Q1. Hospitals have invested heavily in EMRs and HIS over the last decade. Do you see RTLS becoming the missing operational layer that connects digital records with what is actually happening on the hospital floor? Can you share examples where real-time visibility has influenced management decisions rather than just improving tracking?
Indian hospitals have built impressive digital infrastructure over the past decade, including EMRs, HIS, nurse call systems, but a gap still exists: these systems focus on billing and to some extant clinical data, but not what is happening in terms of hospital operations. RTLS fills precisely that gap. It creates a live operational layer that bridges structured digital records with actual floor level reality, where a patient is located now, whether equipment is available currently, how staff are deployed at any given moment.
The impact is felt beyond mere tracking. In one deployment, real-time visibility into patient flow across OPD and IPD revealed consistent bottlenecks at admission and pre-operative areas, the real-time intelligence prompted the hospital management to restructure workflows by changing the staff deployment numbers and fixed the issue in this process.
In another instance, RTLS data showed that high-value imaging equipment was sitting idle for nearly 40 per cent of its scheduled operating window due to patient transport delays while the hospital was about to procure an additional unit for a perceived shortage, a finding that directly impacted a capital expenditure decision: instead of procuring additional equipment, the hospital addressed the transport workflow saving an uncalled for purchase.
A good metaphor can be the city corporation addressing a traffic bottle neck. A certain signal may constantly have a pile up during peak hours in the day time and evening – the answer is not necessarily building a flyover, but it could be merely balancing the time given per signal – increasing the time for the choked signal in proportion to the relatively lesser crowded signal to ensure lesser jams is a much simpler solution that solves the problem rather than spending crores on a flyover.
What distinguishes RTLS-driven decisions is that they are grounded in observed behavior based on real-time data, not reported behavior. Management teams are increasingly using live dashboards and historical flow data not just in operational reviews, but in board-level capacity planning discussions. The conversation is shifting from “how many beds do we have” to “how effectively are we using them” and RTLS is what makes that shift possible.
Q2. India’s leading hospital chains are expanding capacity, but many continue to face challenges around staff productivity, bed turnover and equipment utilisation. In your view, what percentage of operational inefficiencies today stem from visibility gaps, and where are hospitals seeing the quickest ROI from RTLS deployments?
Based on our engagements across Indian hospital chains, we estimate that 40–60 per cent of operational inefficiencies in mid-to-large hospitals are directly attributable to visibility gaps. These are not structural or staffing failures, they are information failures. Nurses spend time locating equipment. Beds remain blocked longer than necessary because discharge signals don’t propagate in real time. Staff deployment doesn’t adjust dynamically to patient load. A lot of customer dissatisfaction leading to diminished brand reputation.
Hospitals that are expanding capacity, adding wings, opening new floors frequently discover that the underlying operational patterns simply scale up along with the physical infrastructure. The inefficiencies travel with the expansion and to some extent become amplified after a certain stage.
Where are hospitals seeing ROI fastest? Three areas stand out consistently.
First is a tie between, asset utilisation and staff placement: On asset utilisation, hospitals typically discover that 20–30 per cent of critical equipment inventory is either idle, misplaced, or over-concentrated in one zone. Optimising this reduces unnecessary procurement and rental costs quickly.
With regard to staff placement – hospitals tend to have a surplus of 40-45 per cent additional headcount in comparison to the workload present with respect to patient transport and non clinical tasks. Optmising this has given all of our customers a 30 per cent minimum gain allowing them ample flexibility to redeploy resources where real necessity exists.
Second, bed management: real-time occupancy and discharge tracking reduces average bed turnaround time significantly, which compounds across a busy facility.
Third, patient throughput in health checks and OPD clinics, where visibility into wait times and bottlenecks enables scheduling adjustments within weeks of deployment.
These are not long-horizon benefits, hospitals begin seeing measurable operational shifts within the first quarter of a Trackerwave deployment. For CFOs and COOs, this makes the business case for RTLS considerably more straightforward than many other digital health investments.
Q3. As hospitals increasingly adopt AI and predictive analytics, are you seeing healthcare providers move from descriptive use cases such as asset tracking to predictive use cases such as forecasting patient flow, equipment demand or workforce allocation?
