We are passionate about curating the world's clinical development data to elevate accessibility, transparency, and raise the standard of clinical trials.
We see insight retrieval as a cornerstone of data curation. ZETTAI, powered by GenAI technology, unlocks the hidden potential of clinical development data. It enables effortless navigation through vast datasets, performs advanced data analysis, and retrieves deep, meaningful insights, while ensuring exceptional accessibility.
Clinical development data represents a vital knowledge base of fundamental clinical research on the human body, crucial for benefiting all of humanity. We are building robust data models, enhancing data quality, and cleaning, organizing, enriching, and contextualizing this invaluable information.
Supporting it all is our cloud-based big data platform: CLINICA. It organizes and processes clinical development data on a previously unprecedented scale, leveraging cutting-edge technologies. CLINICA is capable of storing and retrieving vast amounts of related documents quickly and efficiently.
Unlocking the future of clinical development data with GenAI
Clinical development data has the potential to uncover crucial insights, significantly reducing risk and increasing trial success. ZettAI unlocks this hidden potential, especially as it is supported by a robust data platform and high-quality, high-volume clinical development data. With ZettAI, navigating large, diverse datasets becomes effortless, allowing users to derive valuable insights from extensive and complex trial documents.
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CLINICA is a cutting-edge, cloud-based data platform designed to seamlessly integrate complex clinical development datasets such as clinical trial data, regulatory approvals, disease or hospital data - amongst others.
By utilizing advanced data models and state-of-the-art technologies such as GenAl, CLINICA harnesses the power of big data and non-linear knowledge graphs to unlock the full potential of clinical trial data.
Our innovative platform enables the efficient organization, processing, and analysis of vast amounts of information, providing valuable insights to drive trial success and improve outcomes.
Smart data and insights
drive clinical trial success
Clinical development data is essential throughout the drug development process, providing valuable insights and strategic advantages.
Data fuels
successful
clinical development
Long before a new medical intervention is tested in a clinical trial, the manufacturer makes strategic decisions on the clinical conditions, markets and drug types to focus on. They thoroughly investigate the latest advancements in biomolecular research but also business opportunities, market conditions, and competitor activities.
After that, hundreds of compounds are screened to identify a subset of promising candidates, which are then thoroughly characterized in in-vitro and in-vivo studies to ensure basic safety.
High-quality clinical development data offers a comprehensive overview of current and planned competitor activities, helping companies assess future market conditions, anticipate market launches, and gauge market competitiveness.
By analyzing past, current, and planned clinical research activities across the industry, companies can uncover promising opportunities in the development of new medical interventions.
Utilizing clinical development data for competitive benchmarking provides valuable insights into efficiency and performance metrics, such as study duration, number of arms, and participant count. This enables companies to quantify their strengths and weaknesses, as well as to build and leverage competitive advantages.
Clinical study design is a critical phase at the outset of the clinical development process, where the framework and operations for conducting a clinical trial are meticulously planned. This phase involves defining the objectives, methodology, and logistics of the study to ensure it meets regulatory standards and scientific rigor. Key documents developed during this phase include the study protocol, which outlines the trial's objectives, design, methodology, statistical considerations, and organization. Additionally, the informed consent forms, case report forms (CRFs), and data management plans are prepared to ensure proper documentation and ethical management of participant data.
A well-constructed study design not only lays the groundwork for successful trial execution but also plays a pivotal role in the reliability and credibility of the trial's findings. By anticipating potential challenges and incorporating risk management strategies, this phase helps to safeguard the integrity and validity of the entire clinical development process
In the clinical trial design phase, data plays a pivotal role in shaping a robust and effective study. Historic trial data from previous studies can be utilized to identify risks, establish benchmarks, measure trial complexity and feasibility and identify similar past studies for comparisons. Historical data and documents, such as protocols and forms, and relevant scientific literature are analyzed to inform the selection of endpoints, inclusion and exclusion criteria, principal investigators and optimal dosing strategies as well as develop new protocols and consent forms and identify clinical sites for participation.
Statistical data guides the calculation of sample sizes needed to achieve adequate power and detect clinically significant effects. Additionally, demographic and epidemiological data help identify suitable populations for the study, ensuring diversity and generalizability of results. Data on past recruitment rates of clinical sites and dropout patterns are leveraged to design realistic timelines and mitigate potential challenges.
