Clinical care

Clinical care, Personal/public health

Artificial intelligence (AI) and machine learning have great potential to improve people’s lives. From supporting data analysis in research to providing more accurate and quicker diagnostic tools. But their interior workings are questioned by many and understood by few. New models are needed to solve current shortcomings and causal AI might be our way out. By offering a peek inside the black box, it creates opportunities to implement AI in high-risk settings such as healthcare. But how far along are we and where is this journey taking us?
In a world where our health is paramount, fatty liver disease, known as metabolic dysfunction-associated steatotic liver disease (MASLD), is climbing the ranks of health concerns we can't afford to ignore. Imagine your liver, which controls over 500 vital functions, including the body's detox powerhouse, getting clogged with fat. Alarmingly, about 30% of people globally are wrestling against this silent epidemic. It's a complex disease with various risk factors, especially related to gender and sex hormonal differences, making a one-size-fits-all treatment difficult.
After several years of dismal market activity, 2024 is already looking up for deals in the pharmaceutical industry, with a recent flurry of billion-dollar mergers and acquisitions. Is this trend being driven by the impending loss of revenue caused by soon-to-expire blockbuster drug patents? And what does it mean for earlier-stage biotech startups?
Personalized healthcare is characterized by tailoring treatments to each individual patient’s needs. Despite evident benefits, implementing such a system is not straightforward. The ATHENA project consortium has successfully devised a set of building blocks to address the challenges associated. Both technological and governance tools were developed that can now be used on a larger scale to catalyze the transition towards personalized medicine and care.
Belgium is one of the top countries in the world for in vitro fertilization (IVF). Despite the high cumulative success rates of these interventions, disproportionally little attention has been given to the health of both mother and child during and after pregnancy. With the HEART (High risk for pre-Eclampsia after Assisted Reproductive Technology) project, Belgian researchers strive to understand why some women have an increased risk for pre-eclampsia after IVF and whether biomarkers can be identified to estimate these risks early in pregnancy or even before conception. They aim to raise the standard from successful conception to improved child and maternal health.
The patient’s journey in a healthcare setting is influenced by factors beyond the mere medical aspects of the case. Local variations in care procedures among hospitals and caregivers enable benchmarking and adaptation of practices to optimize outcomes. For effective process modeling, it’s essential to collect data and extract insights. To optimize this process efficiently, collaborative efforts should explore alternative data sources and implement novel tools.
Gluten-free dietary products contain more sugar and fats but fewer nutrients and are generally more expensive. A gluten-free diet can even lead to social isolation and stigmatization. Scientists are thus looking for a way that allows people with celiac gluten intolerance disease to enjoy the benefits of gluten in a safe manner.
ATHENA, a VLAIO-funded multi-stakeholder project, clears the path for increased reuse of Real-World Data in scientific research and healthcare, by introducing innovative solutions and responding to current technical and governance challenges. The project has made significant strides in the field of oncology by developing groundbreaking privacy-preserving machine learning techniques for predictive analytics. The project findings will be presented and discussed at the ATHENA symposium on November 23rd, 2023.
After a therapeutic drug hits the market, it is crucial to continuously gather, analyze, and report data regarding its safety and potential side effects, a practice known as pharmacovigilance. Unlike clinical trials, this involves real-world data (RWD), presenting unique challenges in terms of both quality and quantity. The Belgian BELpREG project seeks to employ RWD for monitoring drug utilization during pregnancy and investigating potential safety implications for both maternal and child health. This initiative holds great promise, although it faces substantial hurdles on its path to success.
Patient data is a treasure trove of information, vital to tailoring individual care pathways and generating profound insights to enhance healthcare more broadly. However, achieving interoperability among the diverse systems employed by various healthcare providers remains a critical challenge. Data standards like FHIR can help to streamline the secure exchange and seamless integration of healthcare data across these different systems. This article will take you through the FHIR framework and how it could be used by your organization.
Studies of a drug's effectiveness and safety don’t end with clinical trials – they extend beyond market access when the true value of a drug is demonstrated in a real-world setting. Based on this information, factors such as availability, pricing, and reimbursement are adjusted. To study the actual worth of a treatment, we require real-world data (RWD) from a large and diverse patient population. By actively sharing this information with stakeholders, we can fuel further research and innovation, and even help to inform decision-making on a population level. But in order to unlock the full potential of our patient data, all members of the ecosystem have to work together.
Patient data can be extremely helpful for improving health. If properly utilized, valuable insights can be generated, leading to better care for all. To do this, we need a way to overcome the incompatibility of information systems used by different healthcare providers, so that we are able to conduct large-scale research projects using data from multiple sources. The key to this lies in implementing data standards, such as OMOP and FHIR – specific methods developed for the storage, sharing, and interpretation of healthcare data.
After recovering from metastatic colon cancer, Stefan Gijssels became a patient advocate dedicated to improving the healthcare system that saved his life. As Chair of the Belgian Patient Expert Center (PEC), he has helped to establish a training program turning patients into patient experts who can provide stakeholders like hospitals and companies with valuable insights during the innovation process.
Clinical drug development is a challenging endeavor, but help is at hand! From trial site selection, to patient recruitment, to endpoint characterization – data science integration can help to overcome bottlenecks and improve efficiency in clinical development by generating unique insights to help guide study design and operations. For this strategy to be used to its full potential in Belgium, local partners should optimize data governance and quality, and improve collaboration. This would help the country maintain its status as a leading location for clinical trials.