Indian hospitals are among the most energy-intensive facilities. Where do you see the biggest gaps today in how HVAC and critical utilities are actually managed?
Indian hospitals are inherently energy-intensive, given that they operate 24×7, under zero-tolerance conditions for comfort, safety, and system failure, but the real challenge isn’t the scale of consumption—it’s how that energy is managed over time. The biggest gaps typically emerge not at the design stage, but once systems move from commissioning into live operations.
Most hospitals are well designed and carefully commissioned. However, HVAC and critical utilities are often run on static assumptions in an environment that is highly dynamic. Fixed setpoints, conservative operating margins, and manual overrides are used to ensure clinical safety, but they rarely adapt to real-time occupancy, case mix, or ambient conditions. Over time, this leads to chronic overcooling, inefficient equipment staging, and unnecessary stress on assets.
A deeper issue is what happens after handover. Performance accountability tends to taper off once operations begin. Without continuous optimisation, systems gradually drift— setpoints creep, controls are overridden, and efficiency erodes quietly, even though nothing appears visibly “broken.”
Another gap is limited, fragmented visibility across systems. HVAC, electrical, and backup utilities are frequently monitored in silos, making it difficult to optimise performance holistically or anticipate issues early.
As a result, operations remain largely reactive. Data exists, but it is not consistently translated into actionable insight. Energy, reliability, and comfort are still managed through experience and intervention rather than intelligence and prediction—leaving significant efficiency and resilience gains untapped.
When building management systems fall short, how does that show up on the ground, especially in ICUs, operating theatres or other critical care areas?
In critical care environments, the consequences of BMS shortcomings can be serious. While hospitals are designed with layers of redundancy, failures in HVAC or critical utilities—such as loss of ventilation control, pressure imbalance, or delayed power backup—can directly compromise patient safety if not arrested quickly.
In practice, what prevents these situations from escalating is often human intervention rather than system intelligence. Engineering teams step in, override controls, manually stabilise conditions, or run systems at maximum capacity to stay ahead of risk. While this vigilance is commendable, it also masks underlying fragility.
More commonly, shortcomings surface as instability before outright failure—temperature and humidity drifting in ICUs, pressure differentials fluctuating in operating theatres, or delayed response during load changes. These deviations increase dependence on manual monitoring and narrow the margin for error.
Another challenge is delayed visibility. Traditional BMS platforms tend to react after thresholds are breached rather than highlighting early signs of deviation. By the time an alarm is raised, teams are already in firefighting mode.
Over time, this reactive operating model becomes normalised. Clinical outcomes are protected, but at the cost of higher energy use, accelerated equipment wear, and constant human oversight. In environments where consistency, predictability, and uptime are non-negotiable, relying on intervention instead of intelligence is a risk hospitals can no longer afford.
Beyond rising power bills, what risks or inefficiencies do hospitals often overlook when HVAC systems aren’t optimised?
Rising power bills are often the first visible signal that HVAC systems are not operating optimally. But in hospitals, energy inefficiency is rarely an isolated issue—it is usually the earliest symptom of broader operational drift.
One commonly overlooked risk is reliability erosion. Systems that are run conservatively to stay “safe” tend to operate under continuous stress—pumps at higher speeds, chillers cycling inefficiently, valves and actuators working harder than necessary. While this initially shows up as higher energy consumption, it gradually accelerates equipment wear and increases the likelihood of unplanned breakdowns.
Another issue is operational fragility. When performance depends on frequent manual intervention—overrides, setpoint tweaks, and constant monitoring—the hospital is effectively relying on people to compensate for system limitations. This creates shift-to-shift variability and reduces resilience during sudden load changes or equipment faults.
There is also significant hidden performance loss. Comfort bands may still be maintained, but through inefficient means— overcooling to manage humidity, simultaneous heating and cooling, or suboptimal sequencing of equipment. Because nothing is visibly “failing,” these inefficiencies often go unnoticed, even as energy use and mechanical stress continue to rise.
In healthcare environments, inefficient HVAC operation is not just a cost concern. It is an early indicator that systems are drifting away from stable, predictable performance— something hospitals can ill afford.
How ready are Indian hospitals to rely on real-time data for operational decisions, and how valuable is predictive intelligence in avoiding unexpected downtime?
Most Indian hospitals today have access to large volumes of operational data, but readiness varies in how confidently that data is used for decision-making. Realtime visibility is often limited to monitoring and alarms, rather than actively guiding how systems should run minute by minute.
Where real-time data does add value immediately is in situational awareness— understanding current loads, equipment status, and deviations across HVAC and utilities. However, the bigger gap is moving from visibility to anticipation. Many failures in hospitals don’t occur without warning; they are preceded by subtle patterns—declining efficiencies, longer response times, abnormal cycling—that traditional systems don’t surface early enough.
This is where predictive intelligence becomes critical. By learning how equipment and systems behave under different operating conditions, predictive models can flag emerging risks before they translate into downtime or clinical disruption. Instead of reacting to alarms, teams get time to plan interventions, redistribute loads, or correct issues during non-critical windows.
In a healthcare environment, avoiding downtime isn’t just about redundancy. It’s about foresight—using data not just to see what is happening, but to understand what is likely to happen next.
