The importance of real-world evidence in the early detection and treatment of chronic diseases
Published: 10.01.2024
Insights from Philipp Thiele, Commercial Lead of intermedix and Co-Founder and Managing Director of docmetric, part of CompuGroup Medical
Mr. Thiele, could you give us a brief introduction to the concept of Real-World Evidence (RWE) and explain why it is becoming particularly important in modern medicine?
Real-World Evidence (RWE) is based on the analysis of real-world data (RWD), for example from electronic patient records, health insurance and billing data or wearables, to generate clinical evidence. As they allow a broader, more diverse and more realistic view of the efficacy and safety of treatments in the general population, RWE studies are a valuable addition to the findings from randomized controlled trials. They also allow the observation of long-term effects and help to conduct cost-benefit analyses, which is highly relevant for decision-makers in the healthcare sector. RWE can also reveal differences in the effect of treatments on different patient groups. Health authorities such as the FDA and EMA are increasingly recognizing the value of RWE for regulatory purposes, which can accelerate the approval of new therapies and the expansion of existing indications.
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With the help of CGM and Alcedis, AstraZeneca conducted the InspeCKD study, in which anonymized health data from the CGM research panel docmetric was examined. What were the findings?
In Germany, the diagnosis of chronic kidney disease is often only made at an advanced stage. Patients suffering from high blood pressure, diabetes mellitus and/or cardiovascular disease are at increased risk and should therefore be regularly screened and monitored in accordance with the guidelines. As part of the InspeCKD study, anonymized electronic patient data records from these risk groups from German GP practices were analyzed. The results of the RWE study indicate inadequate laboratory diagnostic care for patients at increased risk of CKD and underline the importance of intensified education and implementation of guideline recommendations in GP care in order to improve early detection and treatment of CKD.
In your opinion, which regulations and technological innovations are crucial for optimizing the collection and analysis of RWE data?
First of all, we need standardized data formats and protocols to bring together and analyze data from different sources. This data should be collected in cloud-based platforms in order to be able to store and process large volumes of data. Of course, this requires binding rules. The FDA and EMA have already started to develop corresponding guidelines. These guidelines help to establish clear criteria and methods for the collection, analysis and interpretation of RWE data. Artificial Intelligence (AI) and Machine Learning (ML) are already being used to identify patterns and trends in large data sets. Both can help to improve the efficiency of treatments and enable personalized medicine. Natural language processing (NLP) could play a particularly important role with unstructured data from text sources such as clinical notes, doctors' reports and patient feedback. The expanded bandwidth of the data can further improve RWE analysis. Last but not least, blockchain also offers a secure and transparent way to verify and manage healthcare data.
What are the biggest challenges in integrating RWE into clinical practice, especially in terms of data quality and data protection?
RWE data comes from various sources such as electronic health records, health insurance data, patient registries and wearables. These data sources use different formats, terminologies and quality standards, making data integration and comparability difficult. Data from the real world can also be incomplete, incorrect or contain biases, which is why data transformation is extremely important. When it comes to data protection, patient privacy is of utmost importance. The collection, storage and analysis of RWE data must therefore comply with legal data protection requirements. All data must be anonymized. In addition, patients should be fully informed about the collection and use of their data. Patient trust can only be maintained through great transparency and clarity in communication.
What future developments and trends do you see in the use of RWE to improve healthcare?
I see several promising developments here. The integration of Artificial Intelligence (AI) and Machine Learning (ML) will further advance the analysis of RWE data by enabling more accurate predictions and personalized treatment plans. In addition, wearables and mobile health devices will become increasingly important to continuously collect real-time data, enabling closer monitoring and better health interventions. Furthermore, interoperability between different healthcare systems and data sources will be improved through standardized data formats and APIs, resulting in more comprehensive and coherent data sets. I also believe that patient participation in data collection and use will increase through patient-centric approaches and platforms. This will build trust and lead to a more active role for patients. Regulatory authorities will increasingly involve RWE in their decision-making processes, which may lead to faster approvals and wider adoption of new therapies. Finally, the use of blockchain technology will increase data security and transparency, ensuring the integrity of the data collected. All of these developments together will significantly improve the quality and efficiency of healthcare.