Currently the demand for digitalization is dramatically increasing due to its ability to improve the quality of healthcare services provided to patients. The reshaping of clinical trial design, thus the decentralization of clinical studies, is not only beneficial for pharma companies and professionals, but also for patients. The virtualization and remote medical practices enables easier documentation and monitoring while has the potential to make the recruiting process more efficient. SCARLETRED´s technology offers features which assists this shift of clinical trial designs, offering an objective and standardized way of documenting, monitoring and assessing visible skin changes and lesions, contributing to the development of new products.
Why decentralized clinical trials?
The increasing demand of digitalization is primarily due to the current pandemic, however, also because of its potential in improving healthcare services. The implementation of digital technology and AI in clinical trials can be beneficial for pharma companies and facilitate the reshaping of clinical trial designs. In traditional clinical trials, participants are required to make frequent in-person visits to a clinic or hospital. On the other side, the virtualization of some aspects of trials or remote medical practices is allowing researchers to recruit patients, obtain informed consent and monitor safety.
As part of the decentralization of clinical trials, the professionals are able to collect data without the subjects having to leave their home, which is one of the main factors to consider when implementing a new study design where remote monitoring is desired. The use of AI enables healthcare service providers to improve patient management processes through remote monitoring, ensuring access to day-to-day care and creating a wider information base for clinical decision making. Next to the benefits for the patient, digitalization of clinical studies also potentially improve the accessibility to care and collaboration among professionals, while optimizing the use of their time and encouraging innovation in therapeutic treatment plans.
Using AI to reshape clinical trial designs
In a clinical setting, having a reliable, accurate and comparable method for the assessment of disease severity has utmost importance. SCARLETRED is a digital health company introducing standardization and objectivity in the process of skin imaging and assessment.
Scarletred®Vision automatically standardizes the acquired images taken with the app, overcoming inaccuracies resulting from the subjectivity of the currently available assessment tools and methods. Due to compliance to the highest international standards, it is a software well accepted by regulatories for usage in clinical trials worldwide.
SCARLETRED’s CE medical device certified technology has the potential to accommodate this shift in clinical trial designs in compliance with the present restrictions and regulations and at the same time, it encourages virtualization and the use of novel AI tools in clinical research. This way, using decentralized clinical trial design, CROs and sponsors are able to remotely monitor their patients taking part in a clinical trial as well as making it possible to engage with a more diverse patient population, accelerate recruiting and lower the burden of participation.
Features of SCARLETRED´s technology accommodate the shift of clinical study design
Even though every clinical trial has different requirements, patient groups and end points, this innovative technology offers a powerful and objective solution for the standardization of documentation and monitoring of visual changes on the skin surface. Using this technology, innovative trial design can be created with potential to improve patient experience as well as compliance, to act as recruitment and retention tools, and to establish novel endpoints in clinical studies.
The scope of usage of SCARLETRED®Vision can range from local medical routine applications or small, local single-centric clinical trials, up to large international multi-centric cooperative activities between different hospitals and/or within clinical trial stages I-IV.
The system consists of a mobile app, a web platform and skin patches. Skin patches can be easily applied even on sensitive skin types and are automatically detected by the software and the reference colors on the patches are automatically analyzed by the technology in order to eliminate exposure variations, which otherwise would influence data quality. In the context of clinical trials, the resulting color intensities of individual marker regions can be objectively compared with the observed skin regions and precisely quantified over various observation periods.
The app, together with the platform that serves as an analytical and documentation tool for the digital information and image documentation acquired during clinical trials, while in compliance with the highest international regulatory and safety standards. Due to the broad range of applications offered by the GCP grade and ISO13485 certified technology of the software, it is feasible to be used in clinical settings without risking breaches in conformity with regulatory standards. Moreover, it creates a secure data storage and information flow between patients and professionals, benefiting the quality of healthcare service provided. Data security and anonymity is ensured by the generation of unique and personalized Scarletred®QR code.
Importantly, each subject can be individually assigned into groups with the help of the platform outlining the effectiveness of documentation in a clinical setting. The platform provides optimized analysis of the severity of visible skin changes and lesions through the integrated remote data monitoring and the built-in customizable service tools. In the analytics and measurement tools, the surface area of the area of interest (AIO) can be quantified as well as the standard erythema value (SEV), lightness, redness (+a*) and yellowness (+b*) of the skin lesion or change. Moreover scores can be assigned to the whole image or single areas per image, which can contribute to a more focused analysis for clinical use.