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
Good Health's operational complexity is increasing as it adds sites, physicians, diagnostic capacity and patient volume. The business already generates substantial operational data through its EMR, scheduling processes and internal reporting; the challenge is turning that data into timely, prioritized action. Across the referral-to-report journey, multiple queues can deteriorate independently: referrals remain unbooked, waitlists age, capacity goes unused, appointments are cancelled, studies wait for interpretation and site-specific bottlenecks emerge. Identifying these issues today depends on dashboards, spreadsheets, manual review and the experience of staff who know what to look for.
The central problem is to design a scalable operational intelligence system that distinguishes normal variation from situations requiring intervention and helps answer: What needs attention now? Why? Which cases or queues should be prioritized? What response is appropriate? The project is operational, not clinical: it must not diagnose, recommend treatment, replace physician judgement or autonomously alter patient care.
Main objectives
- Map the end-to-end operational workflows and identify the highest-value points where delays, backlogs, errors or unused capacity occur.
- Define an operational exception taxonomy (aging waitlists, unbooked referrals, unfilled capacity, cancellations, reporting delays) with measurable service-level indicators and thresholds.
- Develop transparent prioritization logic that ranks exceptions by urgency, patient impact, age, recoverability and available capacity.
- Identify root-cause signals and design recommended-action logic that supports staff decisions without automating clinical judgement.
- Prototype a control-tower dashboard or decision-support application that consolidates prioritized exceptions and suggested actions.
- Validate the prototype against historical, de-identified or simulated scenarios and deliver an implementation roadmap for Good Health's data and management environment.
Scope of work
Phase 1 - Current-state discovery. Map the referral, scheduling, diagnostic, reporting and follow-up workflows in scope; interview operations and clinical/administrative leads on escalation practices and tacit decision rules; inventory data fields, timestamps, dashboards and reports, noting data-quality limitations.
Phase 2 - Exception and KPI design. Define a prioritized set of operational failure modes and service-level indicators with measurable thresholds and escalation logic; separate operational exceptions from clinical decisions that must remain outside the system.
Phase 3 - Data analysis and modelling. Analyze historical patterns in backlog formation, unused capacity, turnaround time and cancellations; evaluate rules-based, statistical and/or machine-learning approaches for predicting workflows at risk of breaching targets; build a priority model that can be explained to end users.
Phase 4 - Prototype development. Build a functional or high-fidelity control tower with prioritized work queues, filters, trends, root-cause indicators and recommended actions, fed by a lightweight, repeatable import of de-identified extracts rather than direct production integration.
Phase 5 - Validation and implementation planning. Back-test against historical cases and run structured scenario testing with staff; compare against the current manual review process (time-to-detect, time-to-prioritize, false alerts, actionability); recommend phased implementation, governance, data architecture and ownership.
Deliverables
- Report
- Presentation
- Resources
- New protocols/procedures
- Functional or high-fidelity control-tower prototype (dashboard / decision-support application) built on de-identified or simulated data, plus an operational KPI catalogue and exception taxonomy, a prioritization and escalation framework, validation results and a phased implementation roadmap.
Frequency
- Bi-weekly
Skills and training required
Systems thinking, industrial engineering, operations research or process improvement; data analysis (Python, R, SQL, Excel, Power BI); dashboard, UX or information-system design; stakeholder interviewing, process mapping and documentation. Statistical modelling or machine learning is valuable but not mandatory where a rules-based approach is more appropriate. Healthcare experience is helpful but not required; comfort with healthcare privacy requirements is essential. Good Health provides workflow and privacy orientation.
Resources required
De-identified or aggregate operational extracts (referral/booking dates, appointment status, service type, clinic, provider, waitlist age, cancellations, no-shows, completion and report-status timestamps); existing dashboards, reports, workflow documents and service-level targets; access to operations leadership and administrative/clinical leads; clinic observation or remote workflow demonstrations where practical; synthetic or controlled test extracts for prototyping. Standard University analytical tools (computing, Python/R, SQL, Excel, Power BI or equivalent dashboard software) are sufficient; no production EMR access is required.
NDA or a commercialization agreement for this project?
Yes