Enhanced Security with SOS-enabled BLE Badge

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Enhancing Staff Security with BLE Badge Featuring SOS Buttons

Imagine what would a dedicated nurse devoted to the patients and the hospital expect at minimum from the management?
Surely she would expect that her safety and security are taken care of while she is inside hospital.
Healthcare workers are often required to walk throughout the facility, sometimes in high-risk situations, and their safety should never be compromised. A practical, efficient solution to improve staff security is the introduction of BLE-enabled ID badge or BLE wristbands equipped with SOS buttons, offering a simple yet powerful way to alert security in moments of distress. BLE-enabled badge works for dual-purpose : as an ID badge as well as safety SOS device.

BLE Wristbands

BLE wristband with SOS buttonBluetooth Low Energy (BLE) technology allows for seamless, low-energy communication over short distances, making it ideal for environments like hospitals where staff are constantly on the move. A BLE wristband with an integrated SOS button ensures that the wearer can discreetly and instantly alert security personnel in the event of an emergency, regardless of their location within the hospital.

Patient Safety and Location Tracking

The wristband can be used for patients too. The wristband enables the hospital locate the patient in real-time when in need. It also enables the Patient to alert the nurse-station when in need. Pressing the SOS button sets off a buzzer in the nearest nurse station. It also flashes the location of the patient in App notification.

App Alerts for Immediate Response

A RTLS works in tandem to ensure that information flows in real time. The wristband SOS enables the staff send an immediate notification to the hospital security teams at the push of a button. Once activated, the SOS button triggers an alert, pinpointing the exact location of the distressed staff member within the facility. This swift communication allows security to respond faster, reducing the time it takes to reach the scene of an emergency.

Empowering Staff to Feel Secure

One of the most significant benefits of the wristband is the psychological comfort it provides. Knowing that help is just a button press away can make staff members, particularly women, feel more secure while performing their duties. They can focus on their work without constantly worrying about their safety, knowing that help is easily accessible in any situation—whether it’s an incident of harassment, a medical emergency, or a physical threat.

Discreet and Non-Intrusive

The Badge is designed to be lightweight, discreet, and comfortable, ensuring that staff can wear it throughout their shift without feeling encumbered. The SOS button is intuitive to use, and its small form factor allows it to blend seamlessly into the work attire of hospital staff. This non-intrusive nature ensures that it does not interfere with daily tasks but remains within easy reach in moments of need.

Building a Safer Work Environment

By adopting BLE wristbands with SOS buttons for all hospital staff, we create a safety net that reinforces the hospital’s commitment to employee well-being. This proactive measure is an investment in a safer, more supportive work environment where staff can feel protected and valued. In an unpredictable workplace like a hospital, staff members deserve the peace of mind that comes from knowing that their safety is a priority.

RFID or BLE for your Hospital RTLS?

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BLE or RFID? - Things to consider before you decide for your hospital

A hospital RTLS primarily is a software that uses wireless technologies e.g. RFID, BLE or UWB, to provide you real time indoor location intelligence (for example, which asset is currently in which zone?) for your hospitals. Most RTLS vendors tie their software to a specific technology, which means deciding vendor itself selects the technology. But is the optimal? Would you not like to understand the cost-benefits independently before you let your vendor choose for you?

When it comes to hospital RTLS, hospitals in US have predominantly adopted RTLS based on RFID though cost is very high. It's also true that RFID (especially active RFID) based RTLS is there for a very long time. BLE in fact is a recent addition. Interesting thing is both BLE and active RFID uses 2.4 GHz band but while BLE standard is open, active RFID is often proprietary and therefore costlier.

Understanding RFID

RFID is wireless radio frequency (RF) based standard and works on multiple RF bands, designated as LF (Low Frequency), HF (High Frequency) and UHF (Ultra High Frequency). Accuracy and speed increases from LF to UHF. Main components of this technology is RF Tag, RF Antenna and RF Readers. RF Tag can be active which means the tag has a battery and sends data on its own. Passive RF tag on the other hand is without battery and it can only send the data using the reflected RF energy (from the reader). The advantage of passive RF Tag is that it has almost unlimited life (no battery replacement) and also costs 10 times less than an active RF tag. To read passive RF Tags, the RFID reader must use high-gain antenna.

UHF RFID operates at 860-930 MHz (For India designated band is 865-867 MHz) but the high gain makes it a little unattractive to use in radio-sensitive places inside the Hospital.

