Q1. To begin, could you briefly walk us through your 25+ year journey in digital healthcare—especially your experience transforming patient care through EHR implementations, AI-driven innovations, and large-scale digital transformation programs across provider ecosystems?

With over 25 years of dedicated experience in healthcare technology and digital transformation, I have led several strategic healthcare programs across providers, payers, and consulting ecosystems fundamentally bridging clinical operations, patient experience, digital strategy, and enterprise modernization. My career has been driven by a singular focus on translating technology investments into measurable business and clinical outcomes that elevate patient care, physician experience, and healthcare organizational performance.

Reflecting from the past decade, Healthcare’s slower digital maturity when compared to other industries has always represented, to me, a powerful window of opportunities for innovation. Having Worked with leading healthcare systems & consulting organizations in US, Middle East & APAC regions has provided amazing learnings, great insights, and has sharpened the strategy for a better tomorrow.       

This has transformed the thinking that’s required to get strategy along with technology. In turn, this thinking has shaped up to accelerate digital adoption, bridge care delivery across the enterprise by driving EMR/ EHR transformations across multiple product vendors such as Epic, Oracle Cerner, Meditech, Allscripts (veradigm) and regional HIS/EMR platforms. My approach has consistently been focused on balanced governance, clinicians’ alignment, key stake holders’ engagement, and technology execution to ensure sustainable transformation. 

Having worked extensively with CXO’s, clinicians and care delivery teams, I am constantly looking for avenues to digital transformation, emphasize change leadership, workforce enablement, and operational excellence as the foundation for system adoption. While i bring a blend of technical depth, strategic foresight, and leadership maturity to every transformation journey, I am constantly reminded on my mission to harness digital, data, and AI adoption (be it, GenAI, Agentic AI, Voice AI & many more AI’s) to reimagine care delivery for healthcare organizations and operational resilience in modern healthcare enterprises.

Pedigrees are often critical for your foundational learnings, and I constantly go back to refresh my mind often with byte size learnings as an everyday routine to sharpen the axe. Now, for more than a decade I am fortunate to speak at forums for those sharp young minds at universities and colleges. Was lucky to do blend my knowledge with dual master’s degrees in computer science and business administration, and currently in the pursuit of a PhD in Leadership Management.

 

Q2. With GenAI now embedded into clinical workflows—such as ambient documentation, AI-powered clinical assistants, multimodal data interpretation, and automated care navigation—how are healthcare systems integrating these capabilities into Epic, Cerner, and other EHRs while ensuring accuracy, clinician trust, and responsible AI governance?

AI’s journey in healthcare started much earlier than most realize. Back in early 2000, we had what was called CDSS (Clinical Decision Support Systems) early forms of healthcare AI designed to predict disease progression, assess risk factors, and guide interventions. That was the foundation of what we now call healthcare analytics and predictive AI.

With Generative AI (GenAI), the landscape has changed dramatically. GenAI allows us to go beyond predictions it helps create and structure new clinical information. For example, AI can now auto-generate SOAP notes (Subjective, Objective, Assessment, Plan) from doctor–patient interactions using ambient listening and AI scribes. These tools can also generate discharge summaries, referral notes, and care instructions directly within EHR systems such as Epic and Cerner. Notably, my favourites from Epic EHR AI systems are “Emmie, Art & Penny”.

Similarly, in medical imaging and diagnostics, GenAI is used to generate synthetic datasets that strengthen model training for radiology, pathology, and even in ophthalmology. In administrative functions towards healthcare payers, AI automates claims generation, eligibility verification, denials, and appeal letters thus streamlining the entire revenue cycle.

We are also in the era of Agentic AI a more autonomous form of artificial intelligence capable of executing strategies and actions, not just generating text or recommendations. These “agentic” systems will redefine automation and decision support in healthcare.

Voice based AI technologies present significant advantages for healthcare systems, offering new ways to streamline operations and enhance clinical efficiency. These solutions have the potential to revolutionise how healthcare professionals interact with technology, facilitating smoother access to patient data and enabling hands free documentation, which can substantially reduce administrative burden & give back the “Pyjama time” for the physicians.

However, the adoption of voice-based AI is not without its inherent challenges. Issues related to accuracy, privacy, and seamless integration into existing workflows must be carefully navigated. Despite these hurdles, the opportunities for disruption in the healthcare technology space remain abundant. Voice AI stands poised to transform operational norms and redefine the future of healthcare delivery, making it an area of great promise within the sector.

