Description of Organization
Good Health Cardiac Clinics is an Ontario outpatient cardiology organization that collaborates with cardiologists to build and operate specialized cardiac diagnostic clinics, currently in Guelph and Markham with expansion into Cambridge planned. Services include cardiology consultation, electrocardiography, ambulatory cardiac monitoring, echocardiography, exercise stress testing, stress echocardiography and ambulatory blood-pressure monitoring. Delivering these services requires close coordination between referrals, scheduling, administrative staff, cardiovascular technologists, sonographers, physicians, equipment, rooms and reporting workflows. As the network grows, Good Health is focused on preserving rapid patient access while building scalable, data-informed operating systems.
Problem area
As Good Health grows from a small number of clinics into a broader outpatient cardiology network, capacity decisions are becoming increasingly interdependent. A change in referral demand may call for additional appointment slots, but the true constraint may be physician availability, a specific staff skill set, room availability, diagnostic equipment, device inventory or reporting capacity. These decisions are currently informed by operational experience, historical volumes and management judgement, which is workable at small scale but harder to sustain as multiple sites and service lines interact. Adding capacity too late creates waitlists and access problems; adding it too early creates avoidable labour, equipment and facility cost.
The central problem is to develop a reusable network model that forecasts demand and evaluates the interaction between patient volumes, appointment templates, staffing, physicians, rooms, equipment and service mix, so that Good Health can test scenarios before making operating or capital decisions and identify the conditions under which additional capacity becomes necessary. The model is intended for practical management use, not a one-time academic forecast: operations leaders must be able to change assumptions and explore growth scenarios for current and future locations.
Main objectives
- Build a structured baseline model of current clinical capacity by site and service line, quantifying scheduled capacity, actual throughput, utilization, cancellations and idle capacity.
- Forecast demand by service, location and time horizon from historical referral and appointment data, with baseline, conservative and high-growth scenarios.
- Identify the binding constraints on throughput under current and future demand by modelling the interaction between staff, physicians, appointment templates, rooms, equipment and diagnostic-device inventory.
- Develop a simulation and/or optimization tool that allows management to test alternative capacity configurations.
- Define practical trigger points for when Good Health should add staff, clinic sessions, rooms, devices or other capacity.
- Evaluate growth scenarios for Cambridge and future sites, without depending on any one launch date, and produce a reusable decision framework for budgeting, workforce planning, capital allocation and site design.
Scope of work
Phase 1 - Baseline capacity model. Define capacity units for the major service lines (consultations, echocardiography, stress testing, ambulatory monitoring and other diagnostics); map resource constraints including staff competencies, physician sessions, rooms, equipment and device inventory.
Phase 2 - Demand analysis and forecasting. Analyze historical referral and appointment demand by site, service and period; assess seasonality, growth trends and volatility; select forecasting methods appropriate to the available data.
Phase 3 - Simulation and optimization. Model how demand flows through constrained resources and appointment templates; evaluate alternative service configurations, staffing models and clinic schedules; estimate the marginal benefit of adding each resource type; where data support it, optimize a defined objective such as meeting target access times at minimum incremental cost.
Phase 4 - Growth scenarios. Apply the model to current sites and to expansion scenarios including Cambridge and hypothetical future growth; test when to add a second clinical resource, expand a diagnostic day or increase device inventory, and which constraint is likely to emerge next.
Phase 5 - Management tool and validation. Build an interactive scenario-planning tool that lets Good Health modify assumptions without rewriting the model; validate outputs against historical periods and management knowledge; document limitations and how the model should be refreshed as the network grows.
Deliverables
- Report
- Presentation
- Resources
- New protocols/procedures
- Demand-forecasting model and a simulation and/or optimization model linking demand to staff, physician, room, equipment and device constraints; an interactive management tool or dashboard for capacity and growth scenarios, with a capacity-trigger framework, scenario analysis, data dictionary, model documentation and user guide.
Frequency
- Bi-weekly
Skills and training required
Operations research, industrial engineering, management science or systems modelling; forecasting and statistics; discrete-event simulation, optimization or queueing analysis; strong Excel, Python, R or MATLAB skills (SQL useful); the ability to build a scenario-planning interface for non-technical managers. Business/financial modelling helps in weighing capacity, cost and service trade-offs. Healthcare experience is not required; Good Health provides orientation.
Resources required
Historical de-identified or aggregate referral, appointment and throughput data by site and service; appointment templates, operating hours, room usage, staffing structures and skill constraints; physician session assumptions and service-specific operating rules; equipment and ambulatory-monitor inventory with turnaround constraints; wait-time and utilization information; cost ranges for capacity options where financial comparison is in scope; Cambridge and growth assumptions; access to operations leadership and staff leads for validation. Standard University computing and analytical/simulation software (Excel, Python, R, MATLAB, SQL or equivalent discrete-event simulation and optimization tools) is sufficient; no production EMR access is required.
NDA or a commercialization agreement for this project?
Yes