Category Archives: healthcare RTLS

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.

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.

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.

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.

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.

AI Doctor is almost ready

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For a hospital’s managing director, most crucial and expensive resource is doctors’ bandwidth. Imagine an ailing patient entering the hospital for the first time.  In US, a GP would visit him and run the first-level diagnosis. Based on his analysis a line of treatment would be indicated or the patient would be referred to a specialist.

In India, the patient would go to the the front desk and they will guide to the appropriate department based on patient’s answers.

In both cases, It is humanly impossible to remember about vast number of medical issues with correlated symptoms. This means that initial diagnosis could take a little while, sometimes at the cost of patient’s time and patience.

Now imagine that front-desk is equipped with doctor-grade AI or the GP is assisted with doctor-grade AI, like ChatGPT, trained on vast corpus of medical data and researches. It is somewhat like all the specialists are standing behind the GP to help him reach correct diagnosis at the quickest time possible.
With the help from AI, pin-pointing the right department would be faster for the front-desk assistant. AI can help the GP handle a lot more load with higher accuracy.

Medical Generalist AI

With faster initial diagnosis, hospital’s productivity increases by magnitude. The fact is technology already has reached this level. Commercialization may take another year for the regulation bodies to create guard-rails for the use of such technology ubiquitously. But advanced hospitals may start utilizing the generalist AI service much earlier.

DeepMind Med-PaLM

Particularly two companies are at the forefront now. Google’s DeepMind is almost ready with Med-PaLM. Med-PaLM is a multi-modal large language model (LLM) trained on vast medical data. In the recent report it claims to be 90% accurate in its responses. It’s repertoire is vast if not comprehensive. It can understand Clinical Languages. It can analyze images (radiology etc). It is claimed to speak Genomics too!

OpenEvidence

And there is OpenEvidence, promoted by Mayo Clinic. Theirs’ also is a multi-modal large language model but with an extra chip on its shoulder. Unlike other LLMs like ChatGPT, it is trained with real-time data. With real-time data streaming into it, it would be up-to-date about latest medical findings on every sector –or that’s what they claim when the service will be commercially ready. It could be very soon since they recently claimed[2] it to score above 90% in United States Medical Licensing Examination.

OpenEvidence AI claims to become the First AI in History to Score Above 90% on the United States Medical Licensing Examination (USMLE)

Opportunity or Threat?

GPOnline finds that England needed nearly 7,400 more full-time GPs to manage the patient care. According to a 2021 WHO report, India was just above the 2006 standard of 22.8 healthcare workers per 10,000 population. With professional doctor-grade generalist AI at disposal of every hospital, relief is not going to be quantitative alone, it can add qualitative improvements in patient diagnosis in terms of reducing diagnostic time, improving accuracy and potentially reducing number of tests for the patient.

With a professional team always monitoring the model’s answers and fine-tuning the model’s learning, the improvement will be continuous and so will be the efficacy of the system.
Following the current trend, entire service most likely will be made available as SaaS for the hospitals and clinics. SaaS model also will help bringing incremental cost of adoption down to an affordable basis.

In summary, it looks like that generalist medical AI is going to bring paradigm-shift in patient care in not a very distant future. How pervasive the adoption would be though would depend on a hospital’s patient-care economics.

Read more

1.DeepMind announces Med-PaLM
2.First AI in History to Score Above 90%
3.OpenEvidence official site
4. Med-PaLM official page

Why hospitals need relook at equipment service model

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A hospital’s visible operation is about its patients and its doctors and other care-giving staff. But behind a smooth healthcare service, equally crucial is break-free running of plethora of medical equipment. Breakdown of a single equipment can severely impact hospital’s schedule and therefore operational cost.

To appreciate this better, imagine a patient, scheduled for a non-trivial medical intervention, arrives the hospital. Typically the staff needs 30 minutes to make the patient ready. The doctor arrives 20 minutes before the scheduled slot and asks his team to check the readiness of the instruments. The team while checking found a critical instrument faulty. Administrative team now have to replace the equipment but back-office finds that standby equipment has missed service window and is out of service contract now.

How can the hospital owner avoid such costly oversight?

Solution 1:

An asset and service inventory that is always updated, can help the hospital manager avoid such surprises.

1. Back office can maintain a service schedule and contract documents for all assets online.

2. Hospital management can review it in its operational meeting every week and ensure that no equipment misses the service schedule and no impending service contract renewal go unchecked.

But this can add a lot of book keeping overhead on the team and management. How can we make it painless for the team?

Solution 2:

Use a tool like INDTRAC service reminder.

INDTRAC App lets you add you reminder for every aspect related to service or contract renewal. All one needs is add the details of service date, periodicity (monthly, yearly, half-yearly etc..) of every asset service date and every contract. The App will start sending daily reminders to the administrative team days in advance for every service and contract date so that you do not miss any of them.