Site selection and preparation are critical phases in the setup of clinical studies, directly influencing the trial's success. The importance of site selection lies in identifying locations that not only meet the logistical and regulatory requirements but also have the capability to recruit and retain the target patient population. Selecting the right sites involves evaluating their infrastructure, access to suitable participants, experience in conducting similar trials, and ability to adhere to the study protocol. Effective site preparation includes training the site staff, setting up necessary equipment, and ensuring compliance with regulatory and ethical standards.
Thorough site selection and preparation are crucial for minimizing trial delays and ensuring that data collected is both reliable and of high quality. By fostering strong communication and collaboration with site teams, sponsors can enhance trial efficiency and optimize the likelihood of achieving successful outcomes.
Clinical study data is invaluable in the site selection process. Historical performance data from previous trials can highlight sites with strong track records in patient recruitment, retention, and data accuracy. Leveraging historic participant recruitment rates helps ensure targets are met, while data also provides insights into site experience with desired medical conditions. Additionally, identifying planned and ongoing competing studies helps avoid or mitigate recruitment risks.
Data analytics can assess the demographic and epidemiological characteristics of potential sites to ensure they match the study's requirements. Predictive modeling will forecast site performance and identify potential challenges, enabling proactive mitigation strategies. Additionally, data from feasibility studies can provide insights into the operational capabilities of sites, ensuring that only those with the necessary expertise and infrastructure are chosen.
The phase of participant recruitment is pivotal in the clinical trial process, directly impacting the validity and success of the study. This phase involves identifying, screening, and enrolling participants who meet the study's eligibility criteria.
Ensuring informed consent is a crucial step, where potential participants are thoroughly educated about the study's purpose, procedures, risks, and benefits, allowing them to make a well-informed decision about their participation. Once participants are enrolled, randomization is employed to assign them to different study groups in an unbiased manner, ensuring the integrity of the study and reducing selection bias. Effective patient recruitment is essential for achieving the required sample size, maintaining study timelines, and ensuring the reliability of the study outcomes.
By leveraging clinical study data, sponsors can improve patient recruitment and enrollment efficiency, enhance participant retention, and ensure ethical and effective randomization processes.
Historical recruitment rates and site enrollment performance metrics help identify candidate sites and set expectations for the pace of patient enrollment in the study. If recruitment proves challenging, this data can assist in identifying additional sites with promising recruitment rates.
Participant demographics can guide targeted recruitment strategies and pinpoint regions with higher prevalence of the medical condition under study.
Data analytics can assess and predict recruitment challenges and participant dropout rates, enabling proactive adjustments. Monitoring competing recruitment activities by other sponsors and potentially interfering competitor studies helps mitigate recruitment risks.
Predictive modeling can forecast site performance, while GenAI-based technologies can streamline the generation of informed consent documents based on previous trials.
Once a clinical study is underway, the focus shifts to the operational phases of clinical trial monitoring, data collection, and quality assurance. Clinical trial monitoring involves regular oversight of the trial's progress to ensure compliance with the study protocol, regulatory requirements, and ethical standards. Monitors conduct site visits to verify the accuracy and completeness of the data, ensuring that the trial is conducted according to the approved plan. This process includes checking informed consent forms, reviewing source documents, and confirming that adverse events are reported accurately. Effective monitoring helps identify and address any issues early, ensuring the integrity of the trial and the safety of participants.
Additionally, robust data collection processes are vital for maintaining the credibility of the trial's outcomes, as they ensure that the information gathered is reliable and can withstand regulatory scrutiny. Quality assurance measures, such as audits and independent reviews, further safeguard the trial's validity, helping to build confidence in the final results.
Clinical study data aids in monitoring ongoing or planned competitor activities that might directly impact participant recruitment. This data can help foresee when a competitor trial may be launched at a participating hospital, potentially posing challenges for patient recruitment. Additionally, it enables the creation of participant enrollment projections to establish benchmarks, making it clear early on if a study falls behind recruitment expectations. Furthermore, the data helps identify potential competitive situations within a competitor's portfolio.
Data collection and quality assurance are fundamental to the success of ongoing clinical trials. Accurate and timely data collection is achieved through the use of electronic data capture (EDC) systems, which streamline the process and reduce the risk of errors. Rigorous quality assurance procedures, such as data validation and audit trails, are implemented to maintain data integrity. Regular data reviews and interim analyses are conducted to ensure that the collected data meets predefined quality standards and to make any necessary adjustments to the trial. These processes are crucial for generating reliable and valid results, which are essential for regulatory submissions and for advancing medical knowledge.
