Exploring the reuse of clinical data for advanced AI pharma research purposes
By Anna Choma
Accessing medical data for research purposes can be exceedingly challenging, just to mention privacy concerns and regulatory restrictions. Striking a balance between protecting patient confidentiality and enabling valuable research is a complex and ongoing dilemma in the healthcare field. However, as a company providing custom algorithms development services with a research team onboard, we believe in an obligation to maximize the value and utility of this resource. The reuse of clinical data sets can provide significant benefits to both patients and sponsors. Reusing clinical data has been recognized for decades as a valuable approach to improving healthcare outcomes and reducing costs  and it is still an area of increasing interest across the pharmaceutical industry.
Clinical data reuse – accelerating clinical research
Using clinical trial data in drug discovery and other health-related scientific investigations boosts the process of discovering and advancing new, improved treatments, ultimately improving patients’ lives and benefiting society as a whole.
Clinical data reuse can accelerate clinical research by expediting patient recruitment, enabling hypothesis testing, providing cost-effective access to a broader range of clinical information for research applications, and running feasibility studies to use historical data in clinical trials. But what are the other goals you may achieve by clinical data reuse or secondary use?
Goals you may achieve by secondary use
The European Federation of Pharmaceutical Industries and Associations (EFPIA) in its framework for secondary use of clinical data  provides examples of health research purposes for which clinical trial data might be re-used:
- to explore novel hypotheses that would otherwise necessitate launching a new research study involving medical interventions on patients. These fresh hypotheses might focus on investigating alternative treatments or enhancing our comprehension of disease mechanisms, among other possibilities
- for regulatory purposes, such as conducting safety studies at the request of authorities and for research (often done in collaboration) involving the pooling of clinical trial data from multiple studies to support in treatment evaluation and drug development.
- to enhance the efficiency, design, and methods of future clinical trials as well as to allow independent researchers to validate or scrutinize the original results
- and last but not least: to create and test new healthcare technologies, such as AI-based algorithms.
In particular, in pursuit of this final objective, we bring extensive expertise to the table. Throughout the years, we’ve refined our skills and deepened our knowledge in utilizing clinical data to propel the creation of state-of-the-art AI solutions.
Clinical data reuse to create and test AI-based algorithms
Particularly when it comes to the aforementioned objective, we possess a wealth of extensive expertise and knowledge, just to mention projects described in scientific papers.
- We introduced a complete algorithm for the automated detection of cirrhosis using CT that additionally benefits from the clinically inspired and radiomic features.
Check out our joint paper: Krzysztof Kotowski, Damian Kucharski, Bartosz Machura, Szymon Adamski, Benjamín Gutierrez Becker, Agata Krason, Lukasz Zarudzki, Jean Tessier, Jakub Nalepa, Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features, Computers in Biology and Medicine, Volume 152, 2023, 106378, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2022.106378
- We developed an algorithm that in the first step identifies tumor sub-regions such as the enhancing tumor, peritumoral edema, and surgical cavity, and then calculates volumetric and bidimensional measurements based on the current Response Assessment in Neuro-Oncology (RANO) criteria.
Check out our joint paper: Jakub Nalepa, Krzysztof Kotowski, Bartosz Machura, Szymon Adamski, Oskar Bozek, Bartosz Eksner, Bartosz Kokoszka, Tomasz Pekala, Mateusz Radom, Marek Strzelczak, Lukasz Zarudzki, Agata Krason, Filippo Arcadu, Jean Tessier, Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients, Computers in Biology and Medicine, Volume 154, 2023, https://doi.org/10.1016/j.compbiomed.2023.106603
With a proven track record of success in the field, we’re excited to collaborate with like-minded partners and continue our mission of pioneering AI-driven innovations that have a tangible impact and tackle complex challenges.
AI algorithms and clinical data-driven innovations
Reusing clinical data to create and test new healthcare technologies, especially AI-based algorithms, has the potential to improve patient care, increase healthcare efficiency, and advance medical research. Are you ready for secondary use of the data to drive groundbreaking research and innovations? Our research team is comprised of experts from diverse fields – data scientists, clinicians, engineers, and more. We are not just developing algorithms; we have experience in applying them in real clinical settings.
If you’re excited about the prospect of reusing clinical data and powering it with AI, reach out. Your team’s expertise, combined with our technological know-how, will be a dynamic force for change. Let’s design and fine-tune AI-based algorithms together to predict, diagnose, or recommend treatments for various medical conditions.
 Safran C, Bloomrosen M, Hammond WE, Labkoff S, Markel-Fox S, Tang PC, Detmer DE, Expert Panel. Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. J Am Med Inform Assoc. 2007 Jan-Feb;14(1):1-9 https://doi.org/10.1197%2Fjamia.M2273. Epub 2006 Oct 31. PMID: 17077452; PMCID: PMC2329823.