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Healthcare

Improve patient flow without ever identifying a patient

VIDEO.MA measures waiting room occupancy, admission queues and visitor flows on a strictly anonymous basis, with on-site processing preferred. No facial recognition, no patient identification: only aggregated counts and durations.

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Illustration — fictitious demo data

What it is costing you today

Free audit

How long do your patients really wait before admission?

The wait patients experience is often very different from the one staff estimate. Measuring it anonymously, by time slot, helps adjust how reception is organised without identifying anyone.

Do your waiting rooms overflow at predictable times?

An overcrowded waiting room hurts patient comfort and puts pressure on staff, yet peaks are rarely quantified. Measuring occupancy helps spread appointments and reception resources more evenly.

How many entries into sensitive areas go unnoticed?

Technical or restricted areas are hard to supervise continuously. Access alerts on these zones, without identifying anyone, let you react sooner and review how they are organised.

Healthcare & Clinics

Industry challenges

01

Overcrowded waiting rooms

Waiting room peaks are endured, with no real-time visibility to redirect patients or reinforce the front desk.

02

Admission waits that go unmeasured

Time spent at the admissions or billing desk weighs on the patient experience, yet it is rarely tracked objectively.

03

Sensitive areas that are hard to supervise

Pharmacy, storerooms and technical areas must remain restricted to authorised staff, on sites with heavy traffic.

04

Limited operational data

Decisions on reception hours or space layout lack reliable, anonymous attendance data.

VIDEO.MA

What VIDEO.MA delivers

Waiting room occupancy

Per project

Anonymous count of people present in each waiting room, with alerts above a defined capacity.

Anonymous patient and visitor flows

Per project

Aggregated counts of entries and movements between departments, with no identification of individuals.

Admission queues

Per project

Estimated number of people queuing and wait times at admission or billing desks.

Restricted-area access alerts

Per project

Detection of presence in a defined zone outside authorised hours, complementing existing access control and without identification.

Equipment zone monitoring

Feasibility study

Detection of presence or congestion in defined technical or equipment storage areas.

Correlation with appointment volumes

Feasibility study

Aggregated comparison of measured attendance with scheduled appointment volumes, with no personal or health data.

Example metrics

  • Waiting room occupancy

    Video data

    Number of people present per room, counted anonymously by video.

  • Average admission wait

    Video data

    Average time spent queuing at the desk, estimated from video without identification.

  • Entries per hour

    Video data

    Anonymous entries per access point and time slot, derived from video.

  • Out-of-hours restricted-area access

    Video data

    Number of presence events detected in defined zones, by video.

  • Scheduled appointments per slot

    Video + business data

    Aggregated appointment volumes from your scheduling software, with no personal data, compared with measured occupancy.

Dashboard preview

Dashboard · Clinic — Reception and outpatientsDemo data

Entries today

642

Waiting room A occupancy

28 / 40

Average admission wait

7 min

Restricted-area alerts

1

Activity per hour

Events

  • Queue > 8 people — Admissions
  • Waiting room B at 95% capacity
  • Out-of-hours presence — Pharmacy storeroom
Illustration — fictitious demo data

Deployment approach

  1. 1

    Audit and impact assessment

    Review of spaces, existing cameras and privacy risks, to define a scope strictly limited to the necessary areas and indicators.

  2. 2

    Privacy-by-design configuration

    Setup of counting and alert zones, on-site processing preferred, masking or exclusion of sensitive areas and aggregated-only outputs.

  3. 3

    Supervised pilot

    Testing in one waiting room and one admissions desk to validate accuracy, with patients and staff informed beforehand.

  4. 4

    Rollout and periodic review

    Gradual extension to other areas and regular review of scope, retention periods and indicator relevance.

Prerequisites, limitations and privacy

  • No facial recognition and no patient identification: the proposed solutions produce only anonymous counts, durations and events.
  • Health data is sensitive data under Law 09-08. No medical data is processed by video analytics, and treatment areas, patient rooms and consulting rooms are excluded from scope.
  • On-site processing on Edge AI equipment is preferred to limit the circulation and retention of images; only aggregated indicators are transmitted.
  • As data controller, the facility must complete the CNDP formalities and inform patients, visitors and staff; VIDEO.MA can help prepare them.
  • Access alerts do not replace access control or certified fire and intrusion systems.

Frequently asked questions

Are patients identified or recognised?

No. No facial recognition or biometric identification is used. The system detects anonymous silhouettes to produce counts and durations, with no link to any patient record.

Are images sent to the cloud?

On-site processing, on an Edge AI device installed in the facility, is preferred. Only aggregated indicators are sent to the dashboard, and the architecture is validated with your IT team and, where applicable, your data protection officer.

Which areas can be analysed?

Reception areas, waiting rooms, desks, corridors and certain technical zones. Patient rooms, treatment rooms, consulting rooms and any space where patient privacy is at stake are excluded.

What formalities are required?

As data controller, the facility must complete the formalities required by Law 09-08 with the CNDP and inform the people concerned. VIDEO.MA can provide the technical documentation needed to prepare the filing.

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