A specialist healthcare provider needed to translate population need into practical decisions about services, diagnostics, staff and patient flow. Early work showed that demand could not be inferred from population growth alone because referral patterns, acuity, test mix and pathway constraints changed capacity requirements. Marketways connected the investment question to the daily operating pathway.
The engagement objective
Through the initial discovery, Marketways defined the objective: connect demand, investment, workforce and operational pathways.
How Marketways translated the problem
We began with a practical question: Which specialties, locations and resources will face gaps? The first analysis used population, utilisation, referrals, morbidity, capacity, workforce and geography.
That evidence could not be read in isolation. Population growth does not translate evenly into beds, visits, diagnostics or procedures. Optimising one unit can move queues downstream or encourage premature discharge.
Population growth alone produced an attractive demand story but not a safe capacity plan. Referral, acuity, test mix and pathway constraints changed how population need became work inside the service.
We did not judge each component by its isolated KPI. We examined how people, assets, decisions and constraints affected one another, then used the evidence to test whether an apparent improvement would strengthen the complete system or merely move cost, pressure or risk elsewhere.
How the engagement developed
The initial work on clinical service demand and capacity plan exposed dependencies with patient flow and waiting-time redesign, healthcare workforce and roster capacity, diagnostic network and laboratory feasibility. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Clinical service demand and capacity plan: Guide service expansion, workforce and investment priorities.
- Patient flow and waiting-time redesign: Improve throughput while protecting quality and clinical prioritisation.
- Healthcare workforce and roster capacity: Align staffing with patient need and safe working conditions.
- Diagnostic network and laboratory feasibility: Define viable capacity and service boundaries before investment.
Evidence we examined
- Population, utilisation, referrals, morbidity, capacity, workforce and geography.
- Arrival, acuity, pathway events, resources, discharge and readmission.
- Demand, acuity, rosters, skills, absence, agency use and productivity.
- Referrals, test volumes, geography, equipment, workforce, prices and regulation.
Industry conditions we accounted for
- Population growth does not translate evenly into beds, visits, diagnostics or procedures.
- Optimising one unit can move queues downstream or encourage premature discharge.
- Headcount can hide specialty, credential, shift and supervision constraints.
- Test mix, referral behaviour, turnaround time and quality requirements shape economics.
How our engagement contributed to business impact
We connected every method to a decision and a business measure. The organisation could assess the engagement through operating results as well as model performance.
- Clinical service demand and capacity plan
- Method: Forecasting, Risk & Optimisation, Statistics & Econometrics.
- Evidence: Population, utilisation, referrals, morbidity, capacity, workforce and geography.
- Decision supported: Guide service expansion, workforce and investment priorities.
- Impact measure: Access, utilisation, gap closure and sustainable capacity.
- Patient flow and waiting-time redesign
- Method: Process, Workflow & Systems, Forecasting, Risk & Optimisation.
- Evidence: Arrival, acuity, pathway events, resources, discharge and readmission.
- Decision supported: Improve throughput while protecting quality and clinical prioritisation.
- Impact measure: End-to-end time, safe throughput, cancellations and readmission.
- Healthcare workforce and roster capacity
- Method: Forecasting, Risk & Optimisation, Data Foundations & Business Intelligence.
- Evidence: Demand, acuity, rosters, skills, absence, agency use and productivity.
- Decision supported: Align staffing with patient need and safe working conditions.
- Impact measure: Coverage, overtime, agency dependency and continuity.
- Diagnostic network and laboratory feasibility
- Method: Statistics & Econometrics, Research & Evidence Collection.
- Evidence: Referrals, test volumes, geography, equipment, workforce, prices and regulation.
- Decision supported: Define viable capacity and service boundaries before investment.
- Impact measure: Volume adequacy, turnaround, utilisation and investment robustness.
Implementation
The engagement was structured as long-range plan with near-term pilots. We connected the analysis to the decisions, operating constraints and measures that the organisation would continue to use.
How success was assessed
The overall assessment considered access, safe capacity, waiting time and investment confidence. The supporting measures included:
- Access, utilisation, gap closure and sustainable capacity.
- End-to-end time, safe throughput, cancellations and readmission.
- Coverage, overtime, agency dependency and continuity.
- Volume adequacy, turnaround, utilisation and investment robustness.
Services and methods used
Services: Market Research & Demand Assessment, Business Feasibility Study, Process & Workflow Analysis & Redesign, Operational Performance Diagnostic, Workforce Planning & Capacity, Workforce Performance & Capability Assessment.
Methods: Forecasting, Risk & Optimisation, Statistics & Econometrics, Process, Workflow & Systems, Data Foundations & Business Intelligence, Research & Evidence Collection.