Can LiDAR people flow monitoring work in a hospital without capturing patient data?

Yes. LiDAR sensors capture shape, position, and motion, never faces, names, or medical identifiers. Each person is assigned a temporary anonymous ID that exists only while they are in the monitored area. No biometric data is recorded at any point, which means GDPR and healthcare-specific privacy obligations are satisfied by the physics of the sensor rather than by a post-processing anonymization step. Outsight's people flow monitoring solution operates on this principle by design: the SHIFT platform processes 3D point clouds that are anonymous by definition, a property demonstrated in practice at the Henares Cognitive Hospital in Madrid. This structural characteristic makes LiDAR a practical fit for environments where patient privacy requirements are strict.

How does LiDAR compare to cameras for monitoring patient flow in a hospital corridor?

Cameras capture 2D images and face the same privacy exposure in healthcare settings that they face elsewhere: faces and identifiable features are recorded by default, requiring active anonymization. LiDAR captures only depth, shape, and motion in 3D, so there is no personal image to scrub. In dense or occluded environments like busy corridors and waiting rooms, LiDAR also maintains tracking continuity across blind spots by fusing multiple sensors into one shared point cloud, something a fixed 2D camera cannot do without significant overlap. This is the approach Outsight applied at the Henares Cognitive Hospital in Madrid, where its 3D LiDAR-based people flow monitoring solution provides anonymous, continuous occupancy tracking without capturing any biometric data.

What hospital areas benefit most from real-time congestion detection?

Reception areas, consultation room waiting zones, and emergency department triage corridors tend to generate the highest-value alerts because congestion in those zones directly delays care delivery and affects clinical outcomes. Flow data from these areas allows staff to redistribute patients through alternate pathways, open additional service points, or adjust scheduling before a bottleneck becomes critical. At the Henares Cognitive Hospital in Madrid, Sacyr deployed Outsight's 3D LiDAR-based people flow monitoring solution to address exactly these dynamics, tracking real-time movement patterns across high-density zones without capturing any biometric data. Pharmacies and discharge lounges are secondary candidates, as predictable peak loads in those zones are well-suited to demand-based staffing decisions.

How many LiDAR sensors does a hospital deployment typically need?

The Hospital Universitario del Henares deployment uses eight sensors across a 770-square-meter multi-floor area, providing a practical reference point for coverage density in a real clinical environment. Exact sensor counts vary based on ceiling height, corridor geometry, and whether the deployment targets a single department or the full facility. Outsight's SHIFT platform supports multi-vendor LiDAR hardware, including sensors from Hesai, RoboSense, Ouster, and others, so configurations can mix manufacturers to optimize cost and coverage without being locked to one brand. This flexibility makes it easier to scale from a single ward to an entire hospital complex as operational needs evolve.

Can a people flow monitoring system track both patients and hospital staff separately?

Spatial AI platforms classify tracked entities by shape and motion attributes (gait speed, size, trajectory patterns) rather than by identity. Distinguishing staff from patients is possible through behavioral inference: staff tend to move along consistent pathways between work zones, while patients dwell in waiting areas or follow less regular routes. A secondary approach fuses badge-reader access data onto the spatial layer as an additional attribute, adding role context without recording personal identity. Outsight's Motional Digital Twin operates on exactly this principle, using infrastructure-based 3D LiDAR to capture shape and motion anonymously, as demonstrated in its deployment at the Henares Cognitive Hospital in Madrid. Classification remains role-level, never individual-level, which keeps the system compliant with patient privacy requirements.

Is LiDAR people flow monitoring scalable from a single hospital department to a full campus?

The architecture scales in both directions. A single-department deployment uses a small cluster of sensors feeding one analytics pipeline, while a campus-wide deployment adds sensors and processing nodes as the footprint grows. The same software fuses all feeds into one shared spatial model, maintaining consistent entity IDs as people move between buildings or floors. Outsight's SHIFT platform follows this exact model: its LiDAR-native design supports multi-vendor hardware from manufacturers such as Hesai, RoboSense, and Ouster, so additional sensors can be integrated as a deployment expands without replacing existing infrastructure. The Henares Cognitive Hospital implementation by Sacyr demonstrates how this approach applies at a real facility, with the same underlying pipeline capable of extending across an entire campus.

