Liesbeth Demuyser

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.
The emergence of artificial intelligence and machine learning accelerates research and innovation across various sectors, particularly in healthcare. The potential for the development of innovative diagnostic tools and therapies based on insights from health data is limitless. However, progress should never compromise the privacy of patients. It’s a delicate balance that is essential to maintain.
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.
While a vast majority of women experience vaginal yeast infections, research has fallen short in providing an effective treatment approach. However, hope has emerged recently with the development of new model systems that allow exploration of the complex vaginal environment. Organ-on-chip models enable researchers to examine the interactions between human cells and microbes in a more accurate manner, offering the potential for the development of new therapies.
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.
Similar to the growing use of large-scale scientific data to guide laboratory research, urban data presents significant potential for informed decision-making in city governance. Extensive data on waste collection, traffic, pollution, and various other facets of city management can be collected and analyzed to help policymakers identify challenges and develop prediction-based solutions. Thomas Van Oppens, Deputy Mayor of Leuven, underscores the importance of local collaboration in establishing such a data-driven growth model for city governance.
Even though endometriosis impacts millions of women globally, the condition remains poorly understood and researched, leading to delayed diagnosis and lack of effective treatment. Encouragingly, there are promising recent developments in the field coming from Belgium. Professor Hugo Vankelecom’s research group at KU Leuven uses advanced cellular models, known as organoids, to delve into the disease and expedite the drug discovery process. The Danish BioInnovation Institute now offers the team an incubation program to pave the way towards industrial success.
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.
Rapidly spreading diseases and unpredictable global shifts cause increased strain to the pharmaceutical supply chain, jeopardizing the secure delivery of drugs to those who are in need. Artificial intelligence can maximize efficacy of the value chain and allow optimal responses to changing market demands. Two experts share insights into the possibilities and hurdles associated with integration of machine learning tools into the pharmaceutical supply chain.
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.
Many drugs fail clinical trials, often because preclinical animal models fall short of replicating human physiology. To improve animal welfare, speed up drug development, and reduce costs, we need to rely less on animal models, while also minimizing the number of failures early in the drug development process. Artificial intelligence and machine learning are powerful tools that can help us achieve these goals by predicting a drug's efficacy, safety, and uptake in preclinical studies. These technologies can help researchers to make informed decisions and optimize testing strategies, improving drug development for both animals and people.
Femtech is a growing field that has rapidly expanded from niche market to global ecosystem. From period-tracking apps and smart pelvic floor trainers to wearable breast pumps – both start-ups and well-established multinationals are prioritizing tech innovation in women’s health. But are investors keeping up with this trend, or is the strong gender skew in venture capital hampering the femtech field?
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.