To obtain marketing authorization, new drugs and medical devices require a New Drug Application (NDA). Generic drugs use an abbreviated NDA. For devices, a different type of submission is required. This can either be a 510(k) submission if there is a similar device that has already received marketing authorization, a De Novo submission for low to medium risk devices, or a Pre-Market Authorization (PMA) for Class III, high-risk devices.
Once a compound is validated through clinical studies, the approved drug must be monitored as it is used by a significantly broader population. Post-market surveillance ensures ongoing drug safety and efficacy through robust pharmacovigilance and proactive risk management. This includes adverse event reporting, additional studies, and comprehensive data analysis to identify and mitigate risks, thereby safeguarding public health and ensuring market success. Ongoing monitoring of critical data and engagement with regulatory agencies and healthcare professionals helps to promptly address any emerging safety concerns and adjust safety protocols as needed.
Clinical study data plays a crucial role in the regulatory submission process for new drugs and medical devices by providing evidence required for New Drug Applications (NDAs) and various device submissions such as 510(k), De Novo, or Pre-Market Authorization (PMA). This data supports the demonstration of safety, efficacy, and performance, which is essential for obtaining marketing authorization. Additionally, once a compound is approved, clinical study data aids in post-market surveillance by facilitating ongoing monitoring of drug safety and effectiveness through robust pharmacovigilance, adverse event reporting, and risk management.
GenAI can significantly enhance this process by automating the drafting of essential documents for regulatory submissions, such as NDAs and device applications. It can generate comprehensive and accurate reports based on clinical study data, streamline the preparation of post-market surveillance documentation, and assist in developing risk management plans. By leveraging advanced algorithms, GenAI ensures that the documentation is both thorough and compliant with regulatory requirements, ultimately supporting a smoother and more efficient submission and monitoring process.
Our team consists of seasoned professionals with a wealth of experience in the life-science and pharmaceutical sectors. With decades of collective knowledge under our belts, we bring a diverse skill set and a passion for driving innovation in the industry.
Dr. Philipp Diesinger is a Data and AI enthusiast with a career dedicated to serving clients in the life science sector. He is driven by his passion for leveraging data-driven decision-making to deliver tangible results with real-world impact.
Stefan Stefanov is a seasoned software engineer with over 7 years of experience in leading and developing Data and AI solutions for the life science sector. He is passionate about transforming intricate data into user-friendly, insightful visualizations.
Dr. Andreas Thomik is a data scientist with nearly a decade of experience in leveraging data and AI to generate business value across multiple industries, with a particular focus on the life sciences field. He is dedicated to advancing innovative data-driven strategies within the sector.
Works as a data and technology expert in the pharma industry, solving problems across the pharma value chain.
Dr. Peter Roche is a data expert in the life science sector, specializing in data systems architecture, and backend development. He has 10 years of experience as a quantitative analyst in capital markets and asset management, and an additional 8 years as a senior data engineer and data scientist.
Experienced Data and Backend Engineer with deep expertise in cloud technologies, automation, system integration, end-to-end data pipelines.
Dr. Christian Schilling is a Managing Partner at AVANTECA Partners AG, a privately held asset management company based in Basel, Switzerland. He is a former senior executive of Boehringer Ingelheim and has over 25 years of experience in Big Pharma, including strategic and operational roles in research, clinical development, marketing and sales as well as country management. He also held executive roles in business development and M&A.
Simone Menne, current President of the American Chamber of Commerce is a German business leader. She was CFO of Lufthansa from 2012 to 2016. From September 2016 until end of 2017, she was a member of the management board of Boehringer Ingelheim, responsible for its finance division. Simone is currently a supervisory board member and non-executive director at several companies and runs an art gallery in Kiel, Germany.
Ning Li is a successful entrepreneur best known as the founder of Made.com, an online furniture retailer that transformed the industry with its direct-to-consumer model. He co-founded Made.com after launching MyFab.com to enhance online furniture shopping. Following this success, Ning founded Typology, a skincare brand focused on transparency and affordability. With a proven track record in identifying market opportunities and scaling startups, Ning continues to innovate in the consumer goods space.
Experienced Data and Backend Engineer with deep expertise in cloud technologies, automation, system integration, end-to-end data pipelines.