RFID protocols were designed for reading inventory which makes it little difficult to employ it for indoor location tracking in real time. Given the high cost of UHF RFID readers (above USD 1,000), building RFID infrastructure for a hospital is often prohibitively expensive. While most leading vendors started supporting WiFi in the their RFID readers in latest products, old models typically do not support WiFi, which means it needs its separate network making the installation expensive. For a medium-sized hospital, RFID infrastructure costs have been reported between $200,000 and $600,000, depending on the facility's size and specific requirements.

Due to high cost, a hospital typically uses only a few fixed Reader placed at strategic location to cover entire floor area. But that also brings down effectiveness of tracking. Large inaccuracy of location with RFID based RTLS is a common refrain.

How BLE is different?

Most Hospital RTLS employs active RFID hardware which typically uses the regulation-free 2.4GHz. This is the same frequency band that is used in Microwave Ovens all over the world, as well as Bluetooth Devices and basic WiFi network. Given the low power gain used, it is considered safe.
BLE stands for Bluetooth Low Energy and is used in sensor network ubiquitously. Being lightweight, the devices come with very small form factor.  BLE works in the same way as active RFID. But since BLE standard is open, BLE devices have proliferated a lot faster. That effectively has brought down the cost of BLE tags and BLE gateways. BLE gateways cost 10 times lesser than a RFID reader and it can use the hospital's WiFi network which  in turn brings down the overall installation cost for BLE based RTLS.

For a 40 room hospital, it was reported to cost below USD 100K. However, we should remember, the cost of BLE tags are higher compared to passive RFID tags and battery of a BLE tag typically needs replacement every 1-2 years.

Active RFID vs BLE

Both of them need battery which means it requires periodic battery replacement. Additionally active RFID devices and tags are relatively more costly. More importantly vendor lock-in is one major aspect of using active RFID hardware. In comparison BLE devices are available off-the-shelf and can be upgraded / replaced with different vendor's products at ease without any service downtime.

A ready comparison chart

Feature UHF RFID BLE
Frequency Band 860–956 MHz 2.4 GHz
Range Up to 100 meters (depending on tag and reader / antenna)  less than UHF RFID
Data Rate Up to 640 kbps Up to 2 Mbps
Accuracy Typically within a margin of a few meters. Passive tags sometimes are missed by the Readers during periodic scanning Better than RFID. BLE Tags send the data themselves - eliminating the issue of missed scans.
Power Consumption Passive tags require no power; active tags have batteries Low power consumption; suitable for battery-powered devices
Cost Passive tags are cost-effective; active tags are more expensive Generally low-cost due to widespread adoption
Typical Applications Asset tracking, inventory management, supply chain logistics Asset tracking, personnel monitoring, proximity marketing, indoor navigation
Tag Cost Passive UHF RFID tags: Approximately $0.10 to $0.50 per tag.
Active RFID tags: Approximately $5 to $15 per tag.		 BLE tags: Approximately $2 to $10 per tag.
Reader/Gateway Cost Fixed RFID readers: Approximately $1,400 to $8,000 per unit.

Handheld RFID readers: Approximately $1,000 to $4,500 per unit.

 BLE gateways: Approximately $100 to $500 per unit.

INDTRAC Approach

INDTRAC promises to work with almost all leading BLE and passive UHF RFID readers. INDTRAC also qualifies vendor hardware before they are recommend to you so that you can choose the technology most suited for your use case and budget without any worries. For BLE tags, INDTRAC proactively monitors the battery and alerts you to replace the battery when a tag battery loses the power. BLE tags mostly use ubiquitously available low-cost coin lithium battery which makes the replacement cost very marginal.
There is another advantage with using BLE -- you can seamlessly use the RTLS to monitor zone-wise temperature, humidity or VOC almost at the same expense as that of asset tracking infrastructure.

Responsible AGI and other things

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Google apparently in recognition that they lost LLM game (first to OpenAI chatgpt and then to Chinese deepseek) started drum rolling for AGI. Given how people are afraid of AGI, they brought out a paper on responsible AGI (somewhat similar to earlier responsible AI ). Read the Google post here.

