About Us
We are a pioneering medical AI company committed to leveraging advanced AI and real-world data to revolutionize healthcare and life sciences. Our mission is to empower clinical research by integrating real-world data, cutting-edge machine learning, causal inference and generative AI. Through innovative solutions such as target trial emulation, privacy-preserving
federated learning, and synthetic data/digital twins, we support companies in rapid and reliable evidence generation, enabling them to make data-driven decisions that improve outcomes and drive innovation.
Our Solutions
At Ividence.AI, we offer a comprehensive suite of real-world data solutions designed to meet the dynamic needs of healthcare and life sciences. Our advanced services empower organizations to generate reliable clinical evidence, enhancing decision-making and driving innovation.
Advanced Target Trial Emulation
Our platform leverages advanced causal inference methods to emulate target trials using observational data such as sequential trial emulation and cloning approach. Sequential trial emulation enhances the traditional trial emulation by addressing biases such as immortal time bias, ensuring more accurate and reliable estimates. This approach involves creating a sequence of trials with varied start times, allowing for comprehensive data utilization. Additionally, the cloning approach is used when treatment strategies are not defined at baseline, creating clones of individuals assigned to different treatment arms and using censoring and weighting techniques to adjust for selection bias. By integrating these methods, we generate high-quality evidence, support regulatory decision-making, and accelerate the development of new therapies. We have developed advanced tools to generate real-world effectiveness of BNT162b2 against infection and severe diseases in children and adolescents, as well as its protection against post-acute sequelae of COVID-19 (PASC).
Comprehensive Systematic Reviews and Meta-Analysis
Our platform provides advanced tools for conducting systematic reviews and meta-analyses, crucial for pharmaceutical companies aiming to synthesize evidence across multiple studies. Utilizing state-of-the-art methodologies such as multivariate meta-analysis, network meta-analysis, and diagnostic test meta-analysis, we ensure robust and reliable synthesis of data. Our innovative approaches address issues like correlated outcomes, publication bias, and small study effects, offering comprehensive insights into treatment efficacy and safety. Additionally, we include meta-analysis of single-arm studies, providing valuable insights when randomized controlled trials are not feasible. By integrating real-world data and cutting-edge algorithms, we deliver high-quality, actionable evidence to support regulatory submissions and strategic decision-making in drug development.
Secure and Efficient Federated Learning and Federated Trial Emulation
Our platform offers a cutting-edge framework for privacy-preserving federated learning, designed to integrate biomedical data from multiple institutions without sharing individual patient-level data. By employing advanced algorithms, we ensure that only aggregated data is communicated, maintaining high accuracy and efficiency. Our solutions account for data heterogeneity across sites and are optimized for minimal communication rounds, making them ideal for large-scale collaborations. Pharmaceutical companies can leverage these methods for robust and generalizable findings, improving drug development and clinical research while ensuring data privacy and regulatory compliance. Additionally, our platform features federated trial emulation for drug repurposing signal generation and pharmacovigilance studies, enabling the identification of new therapeutic uses for existing drugs and monitoring drug safety across diverse populations.
Advanced Pharmacovigilance and Drug Safety Monitoring
Our platform offers advanced statistical and informatics methods for evaluating vaccine and drug safety signals, utilizing extensive data from the Vaccine Adverse Event Reporting System (VAERS) and FDA Adverse Event Reporting System (FAERS). Our highly sensitive signal detection methods identify temporal variations in adverse event rates, capturing significant changes in vaccine-associated risks over time. By integrating VAERS data with CDC survey data and U.S. census data, we address limitations such as under-reporting and provide a comprehensive analysis of adverse events across different demographic groups. Additionally, our framework extends to semantic technology and statistical inference for drug-repositioning signal mining, enabling the identification of new therapeutic uses for existing drugs. These innovations empower pharmaceutical companies to conduct robust pharmacovigilance studies, ensuring drug safety and improving patient outcomes.
Synthetic Data and Digital Twins for Enhanced Clinical Research
Our platform utilizes synthetic data to address privacy concerns, amplify small datasets, and enhance data quality. By generating synthetic data from real data patterns, we ensure the statistical properties remain intact while protecting patient privacy. This approach supports internal and external data sharing, correcting biases, and combining diverse datasets. Additionally, we create digital twins using advanced generative AI to simulate patient outcomes. Digital twins enable robust control arms for clinical trials, enhancing study power, reducing costs, and accelerating the development of new treatments. Pharmaceutical companies can leverage these innovations to conduct more efficient and accurate clinical research, driving advancements in drug development and patient care.
AI-Driven External Control Arms (ECA)
Our AI-powered platform utilizes real-world data to create robust external control arms (ECA) for clinical trials. By integrating diverse data sources, including electronic health records and historical clinical trials, we enhance the relevance and reliability of single-arm studies. Our advanced algorithms ensure accurate matching and adjustment of baseline characteristics, enabling valid comparisons and accelerating the development of new therapies. This approach reduces the need for traditional control groups, optimizing trial efficiency and supporting regulatory submissions with comprehensive evidence.
Empowering RCT with Multi-Modal
Real-World Data
Our platform enhances clinical trial design using real-world data (RWD) from diverse modalities such as electronic health records, wearables, and registries. Ensuring that the RWD are fit-for-use, we design studies that generate adequate scientific evidence to meet regulatory requirements. This approach bridges the gap between controlled trials and real-world patient experiences, improving trial relevance, accelerating drug development, and providing comprehensive insights into treatment effectiveness and safety.
Transforming Rare Disease Research with RWE
Our platform leverages real-world evidence (RWE) to advance research in rare diseases, overcoming challenges such as data fragmentation and limited sample sizes. By integrating data from diverse sources, including patient registries and electronic health records, we ensure data fitness for use and adherence to regulatory standards. This approach enables comprehensive analysis of treatment effectiveness and safety, supports regulatory submissions, and improves patient outcomes. Through international collaboration and advanced analytics, we provide robust insights into rare diseases, enhancing clinical decision-making and therapeutic development.
Leveraging Social Determinants of Health (SDOH) to Improve Clinical Trial Diversity
Our platform integrates real-world data (RWD) to analyze social determinants of health (SDOH) and their impact on clinical outcomes. By incorporating factors such as education, income, and access to healthcare, we provide insights into how social and economic conditions influence health disparities. Utilizing data from various sources, including national surveys and electronic health records, we enhance the understanding of underrepresented populations. This approach supports the development of targeted interventions, improves clinical trial diversity, and informs strategies to optimize patient outcomes.