Feb 05, 2025
Patient enrollment in clinical trials is not just a logistical metric - it’s a window into how innovation, competition, and healthcare priorities evolve over time. Global trends across medical conditions demonstrate that enrollment figures reflect broader industry movements and external factors.
Over the past decade, oncology trials - especially for conditions like multiple myeloma and breast cancer - have seen almost explosive growth in patient enrollment. This surge has been fueled by groundbreaking advances in immunotherapy and personalized medicine. Peaks in enrollment often coincide with regulatory approvals or major scientific milestones, followed by declines as competition intensifies and trials compete for the same patient pools.
For diseases like chronic kidney disease (CKD), enrollment has seen steady growth in recent years. This reflects not only a growing recognition of the global burden of such diseases but also advances in therapies addressing high-unmet needs.
Conditions such as asthma and diabetes mellitus have seen flat or declining enrollment. This trend is not due to reduced prevalence but rather market saturation of effective therapies, fewer novel breakthroughs, and challenges in demonstrating significant advantages over existing treatments. Chronic obstructive pulmonary disease (COPD) is a stark example of declining enrollment, likely driven by reduced industry focus and fewer promising innovations in the pipeline.
These changes highlight the dynamic nature of clinical development on a global scale. Scientific and technological advancements, coupled with shifting global health priorities, continue to reshape the clinical trial landscape.
Jan 29, 2025
The focus of interventional studies has shifted significantly across various medical conditions over the past decade.
Breast Cancer and Multiple Myeloma have shown consistent growth in clinical trials. Breast cancer, already a major focus in oncology, continues to benefit from advances in personalized medicine and immunotherapy. Multiple myeloma has seen even stronger growth, particularly between 2016 and 2020, likely driven by breakthroughs in treatment options such as proteasome inhibitors and CAR-T cell therapy, as well as increased funding in hematologic oncology.
In contrast, Diabetes Mellitus and Chronic Kidney Disease (CKD) have experienced a steady decline in research activity. This could reflect a maturing market with numerous established therapies already available, along with potentially limited progress in developing novel treatments. At the same time, there has been an increasing shift towards addressing and managing these conditions through the adoption of lifestyle changes. In addition, resource allocation may be shifting to diseases with greater unmet needs or emerging therapeutic opportunities.
Asthma and COPD, both chronic respiratory conditions, showed initial growth followed by significant declines after 2015. This might suggest that research in these areas has reached a plateau, with existing therapies addressing much of the patient population's needs. It may also be the result of a shift toward broader initiatives in respiratory medicine, such as addressing environmental triggers and co-morbid conditions.
Overall, these trends provide a snapshot of how research adapts to scientific progress, patient needs, healthcare priorities as well as market dynamics. They reflect the continuous balancing act of addressing unmet medical needs while building on prior successes.
Jan 21, 2025
Over the last two decades, clinical trials on many types of conditions have undergone a remarkable transformation in scope and focus. While the average number of conditions studied per trial has remained stable, the total number of conditions being investigated has grown significantly. This trend signals an important shift toward a steady diversification of clinical research.
The chart illustrates this evolution for heart disease conditions. The blue line shows a steady rise in the total number of conditions studied, reflecting the expansion of clinical research into new and diverse areas. Meanwhile, the red line remains stable, representing the average number of conditions investigated per study.
Why is this happening?
Advances in genetics, molecular biology, and imaging technologies have allowed researchers to identify new subtypes of heart disease. This has expanded the conditions being studied, moving beyond broad categories like "heart failure" or "coronary artery disease" into more granular distinctions (e.g., heart failure with preserved ejection fraction).
With a growing emphasis on tailoring treatments to individual patients, researchers are exploring diverse disease presentations and patient populations to address unmet medical needs. Regulatory incentives such as the Orphan Drug Act and increased funding for rare diseases have encouraged studies into underrepresented and previously overlooked heart conditions.
Aging populations and rising rates of comorbidities like diabetes, obesity, and chronic kidney disease have made it essential to study heart disease in the context of overlapping health issues. This complexity naturally increases the range of conditions studied in trials.
The shift toward individualized treatment strategies has spurred research into unique patient subgroups and niche conditions, diversifying trial designs and target populations.
The expansion of clinical trials into diverse geographic regions has also brought attention to heart disease conditions that may be more prevalent or uniquely expressed in specific populations.
