Our Products
An automated data extraction software for fast Systematic reviews & Meta Analysis for Health
- Customizable Extraction Templates
- Machine Learning Algorithms to extract data
- Auto-Coding and Categorization
- Validation and Quality Control
- Support for Multiple Study Designs
- Integration with Reference Management Systems
- Export and Reporting Options
- Adherence to Data Standards
Real‐world evidence (RWE) is the evidence of the potential benefits of an intervention/ product/ service in a hospital / clinical setting derived from real world data
Generate queries by decision makers
Automated data cleaning through AI/ML (customised for queries)
Identify vital events & structural issues in data (automatically)
Able to link follow-up data of same patients, based on UIDs
Connect with IoT and automatically identify variables
Conduct regression analysis (automated)Manage projects
Transform evidences into simple and understandable languages Standards
Model Creation Interface: A user-friendly interface for constructing Markov microsimulation models, allowing users to define health states, transition probabilities, time horizons, and other model parameters.
Library of Health States and Parameters: A comprehensive library of predefined health states, facilitating model development and customization.
Data Import and Integration: Ability to import and integrate real-world data sources, such as clinical trials, cohort studies, and administrative databases, to inform model parameters and validate model assumptions.
Simulation Engine: A robust simulation engine capable of running stochastic microsimulation experiments to estimate long-term health outcomes, costs, and cost-effectiveness of different healthcare interventions under uncertainty.
Sensitivity Analysis: Built-in tools for conducting sensitivity analysis to assess the impact of parameter uncertainty and variability on model outcomes, including one-way, multi-way, and probabilistic sensitivity analyses.
Scenario Analysis: Capability to perform scenario analysis to explore the implications of alternative assumptions, model structures, and policy scenarios on decision-making and resource allocation in the health sector.
Graphical Visualization: Graphical visualization tools to represent model structures, simulation results, and sensitivity analyses in intuitive and informative ways, including diagrams, graphs, and charts.
Reporting and Export
Validation and Calibration
User Support and Training
Compliance with Regulatory Standards
Personalized Learning Paths: AI-driven assessment of individual skills and competencies to create personalized learning paths tailored to each user’s strengths, weaknesses, and learning goals.
Adaptive Assessments: Dynamic assessments that adapt based on user performance, providing targeted feedback and resources to address areas needing improvement and reinforce mastery of skills.
Multimodal Assessment: Integration of various assessment modalities, including written tests, clinical simulations, video analysis, and peer evaluations, to comprehensively evaluate medical skills across different contexts.
Real-world Scenario Simulations: Immersive simulations of real-world clinical scenarios using AI-powered virtual patients, allowing learners to practice clinical decision-making, diagnostic reasoning, and patient management in a safe and realistic environment.
Competency Mapping: Automated mapping of assessed skills and competencies to established frameworks and competency standards (e.g., CanMEDS, ACGME milestones), enabling users to track progress and identify areas for further development.
Performance Analytics: Robust analytics dashboards providing detailed insights into individual and group performance, including proficiency levels, learning trends, and competency gaps, to inform curriculum design and instructional strategies.
Continuing Professional Development (CPD) Tracking: Automated tracking of continuing education activities, certification requirements, and CPD credits earned through the application, helping users meet licensure and maintenance of certification obligations.
Integration with Electronic Health Records (EHRs): Seamless integration with EHR systems to enable contextualized learning experiences, allowing users to practice documentation, order entry, and other clinical tasks within the application using realistic EHR interfaces.
Accessibility and Mobile Support: Accessibility features and mobile support to accommodate diverse learning preferences and facilitate on-the-go learning, enabling users to access assessment tools and learning resources anytime, anywhere, and on any device.
Data Security and Privacy: Implementation of robust data security measures and compliance with privacy regulations (e.g., HIPAA, GDPR) to safeguard sensitive user data and ensure confidentiality and privacy protection
