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Performance measurement systems in primary health care: a systematic literature review

BMC Health Services Research volume 25, Article number: 353 (2025) Cite this article

Abstract

Background

Performance measurement systems (PMS) are increasingly recognized as essential tools in healthcare services. However, there remains a significant gap in the literature regarding their development, implementation, and impact on primary health care (PHC). This study aims to systematically review peer-reviewed literature to identify and analyze existing constructs, methodologies, and challenges associated with PMS in primary care settings worldwide.

Methods

This systematic review follows the PRISMA guidelines regarded as the gold standard for evidence synthesis in scientific and grey literature. The quality of the selected studies was assessed using the Rosalind Franklin Qualitative Research Appraisal Instrument (RF-QRA), focusing on transferability, reliability, credibility, and confirmability.

Results

From an initial pool of 167 articles, 14 studies were selected for in-depth analysis. These studies highlighted several challenges, including difficulties in evaluating PMS post-implementation within primary care units, limited evidence on the longitudinal monitoring of performance indicators, and inconsistencies in methodological approaches. The findings also revealed that regional, operational, and cultural contexts influenced the most PMS adaptations.

Conclusions

This systematic review offers a comprehensive diagnosis of the best PMS models implemented globally over the past five years, emphasizing heterogeneity, diversification, and reliability. The findings underscore the potential for PMS to inform public policies to achieve high-performance primary healthcare systems and enhance decision-making processes at both operational and managerial levels.

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Introduction and background

Definition and importance of primary health care

A healthcare system based on Primary Health Care (PHC) organizes its structures. It functions around the principles of equity, social solidarity, and the universal right to the highest attainable standard of health. This encompasses every human being, regardless of race, religion, political ideology, or socioeconomic status [1, 2]. PHC operates as the first level of care, delivering both individual and collective health actions. These include health promotion, disease prevention, diagnosis, treatment, rehabilitation, harm reduction, and health maintenance, all aimed at providing comprehensive care that positively impacts community health [3].

The Alma Ata Declaration, which marked its forty-fifth anniversary in September 2023, remains a foundational document guiding global PHC strategies. Its commitments continue to influence decision-making at all levels of PHC [4,5,6]. This declaration set the groundwork for global health equity, emphasizing prioritizing primary healthcare in global health agendas. Despite significant advancements in access, infrastructure, and communication in PHC globally [7,8,9], the progression anticipated by the World Health Organization (WHO) remains hindered. Specifically, the absence of effective measurement of internal processes within care units across service delivery, regional, and central levels limits operational efficiency and the achievement of global health targets [10,11,12]. Addressing this gap is crucial, as every person, everywhere, has the right to achieve the highest possible level of health [13, 14]. The Astana Declaration reaffirmed and redirected global efforts in primary healthcare [5]. This declaration sought to ensure equitable access to the highest attainable standard of health worldwide [15,16,17]. With a focus on achieving high performance in PHC, the WHO, during its 75th anniversary, collaborated with member countries to address the post-COVID-19 public health landscape. The urgency highlighted by the pandemic reinforced the need for universal health coverage (UHC) and robust PHC systems [18,19,20]. The commitment to UHC and the Sustainable Development Goals (SDGs)—specifically SDG 3: Good Health and Well-being—places PHC at the center of strategies for achieving global health equity [21]. This entails addressing current limitations in PHC, particularly in measuring, monitoring, and improving internal processes to ensure the sustainability of health outcomes [22, 23]. PHC can continue to evolve by focusing on these challenges, serving as the foundation for equitable and accessible healthcare worldwide.

Performance measurement: definition and general applications

Performance measurement refers to systematically evaluating processes, practices, and outcomes within an organization using key performance indicators (KPIs) [24,25,26]. It is widely applied across public and private sectors to identify efficiency gaps, evaluate quality, and prioritize interventions [27, 28]. In healthcare, performance measurement traditionally focuses on standalone indicators, such as mortality rates or operational efficiency metrics [29, 30].

In contrast, performance measurement systems go beyond individual metrics by analyzing the interconnections between indicators and their mutual influences [31]. These structured systems allow for more comprehensive insights, revealing one KPI's proportional or non-proportional impact on another [32, 33]. While performance measurement provides snapshot evaluations, performance measurement systems deliver integrated perspectives, making them essential for managing complex services [34, 35].

Performance measurement applied to the healthcare sector and primary care

In healthcare, performance measurement systems have gained prominence for identifying structural vulnerabilities, improving resource allocation, and assessing the impact of organizational and clinical interventions [36, 37]. However, while the private sector often employs established methodologies such as the Balanced Scorecard (BSC), public sector applications face challenges in adapting these approaches to the unique cultural, operational, and economic contexts of PHC [38, 39].

