Helping Healthcare Organizations Provide High Quality, Efficient Patient Care
The World We Envision
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.
A little more about us...
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.
Founder and CEO
BASc in Engineering Science MASc, Industrial Engineering University of Toronto
BASc, Industrial Engineering MASc, Industrial Engineering University of Toronto
BASc in Electrical Engineering MEPP McMaster University
BASc, Industrial Engineering Ryerson University
BASc in Engineering Science MASc, Clinical Engineering PhD, Biomedical Engineering University of Toronto
Dr. Amy Liu, PhD
Principal Biostatistician, University Health Network
Assistant Professor, Dana Lana School of Public Health, University of Toronto
Dr. Ladan Ahmadi, MD
Internal Medicine, Lenox Hill Hospital, Manhattan, NY
Northwell Health, New York, NY
BASc in Engineering Science
Serial Entrepreneur Partner, Bloom Ventures
Hospital Capacity Flow (COVID and non-COVID)
Allocate capacity across the hospital to manage the COVID surge
Hospital Planning Parameters to Patient Flow
Visualize the future patient flow from planning parameters (in the functional program documents)
Hospital Parking Demand
Visualize the future parking demand from planning parameters (in the functional program documents)
Patient Clinic Scheduling
Maximize the number of patients through a clinic
Surgical Supply Management
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 time prediction/improvement
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.
Hospital Patient flow Simulation
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.
Surgical Inpatient Bed Leveling
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.
Dialysis Policy Analysis
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.
Medication Ordering Process Simulation
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.
Does the state of our healthcare system keeps you up at night? Are you passionate about healthcare and healthcare operations? Do you want to be part of a group of professionals tackling the cutting edge challenges healthcare systems/hospitals are facing? Do you want to help us buid our vision?