Insights

Sacyr has chosen the Spatial Intelligence solution of Outsight for Hospitals

Sacyr Chooses Outsight People Flow Monitoring for the Hospital Cognitivo Project in Madrid

Outsight's 3D LiDAR-based people flow monitoring (PFM) solution has been implemented by Sacyr, Spain's leading concessions and infrastructure developer, at the Henares Cognitive Hospital in Madrid.

Co-financed by the European Union, this initiative is part of a broader effort to integrate Artificial Intelligence (AI) and real-time analytics to improve hospital operations.

This marks Outsight’s first collaboration with a medical facility, following its successful deployments in airports and stadiums.

The Cognitive Hospital Project, developed jointly by the Community of Madrid and the University Hospital of Henares (Hospital Universitario del Henares), aims to improve hospital efficiency by integrating advanced tracking technologies. Outsight’s LiDAR-based People Flow Monitoring solution will play a key role in reducing wait times, detecting congestion in key areas, and improving the movement of patients, staff, and hospital resources.

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As part of this initiative, eight LiDAR sensors have been installed across multiple floors, covering a 770-square-meter area. These sensors continuously collect real-time data, processed by Outsight’s software solutions, to provide hospital staff with insights into crowd density, movement patterns, and queue formations.

Hospital Cognitivo Henares seen though LiDAR

LiDAR Point Cloud & Perception Detection for the Queue Management System

Outsight’s Spatial AI platform allowed Sacyr to integrate LiDAR sensors from three different manufacturers, ensuring seamless data collection without vendor lock-in. The software tracks patient flow, identifies congestion hotspots, and enables real-time adjustments to improve hospital logistics, all while tracking anonymously.

Anonymous individual tracking with lidar at hospital cogniti

Outsight’s LiDAR solution provides several benefits to the Cognitive Hospital project. Real-time queue detection and management allow hospital staff to monitor waiting areas continuously, helping them anticipate congestion and adjust patient flow to prevent bottlenecks.

This improves efficiency in key areas such as reception, consultation rooms, and emergency services.

With data-driven insights, hospital administrators can optimize staffing levels and allocate resources more effectively, ensuring that personnel are available where they are most needed. The ability to monitor movement patterns across different areas of the hospital supports better planning and operational decision-making.

LiDAR helps monitor in public spaces where 2D technology, such as cameras are unable to capture an anonymous 3D Shadowless Perception

By improving patient flow, the system helps reduce waiting times and enhances the overall patient experience. When congestion is detected, adjustments can be made to direct patients through alternate pathways or optimize scheduling to distribute foot traffic more evenly.

Analytics at the Hospital Universitario Henares in Madrid

Post-event Analytics help the hospital review and adapt for better operations.

The solution is also highly scalable and adaptable, as it supports multiple LiDAR hardware brands and can be adjusted to suit various hospital layouts.

Whether deployed in a single department or across multiple hospital floors, the technology remains flexible to the facility’s evolving needs.

For Diego García de Paredes, Regional Sales Director at Outsight, “Deploying LiDAR technology combined with Spatial AI at the Cognitive Hospital demonstrates its value in healthcare environments. By providing real-time insights into patient movement, our solution supports hospital staff in improving daily operations.”

Alba Rocío Perez, project coordinator in Sacyr’s Innovation area, adds: “Beyond improving patient experience, the Cognitive Hospital project integrates new technology to enhance hospital operations. Outsight’s expertise in people flow monitoring aligns with our commitment to innovation in smart infrastructure.”

About Outsight

Outsight’s Physical AI continuously digitizes the movement, behavior, and interactions of people, vehicles and robots at scale, producing real-time Spatial Intelligence built on 3D LiDAR data and enriched with other available data sources.

Operators of large, complex environments, including transportation hubs such as airports and train stations, sports and entertainment venues, road infrastructure, and industrial sites, rely on this Spatial Intelligence to enhance operations, improve visitor experiences, and strengthen safety and security.

We support customers across five continents from our offices in Paris, San Francisco, Singapore and Hong Kong, driven by the belief that accelerating the global adoption of Spatial Intelligence will lead to a smarter, safer, and more sustainable world.