The evolution is certainly happening, when hospitals begin an RTLS deployment, the initial value is largely descriptive, knowing where assets are, seeing real-time bed status, understanding patient location. But the moment that data begins to accumulate, hospital leadership starts asking a different set of questions: not “where is this equipment right now” but “What is the predicted utilisation of our OT’s and is there a need for increasing our capacity to plan better? (The OT being the most expensive resource in a hospital)”
We are actively working with hospital partners on predictive visibility built on RTLS data streams. Patient flow forecasting, anticipating surges in emergency admissions or OPD load based on historical patterns and seasonal indicators is one area where the data foundation RTLS provides is very valuable.
Equipment demand forecasting, particularly for shared resources like infusion pumps and portable monitors, is another use case gaining traction.
Workforce allocation is perhaps the most consequential frontier. Real-time data on staff location, patient acuity, and zone congestion, combined with predictive models, allows shift supervisors to proactively redistribute staff before bottlenecks form rather than reacting after they do.
The hospitals making this transition most effectively are those that treat RTLS not as a tracking tool but as an operational decision support platform, one that feeds AI and analytics layers. The infrastructure is the same; the ambition is different. And increasingly, Indian hospital CIOs are arriving with that ambition from day one.
Q4. Much of the conversation around healthcare infrastructure focuses on adding beds and building new facilities. However, can technologies like RTLS help hospitals extract more capacity from existing infrastructure before they invest in expansion? How are boards and CXOs evaluating that trade-off today
As I had mentioned in my earlier answers, this is one of the most important conversations happening in Indian healthcare today, and RTLS is increasingly at its centre. The instinct to solve capacity constraints by building more … more beds, more floors, more facilities … is understandable but often not required. Our experience suggests that most hospitals operating at 80 per cent+ occupancy have significant latent capacity that remains untapped due to operational inefficiencies rather than physical constraints.
Consider what happens when average bed turnaround time drops by 30–40 minutes across a 500-bed hospital. Or when equipment availability improves so surgical lists don’t get pushed due to missing instruments. Or when staff are deployed in real time based on where patient load is highest rather than on a static shift plan. Each of these improvements can yield an effective equivalent of additional capacity without capital expenditure.
Boards and CXOs are increasingly receptive to this framing, particularly in an environment of rising construction costs and compressed operating margins. We are seeing hospital groups use RTLS data in pre-investment analyses, essentially asking: “Have we fully used what we have before we commit to building more?” In several cases, this exercise has deferred or downsized expansion plans by 12–18 months while operational improvements are implemented.
The trade-off is shifting from a binary “build or don’t build” decision to a sequenced strategy: optimise first, then scale from a higher baseline. RTLS is the evidence base that makes that sequencing possible.
Q5. Government hospitals have traditionally faced challenges around resource constraints and operational complexity. Through your recent engagements with Maharashtra and SCB Cuttack, what operational gaps are public healthcare providers looking to solve through RTLS, and how does the implementation approach differ from private hospitals?
Public healthcare institutions face challenges that are fundamentally different from private hospitals, and our engagements with Maharashtra’s government health system and SCB Medical College and Hospital in Cuttack have reinforced this clearly.
The operational gaps in government hospitals are not primarily about efficiency optimisation, safety and they are about basic visibility. In large public facilities handling thousands of patients daily with constrained staff-to-patient ratios, the core problem is that administrators and clinical leads often lack reliable, real-time information on what is happening across the hospital at any given moment.
With regards to Patient and Asset Tracking – Equipment disappears between departments without warnings. Patient movements are not tracked systematically. Bottlenecks in casualty and OPD go unaddressed because they aren’t measured. All of these things lead to significant stress on care providers and administrators.
With regards to safety of infants, abduction is very much prevalent. Every other day the media reports cases across the nation and there are several that go unreported.
In these contexts, RTLS addresses foundational visibility and safety first, reliable asset tracking, patient flow mapping, infant safety and tracking and staff deployment intelligence before any layer of predictive analytics.
The implementation approach also differs significantly. Government hospitals require solutions that integrate with existing infrastructure at minimal incremental cost, are operable with limited technical staff, and deliver value in high-patient-volume, resource-constrained conditions. Procurement and deployment timelines involve multiple institutional stakeholders.
Trackerwave’s approach in these engagements has been to co-design with department heads and administrative leadership, ensuring the solution maps to their operational reality rather than imposing a private-sector framework. The results demonstrate that RTLS delivers meaningful impact in public healthcare, but the pathway looks different.