In PHC, the adoption of performance measurement systems is constrained by a lack of methodological standardization and fragmented literature. Studies such as those by Prates et al. [40] and Gartner and Lemaire [41] explore complementary aspects but do not fully address performance measurement systems in PHC. Prates et al. [40] evaluate PHC attributes using the PCATool, focusing on quality from the perspectives of users and professionals. Gartner and Lemaire map dimensions of performance and KPIs in broader healthcare contexts but do not address the specific methodological needs of PHC. This gap highlights the need for a sharper focus on performance measurement systems tailored to PHC, considering its unique operational and organizational requirements. By bridging this gap, it is possible to enhance service delivery and improve the quality of care in PHC settings.

Research question, relevance and objective

The increasing complexity and demand for primary healthcare services highlight the need for robust and adaptable performance measurement systems (PMS). However, the literature still reveals significant gaps regarding the development, implementation, and validation systems across different regional and technological contexts. This study aims to explore how PMS can be specifically designed and applied to address the operational and social particularities of diverse global realities.

The central research question is: what are the constructs, methodologies, and indicators used in performance measurement systems in primary healthcare, as documented in peer-reviewed literature, and how can these systems be adapted to address regional specificities by incorporating technological and methodological innovations to enhance management and operational outcomes? The research question is guided by three main dimensions: Regional adaptability and local contextualization, highlight that healthcare systems facistinct challenges in different regions, ranging from infrastructure limitations in low-income countries to organizational complexity in high-income nations. The effectiveness and equity of primary care services depend on the ability of performance measurement systems (PMS) to adapt indicators and approaches to local specificities. Moreover, cultural, social, and economic factors play essential roles in defining priorities and ensuring the efficient use of healthcare resources [42, 43]. Simultaneously, the incorporation of emerging technologies, such as artificial intelligence, machine learning, and digital platforms is transforming performance measurement by enabling real-time data collection and analysis. While these innovations promote faster and more accurate decision-making, their integration into PMS requires robust methodologies that ensure effectiveness even in contexts with varying levels of technological maturity [44, 45].

This systematic review aims to bridge these gaps by synthesizing peer-reviewed literature to identify constructs and methodologies applied to performance measurement systems in primary care. By exploring diverse methodologies and their practical applications, this study offers unique contributions to the academic and operational understanding of performance measurement in primary healthcare settings.

Materials and methods

This section outlines the methodology employed as well as the methods and instruments used to conduct the systematic review focused on the operational performance measurement system in primary health care. The aim is to synthesize the information from the researched literature with a qualitative base that is structured, reproducible, and transparent. This section will be divided into two subsections. The first will focus on the adopted strategy for literature search, aiming to gather the primary references on performance measurement systems in primary health care. The second subsection will describe the instruments used, as well as the methodology for constructing the systematic review and the variables considered in the analyses. The research strategy of Kringos et al. [45] was used as a foundational framework to describe the core dimensions of primary health care and guide the conceptualization of this study. The structure of a primary healthcare system consists of three dimensions: 1. governance; 2. economic conditions; and 3. workforce development. The process of primary health care is determined by four dimensions: 4. access; 5. continuity of care; 6. care coordination; and 7. comprehensiveness of care. The outcomes of a primary health care system encompass three dimensions: 8. quality of care; 9. efficient care; and 10. health equity.

  1. a

    First Stage: Systematic Review following PRISMA

This systematic review follows a robust quantitative approach, systematically grounded in literary documents and pre-selected scientific research, as outlined by Levitt (2018). The review adheres to the PRISMA 2020 Statement [46], which provides a 27-item checklist recognized as the gold standard for evidence synthesis and reporting of systematic reviews and meta-analyses. PRISMA has been widely endorsed by EQUATOR (Enhancing the Quality and Transparency Of Health Research), promoting reliability and transparency in health services research [47].

In addition to PRISMA, this review integrates methodological guidance from the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis [48] and the Cochrane Handbook for Systematic Reviews of Interventions, version 6.4 [49]. These complementary frameworks ensured rigor throughout the stages of study selection, data extraction, and synthesis. The PRISMA checklist for this review has been completed and is included as an appendix to ensure transparency and reproducibility. Furthermore, to ensure the relevance and originality of this review, a thorough search was conducted in Cochrane Database, JBI Database of Systematic Reviews and Implementation Reports, and PROSPERO. This search confirmed the absence of prior systematic reviews or protocols that specifically investigate the methodologies for developing performance measurement systems in primary health care. This finding emphasizes the need for a comprehensive study that not only consolidates existing knowledge but also provides actionable insights for improving health services globally and regionally.

