Dermatology clinical trials are not much more different than those of other therapeutic areas, but they have certain unique characteristics. Topical drug development makes up a large portion of clinical research for treating skin diseases, but because these are often combined with other topical vehicles (whether placebo or another inert emollient), the placebo rates might be higher in dermatology than in other specialties. Data management strategies ensure consistent and reliable quality of clinical trial data, otherwise sponsors and contract research organizations (CROs) would run into notable data quality risks. Without choosing the right data management plan with appropriate advanced statistical methods, a clinical study cannot accurately demonstrate the safety and efficacy of an experimental dermatological drug.

Several solutions have emerged at the forefront of data management innovation within dermatology clinical trials. In this article, we will delve into various digital tools—from AI-powered data cleaning to wearable devices—that are streamlining patient data collection, improving trial efficiency, and enhancing patient outcomes in this therapeutic area. Continue reading to discover how these advancements are furthering clinical research in dermatology and driving more accurate, efficient, and patient-centered clinical trials!

 

1. Artificial Intelligence for Efficient Data Cleaning Processes in Clinical Trials

Dermatology clinical trials, like all other studies, generate massive volumes of data throughout their lifecycle, which then must undergo data cleaning before they can support clinical decision-making. Data cleaning involves identifying and correcting or removing errors, inconsistencies, and inaccuracies in datasets, making this a fundamental process for interpreting study results accurately. If done manually, this process can be tedious, time-consuming, and prone to human error for data scientists, but the advent of artificial intelligence (AI) offers a more efficient alternative solution for these clinical trials.

Probabilistic programming is a subspeciality of AI technology that uses statistical methods to infer uncertain statements. In 2021, MIT researchers introduced a digital system called “PClean,” which uses several probabilistic programming languages and a knowledge-based approach to automate the data cleaning process. This article explains that users can provide PClean with background knowledge about the dataset, such as correct study data in the case of clinical trials, and how it can be corrupted with errors. PClean’s repairs are based on Bayesian reasoning, an approach that weighs alternative explanations of ambiguous data by applying probabilities based on prior knowledge to the data at hand.

A previous analysis estimated that inaccurate data could be costing the US as much as 3.1 trillion dollars a year, highlighting a significant opportunity for AI-powered data cleaning tools to streamline the process. Syneos Health, another CRO, reported that using AI and machine learning (ML) technologies for data capture cleaning can reduce the time burden of manual data cleaning by more than 3000 hours. Systems like PClean, Quadient Data Cleaner, and many others can be applied in dermatology clinical trials to run data cleaning and validation tasks in real-time for even the most complex and large-scale datasets.

Read our recent article here to discover the role of AI in clinical research.

 

2. Patient Wearable Devices Enable Real-Time Data Monitoring

There is currently no standardized methodology for tracking disease progression and treatment response in dermatology clinical trials. More importantly, many skin conditions deeply affect a patient’s quality of life but measuring this impact can only be done using patient-reported psychometric outcomes. With mobile health (mHealth) and wearable technology advancing rapidly in recent years, more clinical trials are now able to integrate these tools into dermatological research to collect real-time continuous data on a patient’s skin health.

Smart devices worn on the body can now monitor a wide range of parameters relevant to dermatology, including UV exposure, skin hydration levels, and even early signs of inflammatory responses. Sponsors and CROs can implement these devices during a study to remotely collect longitudinal data (including day-to-day fluctuations) on participants’ skin conditions, providing patients with greater convenience. The use of electronic devices also provides new opportunities to introduce standardization procedures into data collection for dermatology clinical trials, making robust inter-study analyses possible.

Furthermore, these devices enable a dermatology study to collect a wider variety of data from which ML algorithms can be applied to gain insights, such as identifying groups who are high-risk for flare-ups of their psoriasis or eczema, detecting optimal patterns to improve treatment adherence, or leveraging these data to make predictions of disease outcomes. These devices, with or without the use of AI/ML tools, are applicable broadly to several skin conditions and have demonstrated their potential for promoting personalized, data-driven dermatology clinical trials.

