Dr. Trushar Dungarani on Synthetic Data’s Role in Healthcare

Beyond the Bedside: How Synthetic Data is Arming Clinicians for Healthcare’s Next Frontier

Healthcare is drowning in data yet starving for insight. In an industry that generates an estimated 30% of the world’s data volume, frontline clinicians—the very individuals trained to synthesize complex information and make life-or-death decisions—are often the last to gain meaningful access to the operational and population-level data generated by their own work. This paradox creates a frustrating chasm between the clinical teams at the bedside and the administrative leaders in the boardroom, hindering innovation, perpetuating inefficiencies, and ultimately impacting patient care. The traditional pathways to data, guarded by overwhelmed analyst teams and months-long regulatory approvals, are no longer sufficient for a system demanding agility and precision.

Bridging this chasm requires more than just new technology; it demands a new philosophy. It requires leaders who can speak the languages of both patient care and data science. Trushar Dungarani, DO, SFHM, Director of Clinical and Data Science at MDClone, embodies this new archetype of clinician leader. With a journey that has taken him from the front lines of hospital medicine at Northwell Health and Johns Hopkins Medicine to the forefront of data innovation, Dr. Dungarani has witnessed firsthand the “beauty and the dysfunction” of modern health systems. His work now focuses on empowering organizations with a transformative tool that promises to democratize data, protect patient privacy, and unlock the insights trapped within the electronic health record (EHR): synthetic data.

The Clinician’s Dilemma: A Culture of Data Distrust

For many healthcare professionals, the relationship with organizational data is fraught with frustration. Clinicians are trained to be masters of data interpretation for a single patient, but when it comes to system-level performance, they are often handed reports and directives with little context or ability to investigate the source. Dr. Dungarani explains that this top-down approach, combined with a lack of access, breeds a natural skepticism.

“These clinicians aren’t getting access to data, so they’re being told something without that ability to see, see the data,” Dungarani notes. “There’s always a kind of a defensiveness to it… we have to teach our providers and our clinical team members about data, give them access and, and let them make changes for themselves.”

This challenge is rooted in several systemic issues that Dr. Dungarani identifies from his experience in quality, operations, and health information management:

• Fragmented Systems: Health systems often consist of patchworks of technology. Data from a previous EHR may not be integrated into the current one, making longitudinal studies nearly impossible without significant manual effort.
• Workflow Silos: Data is often trapped within departmental workflows. An emergency department physician may have no visibility into inpatient bed capacity, even though the two are inextricably linked. This makes it difficult for teams to understand and solve cross-functional problems like patient throughput.
• The Bottleneck of Access: The traditional process for obtaining data is fundamentally broken. A clinician with a research idea or an operational question must submit a request to a small team of data analysts, who often have a backlog of projects from across a 30,000-person organization. The wait can be weeks or months, extinguishing the spark of curiosity and innovation before it can even ignite.

This environment not only stifles quality improvement but also contributes to clinician burnout. Being held accountable to metrics without the tools to understand or influence them is a recipe for disengagement. Empowering clinicians means giving them the agency to solve the problems they see every day, and that agency begins with access to data.

Forging a New Path: The Intersection of Leadership, Culture, and Technology

Solving the data access problem is not merely a technical challenge; it is a leadership and cultural one. According to Dr. Dungarani, effective change requires clinician leaders who can act as translators, bridging the gap between administrators, data scientists, and frontline caregivers.

Bridging the Great Divide

Administrators, clinicians, and data analysts often look at the same problem through vastly different lenses. The CFO sees cost, the pharmacy team sees medication adherence, and the physician sees a patient’s clinical journey. A successful data-driven culture requires a “common language” to align these perspectives.

This alignment is crucial because the narrative told by the data and the one experienced by the clinician can often diverge. “A clinical workflow and a clinical story can be very different from a data journey,” Dr. Dungarani explains. “The assumptions and the expectations of how data flows with a patient story need a lot of communication… you need that same type of multidisciplinary team to really communicate how that data is telling that story.”

For example, a report might show a delay in administering a specific medication. The data simply shows a timestamp. The clinical story, however, might involve a patient who was temporarily off the floor for a procedure, a pharmacy delay, or a nurse attending to a more critical emergency. Without the ability for clinical teams to easily explore the associated data points themselves, incorrect assumptions are made, and ineffective solutions are implemented. Clinician leaders are essential in facilitating this dialogue, ensuring that data insights are grounded in clinical reality.

The Leadership Mandate

Ultimately, fostering this culture starts at the top. Leadership must transition from a compliance-oriented mindset—”Your throughput times are terrible, fix it”—to an empowerment-oriented one: “Here are the tools to explore the data. Find the bottlenecks and tell us what you need to solve them.” When teams are given the tools to identify problems themselves, they become far more invested in creating and sustaining the solution. This shift democratizes problem-solving and builds a true learning health system, where improvement is a continuous, collaborative effort rather than a series of top-down directives.

Demystifying Synthetic Data: A New Frontier for Privacy and Innovation

If the goal is to provide widespread access to data, the primary obstacle has always been patient privacy. The Health Insurance Portability and Accountability Act (HIPAA) and other regulations rightly place strict controls on the use of protected health information (PHI). The traditional method for creating “safe” data for research has been de-identification, a process of stripping 18 specific identifiers like name, address, and Social Security number. However, this method is increasingly seen as inadequate. In a world of big data, re-identifying an individual from a “de-identified” dataset can be surprisingly easy with enough external information. Furthermore, to be safe, de-identification often requires removing or aggregating key variables—like zip codes or specific dates—that are critical for studying health equity or disease progression.