  • Eligibility

The research focused on selecting full peer-reviewed articles published between 2016 and 2023 written in English and available in full text online. The search was limited to the years 2016 to 2023 based on significant advancements in global health and performance measurement beginning in 2016. This year marks a turning point with the implementation of the Sustainable Development Goals (SDGs) established in 2015, which included specific targets for universal health coverage and strengthening primary health care [22, 50]. Additionally, the literature on the Primary Health Care Performance Initiative (PHCPI), launched in 2015, began to emerge in 2016, strongly influencing performance measurement approaches in primary health care [51, 52].

The inclusion and exclusion criteria for this systematic review were structured using the Population, Concept, and Context (PCC) framework, as recommended by methodological guidelines [48, 49]. The population included studies involving healthcare systems, facilities, or professionals working within primary healthcare settings globally. The concept focused on articles that explore, develop, or evaluate performance measurement systems, performance indicators, or methodologies applied to primary healthcare, emphasizing operational and organizational dimensions. The context covered studies conducted in primary healthcare settings across diverse geographic and socioeconomic environments, with an emphasis on improving healthcare delivery, resource allocation, and patient outcomes. Articles addressing operational and organizational performance measurement systems in primary healthcare were eligible. As shown in Table 1, studies focusing on the performance of clinical equipment and their calibrations, metrological performance, or those lacking complete conceptual texts on performance measurement systems, not written in English, or dealing with performance in laboratory settings were excluded.

Table 1 Inclusion and exclusion criteria applied to the research
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  • Research strategy and organization of sources

The search strategy was designed to ensure rigor and adaptability to each database, following the guidelines of the PRISMA framework. The selected databases—PubMed, SCOPUS, Web of Science, SciELO, and Springer—are recognized for their high-impact references and manuscript quality in areas related to performance measurement systems, performance intelligence, and operational performance in primary health care.

Search fields were structured around the introduction of keywords, including “health performance measurement,” “performance measurement,” “performance measurement systems,” and “performance intelligence.” Boolean operators (AND, OR), wildcards (*), and parentheses were employed to refine the search logic and maximize relevant results. The strategy was tailored for each database to align with its unique indexing and functionality, ensuring comprehensive retrieval of relevant studies.

The searches were conducted between May 5, 2023, and July 7, 2023. The complete search strategies, including specific adaptations for each database, are provided in the appendix to ensure transparency and reproducibility.

  • Selection of manuscripts

The selection of studies followed a rigorous double-blind process to ensure objectivity and minimize potential bias, as recommended by systematic review methodological guidelines [48, 49]. The article selection process adhered to the PRISMA framework, encompassing four stages [53]. Initially, two independent reviewers (Reviewer 1 and Reviewer 2) conducted the screening of titles and abstracts independently, without knowledge of each other’s decisions. Both reviewers applied predefined inclusion and exclusion criteria strictly.

Records from the years 2016 to 2023 were included, and abstracts were assessed to identify articles relevant to the research question, as shown in Fig. 1. This process resulted in the selection of 14 articles that discussed performance measurement systems in primary health care. During the initial screening, 12 duplicate records were removed, followed by the exclusion of 128 manuscripts due to their lack of relevance to the main research theme, and 13 articles were excluded for the following reasons: six articles did not address performance measurement systems in primary healthcare; four articles focused on secondary or tertiary healthcare settings, which were beyond the scope of this review; two articles lacked sufficient methodological details to satisfy the eligibility criteria; and one article was not available in full text. These exclusions are transparently documented in the PRISMA 2020 flowchart (Fig. 1) to ensure alignment with best practices for systematic reviews [46]. In the second stage, the 14 selected articles underwent full-text review. Both reviewers independently assessed the manuscripts, focusing on methodology and alignment with the central research theme. Any disagreements during this process were resolved through discussion, and when necessary, a third reviewer was consulted to achieve consensus. This rigorous approach ensured that all chosen articles fully met the eligibility criteria and aligned with the research objectives.

Fig. 1
figure 1

Review protocol based on Page et al. [46]

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  1. b

    Second stage: systematization of information collection and summarization

The data synthesis was conducted using an integrative approach to capture both qualitative and quantitative insights into performance measurement systems in primary healthcare. The manuscripts were selected, and the following data were extracted and organized into a database using Microsoft Excel for subsequent analysis: (1) research database used; (2) keywords used to locate the article; (3) type of manuscript (full article or abstract); (4) journal name; (5) manuscript title; (6) publication year; (7) country where the study was conducted or the corresponding author's country (when the research location was not clearly defined); (8) journal's cite score; (9) journal's impact factor; (10) main research question; (11) whether the manuscript provided a methodological framework for performance measurement systems rather than isolated indicators; (12) main methodology used in the article; (13) auxiliary methodologies supporting the manuscript's framework; (14) key research findings; and (15) important secondary results validating the proposed methods. For qualitative data, the four categories of the Rosalind Franklin Qualitative Research Appraisal Instrument (RF-QRA)—transferability, reliability, credibility, and confirmability—were applied to assess the quality of eligible studies [54]. The risk of bias for all included studies was deemed low, and the peer-reviewed articles were considered to have high reliability. The RF-QRA framework ensured that the synthesis maintained methodological rigor and reliability.

