To obtain the MEng Certificate in Healthcare Engineering, a student must complete 3 required courses and either:
- 7 elective courses, or
- 4 elective courses and a project, co-op or internship (MIE8888Y).
The project/co-op/internship option can only be taken with approval by CHE, and it is often in the form of a summer internship in a healthcare setting.
Students should notify CHE of their interest to undertake a project, co-op or internship as part of their studies to obtain their MEng Certificate. CHE will then endeavour to find appropriate opportunities, and notify those who have expressed interest.
Before undertaking a project with an industry sponsor, it is highly recommended that students complete at least one of the industrial engineering courses covering the healthcare sector; in particular, MIE 561.
A list of current MEng project opportunities can be found on the MIE website.
From time to time, project/co-op/internship opportunities will be advertised on this page. This list is typically compiled for projects to be conducted during the summer session after a call for proposals is sent to industry contacts in the healthcare sector at the beginning of the winter semester. To obtain approval to undertaken such an opportunity, a student should send to CHE:
- covering letter explaining the reason(s) for being interested in the opportunity
- curriculum vitae (which will be used for our discussions with the host)
- copy of up-to-date university transcript(s)
Current Project/Co-op/Internship Opportunities
- New opportunities for summer projects should start to appear in the middle of the winter semester.
Past Project/Co-op/Internship Opportunities
- Machine learning classifier to facilitate decision making by multidisciplinary tumor boards (Princess Margaret Cancer Centre): Multidisciplinary tumor boards (MDTs) are an opportunity for specialists to meet and discuss the diagnosis and management of cancer patients. At the Princess Margaret Cancer Centre, patients with lung metastases are reviewed at a specialized tumour board, which is a meeting of radiation oncologists, medical oncologists, surgeons, and radiologists to discuss the patient cases. During these meetings, treatment recommendations for patients are made after a careful review of the patients’ clinical history and relevant investigations. The objective of this study is to develop a machine learning classifier which reproduces MDT decision making and can be used as a decision support system to facilitate quality assurance and knowledge sharing in future MDTs. The dataset currently contains primarily numerical and categorical data, but is expected to grow to include imaging data from CT scans or other imaging techniques, and this image data should be incorporated into the classification when available.
- Predictive modelling for Emergency Department stay (Sunnybrook Health Sciences Centre): The objective of this project is to enhance patient experience by creating more transparency around certain aspects of an ED visit: How long will it take to see a doctor? Will this ED visit lead to hospital admission? Decision support is looking for an engineering student who will help us leverage machine learning models to answer these types of questions. Our goal is to employ these predictive models to create real-time tools/dashboards which can both inform patients of their expected wait times and outcomes, and also be used by hospital clinicians and decision makers to understand demand and respond to future needs (e.g., bed utilization, staffing needs, added supports for patients etc.).
- Forecast of human resources required to address anticipated stroke care demand (CorHealth Ontario): There are over 25,000 stroke events per year in Ontario, resulting in over 15,000 related hospitalizations – it is a leading cause of mortality, morbidity and reduced quality of life. Outcomes of stoke can be devastating to patients and caregivers. Optimal patient outcomes following a stroke are dependent on rapid access to specialized hospital care, namely tPA (tissue plasminogen activator – the clot busting drug) and EVT (endovascular thrombectomy – a new catheter-based treatment to physically remove blood clots from large vessels in the brain). One million brain cells are lost for every minute that a stroke goes untreated. The unpredictability and highly urgent nature of stroke necessitates the presence of an on-call specialized care teams 24 hours a day, 365 days a year. Due to the continued growth of stroke events, changes to health care provider supply and behaviour, and changes to practice, demand being placed on these care teams is exceeding supply. CorHealth is in need of engineering expertise to model and forecast the human resources required provincially and at a hospital level to address anticipated stroke care demand. Interested students would have access to historical stroke event data, provincial and regional experts in stroke care, and current information regarding stroke system resources.
