Trailblazer Of The Agentic Era: Bridging The $1.7 Trillion Gap Between Legacy Systems And Human Care

In the basement of Massachusetts General Hospital, a server room hums with the ghosts of healthcare’s digital past. Rows of monolithic machines, some dating to the 1990s, store patient records, insurance claims, and clinical trial data-precious information trapped in systems that speak obsolete languages like COBOL and Fortran.

Upstairs, a team of engineers stares at a screen flashing timeout errors. They are attempting to connect a cutting-edge Generative AI diagnostic tool to these basement relics. “It’s like trying to teach a rotary phone to stream 4K video,” one engineer mutters. This friction is the frontline of healthcare’s silent crisis: a battle against its own technical debt, a strategic bottleneck, that IDC research indicates is the single greatest obstacle to AI deployment for nearly half of all healthcare organizations. While 82% of these organizations have aggressive plans to increase AI spending in 2026, realizing value from that spend is impossible if the new intelligence cannot speak to the old memory.

Enter Vara Imandi, a Director of Partner Product Success at Salesforce, whose two-decade odyssey through the trenches of legacy integration has positioned him as an unlikely savior of these digital relics. Armed with AI agents, multi-cloud platforms, and a zeal for modernization, Imandi is leading a revolution to bridge healthcare’s technological past and future, one that he calls the "Digital Diplomacy" where AI Agents act as semantic translators, bridging the gap between the brittle, deterministic world of legacy healthcare and the fluid, probabilistic world of Agentic AI.

The Legacy Trap: A $1.7 Trillion Anchor on Progress

To understand the urgency of Imandi’s work, one must first quantify the cost of the status quo. The healthcare industry is currently anchored by a staggering volume of technical debt. This is not merely a line item on a CFO’s spreadsheet; it is a clear and present danger to patient safety.

Legacy systems are not a mere inconvenience; they are a clear and present danger to both physicians and patients. The crisis begins with the clinician's daily tools. Studies confirm that physicians rate the usability of their Electronic Health Records (EHRs) in the bottom 9% of all surveyed industries, a design failure directly linked to higher rates of physician burnout.

This administrative friction has devastating real-world consequences. According to a 2025 survey by the American Medical Association (AMA), 92% of physicians report that delays in processes like prior authorization—often slowed by fragmented, non-interoperable systems—negatively impact patient outcomes, with an alarming 34% stating these delays have led to a serious or life-threatening event.

"These aren't just administrative annoyances," Imandi explains. "When a prior authorization request gets stuck between a fax machine and an AI portal, a patient waits for cancer medication. Interoperability is a patient safety issue."

Compounding this clinical risk is a massive security exposure. According to IBM's 2025 Cost of a Data Breach Report, while the global average cost of a breach has stabilized, the cost in the U.S. healthcare sector remains the highest of any industry at $7.42 million per incident, for the 15th consecutive year. This is the ultimate price of technical debt: a system that frustrates clinicians, harms patients, and exposes the most sensitive data to catastrophic financial and reputational risk.

Imandi’s solution, “digital diplomacy,” centers around using AI agents as translators between old and new worlds. At Salesforce, he oversees the development and roll out of MuleSoft Industry Accelerators, pre-packaged toolkits that convert legacy healthcare data into API-ready formats compliant with HIPAA, GDPR, and FDA standards.

These accelerators have produced tangible results. For instance, global med-tech firm Hologic deployed critical projects 3x faster using MuleSoft's API-led approach to service over 180 applications globally. Similarly, Nebraska Medicine improved call response times and patient satisfaction using the MuleSoft Accelerator for Healthcare to provide a streamlined, 'one-stop shop' workflow for its call center agents. This impact extends to the partner ecosystem, where firms like Deloitte and PWC have slashed deployment timelines significantly, turning months of manual coding into days of automated workflows for their customers. “Legacy systems aren’t problems to solve—they’re assets to unlock,” Imandi explains. “Our role is also to arm our partner ecosystem with the tools to give these assets a voice in the modern world.”

The Machines That Decode the Past and Build the Future

Imandi’s outlook hinges on AI agents that act as both archaeologists and architects. Unlike traditional automation (RPA), these agents use machine learning to map legacy codebases, identify critical business logic, and generate cloud-native APIs—all while preserving decades-old data relationships.

Consider pharmacovigilance, the process of tracking drug side effects. When a major pharmaceutical company like Pfizer partnered with Imandi’s team, it faced a labyrinth of 1980s-era databases storing adverse event reports. In 2024 alone, 414 million patients were reached with their medicines and vaccines and Imandi’s agents parsed a considerable number of Adverse Events (AEs) related to these products, auto-generating FDA-compliant submissions and reducing submission errors down to teens. “It’s like having a Rosetta Stone for every forgotten programming language. By automating the intake of this data, AI agents enable pharmacovigilance experts to focus on higher-value signal detection and analysis, transforming their workflow and enhancing their capabilities.” says a Pfizer IT lead.