For quantitative data, metrics such as publication details, journal impact scores, and the number of recurring themes across studies were extracted and aggregated to identify patterns, trends, and consistencies in performance measurement practices. To organize these findings, the ranking cohort technique [55] was employed, which classified unique methodological themes into hierarchical categories. These categories were tabulated and analyzed to synthesize common measurement methodologies. Finally, a convergent synthesis design was applied to integrate qualitative and quantitative findings. The categorization into these nine areas was derived through a systematic content analysis of the most recurring performance measures identified across the studies. This approach ensured that the selected areas comprehensively represented the key dimensions of performance measurement in primary health care. By grouping these measures into overarching categories, the research offers a holistic view of the structures adopted by the manuscripts. Qualitative themes were matched with quantitative patterns to provide a comprehensive understanding about methodologies and outcomes. Divergent results were noted and discussed to identify gaps or inconsistencies in the literature. This dual-layered approach allowed for a robust synthesis of data, capturing the multidimensional aspects of performance measurement systems in primary healthcare.

  1. iii.

    bibliometric analysis

To complement the systematic review and deepen the understanding of the research landscape on performance measurement systems (PMS) in primary healthcare, a bibliometric analysis was conducted. This approach enabled the identification of patterns, trends, and thematic clusters within the selected literature, offering valuable insights into key focuses and gaps in the field.

The bibliometric analysis was carried out using the full text of the 14 articles included in the study. The software VOSviewer 2020 was employed to map the co-occurrence of terms, identify thematic clusters, and visualize connections between keywords and relevant concepts. The process began with data extraction, during which titles, abstracts, keywords, and terms from the full text of the articles were organized into a database using Microsoft Excel. Key bibliometric indicators analyzed included the frequency of term occurrences, their connections, and the evolution of thematic clusters over the analyzed period (2016–2023).

Using this database, terms were analyzed with VOSviewer, focusing on identifying clusters that represented thematic areas related to PMS. These clusters were categorized into implementation methodologies, such as the Balanced Scorecard and Delphi method; contextual applications, including social and economic factors; and technological advancements, such as artificial intelligence and digital tools. Furthermore, network maps were generated to illustrate the relationships between terms, highlighting the main research focuses across the selected studies.

Results

Selection of studies

Table 2 displays the database extracted from the selected manuscripts that met the eligibility criteria. The inclusion of RF-QRA test analyses was conducted to assess the reliability of data incorporation into the manuscript.

Table 2 List of records included in the extraction data, eligibility criteria and reliability of records for projects studying Performance Measurement Systems
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3.1.1. Impact of Performance Measurement Systems in Primary Care Performance measurement systems have shown diverse impacts on primary care settings. Rashidian et al. [42] evaluated the performance of medical sciences universities in Iran, ranking them using models such as Weighted Factor Analysis (WFA), Stochastic Frontier Analysis (SFA), and Data Envelopment Analysis (DEA). These findings emphasize the importance of integrating multiple models into measurement systems to avoid misleading results. Similarly, Peled, Porath, and Wilf-Miron [56] analyzed clinical indicators in Israel, demonstrating significant improvements over time and the benefits of setting accurate performance goals for resource allocation. Varela et al. [57] assessed the implementation of a patient-centered care model in Chile, identifying critical factors such as population size, healthcare team coordination, and additional training as drivers of successful implementation.

Challenges of performance measurement systems in primary care

Several challenges hinder the effective application of performance measurement systems in primary care. Bresick et al. [52] identified low utilization of validated measurement instruments across African primary healthcare systems, despite recommendations for their adoption. Munar et al. [58] highlighted significant evidence gaps, including underuse of audits and feedback mechanisms, especially in low- and middle-income countries. In Malawi, Dullie et al. [43] found deficiencies in first-contact access, continuity, and comprehensiveness, with patient experiences influenced by characteristics such as gender, geographic location, and health status. These findings underscore the need for robust methodologies tailored to diverse contexts.