- Access and adoption of transcatherer aortic valve implants (CorHealth Ontario): Aortic stenosis is a cardiac condition affecting thousands of Ontarians. Affected persons often present for care in later stages of disease due to its asymptomatic nature and will die within a few years if they do not receive treatment. Until the last decade, the primary treatment was replacement of the aortic valve via open heart surgery. The past decade however, has seen a rapid growth in catheter-based minimally invasive valve replacement – transcatheter aortic valve implant (TAVI). Full adoption of TAVI in Ontario has numerous benefits such as improved quality of life and reduced hospitalization but represents a significant practice change for hospitals in Ontario, including but not limited to patient referral and assessment, pre-operative care, treatment location (OR vs cath lab), post-operative care and discharge. The proposed project would include the following:
- Review and mapping and documentation of current processes associated with referral and assessment and triage for surgical aortic valve implantation and TAVI in Ontario;
- Review, mapping and documentation of processes associated with the delivery of TAVI in jurisdictions with more advanced access to TAVI;
- Analysis of Resource and infrastructure impacts associated with increased adoption of TAVI;
- Develop recommendations and tools to assist Ontario hospitals with process changes necessary to achieve greater access and adoption of TAVI.
- Operationalizing dynamic response to funded surgery volumes (UHN OpenLab): The proposed project focuses on the spine surgery department at Toronto Western Hospital and the need for a tool or system to operationalize a process to identify and respond to funded surgery volumes. The surgeon is provided blocks of operating room time, where they must schedule both surgeries that are volume funded (i.e. the government has set targets for them to complete a certain number of surgeries of certain types and provides funds for each case completed) and that are not volume funded, but needed by patients. Although there are records of what surgeries are both scheduled and performed, they do not specifically track the volume funded cases and often find themselves scrambling to complete their targets at the end of the year, representing lost revenue.
- Developing an understanding of demand and supply of services in mid-west Toronto for people living with substance use challenges (UHN OpenLab): The proposed project focuses on the demand and supply of services in Mid-West Toronto for people living with substance use challenges. A group of over 60 organizations in Mid-West Toronto, including hospitals, primary care providers, and community agencies have formed a coalition with a goal to create integrated services for people living with substance use challenges. Current work, ongoing until May, is exploring the current state and identifying major touch points and opportunities to focus on when creating solutions. This project will work within one or two identified opportunity areas to understand the demand and supply and provide recommendations for improvement.
- Integrated care pathways for patients with high needs at UHN (UHN OpenLab): The proposed project focuses on the need to better identify and address the social determinants of health that impact patients at UHN. Current work, ongoing until April, is exploring the experience and preferences of patients that have high needs (i.e. patients who have over 10 visits to the ED each year and/or over 3 inpatient stays of four days or more). This project will use patient experience and preferences as well as best practices in integrated care pathways to design a new pathway for patients with high needs at UHN.
- Organ Donation and Transplantation System Efficiency Project (Trillium Gift of Life Network): TGLN is looking to better understand the drivers of the increase in case length and to identify specific constraints (“bottlenecks”) in the complex organ donation and transplantation pathways. The project aims to identify opportunities to increase consent for organ donation, build system capacity and improve the efficiency of the system as a whole. Project Description
- Examining Provider Reported Safety Issues in Primary Care (Women’s College Hospital): Women’s College Hospital Family Practice Health Centre is currently undergoing a change in our model of care. As the change process unfolds, areas needing improvement will be assessed. The student is expected to work with the health care team to determine data collection methods for the model of care.
- Patient Flow & Forecasting Model – Input/Operationalization Study (Women’s College Hospital): The project proposed is to process map patient journeys for 3 types of encounters (AN/pregnancy visit, chronic disease management, urgent care), experienced at Women’s College Hospital Family Practice Health Centre.
- Mapping Current State of Day-to-Day Operations of Direct Patient Care (UHN Digital): To address the challenge of predicting short-term hospital capacity at Toronto General Hospital and test the use of its overcapacity protocols, a Generalized Hospital Simulation Model is currently being built. This model will be able to create forecasts centered on real-time patient data (based on historical behaviour). This project’s objective is to design a dynamic input tool for the Generalized Hospital Simulation Model, in order for the model to use real-time patient data that allows for data visualization and interpretation by Hospital Managers and the Flow Management Team.