 

3. Stringent Data Security Regulations Must Safeguard Patient Data

Dermatology clinical trials are unique in that they often rely heavily on visual data in the form of photographs of participants to represent disease progression and treatment responses. Without the appropriate safeguards in place, these clinical images have the potential to infringe on a patient’s right to data security and privacy, as well as cross the ethical boundaries of patient-physician interactions. In the United States (U.S.), the most notable regulation governing patients’ rights to privacy is the U.S. Health Insurance Portability and Accountability Act (HIPAA).

The HIPAA provides valuable guidance for sponsors, CROs, and data-related stakeholders to remain compliant with best practices for stringent data security in dermatology clinical trials. For example, to protect a patient’s identity in clinical photographs, the images should be cropped to remove nonessential identifying details, such as clothing, tattoos, birthmarks, or jewelry. This must be done so that the cropping does not remove any diagnostic disease features or details of response to treatment. This 2023 article by Shinkai et al. also notes that masking or blurring the eye region in photographs does not assure anonymity and is not acceptable.

In terms of promising digital tools, blockchain technology could provide a reliable framework for securely storing and sharing medical records, clinical trial data, and research findings. Blockchain is a decentralized, unalterable digital ledger of all data transactions, ensuring a greater level of transparency and traceability in dermatological research. Importantly, blockchain’s cryptographic protocols allow for secure sharing of sensitive patient information among authorized parties while maintaining strict control over access rights. Learn more about how blockchain technology is improving clinical research quality here!

 

4. Pursuing Data Management Solutions that Offer Scalability Across Therapeutic Areas

Dermatology is not the only therapeutic area that can benefit from the latest innovations in data management solutions. As the clinical trials industry increasingly implements more efficient methods of data collection, analysis, and storage, the ones with the greatest potential are those that offer scalability across other medical specialties. For example, the earlier article about MIT’s PClean explained that this AI-powered data cleaning system uses generic common-sense models that can be customized to various databases and types of errors.

Image-recognition technology in ML deep learning models that can glean insights from clinical images of dermatological diseases can also be applied to fields like radiology or diagnostic imaging to enable more accurate diagnoses. Similarly, wearable devices and mHealth are already being utilized in cardiology to track patients with heart conditions in real-time, enabling faster medical intervention in the case of an emergency. As these technologies are implemented across different fields, they can create a more interconnected healthcare ecosystem that enhances the quality of care in individual specialties beyond dermatology.

 

The Future of Data Management in Dermatology Clinical Trials

As we have discussed in this article, there are several new opportunities for innovation in data management technology within dermatology clinical trials. However, realizing the full potential of these technologies will require overcoming several hurdles. First, one of the challenges of utilizing available databases to their fullest extent is the challenge that lies in standardizing data formats and protocols across different systems and institutions. Although AI/ML data cleaning systems may alleviate interoperability issues caused by this limitation, additional measures may be required to set standards for data collection and formatting in clinical trials.

Concerns about data security and privacy must also continue to be addressed, especially as emerging technologies enable greater data collection from patients. Providing training and education to physicians, researchers, and patients about devices like wearables, AI-based data management tools, and the latest updates to regulatory policies can better safeguard patient data in clinical trials.

Interested in learning more about the latest trends emerging this year in dermatology clinical research? Visit this article here!

Despite these challenges, the technologies and solutions discussed in this article present an exciting future in dermatology where the drug development process is increasingly streamlined, more personalized, and more secure. As these technologies continue to evolve, sponsors and CROs may also be able to promote new data management standards in therapeutic areas beyond dermatology clinical trials.

 

TFS CRO: Your Industry Partner in Data Management

At TFS HealthScience, we understand that strong data management is the foundation of a successful clinical trial. Our Data Management & Biostatistics team combine cutting-edge technology with seasoned expertise to deliver unparalleled accuracy and efficiency in clinical trials for clients worldwide. From study design to database lock, you can expect data integrity, compliance with regulatory standards, and seamless integration across all study phases. Our adaptive approach allows us to tailor our services to your unique needs, ensuring that your data not only meets but exceeds industry standards.

Ready to transform your clinical data management and biostatistics experience? Contact a TFS representative today or visit our website to learn what solutions we can offer for your next clinical trial!