Does it mean that LLM ≠ AGI

There were posts earlier where OpenAI said chatgpt is almost AGI. Even some in Google Gemini team said Gemini is almost sentient. But if people are talking about AGI seperately from LLM, perhaps that is acceptance of that fact that LLM may not ever reach human capability of intelligence. In fact there was a recent study that asserted that LLM could not match human ingenuity of ‘zero shot abstract thinking’.
Martha Lewis, a coauthor of the study, tells LiveScience, – while we can abstract from specific patterns to more general rules, LLMs don’t have that capability. “They’re good at identifying and matching patterns, but not at generalizing from those patterns.”  Read the full LiveScience post.

Can the AGI be responsible?

AGI has to evolve from current generative AI framework. Google Deepmind has categorized the challenge into four baskets:

  • Misalignment
  • Misuse
  • Mistake
  • Structural Risk

Misalignment refers to AI model doing something that developers did not intend it to do. Misuse refers to the model being misused by a human controller/user to work as adversary to humanity. Mistake refers to AI model doing something bad without triggering internal checks and balances. Structural Risk ensues from multi-agent dynamics without any fault from individual model. Example for this can be, a new path opens up due to complex interlinking of activities between AI models which developers didn’t intend to.

Of all the four types, Misalignment and Structural Risk are the most difficult to address because they are the most complex and difficult to uncover. We will limit ourselves to the issue of Misalignment for this post. Marcus Arvan explained in a LiveScience post, ‘If any AI became ‘misaligned’ then the system would hide it just long enough to cause harm’. He has given real life examples where LLM shocked the user with the answers.

‘The basic issue is one of scale. Consider a game of chess. Although a chessboard has only 64 squares, there are 1040 possible legal chess moves and between 10111 to 10123 total possible moves — which is more than the total number of atoms in the universe. This is why chess is so difficult: combinatorial complexity is exponential.

LLMs are vastly more complex than chess. ChatGPT appears to consist of around 100 billion simulated neurons with around 1.75 trillion tunable variables called parameters. Those 1.75 trillion parameters are in turn trained on vast amounts of data — roughly, most of the Internet. So how many functions can an LLM learn? Because users could give ChatGPT an uncountably large number of possible prompts — basically, anything that anyone can think up — and because an LLM can be placed into an uncountably large number of possible situations, the number of functions an LLM can learn is, for all intents and purposes, infinite.’ He argues.

Google’s approach to tackle misalignment

Google plans to use second AI model to validate a model’s answer. It’s sort of AI police to police AI model. Can this work? To find answer, we should ask, does policing work for human citizens? You could say mostly. But we should not miss that mostly it’s willingness of human citizens to follow rule and respect police that makes the job of police manageable. We surely cannot assert that for AI citizens. But given that entire AI paradigm works on goal-seeking principle, meticulous control of goal dynamics may provide an understanding of AI motivation.
Demis Hassabis, the Nobel Laureate CEO of DeepMind has a sobering thought about the approach to AGI. In a lecture in Cambridge University, he said, “Lot of Silicon Valley Companies work with the principle of ‘Move Fast, Break Things’ but I think it is not appropriate, in my opinion, for this type of transformative technology. I think instead we should be trying to use scientific method and approach with humility and respect this kind of technology deserves. We don’t know lot of things. Lot of unknowns about how this technology is going to develop. With exceptional sort of care and foresight we can get all the benefits and minimize the downside of it.” He invites people to focus on research and debate now and be mindful that the technology does not really go out of hand.
We are with him on that thought.

Why surgical instrument tracking is must-have for a hospital now

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surgical scissors left inside the body after operation – photo courtesy – Researchgate

In a recent shocking news update, a woman was found to carry a retained surgical scissors inside her body for 12 long years. It was sheer negligence that happened 12 years ago in an operation that removed her appendix. The lady experienced sustained pain in her body for 12 long years.
This created a huge uproar in the state with people demanding prosecution and cancellation of license.

The fact is that it is a simple procedural mistake but it is also true that it carries a huge ramification for the hospital and the doctor involved. It can cause both the surgeon and the hospital significant reputation damage if not the business. If you are wondering if this happens only rarely, US National Library of Medicine asserts that for every 10,000 operation 1.3 such incidences can happen in average. In India the number most likely is much higher.

Can a hospital eliminate this risk?

Yes, absolutely. A hospital needs to upgrade its CSSD operation to ensure that after every operation all instruments returned are tallied against used list of instruments. It is tedious considering the number of operations a hospital has to do every day.

RTLS makes it fast and error-free

Using a special autoclave-safe RFID tag attached to every surgical instrument, a hospital CSSD can track every instrument. INDTRAC provides custom-made solution to suit the special needs for every CSSD.