- Can a short-stay unit with predictive analytics reduce wait times, length-of-stay, and cost for General Internal Medicine patients in the emergency room? (St. Michael’s Hospital): This summer project involves working with two innovative clinical and analytics teams at St. Michael’s Hospital to develop a simulation model to determine whether a short stay unit supported by predictive analytics can reduce wait times, length-of-stay, and cost for General Internal Medicine (GIM) patients in the emergency room (ER). GIM patients account for up to half of all ER admissions to hospital. One in five GIM patients are discharged from hospital within 48 hours. Many hospitals have short stay units to streamline care for patients with a short length of stay but there is little evidence about the effectiveness of these units and St. Michael’s Hospital does not have one. Predictive analytics will be used to identify patients who are likely to have a short stay and who might benefit from a short stay unit. The goal of this project is to develop a simulation model to explore how a short stay unit might affect patient flow and resource utilization in hospital. This will be a novel and impactful project that will inform St. Michael’s Hospital about whether to implement a short stay unit.
- Minor Traumatic Brain Injury: Follow Up CT Head Decision Analysis (Sunnybrook Health Sciences Centre): Minor traumatic brain injury is common. The initial CT head may reveal small contusions or minor hemorrhages, without other concerning findings, such as brain swelling. In these situations, it is clinical routine to repeat the CT head at or within 24 hours. This is costly in terms of hospitalization costs, imaging costs, and the sequelae of additional radiation in often young patients. In general, there is no clinically significant change in the traumatic injuries on these follow-up CTs. This research study will assess the frequency of significant findings or changes on follow up CT head in patients with minor traumatic brain injury. The results will allow a decision analysis model to be created to guide clinicians as to when and when not to order a follow up CT head. The student will gather the clinical data and develop criteria for appropriate follow up CT imaging.
- Predictive Modelling for Emergency Department congestion (UHN Digital): In today’s digital world predictive tools surround us – from traffic to shopping to marketing strategies. Still, in the Canadian healthcare industry we haven’t built effective real-time tools that allow us to see trends, predict demand and better understand and respond to future needs. This project is set to explore the capture-predict-act paradigm and possible “heads up display” digital tools, and to model out the components required to better plan and manage future demand. Both internal and external data will be used to create a framework to better predict Emergency Department demand.
- Workflow Analytics to Optimize External Beam Radiation Therapy Workload Distribution (Odette Cancer Centre, Sunnybrook): Radiation therapy (RT) in medicine is the application of ionizing radiation as a means of treating and/or controlling malignant cells. The term complexity is often used to define the difficulty (and allotted time) in creating a radiation treatment plan and depends on a number a factors including treatment site (i.e. head and neck, prostate), technique (i.e. inverse or forward planning) as well as dose prescription (large dose per fraction). During the RT workflow, electronic tasks called QCLs are sent within the MOSAIQ® Oncology Management System to the responsible dosimetrist indicating that a treatment plan is required. To optimize modern radiation therapy workload, it would be useful to devise a metric of complexity based on quantitative metrics. In this project, planning interval times from QCLs will be captured and categorized based on code captures for technique, site and dose prescription. Statistical analysis will be performed to determine significance between various planning demographics. Using the acquired data, a strategy will be devised to evenly distribute planning workload amongst various site-groups. The long term goal of this project will be to create an artificial intelligence (AI) manager that can “automatically” assign treatment plan to available dosimetrists in an equitable manner, thereby improving workflow and efficiency within an RT clinic.
- Optimization in the Supply Chain and Logistics Portfolio (Scarborough and Rouge Hospital): The Scarborough and Rouge Hospital has embarked on a quality improvement journey to enhance how we deliver patient care by optimizing service delivery and coordination. One such area requiring further optimization is in the Supply Chain and Logistics portfolio (including Linen delivery and Patient Transport). This portfolio is responsible for balancing the service delivery of supply, linen and patient transport services to the various clinical areas with the cost of providing these services on timely manner. Some of the challenges include variation in demand and limited system flexibility to respond to these changes. The need at hand is for the selected candidate to understand the patterns in demand, the various environmental sensitivities and applying appropriate Operations Research components to suggest areas for improvement. Stipend available.