Tools and methodologies for performance measurement

The development of performance measurement systems in primary care relies on various tools and methodologies. Ratcliffe et al. [59] utilized the PHCPI (Primary Health Care Performance Initiative) framework across five countries, demonstrating its feasibility and reliability. Barbazza et al. [60] introduced the PHC-IMPACT tool in Europe, mapping 139 indicators across six domains, including primary care structures and outcomes. Ruan et al. [39] applied the Delphi method to refine a performance measurement system for clinics in China, ensuring alignment with local needs. Similarly, Agarwal et al. [61] employed the Delphi method to develop performance indicators for Community Health Worker (CHW) programs, enhancing programmatic effectiveness. The development of Performance Measurement Systems (PMS) adapted to local realities relies on mechanisms that integrate social, operational, and economic factors into their design. For example, the Delphi Method has been employed to achieve consensus among local experts, ensuring the relevance of indicators. Tools like the PHC Progression Model and adaptations such as the Malawian version of the PCATool (PCAT-Mw) demonstrate the feasibility of customizing frameworks to address regional challenges. These approaches highlight the necessity of aligning methodologies with local cultural, organizational, and infrastructural contexts to ensure their effectiveness.

Contextual applications of performance measurement systems

The application of performance measurement systems is highly influenced by the context of primary care services. Bresick et al. [52] highlighted differences in performance measurement practices based on user and staff engagement in South Africa and Malawi. Kim et al. [44] evaluated healthcare facilities in Uganda, linking effective management practices to improved availability of essential medicines and higher service utilization. Langton et al. [36] examined population segmentation in Canada, using socioeconomic status as a proxy for patient vulnerability to enhance cost analysis and optimize services. Blozik et al. [62] developed performance indicators in Switzerland using health insurance claims data, adapting these indicators to align with local public health priorities.

Categorization of areas

Across the 14 manuscripts reviewed with total of 1,291 performance measures were identified, encompassing 165 subdomains and 75 domains. These performance measures were categorized into 9 major areas: Service and Process Quality; Communication, Information, and Empowerment; Finances; Stakeholders—Customers, Clients, and Patients; People and Culture; Governance and Organizational Management; Infrastructure and Inputs; Productivity, Performance, and Effectiveness; and Scope of Services Provided and Available.

Table 3 presents the structures adopted in each article for the performance measurement systems, along with the number of dimensions, domains, subdomains, and the total number of indicators. The study by Rashidian et al. [42] employed a combination of mixed methods to structure the base of performance indicators: Weighted Factor Analysis (WFA), Equal Weighting (EW), Stochastic Frontier Analysis (SFA), and Data Envelopment Analysis (DEA). The study by Bresick et al. [52] utilized a widely used structure by primary care managers, the Primary Healthcare Performance Initiative (PHPI) [51], and Ratcliffe et al. [59] adapted it to create a framework named PHC Progression Model. Munar et al. [58] demonstrated their set of indicators through the mixed use of Performance Measurement and Management (PMM) and Evidence Gap Maps (EGMs).

Table 3 Measure structures of performance measurement systems in primary care for projects studying Performance Measurement Systems
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Three studies used survey-based approaches in their performance measurement system frameworks: Ruan et al. [39] with the Index Pool (Delphi method and survey), Barbazza et al. [60] with Focus Groups and Survey for monitoring indicators, and Kim et al. [44] with the PRIME-Tool adapted from the World Management Survey. Two authors used classical performance measurement system frameworks: Amer et al. [63] with the Balanced Scorecard (BSC) and Blozik et al. [62] with the IQ (Indicators Quality) framework. The remaining authors either adapted existing methods or created new ones to underpin their research applications: Dullie et al. [43] with the Malawian version of the primary care assessment tool (PCAT-Mw), Peled, R., Porath & Wilf-Miron [56] with the Performance Measurement System (PMS) by HEDIS Measures and Technical Resources, Agarwal et al. [61] with The Community Health Worker Performance Measurement Framework, and Varela et al. [57] with the Multimorbidity Patient-Centered Care Model (MPCM).

The distribution of each area is illustrated in Fig. 2, and Table 3 highlights the detailed structures, including dimensions, subdomains, and indicators.

Fig. 2
figure 2

Number of indicators per classification area

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To illustrate the connections between the main categories of identified indicators, their respective dimensions, and the methodologies applied in the analyzed articles, we developed the framework presented in Fig. 3. This framework organizes the information into three layers: main categories, associated dimensions, and utilized methodologies. It provides an integrated view of the approaches to performance measurement in primary healthcare systems, highlighting the interdependencies between indicators and methodologies. The framework serves as a comprehensive guide for understanding how various methodologies align with specific dimensions and contribute to addressing the operational and contextual challenges in primary healthcare.