Read more here.

Agentic AI for Healthcare

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Agentic AI is Disrupting Healthcare Service

AI is changing almost every industry that we know of. In healthcare, it is proving to be widely disrupting - starting from the way the healthcare data is used to revolutionizing patient care and medical consulting.  You may have already heard of deep impact of AI algorithm in predictive analysis in healthcare. Agentic AI takes it two steps forward. It integrates everything to give a unified and powerful service interface that you haven't seen yet.

Imagine while you are checking your patient, your assistant is already pulling all the reports for her, analyzing the data for predictive patterns that you are looking for, displaying that analysis in a visual format that you are most used it and suggesting possible options for you to pick and recording your advise while you explain that to the patient. The Agentic AI can be this super-efficient highly knowledgeable, smart assistant that you always wanted.

It's an AI system that doesn’t just help doctors and staff make decisions; it makes the decisions itself. It analyzes data, interprets information, and takes action—all with minimal human intervention. Imagine an AI that can read your patient’s medical chart, diagnose their condition, and recommend a treatment plan, all in real-time.

It can simplify regulation compliance for the hospital administrator. It can assist the operation head in analyzing and recommending the area that require closer scrutiny. It can collaborate with other agents in completing a tasks. It can take up the role of front desk agents when needed.

Cut the time in decision making

Imagine this: a patient comes into the emergency room with chest pain. Traditionally, a doctor would order a series of tests, wait for results, and—after all that time—determine if it’s a heart attack. That’s a lot of time for a patient who could be in serious danger. With Agentic AI, the AI can analyze all the data in minutes, compare it with thousands of similar cases, and flag the critical risks immediately. In fact, studies show AI systems can improve diagnostic accuracy by up to 30% compared to human doctors alone. That means fewer mistakes, quicker decisions, and better outcomes.

Fundamental elements of Agentic AI

  • It uses multi-modal Large Language Model as the engine (e.g. chatgpt or anthropic or Google Gemini) trained with specific body of knowledge
  • Autonomy in decision making - given a role, it can take its own decision based on the data it processed.
  • Mimics human goal-oriented behaviour - changes strategy in real time to fulfill the goal
  • Continuous learning - It learns on the job! While processing the data, creating the decision maps it also refines its inference engine based on new learning
  • It works in real time

The Dollars and Cents: What’s the ROI for a Mid-Sized Hospital?

There is no denying that Agentic AI will require sizeable investment.

On average, the cost of implementing Agentic AI in a mid-sized hospital can range anywhere from $1 million to $5 million, as per an estimate. That includes software, integration with existing hospital systems, and training staff to use the new tools.

But here’s where things get interesting: the ROI is rapid. Hospitals that adopt Agentic AI typically see a 20-30% reduction in administrative costs. With less paperwork, fewer human errors, and faster patient turnover, hospitals can start saving money in a matter of months.

Let's break it down to the avenues where it can help

  • Automating routine administrative tasks (like scheduling, billing, and patient inquiries) reduces the need for manual labor, cutting down on staffing costs.
  • Fewer diagnostic errors translate to fewer malpractice claims and a reduction in unnecessary treatments.
  • Better resource allocation means optimized bed usage and staffing, reducing the costs associated with inefficiencies.

Would this be another isolated system like EHR software? Actually the power comes in integrating the agentic AI into your back-end so that it can 'see' the patterns in the data-stream and alert you on beforehand.

Commodity Agentic AI for Hospital

A commodity Agentic AI is expected to be universally usable for every hospital without needing to train the model. Microsoft recently launched Dragon Copilot, a voice-activated AI assistant which Microsoft claims, "has helped clinicians document billions of patient records, and has assisted over 3 million ambient patient conversations across 600 healthcare organizations in the past month alone. With these ambient AI capabilities, organizations have already realized significant outcomes, with clinicians reporting five minutes saved per encounter,[1] 70% of clinicians reporting reduced feelings of burnout and fatigue,[2] 62% of clinicians stating they are less likely to leave their organization,[3] while 93% of patients report a better overall experience."

Pitfalls?

There is one area of concern that everyone is conscious about. It can potentially give access (to the hospital's internal data) to Agentic AI provider. There are both technical and legal remedies available to mitigate that risk.