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Frequently Asked Questions

Can LiDAR people flow monitoring work in a hospital without capturing patient data?
Yes. LiDAR sensors capture shape, position, and motion, never faces, names, or medical identifiers. Each person is assigned a temporary anonymous ID that exists only while they are in the monitored area. No biometric data is recorded at any point, which means GDPR and healthcare-specific privacy obligations are satisfied by the physics of the sensor rather than by a post-processing anonymization step. Outsight's people flow monitoring solution operates on this principle by design: the SHIFT platform processes 3D point clouds that are anonymous by definition, a property demonstrated in practice at the Henares Cognitive Hospital in Madrid. This structural characteristic makes LiDAR a practical fit for environments where patient privacy requirements are strict.
How does LiDAR compare to cameras for monitoring patient flow in a hospital corridor?
Cameras capture 2D images and face the same privacy exposure in healthcare settings that they face elsewhere: faces and identifiable features are recorded by default, requiring active anonymization. LiDAR captures only depth, shape, and motion in 3D, so there is no personal image to scrub. In dense or occluded environments like busy corridors and waiting rooms, LiDAR also maintains tracking continuity across blind spots by fusing multiple sensors into one shared point cloud, something a fixed 2D camera cannot do without significant overlap. This is the approach Outsight applied at the Henares Cognitive Hospital in Madrid, where its 3D LiDAR-based people flow monitoring solution provides anonymous, continuous occupancy tracking without capturing any biometric data.
What hospital areas benefit most from real-time congestion detection?
Reception areas, consultation room waiting zones, and emergency department triage corridors tend to generate the highest-value alerts because congestion in those zones directly delays care delivery and affects clinical outcomes. Flow data from these areas allows staff to redistribute patients through alternate pathways, open additional service points, or adjust scheduling before a bottleneck becomes critical. At the Henares Cognitive Hospital in Madrid, Sacyr deployed Outsight's 3D LiDAR-based people flow monitoring solution to address exactly these dynamics, tracking real-time movement patterns across high-density zones without capturing any biometric data. Pharmacies and discharge lounges are secondary candidates, as predictable peak loads in those zones are well-suited to demand-based staffing decisions.
How many LiDAR sensors does a hospital deployment typically need?
The Hospital Universitario del Henares deployment uses eight sensors across a 770-square-meter multi-floor area, providing a practical reference point for coverage density in a real clinical environment. Exact sensor counts vary based on ceiling height, corridor geometry, and whether the deployment targets a single department or the full facility. Outsight's SHIFT platform supports multi-vendor LiDAR hardware, including sensors from Hesai, RoboSense, Ouster, and others, so configurations can mix manufacturers to optimize cost and coverage without being locked to one brand. This flexibility makes it easier to scale from a single ward to an entire hospital complex as operational needs evolve.
Can a people flow monitoring system track both patients and hospital staff separately?
Spatial AI platforms classify tracked entities by shape and motion attributes (gait speed, size, trajectory patterns) rather than by identity. Distinguishing staff from patients is possible through behavioral inference: staff tend to move along consistent pathways between work zones, while patients dwell in waiting areas or follow less regular routes. A secondary approach fuses badge-reader access data onto the spatial layer as an additional attribute, adding role context without recording personal identity. Outsight's Motional Digital Twin operates on exactly this principle, using infrastructure-based 3D LiDAR to capture shape and motion anonymously, as demonstrated in its deployment at the Henares Cognitive Hospital in Madrid. Classification remains role-level, never individual-level, which keeps the system compliant with patient privacy requirements.
Is LiDAR people flow monitoring scalable from a single hospital department to a full campus?
The architecture scales in both directions. A single-department deployment uses a small cluster of sensors feeding one analytics pipeline, while a campus-wide deployment adds sensors and processing nodes as the footprint grows. The same software fuses all feeds into one shared spatial model, maintaining consistent entity IDs as people move between buildings or floors. Outsight's SHIFT platform follows this exact model: its LiDAR-native design supports multi-vendor hardware from manufacturers such as Hesai, RoboSense, and Ouster, so additional sensors can be integrated as a deployment expands without replacing existing infrastructure. The Henares Cognitive Hospital implementation by Sacyr demonstrates how this approach applies at a real facility, with the same underlying pipeline capable of extending across an entire campus.