This model has been moved to our Coronavirus Models Page.
Helping Healthcare Organizations Provide High Quality, Efficient Patient Care
ORCHID analytics is founded on the vision to create a reality in which healthcare processes have been designed to support seamless care, where individuals receive care without feeling overwhelmed lost or worried and providers are free to focus on patient care rather than the frustrations of system navigation.
We are a boutique organization motivated to contribute our knowledge and expertise in applying operations research to the healthcare industry. We understand that healthcare has unique challenges, structure and stakeholders that makes solving problems more complicated. That challenge, combined with the ability to make a meaningful impact, is what we love about working in this industry!
We remain current on the academic literature, attend conferences and maintain a relationship with the University of Toronto’s Centre for Healthcare Engineering. This allows us to provide you with the latest tools, innovations and techniques.
We work hand-in-hand with clients to develop and customize decision tools that provide insight on current problems, and identify practical, outcome-driven solutions.
Allocate capacity across the hospital to manage the COVID surge
Visualize the future patient flow from planning parameters (in the functional program documents)
Visualize the future parking demand from planning parameters (in the functional program documents)
Maximize the number of patients through a clinic
Optimize the Surgical Supply Flow
Misaligned bed capacity and allocation leads to long wait times, congestion, and inefficient, lower-quality care. We have employed analytical tools to assess the required inpatient bed capacity and allocation under current and future conditions, while considering variability across seasons and through the week as well as effects on the rest of the organization.
Wait times are a struggle across healthcare and are an important factor in patient satisfaction and provider working conditions. We have applied our techniques to predict and improve waiting times in areas such as surgery, home care services, and emergency departments. We consider how current policies, schedules, budgets, resourcing, and operational decisions affect wait times for service.
Disruptions in patient flow lead to poor patient service, strained resources, and inefficient work processes. Patient flow issues are often caused upstream or downstream from where the symptoms occur. We have developed a discrete-event simulation model to simulate how patients move through the entire hospital (Emergency Department, Operating Rooms and Inpatient Beds) to analyse the whole system process. The model is customizable to any hospital and provides metrics on throughput, wait times, surgical cancellation rates, overtime, bed and OR utilization, and congestion levels. Past clients have been able to simulate, test and refine resources and operating plans for current or predicted demand to achieve the best flow.
Many hospitals struggle with large swings in surgical ward occupancy through the week due to an imbalance in the surgical schedule. This leads to inefficient use of resources and restricted surgical throughput. We used a bed leveling algorithm to determine how to reduce variability in bed occupancy while minimizing the disruption to the surgical schedule.
Our healthcare system is poised to undergo large transformations as we look at ways to revolutionize how healthcare is delivered, including a trend away from institution-based care. However, how to make those changes without compromising patient care is not always clear. We employed data analytics to determine the characteristics of patients suitable for home dialysis.
As technology changes how care is delivered, it can be difficult to predict the impact of these changes. Simulation can help clients visualize the changes, highlight unforeseen consequences, test and evaluate the benefits, and refine new processes virtually before going live with changes. We applied simulation modelling to determine the extent to which an electronic medication, ordering, dispensing and administration process would improve turnaround times and error rates compared to the existing manual system.