AI Behind the Firewall: MDClone's New Copilot Aims to Securely Unlock Data

BOSTON, MA – March 05, 2026 – Healthcare technology company MDClone today announced the launch of ADAMS Copilot, a generative AI-powered data assistant designed to radically simplify how health systems interact with their own complex data. The new tool aims to empower clinicians, researchers, and administrators to get answers from vast datasets using simple, conversational language, all while addressing the industry's paramount concerns: patient privacy and data security.

ADAMS Copilot integrates directly into the company's existing ADAMS platform, an innovation framework designed to help organizations generate insights at scale. Through an intuitive chat interface, users can ask complex questions—such as "What is the average length of stay for diabetic patients over 65 with a history of heart failure?"—and receive validated analytical workflows and conclusions without needing to be experts in data science or statistical programming.

This launch comes at a time when healthcare organizations are awash in data but often struggle to access it meaningfully. Fragmented systems, technical bottlenecks, and a shortage of data scientists frequently create long delays between a clinical question and a data-driven answer. By translating natural language into sophisticated analytics, ADAMS Copilot aims to dramatically shorten that path.

Democratizing Data for the Front Lines

The core promise of ADAMS Copilot is the democratization of data. For years, deep data analysis has been the exclusive domain of specialists. A doctor with a research hypothesis or an administrator trying to optimize bed capacity would typically submit a request to an analytics team, a process that could take weeks or months. MDClone's new tool is designed to put that power directly into the hands of the people asking the questions.

By eliminating the need for deep familiarity with data structures or table relationships, the platform allows a wider range of healthcare professionals to engage in exploratory data analysis and hypothesis testing. This could enable a quality improvement leader to rapidly identify trends in hospital-acquired infections or allow a researcher to quickly test the feasibility of a new clinical trial protocol. The system supports a wide range of tasks, from summary statistics and data visualization to temporal mapping and correlation analysis, all initiated through simple dialogue.

A Secure-by-Design Approach to AI

While the potential of generative AI in healthcare is immense, its adoption has been tempered by significant concerns over data privacy. Many general-purpose AI solutions require sending data to external cloud services for processing, an untenable risk for organizations handling protected health information (PHI).

MDClone is positioning ADAMS Copilot as a solution to this dilemma with its 'behind the firewall' architecture. The entire system, including the large language models (LLMs) it leverages, operates securely within a customer's own data environment. This ensures that sensitive patient data never leaves the organization's control, helping maintain compliance with stringent regulations like HIPAA in the United States and GDPR in Europe. Furthermore, the platform can leverage MDClone's established synthetic data technology, which creates statistically identical but fully de-identified datasets for exploration, adding another powerful layer of privacy protection.

"Generative AI in healthcare must be practical, secure, and responsibly deployed," said Bruno Lempernesse, CEO of MDClone, in the company's announcement. "With ADAMS Copilot, we embed AI directly into our customers' own data environments, safely behind their firewall. By translating natural language into secure, validated analytics, ADAMS Copilot dramatically shortens the path from question to insight and helps healthcare organizations become truly data-driven."

Real-World Validation and the Competitive Landscape

This secure, user-friendly approach helps MDClone carve out a distinct niche in a competitive market. While tech giants like Microsoft and Google offer powerful healthcare AI clouds and analytics tools, MDClone's emphasis on on-premise security and self-service for non-technical users is a key differentiator. The strategy directly targets the trust and accessibility barriers that have slowed AI adoption in many health systems.

The technology is not just theoretical. ADAMS Copilot is already live at Sheba Medical Center in Israel, one of the world's top-ranked hospitals and a long-standing partner of MDClone. Clinical and operational teams there are actively using the platform to accelerate insight generation and improve decision-making. This early adoption by a leading academic medical center serves as a powerful validation of the tool's potential impact on both research and operational efficiency.

Reshaping the Healthcare Data Workforce

The introduction of tools like ADAMS Copilot is poised to reshape the roles and workflows of data professionals within healthcare. Rather than being a replacement for data scientists, the technology is expected to augment their capabilities. By automating routine queries and initial data exploration, it frees up highly skilled analysts to focus on more complex challenges, such as developing novel predictive models, validating AI-generated findings, and tackling strategic, high-impact projects.

For IT professionals, the focus will shift towards managing the governance and security of these powerful AI tools, ensuring robust infrastructure, and integrating them seamlessly with existing systems. Meanwhile, clinical informaticists—who bridge the gap between clinical practice and technology—will become even more critical in training users, helping to formulate effective queries, and translating AI-generated insights into actionable clinical and operational changes.

MDClone will be demonstrating ADAMS Copilot live at the upcoming HIMSS 2026 conference in Las Vegas. The company will offer attendees a firsthand look at how healthcare teams are leveraging this solution to transform data into action.