Summary

According to a report by Deloitte, AI in healthcare has the potential to generate $150 billion in annual savings for the US healthcare system alone.The thing is that the technology is almost here with the most competitive lot already moving ahead of the curve. Better care, smarter decisions, streamlined operations, and lower costs—that’s the power of Agentic AI. 

An early Fire Warning for Hospital?

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This month a tragic fire incident claimed lives of 10 babies in a hospital in spite of heroic effort from the hospital staff to save the babies in neonatal unit.

Fact is every hospital is expected to install fire alarms over all patient-sensitve zone and it is also a fact that every year loss of lives happen in fire incidents in hospitals. Here is another incident that claimed lives in hospital in Delhi this year. An extensive study jointly carried by Department of HSE and Civil Engineering, School of Engineering, University of Petroleum and Energy Studies, Dehra Dun and Centre for Pollution Control and Environmental Engineering, Pondicherry University, found electrical short circuit as the predominant reason for such fires. Full lists of the incidents that they studied can be found here .

Electrical short circuit

Often, the post-incident analysis finds electrical short circuits as the reason. But they conspicuously silent about why the short circuit was not detected early?

Ideally electrical circuit monitoring system would alarm for any electrical shorts in the system. Typically modern circuit-breakers switch off a circuit when anomaly is detected. We do not know if that was in place or if it did not work.

There are also some incidents that started from gas appliance or kitchen stove.

Tracking zone-based temperature for better indicator

Every fire incident starts small and if the temperature at sensitive zones are continuously monitored, sudden change of temperature can be used as early indicator for fire and administrator can be warned in advance before the fire become devastating.

Another option could be to monitor ambient light in sensitive zones and any sudden spike of lumens reported could be considered as early warning.

Thankfully both can be easily implemented with a good flexible RTLS system at a very small cost. Not all RTLS require the hospital to adopt every features offered. A hospital can implement only temperature or light monitoring solution at a fractional cost of full-feature RTLS.

INDTRAC RTLS supports modular solution

INDTRAC RTLS has multiple modules and flexible options. It also lets the hospital pick sensor hardware of its choice, unlike most of the existing RTLS vendors. INDTRAC also lets a hospital implement for only a subset of its total area in order that the hospital manage its budget and cost better.

Summary

RTLS enabled microzone temperature monitoring may give a hospital early warnings to act and avoid the extensive fire related damage to its patients and facilities. Write to us to learn more.

Calculating a hospital’s RoI for RTLS

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A common argument from the hospital management is that RTLS is too expensive for the hospital to adopt. We asked an expert to provide us a simple ROI analysis for a 350-bed hospital to demonstrate how long it takes for a hospital to break even.

Let’s get the assumptions out

  • Current Bed Utilization:
    • 70% of the time, bed utilization is below 70%.
    • Current average bed occupancy is estimated at 65%.
  • Time Savings:
    • Implementing RTLS is expected to save 2 hours/day in equipment search time.
  • Staff Costs:
    • Average hourly wage of hospital staff is ₹600.
  • Increased Revenue from Improved Bed Utilization:
    • By improving patient flow, it’s assumed that the hospital can increase utilization from 65% to an average of 75%, impacting overall admissions positively.
  • Operational Costs:
    • Annual operating costs for RTLS maintenance are estimated at ₹8,00,000.

Investment for RTLS

1. Initial Investment

a. RTLS Software and Hardware Costs

  • Software Licensing: ₹20,00,000
  • Hardware (tags, sensors, etc.): ₹30,00,000
  • Installation and Integration: ₹10,00,000
  • Training and Support: ₹5,00,000
  • Total Initial Investment: ₹65,00,000

2. Annual Operating Costs

  • Maintenance and Updates: ₹5,00,000
  • Support Services: ₹3,00,000
  • Total Annual Operating Costs: ₹8,00,000

Expected Annual Savings

a. Insights into Equipment Usage

  • Reduction in Equipment Search Time:
    • Time saved: 2 hours/day
    • Staff cost savings: ₹600/hour x 2 hours x 365 days = ₹4,38,000

b. Insights into Patient Flow

  • Improved Bed Turnover Rate:
    • Average occupancy increased from 65% to 75%.
    • Additional revenue from increased admissions:
      • Current Occupancy: 350 beds x 0.65 x 365 days = 84,875 patient days
      • New Occupancy: 350 beds x 0.75 x 365 days = 96,375 patient days
      • Additional patient days: 96,375 – 84,875 = 11,500
      • Average revenue per patient day: ₹2,500 (assumption)
      • Additional revenue: 11,500 patient days x ₹2,500 = ₹28,75,000

c. Improved Workflow Coordination

  • Reduction in Staff Overtime:
    • Savings from optimized workflows: ₹3,00,000

d. Operational Efficiency

  • Decrease in Equipment Losses and Repairs:
    • Estimated savings: ₹2,00,000

Total Expected Annual Savings

  • Total Savings: ₹4,38,000 + ₹28,75,000 + ₹3,00,000 + ₹2,00,000 = ₹38,13,000

Now let’s calculate Return of Investment (ROI)