Do you think energy efficiency has moved beyond being a sustainability conversation to becoming a core performance and financial metric for hospital leadership?
It has begun to—but not uniformly, and that gap matters. For many hospitals, energy efficiency still appears primarily in sustainability reports or annual disclosures. But leading hospital groups are starting to recognise it as something more fundamental: a proxy for how well their infrastructure is governed and controlled.
In practice, energy performance reflects whether systems are operating as intended, whether assets are being stressed unnecessarily, and whether the hospital is relying on predictable processes or constant human intervention to stay stable. Rising energy intensity is rarely just a cost issue—it often signals performance drift, growing operational risk, and future capex that leadership hasn’t planned for.
What’s changing in boardrooms is the realisation that energy efficiency is measurable, comparable, and actionable. Metrics such as energy per bed, per procedure, or per square foot now offer insight into operational discipline, not just sustainability posture.
Hospitals that treat efficiency as a business metric gain more than savings. They gain visibility, resilience, and confidence in their ability to scale without compounding risk. Those that don’t often wait until cost spikes, downtime, or expansion pressure force the conversation—by which point options are narrower and more expensive.
In that sense, energy efficiency is no longer a reportable statistic. It is an early decision-making signal—one that hospital leadership can choose to act on proactively, or react to later under pressure.
As hospitals expand and add more complex equipment, how should infrastructure planning change to stay future-ready?
Hospital infrastructure planning needs to shift from being capacity-driven to capability-driven, and equally from an upfront-cost mindset to a lifecycle-performance mindset. Historically, futurereadiness has been equated with adding redundancy— larger plants, more backup systems, higher safety margins—often optimised for capital expenditure rather than long-term operation.
As hospitals add advanced diagnostic, surgical, and life support equipment, load profiles become more variable and complex. Planning for this future requires systems that are not just robust on day one, but efficient, adaptable, and predictable over decades of operation. Decisions made purely on lowest initial cost often translate into higher energy consumption, accelerated wear, and increased dependence on manual intervention over time.
Another critical shift is designing infrastructure with performance continuity in mind. Expansion should not create new silos. HVAC, electrical, and backup utilities must be planned as integrated layers, with visibility across the full utility stack, so that future additions do not compound operational complexity or risk.
Finally, future-ready planning must account for how systems will be operated and optimised throughout their life—not just installed and commissioned. Infrastructure that supports continuous performance monitoring and optimisation allows hospitals to scale, adopt new clinical technologies, and meet regulatory requirements without locking themselves into escalating operating costs or hidden risk.
In healthcare, true future readiness is not defined by how much capacity is built upfront, but by how well that infrastructure performs, adapts, and pays back over its full lifecycle.
What kind of organisational or cultural challenges emerge when hospitals move from manual operations to AI-driven infrastructure?
The biggest challenge is not technology—it’s trust and transition. Hospitals are environments where risk tolerance is understandably low, and teams are trained to rely on experience, judgement, and manual control to keep systems safe. Moving to AI driven infrastructure can initially feel like ceding control, especially when systems begin to make recommendations or decisions autonomously.
Another challenge is role redefinition. Automation changes how engineering teams spend their time. Instead of constant monitoring and firefighting, the emphasis shifts to validation, exception handling, and optimisation. This transition requires new skills and, more importantly, reassurance that intelligence is meant to support human expertise, not replace it.
There is also the question of accountability. When decisions are data-driven, organisations need clarity on who owns outcomes— engineering, operations, or leadership. Without that clarity, even capable systems can be underutilised.
Hospitals that navigate this shift well treat intelligence adoption as a change management exercise, not a software rollout. They invest in training, phase adoption carefully, and build confidence through early wins. Over time, teams stop seeing intelligence as a risk and start seeing it as a safety net—one that reduces cognitive load, variability, and dependence on constant manual intervention.
Looking ahead, what do you think will set operationally excellent hospitals apart, and how should CEOs think about evaluating technology partners beyond short-term cost savings?
Operationally excellent hospitals will be distinguished by how predictable their infrastructure becomes under pressure. Not whether systems are installed or certified, but whether temperature, air quality, power, and uptime remain stable during peak loads, equipment faults, or sudden demand changes—without requiring constant manual intervention.
In practice, the gap shows up quickly. Some hospitals run efficiently only when everything goes right. Others continue to perform even when conditions don’t. That difference is rarely about equipment quality alone; it comes down to how well systems are governed, monitored, and optimised over time.
For CEOs, this fundamentally changes how technology partners should be evaluated. Upfront cost and feature lists are easy to compare, but they say little about long-term outcomes. The more important questions are: Does this partner stay accountable once the system is live? Can they demonstrate sustained performance across years, not just at commissioning? And do they understand hospital operations well enough to reduce variability, not add another layer of complexity?
The strongest partners are those who remain present after handover—tracking performance drift, responding to real-world behaviour, and continuously tightening operations rather than walking away once the installation is complete.
In healthcare, operational excellence is not achieved through one-time decisions. It is sustained through partners who are willing to stand by outcomes, not just promises. Hospitals that recognise this early build infrastructure that is not only efficient, but resilient, scalable, and dependable over the long term.
lakshmipriya.nair@expressindia.com
laxmipriyanair@gmail.com