Choosing when, where and how long to treat each cancer patient can be an arduous task that results in a waste of potential treatment bandwidth. GrayOS is a virtual model of your center, which continuously simulates the future to optimize the present.
Predict future patient load and evaluate its impact on your operations.
Thinking of buying another machine? Concerned about the incoming surge of cancer patients? GrayOS can accurately simulate the impact of any hypothetical scenario to optimize decision making.
Never have to manually schedule a patient again. Free up more time to spend with them
GrayOS simplifies how your staff schedules their patients. Not only will the schedules be robust and optimized, a task which normally takes hours is reduced to minutes.
From your patient's perspective, their treatment is one, connected trajectory.
Radiation therapy and chemotherapy are intrinsically connected through the patient. GrayOS operationally connects the two disciplines for both scheduling and visualization purposes.
Is the infusion center falling behind by noon? Did a nurse have to go home sick?
A theoretical schedule often doesn’t go according to plan. A nurse goes home sick. A patient shows up late. A machine breaks. GrayOS will assist your staff in how to appropriately respond, reducing their stress and minimizing the impact of variability.
Engage your patients. Easily accommodate their explicit scheduling preferences & constraints.
Your patient’s preferences can now be explicitly considered in the creation of the schedule. Improve your patients’ satisfaction, without putting any additional stress on your staff.
Seamlessly connect to your existing EHR(s).
Integration ensures your EHR remains the ground truth while moving the scheduling workflow entirely within GrayOS.
GrayOS is the only platform on the market that fully automates patient scheduling while being built from the ground up for oncology. We’ll improve your efficiency without compromising your quality of care.
5% increased resource utilization rates
25% reduced administrative burden
Increased staff satisfaction
Reduced patient wait times
Increased patient satisfaction
PhD Medical Physics, specialized in deep learning for outcome prediction in oncology.
PhD Medical Physics, specialized in optimization and radiation therapy treatment planning.
Canada research chair in healthcare logistics (HANALOG).
Polytechnique Montréal - healthcare logistics
At Gray, we have a vision of an oncology workflow where each patient’s journey through their cancer treatment is personalized for them, and statistically optimized to provide the best possible clinical outcomes. A connected workflow devoid of inefficiencies is the first step in achieving this vision.
We want to hear about your concerns, the nuances surrounding your organization and how we can help optimize your efficiency!
Supported by