Interesting thing is this reminder service can be used independent of indoor tracking service.

That means you do not need BLE (Blutooth Low Energy) or RFID to use this service. All that is needed, is to download the INDTRAC QR App and start using it to manage all assets and service reminders.

Additional Benefits

Medcity News has compiled few important data that can help a hospital owner to decide why this is important.

According to them,

  • The average medical service contract took 3-4 months to activate, with greater than 75% of contracts (relatively) low dollar.
  • 63% of the contracts had different prices for the exact same model.
  • Variability ranged from 40% to 60% in contract costs on the same asset and identical entitlements.

References : Why healthcare needs a new model for equipment service

With the App-driven approach, all the service and contract data would be digitally available and analyzable at any time from one’s phone. That would enable centralised review of all service contracts. Centralised planning and negotiations in advance for service contracts can drastically reduce the variability and cost surprises for the hospital management.

What is RTLS? – This is what ChatGPT says!

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OpenAI ChatGPT logoChatGPT is making a lot of waves. So I thought why not try ChatGPT to answer some basics about RTLS. Here are some excerpts, a little lengthy for my comfort but to be frank, composition and style are comparable to any good marketing content!

Question : What is RTLS?

ChatGPT :

Real-Time Location Systems (RTLS) are a set of technologies and systems that are used to track and monitor the location of people, assets, and equipment in real-time. RTLS typically use a combination of technologies such as radio frequency identification (RFID), WiFi, Bluetooth, and GPS to determine the location of a specific item or person. The data from RTLS is then used to improve operational efficiency, increase safety and security, and provide real-time data to support decision making.

There are several different types of RTLS, including:

  • Active RTLS: which use active tags that actively transmit their location to a reader or a network of readers.
  • Passive RTLS: which use passive tags that only transmit their location when prompted by a reader.
  • Hybrid RTLS: which combine active and passive tags in order to take advantage of the strengths of both types of tags.

RTLS technology is widely used in various industries such as healthcare, manufacturing, transportation, retail, and logistics, among others. It can be used for tasks such as tracking inventory, monitoring the location of employees, and tracking the location of vehicles and equipment.

Question : What would a good RTLS for healthcare do?

ChatGPT :

A Real-Time Location System (RTLS) for healthcare would be designed to track and monitor the location of people, assets, and equipment in a healthcare setting, in real-time. The specific functionality of an RTLS for healthcare can vary depending on the specific needs of the healthcare organization, but some common uses include:

  1. Asset tracking: RTLS can be used to track the location and movement of medical equipment, such as wheelchairs and beds, as well as high-value assets like surgical instruments. This can help to improve equipment utilization and reduce the time and effort required to locate equipment.
  2. Staff tracking: RTLS can be used to track the location of nurses, doctors, and other staff members, which can help to improve communication, collaboration, and patient care.
  3. Patient tracking: RTLS can be used to track the location of patients, including those in critical care, which can help to improve patient safety and reduce the risk of errors.
  4. Environmental monitoring: RTLS can be used to monitor the environment, such as temperature, humidity, and air pressure, to ensure that the conditions are appropriate for patients and equipment.
  5. Safety and security: RTLS can be used to monitor the location of staff, patients and visitors in the facility, to ensure that everyone is safe and secure, and to respond quickly in case of emergency.

It’s important to note that RTLS technology is not a standalone solution and it’s often integrated with other technologies such as IoT, Wireless and Cloud computing to achieve the best results.

RTLS helps hospitals to reduce human error

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Healthcare Industry is constantly upgrading technologies to improve service predictability and reduce cost. One of the trends that we see this year is more pronounced use of automated data capture system instead of manual form filling. There are many benefits for sure. Here are some:

  • it enables data capture consistency
  • it helps in reduction of human error in data capture
  • it frees up the nurses’ time for more important patient-care

Important thing is that these can be integrated with RTLS. In other words RTLS not only helps hospitals to keep tab on all their expensive assets, it also can help a hospital to streamline workflow management  using IOT-enabled identity cards, asset tags and patient tags.

Healthcare IT News posted an article this month. Quoting the article,

It often can be time-consuming and impacted by human error when healthcare staff are left to juggle the needs of their patients and the administrative requirements of manual documentation. RTLS leverages IoT-enabled badges to automate data collection, providing accurate insights while enhancing the patient care experience.

In 2023, I expect more healthcare organizations will use these badges to collect data enterprise wide to gain comprehensive real-time insights on equipment utilization, clinical workflows, patient/staff interactions and safety compliance to reduce operational bottlenecks, increase efficiency and improve the patient experience.

INDTRAC can help you choose the right technology, instruments and implement customized workflow management using RTLS.