MACE as the primary outcome. This model aims to outperform the traditional CHA₂DS₂-VASc score by incorporating both classical and emerging clinical factors. The estimated timeline from clinical validation to commercialization is approximately 48 months. AI-based prediction is expected to enable personalized treatment, reduce the incidence of MACE, hospitalizations, and disability, and improve cost-effectiveness, ultimately decreasing the social and economic burden of AF and stroke in Europe.

This study, titled "Automated Indicator Feedback for Improving the Quality of Discharge Letters: A Cluster-Randomized Controlled Trial" (FIAQ-LS), aims to evaluate whether continuous real-time feedback to hospital teams can improve the quality of discharge letters. Discharge letters are critical for ensuring continuity of care and reducing adverse events by providing detailed information about a patient's hospital stay to both the patient and their primary care physician. The study will be conducted at Grenoble Alpes University Hospital and involve 40 hospital services across three campuses. The trial design includes two parallel arms: an intervention group receiving monthly performance feedback through automated dashboards and a control group with no additional intervention. Services are randomized into these groups using a stratified cluster approach. The primary objective is to assess whether this intervention increases the proportion of discharge letters validated on the day of discharge compared to usual care. Secondary objectives include evaluating patient satisfaction, rates of unplanned 30-day readmissions, and completeness of discharge letter content. The study will include data from approximately 132,000 patient stays over two phases: a pre-implementation observational period (12 months) and an intervention phase (12 months). All data will be collected and analyzed anonymously, with findings expected to inform the broader implementation of quality improvement strategies in French hospitals.