The integration of AI into core clinical workflows within platforms like Epic and Cerner represents both a transformative opportunity and a governance challenge. Leading health systems are embedding AI capabilities such as ambient clinical documentation and intelligent assistants directly within the EHR user interface to reduce physician administrative burden and enhance decision support. 

For instance, Epic’s collaboration with Nuance DAX Copilot and Microsoft’s Azure OpenAI allows clinicians to auto-generate visit notes in real time, which are then reviewed within the Epic note composer before physician’s signoff. This human-in-the-loop validation preserves accuracy while delivering efficiency gains, addressing one of the primary clinician pain points documentation overloads that contributes to the physician’s burnout.

Accuracy and clinician trust hinge on transparent model behaviour and explainability within these integrations across the healthcare enterprise for those secured data exchanges. Mature health systems are adopting structured governance frameworks, often led by interdisciplinary AI councils that include CMIOs, compliance officers, and clinical data scientists. For example, Mayo Clinic and Cleveland Clinic have developed AI validation pipelines where predictive models undergo pre-deployment clinical validation and continuous performance monitoring in production environments. These organizations are also integrating model output dashboards within the EHR that display confidence levels and key input factors, allowing clinicians to interpret AI suggestions contextually rather than blindly following algorithmic recommendations.

Responsible AI governance has emerged as a cornerstone of sustainable adoption. Health systems are institutionalizing policies aligned with HIPAA, HITRUST, and emerging FDA guidance on AI/ML based software as a medical device (SaMD). This includes ensuring patient data privacy, preventing model drift, and addressing potential bias in clinical predictions. For instance, Mount Sinai’s AI governance framework enforces periodic retraining of models using demographically balanced datasets to ensure equitable outcomes. Forward looking organizations are also leveraging federated learning within Epic and Cerner ecosystems to enhance model performance without centralizing patient data. In essence, the most successful healthcare leaders are not merely integrating AI into workflows they are embedding it within a culture of clinical accountability, transparency, and continuous learning.

 

Q3. As global interoperability standards mature and HIE platforms evolve to real-time, patient-centric data exchange, how do you see EHR systems transforming over the next few years to support more connected, decentralised, and collaborative care models?

To understand the future of EHRs, we must look at where they began. The earliest version of an EMR apart from the handwritten notes, dates to the 1960s, when Massachusetts General Hospital developed the first system called COSTAR (Computer Stored Ambulatory Record) in collaboration with Lockheed Corporation. In the 1970s, the U.S. Department of Veterans Affairs built VistA (Veterans Health Information Systems and Technology Architecture) a milestone in government-led digital health.

Fast forward, By the 1990s, commercial EHR systems such as Epic, Cerner, and Meditech entered the market, followed by regulatory frameworks like the HIPAA Act of 1996, which formalized data protection and portability. This era also did not forget in integrating the personal health records (PHRs)—digital versions of a patient’s medical history that could, in theory, travel with them across borders.

The next major leap came with interoperability standards—HL7 v2/v3, CCDA (Consolidated Clinical Document Architecture), and FHIR (Fast Healthcare Interoperability Resources), came through as the successor by HL7 International. FHIR ushered in API-driven, real-time data exchange, allowing seamless integration between healthcare disparate systems and improving continuity of care.

Having said that, EHR systems are evolving from being static systems of records, completely siloed, highly data protected at its own health facility (non-transferable) to dynamic systems of engagement—designed around the patient rather than the facility

Looking ahead, the EHRs of the future will be:

• Data-driven and AI-transformed, operating through federated and agentic AI architectures.
• Cloud based, secure, and omnichannel, enabling real-time access across care settings and geographies.
• Designed with AI-first, patient-centric, and human-AI collaborative frameworks.
• Structured in multi-layer architectures, experience first, compounded intelligence, interoperable data, and security layers working together.

The 21st Century Cures Act already mandates API access for patients and clinicians, paving the way for a truly interoperable, intelligent EHR ecosystem that unites payers, providers, pharma, and patients around a single digital continuum of care.

EHRs will increasingly operate as open(yet secured), API-driven platforms that seamlessly exchange data across providers, payers, and patients in real time. This shift will enable a unified longitudinal patient to record accessible across care settings, supporting precision care, remote monitoring, and AI-driven decision support. The traditional “closed” EHR ecosystem will give way to interoperable, cloud-enabled architectures capable of integrating third-party digital health applications, wearables, and population health platforms.

With decentralization, this will define EHR transformation and care will move beyond hospital walls into homes and community networks. EHRs will embed more intelligent automation, predictive analytics, and agentic AI to deliver context-aware insights at the point of care. 