This diversification can be seen as a positive development for patients and the healthcare system. By broadening the scope of research, we can uncover new insights, develop targeted therapies, and improve outcomes for a wider range of patients. As the field continues to expand, the challenge will be balancing innovation with the need for efficient, well-designed trials. A dynamic, collaborative approach will be key to translating this diversity into meaningful clinical impact.
Jan 14, 2025
The clinical development landscape is undergoing significant changes, and one of the most impactful trends is the growing adoption of mixed-phase studies (e.g., Phase I/II or Phase II/III). These innovative designs are reshaping therapeutic development, offering a more efficient, cost-effective, and patient-centric approach to clinical trials.
Mixed-phase studies accelerate the path to approval by combining exploratory and confirmatory phases into a single protocol. For instance, early safety signals in a Phase II/III study can lead to immediate transitions into confirmatory endpoints, saving valuable time. Biomarker-driven studies often benefit from mixed-phase designs, as early efficacy insights can directly inform later-stage cohorts. This approach aligns well with the focus on precision medicine and targeted therapies. Conducting fewer, more integrated studies reduces administrative and operational costs. Shared resources, such as infrastructure and trial sites, further enhance efficiency. By reducing the need for re-enrollment and separate consent processes, mixed-phase studies minimize participant burden. These designs also support improved patient retention and continuity.
Despite their advantages, mixed-phase studies require rigorous planning and execution. Key challenges include navigating regulatory expectations, managing complex protocols, and ensuring robust interim analysis frameworks. Success depends on cross-functional collaboration and leveraging modern technologies to support adaptive trial designs.
Regulatory bodies, including the FDA and EMA, have provided guidance to support seamless and adaptive trial designs. These frameworks are particularly suited for therapies addressing unmet medical needs, rare diseases, or accelerated pathways. They also encourage sponsors to embrace and adopt mixed-phase designs, enhancing flexibility and efficiency in clinical trials.
The data tells a compelling story: mixed-phase studies (Phase II/III) have shown remarkable growth over the past two decades. From representing merely 10% of studies in 2000, these hybrid trials now constitute 31% of all studies in 2024.
Jan 07, 2025
The oncology field has seen significant advancements in clinical research over the past two decades, as highlighted by the attached charts depicting clinical trial activity for Carcinoma and Leukemia.
Clinical research activity in Carcinoma has grown consistently, with over 15,000 studies underway by 2024 and nearly 500,000 participants currently involved globally. This growth reflects the increasing focus on multi-phase trials targeting both common and rare carcinomas, leveraging advances in targeted and combination therapies. While on a smaller scale, leukemia research has seen steady growth, peaking at around 1,500 trials and 30,000 participants. Emphasis has been placed on immunotherapies (e.g., CAR-T cells) and precision approaches targeting specific molecular pathways.
Cancer remains one of the leading causes of morbidity and mortality globally, necessitating ongoing investment in preventative measures and novel treatments. Research into genetic biomarkers and individualized therapies has made oncology a fertile ground for innovation. Expedited approval pathways for oncology drugs and increased regulatory incentives have spurred development. Oncology continues to be one of the most heavily funded therapeutic areas, reflecting both its societal importance and the potential for return on investment.
However, the increasing complexity of oncology trials, particularly in later phases, can place significant logistical and financial demands on participants. Despite ongoing innovations, disparities in access to these advanced therapies persist across regions and populations. Additionally, there is concern about the over-concentration of studies in similar therapeutic areas, potentially leading to inefficiencies and participant shortages.
The growing trends in oncology trials observed in the charts underscore the field’s critical importance to global health and its pivotal role in driving pharmaceutical innovation. Yet, they highlight challenges regarding the sustainability of participant recruitment and the alignment of clinical research resources to support the increasing demand for trial diversity and innovation.
Dec 17, 2024
Over the past two decades, clinical development in asthma and chronic obstructive pulmonary disease (COPD) has shifted from generalized approaches towards precision medicine. This evolution is marked by advances in biomarker identification, targeted biologics, inhalation technology, and real-world data integration.
