Virtual Medicine - Everywhere for Everyone

The presence of tele-visits and tele-monitoring, when combined with the always-on ambient sensing delivered by AI smart cameras, delivers sufficient digital fidelity creates the potential for virtual medicine applied to the whole hospital.

Key Challenges Facing Inpatient Medicine

  • Staffing crunch
  • Optimizing bed capacity
  • Preventing avoidable harm

Doctors in hospitals are tasked with the important responsibility of caring for the sick and injured. However, with increasing patient loads and limited resources, it can be difficult for doctors to be everywhere for every patient. Ubiquitous telemedicine and video monitoring create a foundational layer that reduces the friction to engage with any patient at any moment.

The use of AI technology can then help create a second set of virtual eyes that is watching their patients all the time for changes in patterns. Pairing this with a virtual medicine doctor or nurse can ensure these patterns are vetted and combined with the entirety of medical information about the patient.

A virtual command center that watches all patients in aggregate can allow hospitalists and other members of the bedside team to focus on each individual patient more closely.

Virtual Medicine

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Visit Patients Anywhere & Everywhere

Quantify Patient Movement

eICU Light for The Entire Hospital​

Key questions

What are specific use cases for Virtual Medicine?

Virtual Medicine can range from applying established patterns like eICU to other parts of the hospital at a much lower cost. In addition, AI monitoring of patient location, movement and environment can tailor that care to events more associated with the floor of the hospital – pressure injuries, rest and recovery, impact on patient behavior of medication changes and more.

What data are you collecting ?

Our computer vision technology extracts information about who is with the patient and the care or support they are providing, information about the patients movement (a wearable without a wearable), and where they are spending time (ie.g in bed all day) and key actions of the patient. This is then processed by AI filtering to nudge the attention of providers to patients who have increased need (e.g. patients not moving in bed for hours during the day)

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