- Time-Study and Modeling of Pharmacist Activities (Holland Bloorview Kids Rehabilitation Hospital): The objectives of this project is to determine key drivers and input variables that impact pharmacist workload, and to develop a cost-of-service model to predict resource requirements for pharmacists at Holland Bloorview. The project will involve conducting a time-study of pharmacist activities, and analysing the data in the context of client referral patterns, workload data, decision support data, and key performance indicators. A stipend will be offered to the student for the duration of this project, which could either on a full time basis over 4 months or a part time basis over 2 semesters. Read more…
- Systems Dynamics project at North York General Hospital (NYGH): North York General Hospital (NYGH) would like a MEng student in Industrial Engineering with some healthcare experience (course and/or hands-on) to develop a functional system dynamic map of the Mental Health Program with connections to external care facilities, where possible i.e. community care, rehab, etc. This work will aid in future stakeholder analysis and will help us understand the inputs/outputs that each program/department/unit provides internally and externally in order to provide the best care possible to our mental health patients. The student is open to use whatever tool they choose to create the system map as long as it is captures our functions well and is easy to read and understand.
- Optimizing Patient Transportation at Toronto General Hospital & Princess Margaret Cancer Centre: There is a massively busy transportation service between Toronto General Hospital (TGH) and Princess Margaret Cancer Centre (PM), involving multiple patient transfers within and between sites each day for tests/interventions/admission. The transportation staff utilize a pager-based system and must find phones to respond to pages related to transferring patients. The ultimate objective of this project is to conduct a thorough review and explore opportunities to optimize the services provided by transportation within and between TGH and PM. This review should be undertaken by building on existing strengths and evaluating current processes. The transportation staff, nursing units and other departments should be directly involved in the review, to collaborate and understand what their unique challenges are.
- Exploring factors effecting unscheduled hospital utilization of patients with chronic diseases (UHN OpenLab): The objective of the overall study is to quantify the increasing impact of chronic diseases (especially in advanced stages) on unscheduled hospital resource utilization (visits and admissions through hospital EDs), and develop a decision-support tool to inform policy making and reallocation of resources. Potential approaches include: (1) Statistical analysis to characterize and find population-level trends of patients with chronic diseases; (2) Simulation modeling to relates care-process characteristics (length of stay, frequency), trends in demographics, and diagnoses to utilization. The final scope of the student project will be customized for the successful candidate. Research will take place at the University Health Network, a network of 5 large academic hospitals in downtown Toronto, Canada (including a regional cancer centre and a rehabilitation institute).
- Reducing patient re-admission rates through a more predictive Discharge Summary model (UHN Digital): UHN is currently completing a three-year program that has significantly improved the quality, completion, and delivery of Discharge Summaries from the organization to primary care providers. This project seeks to determine other variables that caused certain clinical services to not experience the same re-admission rate improvements as their peers; conduct statistical analysis and data modeling to understand indicators, influencers and trends for UHN patients with a high re-admission risk; and generate a more predictive model for the organization.
- Heart Function Clinic Scheduling and Flow (North York General): The Clinic services approximately 300 patients per month with an annual volume growth rate of approximately 14%. The current space is utilized for a number of cardiology clinics including Arrhythmia Clinic, Pacemaker/ICD Clinic, Heart Failure Clinic, Rapid Cardiology Assessment Clinic and Supportive Cardiology Clinic. It is proposed that a simulation model be developed to enable the testing of different scheduling and/or patient flow scenarios.
- Impact of timely and quality Discharge Summaries on re-admission rates to tertiary/acute care hospitals (University Health Network): Timely and quality Discharge Summaries have been shown to decrease re-admission rates elsewhere. As UHN has now improved the quality of its Discharge Summaries, as well as completion rate and delivery time to Primary Care, this project will evaluate the impact on UHN’s 30-day re-admission rates using case studies, publications and the analyses of data collected from on-site interviews and observations.
- Improve data quality from a human factors point of view (Sunnybrook Health Sciences Centre): Data capture and management/reporting is a large issue that can be linked to a number of human factors issues, multiple systems, design of collection methods, etc.
- Software Developer for web-based tool to manage external ad-hoc requests (University Health Network): The Decision Support department at UHN is currently looking for a Ruby on Rails-Postgre SQL developer to help get their site developed and launched. He/she is responsible for working on UI modifications, search and matching engine, and extended features sets implementation and/or integration of applications and services. The developer will creatively enhance the vision of the interaction and visual design. He/she bridges the gap between design and technology by providing technical guidance during the design process — not only bringing awareness to constraints, but also applying their creative thinking and problem solving abilities to create opportunities for innovation.