What is Synthetic Data?

Synthetic data offers a far more robust solution. Instead of simply removing identifiers, a synthetic data generation process studies the entire original dataset to learn its statistical patterns, correlations, and distributions. It then generates a brand-new, artificial dataset that mirrors these statistical properties but contains no one-to-one mapping back to any real patient. Imagine it as creating a statistically identical “twin” population. One row of synthetic data does not represent a real person, but an analysis of 10,000 rows will yield the same conclusions—the same rates of diabetes, the same average length of stay, the same correlation between a lab value and an outcome—as an analysis of the original 10,000 patients.

This approach offers the best of both worlds: it provides a high-fidelity dataset for exploration and analysis while completely severing the link to individual identities, thus protecting patient privacy at its core.

Accelerating the Pace of Discovery

The operational impact of this technology is immense. It effectively removes the regulatory and logistical logjams that plague healthcare research and operations.

“The benefits of synthetic data right now in the healthcare system is a, I don’t have to jump through a million hoops to look at whether my study is feasible,” says Dr. Dungarani. “It may take weeks or months to get a data set to even see if your project is viable. Do I have a magnitude of patients to do this research project?”

With a self-service platform built on synthetic data, a clinician can test that hypothesis in minutes. They can explore the data, refine their research question, and confirm the viability of a study before ever applying for Institutional Review Board (IRB) approval to use the original patient data. This dramatically speeds up the innovation cycle, allowing teams to fail fast on ten ideas to find the one that warrants the time and resources of a full-scale study. It transforms the data analyst from a gatekeeper into a strategic partner, freed from routine data pulls to focus on more complex, high-value projects.

Synthetic Data in Action: From Predictive Models to Health Equity

The applications of this approach are not theoretical. Health systems are already using synthetic data to drive meaningful improvements in patient care and operational efficiency.

Predicting Chronic Disease Progression

Dr. Dungarani shares the example of a health system working to predict which patients with chronic kidney disease (CKD) are most likely to progress to dialysis—an outcome that profoundly impacts a patient’s quality of life and represents a significant cost to the system. Using synthetic data, their teams could rapidly build and test machine learning models to identify the key predictors of progression. This allows them to create targeted interventions for high-risk patients, potentially delaying or preventing the need for dialysis and improving long-term outcomes. Similar work is being done to predict which cardiac patients will develop severe heart failure, enabling proactive care management.

Closing Gaps in Healthcare Equity

Perhaps one of the most powerful uses of synthetic data is in the study of healthcare disparities. Analyzing sensitive demographic variables like race, ethnicity, language, and geographic location is essential for identifying and addressing inequities, but it also carries the highest risk for patient re-identification. Synthetic data provides a safe harbor for this critical work.

Dr. Dungarani describes clients using synthetic data to examine geographic “heat maps” for correlations between environmental factors and chronic disease or to analyze whether a patient’s primary language or ethnicity impacts the time it takes to get to the operating room for a common procedure like a hip fracture. This type of exploration is vital for ensuring equitable care delivery for all populations. The self-service nature of these tools fosters an iterative, curiosity-driven process that is fundamental to discovery.

“We can ask a question, find an insight, and with anything in healthcare, there are 10 more questions or are 20 more questions,” he remarks. “Having that self-service ability to look at synthetic data and ask more questions and really have this dialogue with data is really the power.”

Key Takeaways for Healthcare Leaders

For clinician leaders who want to champion a new era of data-driven healthcare in their organizations, Dr. Dungarani’s insights distill into several actionable principles:

1. Champion Data Literacy: Acknowledge that healthcare has one of the lowest rates of data literacy among all industries. Invest in training that creates a common language and understanding of data workflows between clinical, analytical, and administrative teams.
2. Democratize Data Access (Safely): Move beyond the gatekeeper model. Explore and invest in technologies like synthetic data platforms that can empower your teams with self-service analytics without compromising patient privacy or overwhelming your existing data infrastructure.
3. Cultivate Curiosity, Not Compliance: Foster a culture where data is a tool for exploration, not just a report card for compliance. Encourage teams to ask questions, test hypotheses, and follow their curiosity to uncover the root causes of systemic challenges.
4. Embrace the AI Frontier: The rise of AI and machine learning in healthcare is inevitable. Clinicians must be active participants in shaping how these tools are developed and deployed. This requires a willingness to learn, experiment, and engage with new technologies.

Conclusion: Be a Part of the Change

The future of healthcare will be defined by how well organizations can leverage their data to deliver more personalized, efficient, and equitable care. Achieving this vision requires more than just algorithms and dashboards; it requires a profound cultural shift that places the power of data into the hands of those on the front lines. Synthetic data is a key enabling technology for this shift, providing a safe and agile framework for innovation.

As Dr. Dungarani passionately concludes, skepticism in the face of new technology is natural, but inaction is a choice. The path forward demands engagement and a proactive spirit from today’s and tomorrow’s healthcare leaders.

“Without trying, without learning and, and, and being curious, you know, we aren’t gonna move forward,” he urges. “So I would say if you doubt it, study it, learn it, and, and figure out where the gaps are and be a part of the change.”