Fig. 3
figure 3

Framework of Performance Measurement Systems (PMS) in Primary Healthcare, based on studies included in the systematic review. The framework is structured into three layers: (1) The nine main categories of indicators identified in the review (Layer 1), (2) The subcategories or specific dimensions associated with each category (Layer 2), and (3) The methodological approaches employed in the analyzed studies (Layer 3). This hierarchical visualization illustrates the interconnections between categories, dimensions, and methodologies, highlighting their role in addressing operational, contextual, and technological challenges in primary healthcare systems

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Research characterization

As per Fig. 4, three studies were published by Americans or conducted in the United States [58, 59, 61], and nine research studies were conducted in various countries: South Africa [52], Iran [42], Netherlands [60], Uganda [44], Chile [57], Canada [36], Hungary [63], Malawi [43], China [61], Switzerland [62] and Israel [56].

Fig. 4
figure 4

Number of manuscripts by country where the research took place or corresponding author's country

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As per Fig. 5, the journal with the highest number of publications is BMC Health Services Research, with a total of 9 publications, followed by 3 publications in BMJ Global Health and one each in BMC Family Practice and Human Resources for Health.

According to this systematic review, the years with the highest number of publications on performance measurement systems in primary healthcare were 2019 and 2022, with 4 and 3 publications, respectively. 2018 and 2020 each had 2 publications, while 2017, 2021, and 2023 (up to the end of June) had 1 publication each (Fig. 6).

Fig. 5
figure 5

Number of manuscripts per publication journal

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Fig. 6
figure 6

Number of manuscripts by year of publication of the manuscript

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Co-occurrence of terms

Bibliometric analysis based on the full text of the selected articles allowed the identification of clusters of relevant terms that reflect the main areas of focus in the literature on performance measurement in primary health care (co-occurrence of terms). Using VOSviewer 2020 [64] (https://www.vosviewer.com/), it was possible to visualize the most frequently used terms and their connections, grouped into thematic clusters. Excerpts from full-text articles are available in manuscript appendices. Cluster 1 focuses on terms related to the implementation of performance measurement systems, such as “Balanced Scorecard” and “framework”. They are concentrated in a single term in Fig. 7, Dimensions. This cluster reflects studies that explore the application of standardized methodologies and frameworks to assess organizational performance in primary care.

Fig. 7
figure 7

Network visualization of term co-occurrence in performance measurement systems

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Cluster 2 (Fig. 8) encompasses themes related to equity and access, highlighting terms such as "access" and "health care costs". They are concentrated in the Tracking dimension. This cluster suggests a focus on social determinants of health and the assessment of costs associated with access to health services.

Fig. 8
figure 8

Network visualization of term co-occurrence in social determinants and cost tracking

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Cluster 3 (Fig. 9) highlights terms related to continuity and coordination of care, such as “continuity” and “coordination.” They are concentrated in two poles: comprehensiveness and coordination. This grouping reflects the emphasis on improving integration and continuity of care in fragmented health systems.

Fig. 9
figure 9

Network visualization of term co-occurrence in continuity and coordination of care

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The bibliometric analysis reveals strong connections between terms related to "continuity of care" and "coordination," reflecting the interdependence of these dimensions in primary healthcare performance. "Balanced Scorecard" emerged as a central term, highlighting its relevance as a widely used tool for implementing and evaluating performance measurement systems. The term co-occurrence analysis also uncovered thematic evolution over time. More recent studies (2021–2023) emphasized advances in the use of digital systems and artificial intelligence to support performance measurement, whereas earlier studies focused more on traditional methods, such as the Delphi approach. These findings underscore the need to align performance measurement systems with local realities, integrating social and operational factors. Moreover, the observed connections between access, cost, and continuity reinforce the importance of adopting a holistic approach to measuring performance in primary healthcare.

Discussions

This study conducted a systematic literature review with a limited focus on performance measurement systems in primary healthcare (PHC). Among the pre-selected articles, several authors addressed performance measurements closely aligned with formal systems [65,66,67,68,69,70,71,72,73,74]. However, only a few studies demonstrated application structures or reviews that reflected the actual use of integrated systems incorporating indicators across specific domains that influence others. This highlights a significant gap in the literature regarding the practical implementation of performance measurement systems [75].

Contributions of the review

This review emphasized the importance of assessing PHC services through systematic frameworks, highlighting how researchers adapt their approaches to regional realities and specific operational contexts. Notably, studies such as Bresick et al. [52] and Ratcliffe et al. [59] applied the Primary Healthcare Performance Initiative (PHCPI), while Amer et al. [63] adopted the Balanced Scorecard (BSC), demonstrating the influence of cultural, operational, and economic factors in their implementation. Furthermore, the strong connections observed between "continuity of care" and "coordination" underscore the interdependence of these dimensions in enhancing PHC performance. These findings align with prior studies [76, 77], which emphasize the need for integrated performance monitoring that includes access, cost, and continuity.