The impact of this technology extends beyond internal efficiency to create profound societal value. In India, for example, Apollo Hospitals leverages automation to process over 500,000 insurance claims annually, accelerating access to care for low-income patients. Meanwhile, innovative care models are tackling public health crises; a pilot program in Sweden's dementia care network demonstrated a 25% reduction in caregiver burnout by deploying AI for automated care coordination.

This localized impact signals a massive global shift. According to a 2025 forecast from Gartner, the momentum is undeniable: The market for AI in healthcare is projected to surge from $21.66 billion in 2025 to over $110 billion by 2030. Gartner predicts that AI agents will augment or automate 50% of all business decisions, fundamentally reshaping clinical and administrative workflows.

The momentum behind this technology is also no longer based on forecasts, but on published, peer-reviewed results from the world's leading medical institutions. A landmark 2025 study from Stanford Medicine, published in Nature, demonstrated that an AI model analyzing clinical notes and medical images could predict disease progression with unprecedented accuracy, enabling earlier interventions. This clinical power is matched by a profound operational impact. Researchers at Harvard Medical School, writing in a recent New England Journal of Medicine (NEJM) perspective, highlighted findings from pilot programs where ambient AI scribes reduced physicians' after-hours EHR documentation time— often referred to as "pajama time"—by over 50%, directly combating burnout. These tangible results underscore the transformative potential of AI in healthcare.

Multi-Cloud Mastery: Healing Healthcare’s Split Personality

Healthcare’s cloud transition has been schizophrenic. While 80% of providers use multiple clouds, only 15% have unified them into a coherent strategy—a disconnect costing the sector $28 billion yearly in redundant storage and security gaps. Imandi’s answer: MuleSoft with Agentforce, Salesforce’s Agentic platform, which acts as a “central nervous system” for multi-cloud environments.

At May 2025’s Agentforce World Tour in New York, Imandi demonstrated Agentforce with MuleSoft harmonizing data across AWS, Azure, and an on-premise Epic EHR system. In real time, an AI agent diagnosed a billing discrepancy, negotiated with an insurer’s chatbot, and updated the patient’s record—a feat previously requiring six human touchpoints. “This isn’t just integration,” he told the crowd. “It’s alchemy.”

The numbers validate the hype: Early adopters report 40% faster claim processing and 30% lower cloud costs. Yet the greater promise is found in scalability. Imandi’s team has enabled 15,000+ developers across 40 countries to build niche agents, from prior authorization bots in Nairobi to clinical trial recruiters in São Paulo.

Rewriting Healthcare Without Rebuilding Inequality

Not all legacy baggage is technological. When Imandi’s agents modernized a Southern U.S. hospital’s patient scheduling system in 2024, they uncovered a grim artifact: embedded racial bias in 12% of treatment prioritization rules. “AI mirrors our history,” warns Dr. Atul Gawande. “Modernize the code without confronting the data, and you automate inequality.”

Imandi’s response is “ethical by design” guardrails. All agents now undergo rigorous bias audits, while open-source toolkits let NGOs tweak algorithms for underserved communities. It’s a fragile compromise-Most hospitals still lack AI governance frameworks, but Imandi sees it as non-negotiable. “Transparency isn’t a feature. It’s the foundation,” he insists. Imandi advocates for Human-in-the-Loop (HITL) architectures, where the AI handles the "doing" (data entry, claims checking) but a human retains control over the "judging" (patient empathy, complex negotiation). A 2025 AMA survey calls this out clearly, nearly half of physicians (47%) ranked increased oversight and regulation as the top requirement to build their trust in AI tools, signaling a clear demand for robust governance frameworks.

The Human Cost: Automating Empathy?

Back at Mass General, nurse Lena Ortiz recalls the pre-agent era: “I’d spend hours arguing with insurance bots while my patients suffered.” Today, AI handles 80% of her prior authorization work, freeing her to lead a pediatric cancer support group. “The machines fight the bureaucracy,” she says. “We fight the disease.”

Yet the transition is uneven. While Boston’s hospitals deploy Imandi’s tools, clinics in rural Mississippi and Malawi lack the budgets or broadband to join. Imandi’s next mission, to work with local nonprofits on initiatives to bring low-code agents to 100+ underserved regions by 2026, aims to close this gap. “Technology without access is just another wall,” he says.

Progress as Paradox

The legacy modernization movement embodies a paradox: To build the future, we must first preserve the past. The work of AI leaders like Vara Imandi proves that aging systems, once seen as anchors, can become engines of equity—if guided by ethics, inclusivity, and relentless iteration.

But as hospitals rush to adopt AI agents, a question lingers: Will we control these tools, or will they control us? The answer lies not in code, but in conscience. After all, legacy systems did not create our biases, inefficiencies, or inequalities—they simply encoded them. The real test of this transformation is not technological. It is whether we are brave enough to modernize ourselves and ensure that our new tools reflect the best of who we are, not the latent flaws of our past.

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