First Year Costs

  • Total Initial Investment: ₹65,00,000
  • First Year Operating Costs: ₹8,00,000
  • Total First Year Costs: ₹65,00,000 + ₹8,00,000 = ₹73,00,000

b. First Year Savings

  • Total Expected Annual Savings: ₹38,13,000

c. Net Cost for the First Year

  • Net Cost: ₹73,00,000 – ₹38,13,000 = ₹34,87,000

5. Breakeven Analysis

a. Annual Savings Beyond Year 1

  • Annual Savings (after first year): ₹38,13,000
  • Annual Operating Costs: ₹8,00,000
  • Net Savings per Year: ₹38,13,000 – ₹8,00,000 = ₹30,13,000

b. Breakeven Point

  • Total Investment: ₹65,00,000
  • Annual Net Savings: ₹30,13,000
  • Years to Breakeven: ₹65,00,000 / ₹30,13,000 ≈ 2.16 years (approximately 3 years)

Summary

  • Total Investment in the First Year: ₹73,00,000
  • Breakeven Year: Year 3

Conclusion

Implementing an RTLS in a 350-bed hospital can significantly enhance operational efficiency and patient care. Despite an initial investment of ₹73,00,000, the hospital can expect to reach breakeven in approximately three years, achieving substantial annual savings thereafter. The expected improvements in bed utilization and workflow coordination are crucial for driving this ROI.

A new RTLS App for Hospital launched!

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RTLS App for Hospital

INDTRAC for Hospital earlier was available only as browser App. Now it is available as native App in Google Play store. While you still can continue to use the browser App, the new native App enhances the experience by an order of magnitude.

The App also adds  a few very important functionalities that were missing in the web version.

Examples include :
1. Adding custom reminder : Medical equipment need regular maintenance and AMC renewal. Keeping track of different dates for so many equipment can be error-prone if not tedious. The App lets you feed those details into the system for each equipment once. Automatic reminder will be sent to your team at a regularity you choose. It will ensure that team won’t miss any of those renewal windows.

2. Uploading the AMC reports. : The app lets you scan the report and upload it so that others can use it as reference.

3. The App works in unison with other INDTRAC Apps which means admin function can be segregated based on the service one administers. For example to monitor only the ambient temperatures and humidity, you can use only the INDTRAC Sensor where as other RTLS functionalities can be availed in the INDTRAC Hospital App.

More details are available at our product page. The App also can be downloaded from Google Play store. You are most welcome to download and try yourself. However to appreciate full power of the App, you would need the sensors and wireless readers to be fixed in your premise. We help our customers to run the pilots before full deployment.

Updated App

The March, 2025 update brings many major enhancements. Now a hospital can integrate buzzers and hooters to enable public alerts in Hospital.
Raise Emergency Alarm from the App
Admin can raise an immediate evacuation alarm in any building from a click on the App when all buzzers can will fire. Switching off is also equally easy, entirely from the App. The admin can do that remotely too.
Staff SOS from anywhere in the building
INDTRAC now supports BLE based ID badge with integrated SOS button. The wearer can immediately ask help by pressing the button in her ID badge. Security will instantly know her location, saving precious time in searching for her. It significantly improves Staff security in your hospital.
Location fencing integrated with Buzzer
Now you not only can create indoor fence for assets, you can set up a buzzer which will buzz off when the fence is breached.

Ask for a demo now. Write to us to contact@indtrac.com

Did you know QR code can cut your shipment errors and returns drastically?

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QR Code for shipment trackingIf you are a factory owner or goods producers, this may be for you. Small businesses often struggle with shipment errors and returns. Manual processes and human error can lead to picking the wrong items, sending to incorrect addresses, or damaged goods arriving. These issues not only cost money in return shipping and processing fees but also damage customer satisfaction.

