As India’s healthcare system shifts toward continuity of care and decentralized delivery, remote patient monitoring (RPM) is emerging as a critical enabler rather than a pilot-driven concept. With rising chronic disease burden and expanding digital health infrastructure, in this exclusive conversation with Vivek Tyagi, Managing Director and Country Manager, Analog Devices India, Dr. Asawari Savant from Elets News Network (ENN) explores topics around around making RPM clinically reliable, scalable and cost-effective, which has never been more urgent. Edited excerpts

Remote patient monitoring in India has seen pilots for years, what has changed now that makes it a “need of the hour” rather than a nice-to-have? 

Several forces have converged to make RPM more urgent now. India is managing a sustained rise in chronic conditions that require ongoing follow-up, not only episodic visits; government-released figures citing the ICMR-INDIAB study put diabetes prevalence at 10.1 crore people, which increases the need for continuity of care beyond periodic check-ins. In parallel, India’s digital health backbone is evolving through initiatives such as the Ayushman Bharat Digital Mission (ABDM), which is designed to support interoperable digital health records and more connected care journeys over time.

From a care delivery lens, RPM is also being pulled forward by capacity and access pressures, especially when follow-up needs to extend beyond hospital settings and into homes and community care. ADI does not deliver healthcare services, but we help enable many of the foundational building blocks used in patient monitors, wearables and connected diagnostic devices, including sensing and edge processing that can support clinically meaningful measurements outside a hospital environment.

From your perspective, what are the top 3 reasons RPM programs fail at scale in India, clinical reliability, workflow adoption, or economics, and why?

I would frame the scaling challenge as three opportunities to strengthen the foundations. First is clinical confidence, RPM expands when clinicians can trust that measurements remain consistent in real homes, across motion, device placement variability and diverse use environments, supported by validation that matches the intended use case. Second is workflow fit, RPM works best when data flows support clinical decision-making with clear escalation rules and fewer non-actionable alerts, so care teams can focus attention where it matters rather than managing noise.

Third is economics at scale, programs slow when operational cost rises linearly with every additional patient, through support burden, replacements, connectivity and clinician review time. A practical lever is using edge processing to reduce low-value transmissions and convert raw signals into higher-value summaries or flagged events, which can help improve efficiency without compromising care quality.

“Clinical-grade at home” is a high bar. What are the minimum technical and validation standards you believe home monitoring must meet before clinicians can rely on it at scale?

“Clinical-grade at home” starts with clarity on intended use, whether the device is meant for screening, trending or clinical decision support, and then aligning performance validation to that use. At a minimum, home monitoring should demonstrate measurement accuracy and repeatability in conditions that resemble real use like motion, placement variability, ambient interference and user behavior outside controlled settings. Reliability should also include clear data-quality indicators and safeguards that help clinicians interpret readings appropriately.

As these devices increasingly rely on software and connectivity, clinician confidence also depends on disciplined software development and security-by-design practices appropriate for medical-grade systems, along with robust update mechanisms and failure recovery. We at ADI help enable device makers with building blocks for acquiring physiological signals and processing them at the edge, supporting designs that aim for high signal integrity and low power in wearable and home form factors.

India’s realities include patchy connectivity, power variability, and low-touch support. How should RPM devices and systems be designed so they remain dependable in real-world conditions?

Designing for dependable RPM at scale generally means assuming networks may be intermittent, power may be variable, and support may be limited, and ensuring the system behaves predictably anyway. A practical approach is graceful degradation, devices should continue core measurements locally, store data securely for later upload, and share meaningful summaries when connectivity returns rather than failing when the cloud is unreachable. Using edge processing to filter noise, handle artifacts and create higher-value updates can also reduce dependence on continuous connectivity.

Resilience also depends on usability and maintainability, guided placement, automated self-checks and simple cues can improve correct use without frequent support, while secure remote provisioning and update processes that tolerate interruptions can help keep fleets of devices consistent over time. ADI’s role is typically in the sensing and edge-processing chain that supports reliable measurement and efficient processing in devices used outside traditional care settings.

Cost is often the biggest constraint. Where can technology reduce total cost of care in RPM, device BOM (bill of materials), maintenance, connectivity, or clinician time, without compromising outcomes?

In many RPM models, the biggest cost driver is operational effort, support calls, replacements, unnecessary transmissions and clinician review time, more than the device alone. Technology can reduce total cost of care by shifting from “more data” to “more actionable data”, using on-device processing to generate trends, summaries and exceptions so clinicians spend time where intervention is most likely to matter, rather than reviewing continuous low-value streams.

There are also savings levers in connectivity and maintenance. When devices transmit fewer, higher-value updates, data and backend processing overhead can decline, while better signal quality and fewer false alarms can reduce returns and support burden. BOM optimization is possible through thoughtful integration, but it needs to be balanced against signal integrity and reliability, because those directly influence clinical confidence and downstream operational cost.

Also read: India Pharma Is at an Inflection Point. What Happens Next Will Be Decided at HITEX Pharma Expo 2026 

Looking ahead 5–10 years, how do you see decentralised care reshaping India’s healthcare delivery, what shifts will we see in hospitals, diagnostics, and preventive care, and what role will “edge intelligence” play?

Over the next decade, decentralized care in India is likely to expand in three ways, more routine follow-up for stable chronic conditions shifting outside hospitals, diagnostics and follow-up becoming more distributed through connected tools and care pathways, and prevention moving from periodic snapshots toward more continuous or more frequent monitoring, when devices are usable, affordable and trusted. Hospitals will remain essential for acute and complex care, while more monitoring and early intervention can extend into homes and community settings where access is uneven.

Edge intelligence will matter because it makes decentralized care more practical, it can improve signal quality, reduce data burden, support offline operation and translate raw physiological signals into clinically relevant insights closer to where data is generated. ADI aims to help enable this ecosystem by providing building blocks that transform signals into usable information for devices and platforms used in monitoring and connected diagnostics.

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