By synthesizing existing knowledge, this systematic review advances the field with a diagnostic framework for evaluating PHC performance measurement systems. Its emphasis on heterogeneity and adaptability offers valuable insights for tailoring systems to diverse regional and operational contexts, paving the way for more effective and sustainable implementations.

Gaps and limitations of manuscripts

The 14 analyzed articles highlighted significant challenges in post-implementation evaluation of performance measurement systems at PHC facilities and among professionals, such as general practitioners. This reflects a lack of evidence on the tracking of indicators over time across both infrastructure and professional practice. Moreover, none of the studies compared systems of similar scope and structure across different locations which limits the generalizability and replicability of the findings. The absence of continuous validation, as evidenced in studies by Barbazza et al. [60] and Peled et al. [56], further undermines the consistency of results. Compared to previous reviews, such as those by Prates et al. [40] and Gartner and Lemaire [41], this study goes further by addressing not only the theoretical constructs of performance measurement, but also their practical applications in diverse primary healthcare settings. While previous works focused predominantly on specific regions or dimensions, this review highlights the interplay of technological, operational, and socio-economic factors across global contexts, offering a more holistic perspective.

Thematic relevance and technological advancements

Aligned with the research question, this review explored how performance measurement systems address key challenges in primary healthcare, including the integration of emerging technologies, regional adaptability, and methodological robustness. The findings highlight a clear evolution in the methodologies employed, with traditional approaches, such as the Delphi method, remaining prevalent in earlier studies. However, more recent research (2021–2023), such as Ruan et al. [39] and Varela et al. [57], demonstrates an increased reliance on digital technologies and artificial intelligence for performance measurement. These advancements indicate that emerging tools have the potential to address operational and methodological limitations, offering more comprehensive and adaptive solutions for primary healthcare settings.

Integration with performance indicators

According to Table 3 and Fig. 3, the distribution of indicators across nine primary areas highlighted how performance measurement systems address different PHC aspects. "Scope of Services Provided and Available" emerged as the area with the highest number of indicators (105 records), suggesting a prioritization of clinical excellence and patient engagement. Conversely, "Finances" and "Productivity, Performance, and Effectiveness" presented the lowest records, reflecting challenges in operationalizing these dimensions.

Uniqueness, value, and viability

Studies such as Rashidian et al. [42] highlighted that sites with lower financial inputs performed better than large universities with greater financial resources. This underscores the need for strategies tailored to specific performance conditions [78]. Similarly, Bresick et al. [52] argued that the underuse of validated instruments remains a critical issue decades after the Alma Ata Declaration. The bibliometric analysis revealed that topics such as "Delphi," "Balanced Scorecard," and "artificial intelligence" emerged as central in recent studies. However, areas like "Productivity" and "Effectiveness" remain underrepresented, suggesting challenges in practically implementing these dimensions.

Practical implications

The findings of this systematic review underscore the critical importance of aligning performance measurement systems with the unique realities and challenges of local contexts in primary healthcare. Effective performance measurement requires a nuanced approach that incorporates social, operational, and technological factors to ensure meaningful outcomes. By tailoring systems to the specific needs of each region or facility, decision-makers can create frameworks that are not only adaptive but also capable of driving tangible improvements in healthcare delivery. The integration of advanced technologies, Driven by the pandemic [79], such as digital health tools and artificial intelligence, presents opportunities to address existing gaps in data collection, analysis, and monitoring. These tools can enhance the accuracy and comprehensiveness of performance evaluations, facilitating more informed decision-making processes. However, the findings also highlight the need for robust training programs for healthcare professionals, ensuring they can effectively utilize these technologies and integrate them into their workflows.

Furthermore, the underrepresentation of dimensions such as "Finances" and "Productivity, Performance, and Effectiveness" suggests a pressing need to develop more comprehensive indicators in these areas. By addressing these gaps, performance measurement systems can provide a more holistic view of healthcare operations, linking patient outcomes with operational efficiency and cost-effectiveness. Finally, continuous validation and periodic updates to these systems are essential. Such practices ensure that performance measurement frameworks remain relevant in the face of evolving healthcare demands, technological advancements, and socio-economic changes. By emphasizing adaptability and regional specificity, performance measurement systems can serve as powerful tools for improving the quality, accessibility, and sustainability of primary healthcare worldwide.

Based on the findings of this review, the dimensions 'Service and Process Quality' and 'Communication, Information, and Empowerment' emerge as priority areas for PMS promotion. These dimensions directly influence patient outcomes and operational efficiency. To adapt these dimensions to local realities, mechanisms such as the use of digital health tools, real-time data collection platforms, and training programs for healthcare professionals are recommended. Additionally, integrating culturally sensitive methodologies, such as community-based participatory approaches, can further enhance their applicability in diverse settings.

