Helping Healthcare Organizations Provide High Quality, Efficient Patient Care

Hover over the QA quadrants ORCHID Anaytics and Quadruple Aim Our advanced analytics models utilize value and efficiency measures such as utilization, throughput, overtime, resource alignment, non-value-added time and cost. Significant saving can also be realized by avoiding unsuccessful implementations or pilots that can be ruled out using advanced analytics. It has been shown in the literature that operational decisions also influence patient outcomes. For example, wait times, appropriate resource alignment to patient needs, appropriate lengths of stay, adequate nursing-to-patient ratio, and error reduction processes all contribute to improved patient outcomes. These improvement targets can be included in advanced analytics models. Our models invariably include metrics aimed at improving the patient experience such as wait times, delays, length-of-stay, cancellation rates, error rates, wayfinding/travel time, continuity of care, and appropriateness of care. Patient representatives may also be included in the development of our models to ensure that what is important to their care experience is captured and measured. We provide solutions that improve workflow and staffing schedules; reduce non-value-add time, patient hand-offs, errors, and non-billable hours; increase patient care time and appropriateness of care; and manage workload. Participation by provider stakeholders in our transparent, evidence-based process ensures all perspectives are included, solutions are realistic, and implementation is smooth and successful. Reduced waiting time for surgery by increasing Operating Room throughput and efficiency.• Identified master surgical schedule changes and/or bed reallocations to reduce surgical cancellations. • Optimized capacity planning and bed allocation decisions to prevent hallway medicine and ensure patients have quick access to the most appropriate care.• Created a tool that allowed allied health providers to give non-emergent patients an estimate of their expected wait time for publicly funded care. • Predicted and smoothed bed occupancy levels to balance nurse workloads and reduce overloads.• Provided bed allocation solution to give surgeons confidence that surgeries can proceed as scheduled, and OR time/revenue will not be lost, without jeopardizing medical patients. • Reduced off-servicing patients so that nurses and physician can streamline work and nurses can focus on patients within theirarea of expertise. • Proved cost-effectiveness of increasing budgeted vs surge nurses, thus, inceasing consistency for patients and providers. • Optimized placement of incremental QBP procedures in the surgical schedule without expanding required inpatient bed capacity. • Identified scheduling policies that reduced overtime in Operating Rooms. • Quantified hospital-wide factors impacting OR utilization. • Evaluated alternatives to improve surgical throughput.• Identified opportunities to smooth and/or predict bed occupancy levels to reduce surge staffing costs. Reduced waiting time for surgery with improved OR efficiency.• Reallocated inpatient hospital beds to reduce off-servicing so patients are cared for by the most appropriate care team.• Identified additional funding and resources required to meet provincial surgical wait time targets.• Evaluated impact of morning discharge and other potential bed management policies to find the best combination of interventions that reduce time patients wait to receive inpatient treatment. Improve Patient and Population Health Outcomes Increase Value and Efficiency Enhance Patient and Caregiver Experience Enhance Provider Experience Hover over the quadruple aim quadrants for explanation and click on each one for detailed examples! Click for Examples Click for Examples Click for Examples Click for Examples

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.

Our Team

Founder and CEO

Azadeh

Azadeh Mostaghel

BASc in Engineering Science
MASc, Industrial Engineering
University of Toronto

Associates

Ben

Benjamin Hayes

BASc, Industrial Engineering
MASc, Industrial Engineering
University of Toronto

Mina

Mina Mahdavi

BASc in Electrical Engineering
MEPP
McMaster University

Negar

Negar Deilami

BASc, Industrial Engineering
Ryerson University

Farrokh

Farrokh Mansouri

BASc in Engineering Science
MASc, Clinical Engineering
PhD, Biomedical Engineering
University of Toronto

Advisors

Amy

Dr. Amy Liu, PhD

Principal Biostatistician,
University Health Network

Assistant Professor,
Dana Lana School of Public Health,
University of Toronto

Scientific Advisor

Ladan

Dr. Ladan Ahmadi, MD

Internal Medicine,
Lenox Hill Hospital,
Manhattan, NY

Northwell Health,
New York, NY

Medical Advisor

Rafal

Rafal Dittwald

BASc in Engineering Science

Serial Entrepreneur
Partner, Bloom Ventures

Technological Advisor

Associations

Past Projects

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

Bed Mapping

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.

We're Expanding!

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?

Email Us with Your Resume!

We're Listening!

Have questions or ideas? Drop us a line to get in touch.

info@orchidanalytics.ca

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