Real-Time Location Systems (RTLS) that deliver room-level certainty with low latency are critical in hospitals because they enable precise workflow automation, improving patient care, safety, and operational efficiency. If you only have approximate locations—"somewhere on this floor or within 15 or 30 feet"—you lose the ability to automate tasks such as nurse call response, automated check-ins/outs, inventory tracking to know exactly which device is in which room, or infection-control workflows. A CTO looking to modernize hospital operations must ensure the location data is both reliable (no false positives) and quick (low latency), or the entire digital workflow can break down.
Topics include:
RF-based technologies like Wi-Fi or Bluetooth use signal strength (RSSI) or Angle of Arrival (AoA) to estimate location. While these methods can work for approximate indoor positioning, such as the general area or corridor that someone or something is in, they face challenges providing deterministic room-level certainty. RF signals penetrate walls, reflect off surfaces, and can fluctuate based on environmental factors—like equipment, human traffic, furniture rearrangement. As a result, relying on RF alone often leads to “location jumps” or false positives that degrade real-time automation.
To achieve room-level certainty at a low latency—required for reliable, automated hospital workflows—a deterministic secondary signal is needed:
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Infrared (IR): Does not penetrate walls
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Ultrasound: Also contained by walls—not perfect, but better than RF at precise boundaries.
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Camera Vision: More complex, expensive, and less reliable than IR—occlusions (e.g., staff, equipment, carts), and lighting variation can block or delay detection.
By combining RF’s ubiquitous coverage, for communication and coarse positioning, with a deterministic secondary signal, hospitals get the best of both worlds:
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Room-level “proof” of presence—the deterministic component.
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Timely location updates—the RF component for quick data transfer and baseline positioning.
Bluetooth alone will always carry a risk of “spill-over” between rooms due to its signal propagation through walls or around corners—especially if the rooms share walls or are in close proximity. Even installing one BLE gateway per room attempts to localize, but you can still get:
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Signal Overlap: BLE signals will bleed into adjacent rooms.
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Environmental Interference: Machinery, walls with varying construction, and human presence can shift RSSI values.
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Latency vs. Accuracy Trade-Off: If the BLE system tries to improve accuracy by scanning multiple channels or waiting on advanced algorithms, you add delay.
Some vendors claim they can guarantee room-level certainty with a single BLE gateway in every room. In practice the moment the real environment changes—for example staff crowding, furniture, new medical equipment—the system can and will misread which room the tag is in, eroding trust in the data. With as few as three false positives, frontline staff typically lose confidence and abandon the system entirely.
A “15-foot accuracy” radius is a typical for standard BLE setups. While 15 feet might sound small in open areas, in a hospital it can easily span multiple rooms. For instance, in a setting where rooms are adjacent, 15 feet can mean you accidentally track a patient or clinician as being in the neighboring room. That is unacceptable for clinical workflow automation.
Here’s how this impacts real use cases:
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Use case |
15-Foot Inaccuracy Effect |
Outcome |
|---|---|---|
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Patient Tracking |
Patient tag appears in the wrong room A patient’s location drifts into an adjacent room, making the system think they've relocated when they haven’t. |
Delayed care, misdirected staff Care teams cannot reliably locate patients—delays occur when staff search the wrong room, potentially compromising response times and patient safety. |
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Patient Visit Time |
System logs false exits/entries The system logs a patient as having left or entered a room they never actually exited or entered, because they appear 15 feet away. |
Invalid length-of-stay and occupancy metrics Room‐occupancy analytics become invalid—length‐of‐stay metrics, turnover reports, and bed‐availability dashboards all reflect erroneous timestamps, undermining operational planning and staffing models. |
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Patient and Staff Co-location |
Cannot confirm who’s with whom BLE inaccuracy prevents reliable detection of which patients and staff are physically proximate. A 15 feet drift can make the system think a clinician is in one room when they are actually in another, so it cannot confirm true co-location. |
Missed handoffs, poor coordination Care teams lose visibility into who is with whom; workflow coordination breaks down, response times suffer, and collaboration on critical tasks is delayed or misdirected. |
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Nurse Call Cancellation |
Badge drift cancels or misses calls The system may register a nurse in the wrong room if their badge drifts 15 feet into an adjacent space. |
Patients left waiting, manual override needed Nurse calls remain active in the correct room or get canceled in the wrong room—patients wait longer, and nurses must manually verify and correct call status. |
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Hand Hygiene Compliance |
Misregistered dispenser events A dispenser in one room may trigger an opportunity or dispense event when a clinician’s badge drifts in from a neighboring room—or fail to trigger when they actually enter a room. |
False compliance data, reduced infection control confidence Compliance reports flood with false negatives and false positives, undermining trust in infection‐control data. |
Kontakt.io’s Beam solution combines the openness and ubiquity of BLE with the deterministic advantages of IR to confirm room presence delivering 100% room-level certainty—mandatory for reliable, automated hospital workflow.
Here’s why the Beam solution is best in class:
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Feature |
Benefit |
|---|---|
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Battery-Powered, Scalable Deployment |
No need to wire for power or Ethernet in every room—install anywhere and it's one-device with BLE + IR. This cuts down deployment time drastically—crucial for large hospital campuses with hundreds or thousands of rooms. The Beam is compact and battery-operated, so minimal disruption to hospital operations. |
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Fast to Install and Maintain |
No complex calibrations needed—the IR boundary itself acts as the “calibration,” eliminating the guesswork and repeated site surveys required by purely RF-based solutions. Installation averages 6 minutes per room. For a 200-bed hospital, that’s just 20 hours total—or 2.5 working days for a single technician to complete the installation. |
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Room-Locked IR |
IR signals do not pass through walls, so if a device “sees” the IR tag, you know it is really inside that specific room. |
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Low Latency, High Certainty |
BLE provides quick data upload to the gateway (under 10s is easy to achieve) and IR ensures near 100% certainty of the exact room. |
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Open BLE Ecosystem |
Kontakt.io supports open BLE protocols, ensuring a seamless, easy integration with existing hospital systems, devices, and future technologies. Standard protocols also protect against vendor lock-in and facilitate expansions or upgrades later. |
By combining these factors, Kontakt.io’s BLE + IR Beam solution provides scalable, fast, and deterministic location data—exactly what a CTO needs to ensure seamless workflow automation in critical healthcare environments.
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Room-level certainty is mandatory for reliable, automated hospital workflows.
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RF alone (BLE/Wi-Fi) inevitably suffers from interference and signal bleed—making it impossible to achieve 100% certainty.
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A hybrid approach (BLE + IR) ensures both scalability and deterministic “room lock” in clinical areas needed.
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BLE + IR eliminates the problem of 15-foot accuracy “uncertainty"—ensuring no location “jumps” between rooms and boosting trust in automated workflows.
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Kontakt.io’s Beam Mini offers a plug-and-play, multi-year battery-powered solution that combines open BLE for real-time updates with IR for true room-level confirmation.
Bottom line: To build trust in automated workflows, you need deterministic location data. Kontakt.io’s BLE + IR solution delivers exactly that.