Biomarkers such as blood eosinophil counts, fractional exhaled nitric oxide (FeNO), and genetic markers have become essential for identifying patient subtypes and predicting responses to biologic therapies. The concept of endotypes and phenotypes now guides clinical trial design, allowing targeted interventions for specific patient groups like eosinophilic asthma or neutrophilic COPD. This has paved the way for monoclonal antibodies (mAbs) like mepolizumab, benralizumab, and dupilumab, targeting inflammatory pathways such as IL-5, IL-4R, and IL-13. Dual-target biologics are being explored to address overlapping inflammatory pathways in asthma and COPD.
New drug classes, including PDE4 inhibitors, JAK inhibitors, and BTK inhibitors, are being developed to modulate airway inflammation. Simultaneously, advancements in inhalation technologies like soft mist inhalers and breath-actuated devices have improved drug delivery and patient adherence.
Clinical trial endpoints have expanded beyond lung function (FEV1) to include symptom control, exacerbation reduction, and patient-reported outcomes (PROs). Regulatory bodies have created accelerated approval pathways for drugs addressing unmet medical needs. Trials are increasingly decentralized, using telehealth, wearable devices, and home spirometry for remote monitoring.
Efforts to increase diversity in clinical trials are addressing disparities, as asthma and COPD disproportionately affect lower-income and minority populations. Data-sharing initiatives like the COPDGene study enable data reuse to generate insights and reduce development time. Digital health tools, including smart inhalers and mobile apps, collect real-time adherence data, while artificial intelligence and machine learning improve patient recruitment and trial optimization.
Real-world evidence (RWE) from electronic health records (EHR) and insurance claims is being integrated with clinical trial data to assess long-term safety and efficacy, driving more personalized and effective treatments.
The charts below present all ongoing interventional clinical studies in asthma (n=4761) and COPD (n=3904) each year, showing trends in study phases and annual participant enrollment. Both charts indicate an increase in early-phase studies with fewer participants since 2019. Approximately 10% of studies overlap between asthma and COPD, with 17 studies among the 100 largest exploring both conditions.
Dec 10, 2024
Over the past two decades, the landscape of global clinical trial registration has expanded significantly. The remarkable increase in trial registrations reflects not only growing regulatory requirements for transparency but also rising investments and innovation in healthcare.
This extensive dataset represents an invaluable resource for understanding human health, global health trends, therapeutic advancements, and emerging areas of medical research. While the data is fragmented across multiple national registries, it holds unparalleled potential for researchers, policymakers, and industry stakeholders. This data can help uncover disease patterns, refine clinical trial design, and accelerate the development of life-saving treatments.
Key regulatory milestones have acted as catalysts for this growth, not only increasing the number of registered trials but also elevating the quality and accessibility of data. Despite these advancements, there remains significant room for improvement. While essential for transparency, these regulations have also introduced additional compliance burdens and complexities for sponsors, particularly smaller organizations.
After years of exponential growth, a slowdown in clinical study registrations was inevitable given the high costs, complexity, and other constraints of clinical trials. The chart shows a decline since 2021, driven in part by the COVID-19 pandemic, which spurred COVID-related trials but delayed other research. Rising regulatory complexity, higher costs, and the shift toward real-world evidence (RWE) studies and decentralized trial models may also be contributing factors.
Organizations must revisit and refine their research strategies to align with emerging dynamics in clinical research.
Dec 03, 2024
Participant recruitment is a cornerstone of clinical trial operations, often determining a study's success. Yet, challenges such as insufficient enrollment, participant dropouts, and ethical concerns pose significant risks to these efforts.
The challenge becomes even more complex when multiple sponsors independently study the same medical condition at the same clinical sites.
To explore how enrollment outcomes may be influenced by the number of “competing” studies—defined as trials investigating the same condition and operating at the same sites—we analyzed a dataset of nearly one million clinical studies. Our analysis focused on the “recruitment discrepancy”, or the difference between estimated and actual enrollment, across studies in various therapeutic areas. Here, we illustrate this effect with data from 9,336 breast neoplasm studies initiated after 2000.
The results are interesting:
Our findings suggest a potential statistical relationship between the number of competing trials and elevated recruitment risks. While this is an anecdotal analysis, it underscores the critical need for strategic site and participant recruitment planning, especially in highly competitive therapeutic areas.
Nov 26, 2024
Clinical trial data is stored across numerous national registries worldwide, creating a complex and fragmented data landscape. This dispersion poses significant challenges for researchers, healthcare professionals, and policymakers, as every single clinical study matters when trying to advance medical knowledge and improve patient care.
