Project Option for MEng Certificate in Healthcare Engineering

To obtain the MEng Certificate in Healthcare Engineering, a student must complete 3 required courses and either:

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  • 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:

  1. covering letter explaining the reason(s) for being interested in the opportunity
  2. curriculum vitae (which will be used for our discussions with the host)
  3. copy of up-to-date university transcript(s)

Recent Project/Co-op/Internship Opportunities

  • 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.