The escalating demands on primary healthcare systems require innovative methods for evaluating and improving service efficiency. In contrast to hospital-focused analyses, primary care often lacks tailored efficiency assessments, especially within public health contexts. Traditional methods like Data Envelopment Analysis (DEA) have been employed to benchmark performance across decision-making units (DMUs), but they present limitations in distinguishing efficient units and addressing outliers or specialisation. To overcome these challenges, a novel methodology—DEA Visualisation—has been applied to assess 82 public primary health centres in Madrid, serving citizens aged 65 and above. This approach integrates DEA with multivariate statistical tools to reveal inefficiencies, identify specialisation patterns and ensure inclusiveness in analysis, ultimately guiding resource optimisation and managerial decision-making.
Uncovering Inefficiencies through Factor Analysis
DEA Visualisation begins by examining how efficiently each centre transforms resources (doctors, nurses, administrative staff, infrastructure costs) into outputs (visits, prescription costs, vaccines). The model applies factor analysis to 105 DEA specifications, grouping them based on similar efficiency scores. The first factor primarily identifies cost-efficiency in prescriptions, with centres like Monterrozas (MONR), Condes de Barcelona (COND) and Mirasierra (MIRS) excelling due to lower unitary prescription costs, likely achieved by prescribing generics. Conversely, centres such as Barcelona (BARC) and Las Águilas (LAGU) display high prescription costs, indicating potential inefficiencies or incentives for prescribing specific drugs.
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The second factor highlights the use of infrastructure in administering vaccines, particularly by nurses. Centres like Eloy Gonzalo (ELOY) and Infanta Mercedes (INFA) showed high resource usage but low vaccine numbers, indicating underutilisation. Additional factors explored staffing efficiency and infrastructure usage for generating patient visits. Benita de Ávila (BENI) stood out for employing a large workforce relative to its output, revealing inefficiencies in staff deployment. In contrast, centres like Ramón y Cajal (RAMO) and Jaime Vera—Leganés (JAIL) demonstrated higher productivity with fewer resources, indicating better infrastructure-to-output alignment.
Mapping DEA Specifications with Property Fitting
To enrich the interpretation of these efficiency dimensions, the study applies property fitting (ProFit), projecting DEA specifications and health centres into a common space. This technique visually links specific DEA configurations to performance patterns. Dimensions derived from this mapping confirm initial findings: Dimension 1 underscores prescription cost-efficiency; Dimension 2, infrastructure use in vaccination; and Dimension 3, staff efficiency.
Dimension 4 reveals excessive infrastructure without proportional service output in centres like Benita de Ávila (BENI), whereas Dimension 5 captures reluctance or readiness to embrace preventive medicine. Notably, centres focusing on nurse-led vaccination and education initiatives not only reduce prescription reliance but also operate more cost-effectively. Additional dimensions, such as infrastructure use per visit and non-medical staff efficiency, further expose underlying performance drivers. Centres like Reina Victoria (REIN) exhibit unusual traits, operating with minimal staff but incurring high infrastructure costs and prescription expenditures, suggesting potential inefficiencies masked by traditional DEA models.
Benchmarking Common Practices through Cluster Analysis
To identify realistic performance benchmarks, cluster analysis groups centres with similar operational profiles. This classification aids in comparing like-for-like institutions, avoiding misleading conclusions from unrelated peer comparisons. For instance, Benita de Ávila (BENI), despite being in the same cluster as Barcelona (BARC) and Jaime Vera—Leganés (JAIL), deviates significantly due to overstaffing. Similarly, Reina Victoria (REIN), though efficient in administrative staffing, operates with high prescription costs and infrastructure expenditure, positioning it as a maverick rather than a true benchmark.
Cluster insights show that some centres, such as those focused on preventive care, perform better overall in terms of prescription cost reduction and infrastructure utilisation. These findings challenge assumptions that higher staffing or larger facilities inherently lead to better outcomes. Instead, strategic resource deployment and specialisation in preventive practices emerge as key contributors to performance. By recognising these patterns, managers can avoid one-size-fits-all benchmarks and instead focus on contextually appropriate efficiency goals.
DEA Visualisation provides a comprehensive and nuanced approach to evaluating efficiency in primary healthcare. By integrating DEA with multivariate statistical analysis, this methodology overcomes the traditional model’s limitations, revealing inefficiencies, benchmarking realistic peers and accounting for specialisation and outliers without exclusion. The study of Madrid’s primary health centres highlights varied performance levels driven not solely by resource volume but by how those resources are applied.
Centres prescribing more generic medications reduce public costs and exemplify good practice, while others may warrant scrutiny for favouring costlier drugs. Infrastructure and staff underutilisation suggest opportunities for redistributing patient loads or reassessing capacity. Notably, centres focused on preventive care demonstrate cost-effectiveness, encouraging a shift from curative to preventive service models.
Managers and policymakers should leverage these insights to refine resource allocation, enhance service delivery and guide future investments in primary care. Further research could explore integrating satisfaction and demographic-specific outputs to enrich efficiency models. DEA Visualisation marks a significant advance in healthcare operations management, offering a robust framework for continuous improvement in public primary care.
Source: Health Care Management Science
Image Credit: iStock