When data is scattered across different platforms, inconsistencies in formats, terminologies, and incomplete information become common hurdles. This fragmentation hampers comprehensive analysis, leading to redundant research efforts and slowing down the development of life-saving therapies. These issues are particularly critical in an industry where even small or individual trial data sets can have a profound impact.
To unlock the full potential of clinical trial data, harmonizing data standards and promoting interoperability among registries is crucial. Initiatives like the WHO’s International Clinical Trials Registry Platform are steps in the right direction, aiming to streamline data sharing and enhance transparency in clinical research.
Advancements in AI and natural language processing offer promising solutions to bridge the gaps created by fragmented data. By leveraging these technologies, we can enhance data accessibility, improve research quality, and accelerate medical innovations.
Imagine a world where clinical trial data is seamlessly integrated, providing a comprehensive view that drives informed decision-making and fosters global collaboration. This vision is achievable through concerted efforts toward standardization and the adoption of cutting-edge technologies.
Sep 16, 2024
We are excited to share that our team has published a new article in Issue 8 of the esteemed Data & AI Magazine. Titled “Can large data reveal how sponsor competition affects participant recruitment in clinical trials?", the article investigates the dynamics between sponsor competition and its influence on the recruitment of participants for clinical trials.
In this insightful piece, we analyze how varying levels of competition among sponsors can impact recruitment strategies and participant engagement. By leveraging large datasets, we uncover patterns and insights that can help optimize recruitment processes, ultimately leading to more successful clinical outcomes.
This article embodies our commitment to driving innovation and thought leadership in the field of clinical data analysis. We invite you to read the full article in Issue 8 of the Data & AI Magazine to gain valuable insights into the intricate relationship between sponsor strategies and participant recruitment.
Stay tuned for more updates and thank you for following our journey in transforming clinical research!
Jul 5, 2024
We are thrilled to announce that GoTrial is now forming strategic collaborations with select industry partners in the life sciences sector, with a particular focus on clinical development. This initiative aims to foster innovation and mutual learning by working closely with carefully chosen organizations.
Our goal is to explore new frontiers in clinical development through these early partnerships, leveraging our expertise and insights while gaining valuable perspectives from our collaborators. We believe that by working together, we can drive advancements, address complex challenges, and create impactful solutions in the field.
While we are excited about the prospect of these collaborations, we will be selectively partnering with a limited number of organizations to ensure a focused and productive engagement. If your organization is interested in exploring a partnership that promotes innovation and growth, we would be delighted to discuss potential opportunities with you.
We look forward to building meaningful relationships and achieving great outcomes together.
For more information on how to get involved, please contact us.
Jun 10, 2024
We are thrilled to announce that our team has published a new article in the esteemed magazine The Data Scientist. Titled “Challenges and Opportunities in Clinical Trial Registry Data”, the article delves into the complexities and potential of clinical trial registries in the data science landscape.
In this insightful piece, we explore the multifaceted challenges faced when working with clinical trial registry data, including issues related to data quality, accessibility, and standardization. We also highlight the exciting opportunities that this rich data source presents for advancing research and improving trial outcomes.
Our article reflects our commitment to driving innovation and thought leadership in the field of clinical data analysis. We invite you to read the full article in The Data Scientist to gain a deeper understanding of how leveraging registry data can transform clinical trials and foster new advancements in medical research.
Stay tuned for more updates and insights from our team!
Apr 22, 2024
We are excited to share that we have entered into a strategic partnership with Rewire, a leading data science consulting company. This collaboration represents a powerful synergy between our data expertise and Rewire’s exceptional consulting capabilities. Rewire is renowned for its expertise in data science, data engineering, and large-scale AI transformations.
In this partnership, we bring our life science expertise, advanced data platform, and (Gen)AI tools to the table. Rewire complements this with their top-notch data science, data engineering, and AI transformation talent. Together, we offer a comprehensive and unique value proposition that combines our clinical development insights and analytics with Rewire’s consulting prowess.
This integrated approach is designed to enhance clinical development processes, drive innovation, and deliver actionable insights to optimize trial outcomes.
We are thrilled about the potential of this partnership to create value and advance the field of life sciences.
For more information about Rewire and their impressive capabilities, visit Rewire's website.
Stay tuned for more updates as we embark on this exciting journey together!
If you are interested in a demonstration or wish to get in touch, please use the online form or the email provided below. We always try to respond in a timely fashion.
