At MDClone, innovation is not about adding AI for the sake of novelty, it is about embedding intelligence where it drives real clinical and operational impact. One of the leaders shaping that vision is Dr. Adi Tamir, Product Innovation & AI Lead.
Adi brings the mindset of a scientist to product innovation. With 2 PhDs (science and microbiology), her career has centered on solving complex biological and data-driven challenges with precision and rigor.
Outside of work, she practices karate, DJs, and produces music, all pursuits that demand focus, timing, and continuous refinement. That balance of discipline and creativity shapes how she leads product innovation.
Building the Foundation for Embedded AI
Before leading AI strategy within the platform, Adi spearheaded natural language processing (NLP) initiatives at MDClone, focused on extracting structured insights from unstructured clinical data. That work deepened her understanding of how clinicians and researchers engage with complex datasets and positioned her to shepherd the development of ADAMS Copilot.
In late 2023, MDClone made a strategic commitment to embed AI directly into its ADAMS data exploration platform. The goal was clear: enhance how healthcare organizations interact with longitudinal data without compromising security, governance, or trust.
Adi was selected to lead the initiative.
Engineering Intelligence with Intention
ADAMS Copilot is MDClone’s AI-powered research assistant embedded directly within the ADAMS platform. It enables users to interact with curated healthcare data using natural language to accelerate insight generation, statistical analysis, visualization, and exploratory workflows without manual scripting or data export.
With ADAMS Copilot, users can:
- Run summary statistics
- Generate visualizations
- Evaluate data completeness and quality
- Detect trends, correlations, and patterns
- Explore research questions iteratively
ADAMS Copilot runs within the secure MDClone environment and the client’s private Azure network. No underlying data leaves MDClone, and only encrypted query metadata is securely exchanged to support AI processing.
Depending on user permissions, it analyzes original or synthetic data to ensure sensitive information never leaves the protected ecosystem.
By translating natural language queries into rigorous analytic outputs, ADAMS Copilot acts as an intelligent partner, helping teams move from question to insight faster and more intuitively than traditional manual methods allow.
Co-Pilot Use Case: OR Supply Utilization Optimization
One example of ADAMS Copilot in action is the analysis of Operating Room supply utilization, an area rich with operational and financial opportunity. Because OR workflows are inherently hierarchical (a case includes procedures, and each procedure includes multiple supplies), extracting meaningful insight from consumption data can be complex. Using ADAMS Copilot, teams explored supply usage across cases, procedures, surgeons, and sites to identify standard supply utilization profiles to detect variation in practice patterns, and flag cost outliers. By linking item-level utilization directly to pricing data, stakeholders were able to pinpoint high-impact cost drivers, benchmark performance across clinics and surgeons, and surface anomalies that may signal workflow inefficiencies or documentation gaps. The result was a data-driven framework for supply standardization and continuous monitoring to enable operational optimization and measurable cost reduction without compromising clinical care.
AI Designed for What’s Next
Under Adi’s leadership, ADAMS Copilot represents more than a feature. It reflects MDClone’s broader commitment to responsibly embedding AI into trusted healthcare data workflows.
At MDClone, innovation is engineered with intention, where structure enables creativity, and intelligence is built to endure.
Stay tuned for more!
Warmly,
Vicki Snavely, RN