Governance, security, and consent management will be equally critical, ensuring HIPAA and GDPR compliance in distributed environments. The Healthcare CXO’s mandate will shift toward building connected digital health ecosystems where the EHR becomes the orchestrator of a broader AI based digital fabric supporting collaborative, equitable, and continuous care delivery.

 

Q4. AI-driven front-door solutions—from smart scheduling to automated symptom triage to personalised care recommendations—are reshaping patient experience. Where do you think healthcare organisations still fall short, and how should they redesign patient journeys to fully leverage patient-centric digital tools?

Despite rapid technological progress, AI adoption in healthcare remains cautious and fragmented. 

While AI driven digital front door solutions in healthcare have improved access and convenience, most healthcare organizations still fall short in delivering truly integrated and frictionless digital experiences. Many systems operate in silos, AI chatbots, scheduling tools, and symptom checkers often don’t sync with EHRs or care management platforms, resulting in fragmented handoffs. 

For example, an AI triage bot may recommend an urgent visit, but the scheduling system might not reflect real time clinician availability, forcing patients to re-enter data or call the front desk and defeating the purpose of implementing AI. These gaps erode patient trust, experience, physicians time, leave money on the table and diminish the perceived value of digital investments.

To fully leverage AI and redesign patient journeys, CIO’s must focus on end-to-end orchestration linking AI tools directly to core clinical and operational systems through interoperable APIs and workflow automation. This means using platforms like Epic’s Hello Patient or Cerner’s Virtual Front Door integrated with CRM and RPA engines to deliver continuity from symptom input to follow-up care. 

Embedding personalization via longitudinal health data and social determinants can further tailor experiences, shifting digital tools from transactional to relational. The goal is not more AI touchpoints, but one connected, intelligent front door that adapts to each patient’s context and care needs.

While there’s a greater degree of enthusiasm in the Board/CXO’s, but we often see scepticism at the physician’s fraternity largely due to AI hallucination, regulatory uncertainty, and high infrastructure costs (GPUs, edge systems, cloud resources). Physicians, understandably, hesitate to trust systems that might misclassify or misinterpret data. While the Gen-Z physicians are ready to wrap AI around them, the previous ones approach it with abundant caution.

Take radiology as an example: AI can already detect fractures, nodules, and early-stage cancers with impressive accuracy. But if AI produces a false positive or misses a lesion, it can have serious clinical and legal implications. 
In most of my conversations with physicians, I am constantly reminded my physicians often say, “AI in healthcare cannot afford to hallucinate , whatsoever…Period!”

Beyond diagnostics, AI also helps in clinical coding, triage, and scheduling, yet widespread trust and ROI demonstration remain the biggest barriers. For sustainable adoption, organizations must focus on:

• Clinician engagement and change management to build trust
• Responsible AI governance and bias mitigation
• Redesigning patient journeys around empathy, not just efficiency; and
• Integrating front-door solutions (like chatbots, symptom checkers, and triage systems) within the continuum of care.

In short, healthcare needs to pair digital precision with human compassion, that’s the bridge between technology and true patient experience.

 

Q5. With rising pressures from nursing shortages and operational bottlenecks, how do you see AI-enabled remote care, virtual nursing models, and continuous patient monitoring helping healthcare systems stabilise their workforce and enhance care delivery?

I’d like to view this more as a global workforce shortage problem at health systems across the world, not just a nursing shortage. Well, looking at the rear mirror, the bygone pandemic has amplified mental health strain, burnout, and attrition across the entire spectrum of care workforce and nursing is no exception to it. We constantly see the nurse-to-patient ratio jump from every year to an alarming range which impacts care quality and safety. AI can certainly manage this situation and provide a balance of care across the ecosystem.

AI enabled remote care and virtual nursing are rapidly becoming strategic levers for healthcare systems grappling with workforce shortages and Care givers burnout. Virtual nursing models powered by AI driven communication tools and intelligent triage are allowing remote nurses to manage admissions, discharges, and patient education, freeing bedside staff to focus on direct clinical care. 

For example, Health systems like Mercy Virtual and Advent Health have successfully deployed “virtual command centers” where AI supports nurses with continuous observation, early warning alerts, and patient engagement at scale. This hybrid model not only optimizes staffing efficiency but also sustains care quality across distributed sites.

Remote patient monitoring, enhanced by AI algorithms that detect subtle physiological trends, enables proactive intervention before patient deterioration. This reduces preventable readmissions and nurse workload associated with crisis response. Integration of these capabilities into EHRs and hospital command centers creates a closed-loop operational model. This is where data drives care coordination, escalations, and resource allocation in real time. Ultimately, AI-enabled remote and virtual care ecosystems are helping healthcare organizations rebalance their workforce, reduce burnout, and maintain clinical excellence amid rising demand.

