After a drug or medical device has been approved, phase IV post-marketing studies are beneficial to monitor safety and long-term adverse events in a real-world setting after market release. This allows collecting data from a large number of patients with a variety of medical conditions. As in the earlier clinical study phases, data collection must be efficient, secure, and safe for the patients. The data is usually collected from various health databases, electronic health records, and patient registries. Yet, many post-marketing studies lack a systematic and controlled investigation with a centralized, long-term approach regarding safety and adverse events. To gather high-quality real world data, long-term studies need to be highly organized, and data must be easily accessible for post-marketing analysis. This can be especially valuable for non-interventional studies.
Scarletred®Vision facilitates the collection of this type of valuable data by providing a single platform to compile the post-marketing information given by physicians and the patients themselves. Physicians can take photos, for instance, of drug-related side effects and the progress of treatment in various skin diseases and injection site reactions (ISR). The only required tool is the Scarletred® skin patch and the app on their smartphone. Another critical aspect besides product-related side effects is evaluating the patients’ quality of life. This is possible by implementing standardized and validated questionnaires documenting electronic patient-reported outcomes (ePROs), which can be accessed digitally or provided during your next check-up. With Scarletred®Vision, individual data is collected using a unique QR code, which links the patient images and ePROs for valuable real-world data. Honoring the strict General Data Protection Regulations, the QR code ensures that patients stay anonymous and their medical data is stored safely. This application for post-marketing studies is a highly efficient approach and can easily be implemented on a large scale to provide high-quality data.
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