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Healthcare productivity improves when a health service uses its workforce, skills, time, facilities, equipment and information more effectively to deliver needed care—not simply when it sees more patients or asks staff to work longer. Any claimed gain should be judged alongside care quality, safety, access and fairness.
What healthcare productivity means—and why volume is not enough
Productivity is the relationship between the resources used to provide care and the care produced. Depending on the setting, outputs might include consultations, completed episodes of care, hospital discharges or health outcomes. Inputs may include clinicians’ time, other staff, facilities, equipment and technology.
A higher patient count by itself does not establish higher productivity. A clinic could see more people by shortening appointments, but if that leaves patients’ needs unmet, increases errors or shifts work elsewhere, the apparent throughput gain may not represent better performance. Likewise, longer hours can increase total output over a period without increasing output per hour.
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The right measure depends on the service. A primary-care clinic, hospital and national health system produce different kinds of care using different combinations of labour and capital. A measure that is meaningful for one cannot automatically be used to rank another.
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What drives healthcare productivity?
Workforce skills, time and distribution
Training and experience affect what staff can do, while the balance of skills on a team affects how work is shared. Staffing levels matter, but so do where workers are located, which care settings they serve and whether their capacity matches the work arriving there. WHO identifies inadequate resources, imbalanced distribution across geography and care levels, uncoordinated workforce practices and weak workforce information systems as management challenges.
Teamwork and work organisation
Many care activities depend on several professions working together, supported by equipment and administrative functions. The measured output of an individual clinician may therefore reflect the work of a wider team, not that person’s effort alone. Clear roles, sensible referral pathways and coordination can help reduce avoidable handoffs or administrative burden. OECD’s 2013 analysis distinguishes this kind of “working smarter” from increasing output by working longer.
Facilities, equipment and technology
Staff time is only one input. A shortage of usable rooms, diagnostic equipment or other capital can constrain the work a team can complete. Digital tools can support defined tasks such as identifying patients at risk, assisting clinical decisions, monitoring patients remotely or helping providers communicate. They are enablers, not productivity outcomes in themselves: benefits depend on implementation, appropriate data use, workforce preparation and institutional capacity.
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Services do not all handle the same patients or the same complexity of need. A comparison that ignores case mix can make a team caring for more complex patients appear less productive, even when it is using resources appropriately. Demand also varies by place and level of care, so a staffing or process change that works in one service may not fit another.
How to measure productivity responsibly
Before comparing teams, facilities or systems, define the setting and population, the period being measured, the output, and the inputs included. Possible measures include consultations per clinical hour, completed care episodes per combined labour and capital input, or outcomes achieved per unit of resource. These answer different questions and should not be treated as interchangeable.
- Specify the output. Decide whether the question concerns activity, completed care, outcomes or another result.
- Specify the inputs. State which staff time, roles, facilities, equipment and other resources are counted.
- Make comparisons like for like. Account for differences in case mix, service type, population and period; avoid ranking unlike services without adjustment.
- Pair productivity measures with balancing measures. Track quality, safety, access, equity and patient-centredness alongside throughput and resource use.
A productivity measure is most useful when it makes its trade-offs visible. For example, a rise in completed visits should be interpreted with measures that can reveal whether safety, access or fairness changed at the same time. OECD’s 2023 performance framework highlights people-centredness, resilience, environmental and economic sustainability, and equity, while recognizing that system goals can interact or conflict.
For a whole-system view, WHO’s 2017 monitoring framework groups measurement into service delivery, workforce, health information, medical products, vaccines and technologies, financing, and leadership and governance. Considering these domains helps prevent a single staffing or technology metric from standing in for the performance of an entire health system.
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1. Find and reduce low-value or duplicated work
Map the work involved in a care pathway and look for unnecessary practices, repeated steps, avoidable data entry or duplication between services. OECD’s 2019 report estimated that around one fifth of health-care expenditure in OECD countries—about USD 1.3 trillion annually—was not used to generate better health and sometimes caused harm. This is a cross-country aggregate estimate, not a forecast of savings any particular clinic or health system can recover. Local changes need to identify specific waste and check whether removing it preserves necessary care.
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2. Plan staffing around workload
Use the activities a service performs, the time they require and the skills needed to complete them to assess staffing requirements. WHO’s Workload Indicators of Staffing Need (WISN) method connects staffing needs to workload through activity and time standards. It can inform workforce planning, but the result still needs to be considered alongside skill mix, geography, level of care and local constraints—not just total headcount.
3. Redesign roles and coordination around patients
Review which team members perform each task, where specialist input is needed and how patients move between services. For people with chronic or complex needs, coordination across providers may help align care and avoid disconnected activity. Evaluate the full team and its supporting resources rather than attributing a change in output to one profession in isolation.
4. Choose digital tools for a defined process problem
Start with a care need or workflow bottleneck, then decide whether a data or technology intervention is suitable. Risk stratification, decision support, telemonitoring and provider communication networks are examples OECD has discussed as potential supports for access, effectiveness and productivity. Before adopting a tool, assess implementation costs, data governance, workforce readiness, patient access and the capacity to integrate it into care. A tool’s presence or usage count is not proof that care improved.
5. Check the result across performance dimensions
Compare the intervention with the existing process using the same population, period and definitions where possible. Examine resources required, output or outcomes, care quality and safety, accessibility, equity, feasibility and resilience. If throughput rises but patients lose access, risks increase or the burden becomes unsustainable for staff, the change is not an unqualified productivity improvement.
What makes a productivity gain credible?
A credible gain is specific to a defined service and population, uses a transparent account of inputs and outputs, and is assessed alongside outcomes and balancing measures. OECD and WHO describe system-level opportunities and methods, but no single staffing model, workflow redesign or digital tool is established as a guaranteed productivity improvement in every setting. Case mix, regulation, labour markets, capital, data quality and implementation capacity all shape local results.
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