Additionally, Virtual nursing models are gaining traction, especially in long-term acute care (LTAC). Nurses remotely monitor patients using AI chatbots and IoMT devices, handling routine tasks such as symptom checks and patient education. These systems can alert physicians or trigger follow-ups if abnormalities arise.

 

Q6. Having led multi-geography  EHR modernisation and digital transformation programs, what differentiates organisations that scale transformation successfully—whether in terms of governance, clinical engagement, workflow redesign, or linking technology adoption to measurable clinical and financial outcomes?

Successful organizations start with a clear, purpose-driven vision anchored in security, adaptability, and patient-centricity. Every transformation journey must begin with a solid foundation, ensuring data standardization, strategic platform modernization, and well thought system integration. Typically, large scale transformations follow a phased approach:

Healthcare organizations that scale EHR modernization successfully distinguish themselves through robust governance and deep clinical engagement. The most mature programs are led jointly by IT, clinical, and operational leaders under a unified governance structure that prioritizes outcomes over implementation milestones. 

For example, Cleveland Clinic embedded physician informaticists and nurse champions in every phase of its Epic rollout, ensuring workflows aligned with frontline realities and reduced cognitive load. 

Similarly, NHS England’s Global Digital Exemplars model emphasized board level sponsorship and measurable clinical benefits such as reduced sepsis mortality and faster discharge times rather than purely technical KPIs. These organizations recognize that transformation succeeds only when clinicians are co-owners of change, not end users of technology.

Another hallmark of successful transformation is continuous workflow redesign and outcome linked execution. Systems like Mount Sinai Health System and Intermountain Healthcare established data driven optimization programs that measure post-go-live performance tracking metrics such as charting time reduction, clinician satisfaction, and throughput improvements. 

They use EHR analytics and digital command centers to identify workflow friction points and iterate continuously. Financially, Kaiser Permanente has demonstrated how standardizing care pathways within its EHR led to measurable ROI through reduced readmissions and improved chronic care outcomes. The differentiator lies in execution maturity organizations that treat EHR modernization as a long-term, evolving clinical transformation initiative, rather than a technology deployment, are the ones that achieve sustained value and operational excellence.

Critically, Overall an exemplary governance is the linchpin that covers policy enforcement, audit trails, role-based access, and compliance. Technical success depends on adherence to best practices in DevOps, AI integration, security, and observability.

 

Q7. For investors evaluating digital health, AI-driven clinical tools, interoperability platforms, and next-generation EHR technologies, where do you see the strongest growth opportunities in the healthcare technology ecosystem over the next five years?

Investors should constantly evaluate opportunities through the lens of national health outcomes for improving access, care efficiency, and interoperability for a long term vision. 

The next five years will be defined by strongest growth opportunities in healthcare technology will center around AI-driven clinical decision support, ambient automation, and connected & secured data ecosystems. As hospitals face mounting pressure to improve productivity and clinical quality, AI tools that augment clinicians such as ambient documentation (e.g., Nuance DAX, Suki), predictive analytics for deterioration detection, and precision diagnostics will see rapid adoption. Though the healthcare industry started with expected pessimistic adoption, these technologies deliver measurable ROI by reducing administrative burden, improving diagnostic accuracy, and enabling earlier intervention. Additionally, virtual nursing, remote patient monitoring, and AI-enabled command centres are emerging as scalable models to address workforce shortages and drive continuous, proactive care creating compelling investment opportunities in operational and clinical efficiency.

Equally promising is the growth of interoperability and next-generation EHR platforms that shift from being static data repositories to intelligent, open ecosystems. Standards such as FHIR, TEFCA, and real-time Health Information Exchanges are unlocking new value through seamless data liquidity across payers, providers, and life sciences. Companies building APIs, patient data marketplaces, and AI-ready clinical data pipelines like Redox, Health Gorilla, and Innovaccer are positioning themselves as foundational infrastructure for digital health. 

As (VBC) value based care models mature, investors will gravitate toward technologies that link clinical intelligence with financial outcomes, enabling the next generation of data driven, interoperable, and patient centric healthcare delivery. Undoubtedly, AI will be woven into the entire fabric of healthcare systems. 

To be precise, the need is for an integral “AI fabric” that drives care efficiency, public health insight, and patient-centric value. Winning investors will be those who carefully govern, catalyse, disrupt and augment healthcare to build secure, interoperable, and patient centred platforms that unify the healthcare ecosystem while ensuring measurable clinical efficiency and most importantly, the financial returns.