Study limitations

The authors identified certain limitations that should be reported due to their potential impact on the interpretation of the research findings and data. Despite the large number of articles surveyed, only fourteen studies were classified for inclusion in the systematic review, which may be considered insufficient for a more in-depth analysis. The study conducted by Peled, Porath, & Wilf-Miron [56] did not clearly indicate which indicators were constructed or adapted. Although this article developed a performance measurement system and was applied in primary health care, it met the inclusion criteria; however, it was not possible to quantify or analyze which KPIs were studied and implemented by the authors. Grey literature documents were not searched due to the criteria for inclusion of peer-reviewed articles. This may affect a broader understanding of the extent of performance measurement system applications in the literature. Introducing and discussing some of these documents in more depth was considered to mitigate the lack of this information.

While methodologies such as the Balanced Scorecard and PHC Progression Model have demonstrated strong adaptability and effectiveness across various settings, others have faced limitations. For instance, the Evidence Gap Maps (EGMs), though valuable in identifying research gaps, were underutilized in low-resource settings. Similarly, the application of Equal Weighting (EW) in ranking performance indicators has been criticized for oversimplifying complex healthcare dimensions. These findings underline the need for continuous validation and refinement of PMS frameworks to ensure their applicability across diverse contexts.

Conclusions

Performance Measurement Systems in primary care, though scarce, have been studied and improved in recent years. The development of the systematic literature review process with a methodology that emphasizes heterogeneity, diversification, reliability, and novelty of the works reflected a diagnosis with a five-year parameter of the best implemented PMS models around the world. The findings, for the most part, differ from each other in study methodology and practice, constructing frameworks according to the needs of each country, including those with medium and low income. The models from each research group showed that this diagnosis needs to be monitored over time and be easily implementable in primary health care. Time series are suggested to track the previously created indicators. The participatory use of primary care workforce has become crucial for the methodological progression of indicator development. Future work is important in the context of the effectiveness of the constructed systems, as this step was limited in most of the works included in the review criteria. The ease of application and understanding of performance measurement systems in highly vulnerable countries can be another avenue of study through current discussions on primary care.

Data availability

The datasets generated and/or analysed during the current study are available in the DATA BASE SMD LINK repositor, https://abrir.link/SMD-PHC. Access must be through a formal request to the authors.

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Authors and Affiliations

  1. Federal University of Bahia, Polytechnic School. 02 Prof. Aristides Novis Street, Salvador, 40210 630, Brazil

    Carlos Jefferson de Melo Santos, Ava Santana Barbosa & Ângelo Márcio Oliveira Sant’Anna

  2. Federal University of Vale Do São Francisco, Production Engineering College, Santo Antonio Street, Salgueiro, Pernambuco, 56000-000, Brazil

    Carlos Jefferson de Melo Santos

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CJMS performed the data collection, most of the writing and analysis of the information. ASB collected and edited most of the data, as well as reviewing the writing. AMOS authored the analyzes and revised the English. All authors read and approved the final manuscript.

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Appendices

Appendix 1

Detailed search strategies

The following table provides the detailed search strategies applied to each database during the systematic review. These strategies include keywords, Boolean operators, and database-specific adaptations to ensure comprehensive retrieval of relevant studies.

PubMed

  • "performance measurement systems"[MeSH Terms] OR "performance indicators"[All Fields]

  • AND "primary healthcare"[MeSH Terms] OR "primary care"[All Fields]

  • AND ("2016"[Date—Publication]: "2023"[Date—Publication])

Scopus

  • TITLE-ABS-KEY ("performance measurement systems" OR "performance indicators")

  • AND ("primary healthcare" OR "primary care")

  • AND PUBYEAR > 2015 AND PUBYEAR < 2024

Web of Science

  • TS = ("performance measurement systems" OR "performance indicators")

  • AND TS = ("primary healthcare" OR "primary care")

  • AND PY = (2016-2023)

SciELO

  • "performance measurement systems" OR "performance indicators"

  • AND "primary healthcare" OR "primary care"

  • AND ("2016" TO "2023")

Springer

  • ("performance measurement systems" OR "performance indicators")

  • AND ("primary healthcare" OR "primary care")

  • AND PUBYEAR BETWEEN 2016 AND 2023

All search strategies were executed between May 5, 2023, and July 7, 2023.

Appendix 2

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de Melo Santos, C.J., Barbosa, A.S. & Sant’Anna, Â.M.O. Performance measurement systems in primary health care: a systematic literature review. BMC Health Serv Res 25, 353 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12913-025-12412-6

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12913-025-12412-6

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BMC Health Services Research

ISSN: 1472-6963

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