QR code can make tracking your shipment accurate and super-easy

A smartphone App is the lightest solution you can imagine. Using the App, you can generate QR code for each shipment and also use the same app to track them from anywhere. Each time someone scans, the location and time is recorded in the App. That way anyone in your office can track the item in almost near real-time.

  • Unique Identification: Each product or package would have a unique QR code attached. This code encodes information like the product ID, order number or any other fields you like to include.
  • Scanning for Accuracy: Warehouse staff can scan the QR code with a smartphone during picking and packing. This ensures the correct item is selected and placed in the appropriate package.
  • Real-Time Tracking: More scan adds more real-time updates. This transparency helps identify potential delays or delivery exceptions.

A 2021 survey by Zebra Technologies revealed that 83% of businesses using QR codes reported a decrease in picking errors.

A 2020 study by Deloitte found that real-time shipment tracking with QR codes reduced shipment returns by 15%.

Incidentally INDTRAC QR App does exactly what is needed. What’s more? We will help you in case you need any assistance in setting up at no cost to you.

Cost Savings through QR Code-Based Asset Tracking

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QR Code based Asset inventory
In the ever-evolving landscape of business operations, efficiency and cost-effectiveness are paramount. Many companies are turning to innovative solutions like QR code-based asset tracking to streamline processes and reduce operational costs. Let’s explore how adopting this technology has enabled businesses across various sectors to achieve significant savings.

Case Study 1: Manufacturing Sector

Company Profile:
A medium-sized manufacturing firm with multiple production facilities and extensive machinery.

Challenge:
The company faced challenges in tracking the usage and maintenance schedules of machinery across different sites. Manual record-keeping led to discrepancies, delays in maintenance, and increased downtime.

Solution:
Implemented a QR code-based asset tracking system integrated with their existing maintenance management software. Each machine was tagged with a QR code linked to a digital profile containing maintenance logs, service schedules, and operational data.

Results:

  • Reduced Downtime: By monitoring maintenance needs in real-time, the company minimized unplanned downtime by 30%, leading to increased productivity.
  • Improved Maintenance Efficiency: Scheduled maintenance became more efficient, reducing overall maintenance costs by 20% annually.
  • Inventory Optimization: Better tracking of spare parts and consumables lowered inventory costs by 15%.

Case Study 2: Logistics and Transportation

Company Profile:
A logistics company specializing in warehousing and distribution services.

Challenge:
Tracking and managing a vast inventory of goods across multiple warehouses and during transit was cumbersome and prone to errors. This led to inefficiencies in order fulfillment and increased operational costs.

Solution:
Deployed a QR code-based inventory management system that enabled real-time tracking from warehouse to delivery.

Results:

  • Enhanced Inventory Accuracy: Reduced inventory discrepancies by 25%, minimizing stockouts and overstock situations.
  • Optimized Route Planning: Improved asset visibility allowed for optimized route planning, reducing fuel consumption and transportation costs by 15%.
  • Faster Order Processing: Streamlined order picking and fulfillment processes increased operational efficiency, leading to faster turnaround times and improved customer satisfaction.

Case Study 3: Healthcare Sector

Company Profile:
A network of hospitals and clinics facing challenges in managing medical equipment and supplies.

Challenge:
Ensuring timely availability and tracking the usage of medical equipment and supplies across different healthcare facilities was critical for patient care and operational efficiency.

Solution:
Implemented a QR code-based asset tracking system integrated with their inventory management software and patient records system.

Results:

  • Improved Asset Utilization: Optimized usage of medical equipment reduced unnecessary purchases and extended equipment lifespan, resulting in cost savings of 18%.
  • Enhanced Patient Care: Timely availability of critical supplies improved patient care outcomes and reduced emergency procurement costs.
  • Regulatory Compliance: Maintained compliance with healthcare regulations by tracking equipment maintenance and usage history seamlessly.

Conclusion

Across diverse industries, adopting QR code-based asset tracking systems has proven instrumental in driving operational efficiency and achieving substantial cost savings. Whether in manufacturing, logistics, healthcare, or beyond, companies have reaped the benefits of real-time visibility, improved inventory management, and streamlined operations. By leveraging technology to enhance asset tracking capabilities, businesses not only reduce costs but also position themselves for sustained growth and competitiveness in today’s dynamic marketplace.

Ready to transform your operations and unlock cost-saving opportunities? Try INDTRAC QR App for free. Call us for any help you need.