Ambulatory care facilities play a central role in healthcare delivery, particularly in primary and some specialised services. Ensuring that care supply aligns with patient demand is essential for improving access, minimising wait times and optimising resources. Operations Research (OR) offers valuable tools to support decision-making in practice management, but the effectiveness of these tools depends heavily on the availability and quality of data. By analysing planning levels, data requirements and healthcare systems in Germany, the Netherlands and New Zealand, a clearer picture emerges of the opportunities and challenges facing data-driven optimisation in ambulatory care.
Planning Decisions and Data Needs
Ambulatory care decisions span strategic, tactical and operational levels. Strategic planning includes decisions on location, service design and capacity, requiring long-term data on population demographics, existing facilities and projected service demand. Tactical planning involves workforce planning, panel management and appointment scheduling, dependent on accurate data about patient types, staff availability and historical utilisation. Operational decisions are concerned with real-time activities, such as assigning patients to appointments or managing walk-ins and require up-to-date information about current schedules and patient preferences.
The data needed at each level varies in granularity. Strategic decisions rely on aggregated data, such as regional demand volumes and mobility patterns, while operational decisions require detailed, patient-specific data, including appointment requests, punctuality and service durations. A lack of comprehensive data impairs the ability to make informed decisions, especially in higher-cost, long-term planning domains such as facility location and service capacity.
Barriers to Data Collection and Implementation
Despite the clear benefits of data-informed decision-making, practical barriers often hinder data collection and the application of OR models. Ambulatory practices may lack the infrastructure or incentive to collect detailed data on patient interactions, appointment logistics and service utilisation. Historical data may be incomplete, inconsistent or fragmented across systems, complicating efforts to build predictive models.
Furthermore, the structure of the healthcare system influences data availability. For instance, in Germany, fragmented data ownership and regional differences create difficulties in consolidating information for strategic planning. In contrast, systems with mandatory registration, such as the Netherlands, offer more consistent patient tracking, enabling better panel and appointment management. The ability to access comprehensive data through national systems or intermediaries, as in New Zealand, facilitates the development of predictive and optimisation models, although cost and governance issues may remain.
Internal link: Advancing Healthcare Through Data Interoperability
Even when data is available, integrating OR models into routine practice remains a challenge. Decision-makers may be unfamiliar with OR methodologies or sceptical of model outputs, particularly when they rely on assumptions or proxy data. Overcoming these barriers requires better communication between modellers and practitioners, as well as tools that present clear benefits without adding significant complexity to workflow.
System Comparisons and Practical Realities
Healthcare systems differ markedly in their approach to ambulatory care delivery and data governance, shaping the feasibility of applying OR techniques. Germany, the Netherlands and New Zealand share universal coverage but differ in funding models, patient registration policies and physician incentives.
In Germany, where patients have open access to providers and practices are often physician-owned, data is less centralised and more variable. As a result, planning decisions are often made with limited empirical support. Conversely, Dutch practices require registration, enabling structured data on patient panels and more predictable demand. In New Zealand, the partial payment model incentivises efficiency, while national-level data services support regional planning and practice optimisation.
These system-level differences influence which OR models are feasible and how data should be collected. For example, strategic location planning in sparsely populated regions, such as rural New Zealand, may rely on mobile practices and routing algorithms, while urban planning in the Netherlands may focus on equitable access and appointment scheduling. Practices in all three countries face common challenges, including physician shortages and rising demand due to ageing populations, reinforcing the need for improved data-driven decision support.
Aligning supply and demand in ambulatory care is critical for ensuring patient access, managing workloads and sustaining healthcare systems. Operations Research offers a robust framework for addressing these challenges, but its potential can only be realised through structured data collection and system-level cooperation. The differences between healthcare systems underscore the importance of tailoring models to context, while also highlighting the universal value of detailed, high-quality data. In the future, greater integration between modelling and real-world decision-making will be essential for delivering efficient, equitable ambulatory care.
Source: Health Care Management Science
Image Credit: iStock
