Healthcare productivity improves when health workers, their skills and time, equipment, information, and service organisation combine to deliver more effective care with the resources available—not simply when a clinic sees more people or staff work longer. A credible gain also preserves or improves safety, access, equity, and patient-centredness.
What does healthcare productivity mean?
Productivity is the relationship between the care produced and the resources used to produce it. Those resources include staff time and skills, facilities, equipment, technology, and the work required to coordinate services. The output might be consultations, completed episodes of care, or health outcomes, depending on the question being asked.
That definition makes volume alone an incomplete measure. A service that completes more appointments but misses follow-up, compromises safety, or serves fewer people with complex needs may not have become more productive in a meaningful sense. Nor does an increase in total output necessarily mean greater efficiency: it could reflect longer working hours, more staff, or greater use of equipment.
The right measure depends on the setting. A primary-care clinic, a hospital, and a national health system produce different kinds of care and use different mixes of resources. Comparing them—or comparing them across different populations—without accounting for those differences can mislead.
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What drives healthcare productivity?
Workforce skills, capacity, and distribution
Training and experience affect what work staff can do and how effectively they do it. So does whether the right mix of skills is available where and when care is needed. The World Health Organization (WHO) identifies inadequate resources, imbalanced distribution across locations and levels of care, uncoordinated workforce practices, and weak workforce information systems as workforce-management challenges.
Headcount by itself therefore tells only part of the story. A workforce can be too small for demand, poorly matched to the work, or concentrated in the wrong places. Skills, workload, geography, and the level of care all shape the services that can be delivered.
Teamwork and work organisation
Many services depend on several professions working together, supported by equipment and administrative systems. OECD analysis cautions against treating an individual clinician’s output as the full productivity of a service: the work of other team members and the capital used to provide care also matter.
Clear role design, effective referral pathways, and coordination can help teams spend less time on avoidable administration or duplicated tasks and more time on necessary care. The relevant question is how the whole service works—not whether one professional appears to be producing more in isolation.
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Digital tools can support particular care processes. OECD examples include risk stratification, clinical decision aids, telemonitoring, and technology-supported provider communication networks. Their contribution depends on how well they fit the care need, whether the data are useful, and whether staff and institutions can put them to effective use.
A new system or device is an input, not proof of a productivity gain. Assessment should account for implementation costs, data governance, staff preparation, and whether patients can access the service. A tool that creates extra work or excludes some patients may fail to improve performance overall.
Demand, case mix, and available resources
The work required for a service depends on who needs care and what that care involves. A population with more complex or chronic needs may require more coordination and follow-up than a population with simpler needs. Facilities and equipment also affect how much care can be delivered with a given amount of labour.
For that reason, comparisons need to specify the population and case mix as well as the resources counted. Otherwise, a service treating more complex needs might appear less productive simply because the measure does not reflect the work involved.
How should healthcare productivity be measured?
Start by defining the decision the measure is meant to inform. Then specify the setting, population, period, output, and input denominator. Depending on the question, a measure might be consultations per clinical hour, completed care episodes per combined labour and capital input, or outcomes achieved per unit of resource. These measures answer different questions; one is not a universal productivity score.
| Setting | Possible output | Possible input denominator | Important context |
|---|---|---|---|
| Primary-care clinic | Consultations or completed care episodes | Clinical hours, or staff and capital resources | Consider follow-up, patient needs, and access—not appointment count alone. |
| Hospital service | Completed episodes of care or discharges | Combined labour and capital resources | Consider case mix, quality, and outcomes alongside throughput. |
| Health system | Health outcomes or services delivered | System-wide resources, including workforce and financing | National-level measures can conceal variation in access, distribution, and performance. |
These are examples of possible measures, not standardized benchmarks. Before comparing results, check whether the services, population, case mix, time period, and inputs are comparable. Where they are not, adjust the comparison or avoid presenting the figures as like-for-like.
Pair resource and output measures with balancing measures for quality, safety, access, equity, and patient-centredness. If throughput rises while one of those dimensions deteriorates, the change is not an unqualified improvement. WHO’s 2017 health-system monitoring framework can help broaden the view: it groups monitoring domains into service delivery, workforce, health information, medical products, vaccines and technologies, financing, and leadership and governance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can healthcare productivity be improved?
Remove unnecessary or duplicated work
Review processes for activity that does not add value to care: unnecessary practices, repeated tasks, or avoidable hand-offs. OECD’s 2019 report, Health in the 21st Century: Putting Data to Work for Stronger Health Systems, estimates that around one fifth of health-care expenditure in OECD countries—about USD 1.3 trillion annually—is not used to generate better health and may sometimes cause harm. That is a cross-country aggregate estimate reported by the OECD, not a current-year measurement or a forecast of savings a particular organisation can achieve.
For a local improvement effort, identify the specific activity, the resources it consumes, and what happens to care when it is changed. Removing a step is not beneficial if it was preventing errors or ensuring necessary follow-up.
Plan staffing around workload
Connect staffing decisions to the activities a service must perform, the time those activities require, and the skills needed to do them. WHO’s Workload Indicators of Staffing Need (WISN) approach uses activity and time standards to relate staffing requirements to workload. It offers a planning method for examining staffing needs; it does not remove the need to consider local conditions, such as geography and care level.
Look at distribution and coordination as well as total staff numbers. A different skill mix or a better match between workload and staff may address a service constraint more effectively than adding hours across the board.
Improve role design and coordination
Map how work moves through the team: who handles each task, where referrals go, and which steps create waiting or duplication. Consider whether responsibilities are assigned clearly and whether team members have the skills, support, and authority required for their roles. Because care relies on multiple people and resources, evaluating the whole pathway is more informative than judging each profession by a single output count.
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Use digital tools for a defined process problem
Begin with a specific need—for example, identifying people who may benefit from preventive support, assisting a clinical decision, monitoring a patient remotely, or helping providers communicate. Then assess whether the proposed tool improves that process in practice, including its costs, data requirements, effects on staff work, and reach among patients.
OECD’s 2019 analysis describes digital approaches as potential enablers of access, effectiveness, and productivity, while emphasizing the importance of appropriate implementation. Its examples do not establish that any one tool will raise productivity in every setting.
Evaluate outcomes and trade-offs together
Set measures before making a change. Track the intended output or outcome alongside resource use and balancing measures for quality, safety, access, equity, and staff sustainability. Compare alternatives on the same dimensions, including feasibility and resilience, rather than selecting an option because it improves only one metric.
OECD’s 2023 renewed performance framework makes interactions and trade-offs among health-system goals explicit, including people-centredness, resilience, environmental and economic sustainability, and equity. That perspective helps distinguish a narrow throughput increase from a broader improvement in system performance.
What makes a productivity gain credible?
A productivity claim is most useful when it explains what care was produced, which resources were counted, who was served, and over what period. It should also show whether quality, safety, access, and equity were maintained or improved. This discipline helps readers and decision-makers tell a genuine improvement from a change in volume, hours, staffing, or case mix.
Local results depend on regulation, labour markets, capital constraints, data quality, and implementation capacity. Broad system evidence can identify promising opportunities, but it cannot guarantee that a particular staffing model, workflow change, or digital tool will work the same way everywhere.
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