Demonstrating value using real-world data
Once a medicinal product reaches the end of clinical trials, marketing authorization can be granted by regulatory agencies such as the FDA and EMA. Access to patients in a particular country as well as pricing and reimbursement are regulated on a national or regional level. In order to do so, claims made on the therapy outcome in clinical development should be verified in a real-world setting and the patient population most likely to benefit from treatment, and thus qualifies for reimbursement, identified. Information extracted from randomized clinical trials is limitedly useful in this context. Ton Peters, Associate Director Evidence Generation Benelux at The Janssen Pharmaceutical Companies of Johnson & Johnson, agrees: “A clinical study population typically lacks the heterogeneity of the patient group that potentially benefits from the drug.” To overcome this issue, data obtained from patients in the real world, including electronic health records, health insurance claims, and patient surveys can be used. “Today, regulatory medicine and health insurance agencies use evidence based on RWD to provide budgetary and clinical insights into treatment strategies,” says Marleen Hoebus, External & Government Affairs Director at Janssen. “Pricing and reimbursement schemes are adapted based on real-world patient outcomes.”
“Connecting diverse data sources amplifies the volume and variety within a dataset, enhancing the accuracy and authenticity of the insights we extract.” – Walter Van Dyck
RWD are also vital when determining the value of treatments for orphan and ultra-orphan diseases – conditions affecting less than 1/2,000 or 1/50,000 people, respectively. “For drugs targeting orphan diseases, it is almost impossible to recruit sufficient patients to conduct a randomized phase 3 clinical trial,” says Walter Van Dyck, Associate Professor and Partner at Vlerick Business School. Due to limited and geographically scattered patients, it is common to conduct single-arm trials in orphan diseases, where all patients receive the drug (without a control group). “To facilitate a comparison of treatment effects, we can instead employ a digital twin constructed using historical data from control patients.” RWD can help support this clinical trial design by identifying eligible patients and relevant endpoints.
Our three interviewees all agreed that we need designated data infrastructure to effectively use RWD to assess novel therapies. “Apart from the standard patient health records, we need to enrich our RWD with additional clinical information,” says Peters. “Connecting diverse data sources amplifies the volume and variety within a dataset, enhancing the accuracy and authenticity of the insights we extract,” agrees Van Dyck.
Leveraging patient data for progressive understanding
The usefulness of RWD extends beyond facilitating the transition towards a value-based healthcare system. Ideally, they are also used to drive research, and to answer questions as to why certain patients do not respond well to treatment and how therapy strategies can be optimized and personalized. “Releasing a drug on the market is not a rigid process. Quite the opposite: it should be an adaptive learning system in which RWD analyzed with machine learning tools fuel innovation,” states Van Dyck. Hoebus concurs: “By catalyzing the continuous interplay between research and clinical implementation, we can greatly accelerate health care optimization.”
“By catalyzing the continuous interplay between research and clinical implementation, we can greatly accelerate health care optimization.” – Marleen Hoebus
The importance of collecting data on drug outcomes persists even after market access. Healthcare providers gather data on safety and adverse effects to contribute to the process of pharmacovigilance. If adequately enriched, RWD can even be used to guide health policy development. “The COVID-19 pandemic and the concomitant vaccination campaign serve as a remarkable illustration of how comprehensive data collection can effectively inform decision-making,” says Hoebus.
Avoiding a Belgian backslide on clinical trials
Belgium has a longstanding history as a global leader in clinical research. However, the country is on the verge of losing this position due to limited integration of RWD into clinical strategies. This is in contrast to other countries which have to-date made greater strides in this area. “Regrettably, Belgium’s participation in international initiatives focused on health data-driven innovations is rather limited at the moment,” warns Van Dyck. “We are at risk of a brain drain, where valuable expertise is lost as people and clinical activities move to other countries with a better focus on real-world data.”
A fundamental change in mindset will be necessary for Belgium to reestablish its position. “The establishment of the Belgian Health Data Agency is promising in this respect,” says Hoebus. “It demonstrates the government’s positive intent to actively pursue the essential transition towards a data-driven culture.” However, it is vital for the entire ecosystem to be committed to embracing this shift. “We need a lot of data to minimize the risk of undesirable machine learning behavior like overfitting and the subsequent propagation of inaccurate claims,” Van Dyck argues. “To scale up, we need to combine multiple data sources across the country.”
Read this article to find out how we can unlock the potential of patient data using healthcare data standards!
Belgium needs to establish a robust RWD network – including hospitals, research institutions, companies, and other stakeholders – with strong mutual trust between all involved parties. While dialogue and collaboration are crucial for the establishment of such a network, they alone will not suffice. “The key imperative for our ecosystem is co-creation,” Hoebus states, “Which necessitates the active involvement of all stakeholders, convening around the drawing board to collectively establish shared objectives.” She further highlights the importance of aligning these goals with the European guidelines, while avoiding stricter local implementation. As this latter tendency can impede optimal progress and hinders innovation in Belgium.
There are also technical hurdles which need to be overcome. “A major challenge we face today is finding a way to couple databases to each other,” says Van Dyck. “We need a common vocabulary to allow them to interact.” This does not mean data have to be shared between parties. “Federated learning is a great way to implement privacy by design,” Peters advocates. “Insights can be shared between stakeholders, without the need to exchange data.” The cross-border Federated Data Networks will help researchers to generate new medical insights based on large patient cohorts and within a very short period of time. These new insights will give patients and physicians more robust information for a more personalized approach of healthcare.
The rising awareness that our patients’ privacy is protected, coupled with an enhanced understanding and education about the potential of RWD, should allow Belgium to regain its position as a global leader in clinical trials. Change doesn’t usually come easy or quick though, and Hoebus stresses the urgency of the situation: “While other countries may have made more substantial advancements, it is not too late for Belgium to catch up. With the momentum still present, it is crucial to seize this opportunity and take action now.”
