Emergency medicine literature in the Web of Science category “emergency medicine”: A bibliometric analysis (1980–2024)

Abstract

OBJECTIVES: The objective is to describe long term trends in emergency medicine (EM) research output from 1980 to 2024 and to map study designs, conceptual domains, and collaboration structures within the Web of Science (WoS) Category “EM.”

METHODS: We conducted a retrospective bibliometric analysis of records indexed in the WoS Core Collection from January 1, 1980, to December 31, 2024, using the WoS Categories filter “EM.” The search was performed on October 3, 2025. Only records with WoS document type “Article” were included. Bibliographic data, including publication year, journal, country, institutions, citations, and indexed keywords/metadata, were extracted. A rule based text mining algorithm classified study designs and conceptual domains and was internally validated against a manually reviewed sample of 240 records, showing 75.8% agreement (κ = 0.638) for study design and 86.3% agreement (κ = 0.828) for conceptual domain. Descriptive statistics and VOSviewer based co authorship, co citation, and keyword co occurrence analyses were performed.

RESULTS: Of 161,842 records initially identified, 102,561 articles met eligibility criteria. Annual EM publication counts increased after 2000. Health services and systems research and observational clinical studies predominated, whereas randomized controlled trials and qualitative or mixed methods studies remained uncommon. Trauma, resuscitation, cardiovascular emergencies, and sepsis were longstanding core domains, while digital health, artificial intelligence, point of care ultrasound, disaster medicine, and global EM gained prominence after 2010. Collaboration expanded over time, but multinational co authorship remained limited, and affiliations from low and middle income countries were underrepresented.

CONCLUSIONS: EM has evolved into a high‑volume, thematically diverse field, but its evidence
base remains predominantly observational and geographically imbalanced.

Introduction

Emergency departments (EDs) are a critical interface between communities and health systems, functioning simultaneously as portals for time sensitive conditions, safety net access points, and barometers of system performance. Over recent decades, rising ED attendance, increasing case complexity, and persistent crowding have been documented in many countries, with consistent associations between crowding, delays in care, and adverse outcomes.[1-3] These epidemiological pressures have reinforced emergency medicine (EM) as a distinct academic discipline with its own research priorities, methods, and journals, particularly around time critical conditions such as trauma, cardiac arrest, sepsis, and acute coronary syndromes.

In parallel, the scientific literature on EM has expanded rapidly and diversified. Several bibliometric studies have examined specific elements of this growth, including highly cited EM articles in the Web of Science (WoS) category, 10 year trends in EM research output and topics, national EM publication patterns such as those from Türkiye, and focused domains including COVID 19 related emergency care research.[4-7] More recently, global analyses have mapped EM research networks and highlighted substantial geographic and income related imbalances in authorship and collaboration, particularly between high income and low and middle income countries.[8] Collectively, these studies show that EM research activity is increasing and thematically heterogeneous, but they are typically constrained to single journals, limited time windows, specific subtopics, or particular regions.

To date, there has been no comprehensive, article level bibliometric synthesis of EM research spanning the modern history of the specialty within the WoS Category “EM.” Such a synthesis is needed not only to characterize long term trends in publication volume, study designs, conceptual domains, and collaboration structures but also to identify persistent methodological, thematic, and geographic gaps. By mapping these intellectual structures, bibliometric analysis may help researchers, academic leaders, journal editors, and funding bodies identify underdeveloped areas and better inform future research prioritization in EM. Therefore, the objective of this study was to provide a four decade overview (1980–2024) of EM literature indexed in the WoS Category “EM,” describing temporal trends in publication output, the distribution of study designs and major conceptual domains, and the co citation, co authorship, and keyword networks that delineate the intellectual structure of the field.

Material and Methods

Study design

This was a retrospective, descriptive bibliometric analysis of publications indexed in the WoS Category “EM” within the WoS Core Collection. The analysis was conducted at the article level and followed established guidance for bibliometric reviews, with structured reporting of data sources, search strategy, eligibility criteria, classification procedures, and quantitative synthesis of publication patterns, citation structures, and conceptual domains.

Data sources, search strategy, and eligibility

All data were retrieved from the WoS Core Collection (Clarivate Analytics) using the advanced search interface on October 3, 2025. The dataset was defined by selecting the WoS categories field and applying the category “EM,” with the publication period restricted to January 1, 1980, through December 31, 2024. No additional filtering was applied by individual WoS Core Collection indexes/ components; all records returned under this category definition were considered for screening. No explicit language restrictions were applied, although the WoS Core Collection is predominantly English language.

Records were eligible if they were indexed in the WoS Category “EM,” had a publication year within the predefined time frame, and were classified as “Article” according to the WoS document type. All nonarticle document types, including editorials, letters, meeting abstracts, notes, and corrections, were excluded from the analytic dataset.

Records were exported in WoS format using Full Record and Cited References. Exported fields included title, authors, source title, publication year, author affiliations, document type, abstract, author keywords, Keywords Plus, citation counts, and cited references. For transparency, the list of journals indexed under the EM category at the time of search is provided in Supplementary Table 1.

Data refinement and selection procedure

The initial WoS retrieval yielded 161,842 records across all document types within the WoS Category “EM.” After restricting the dataset to records with WoS document type “Article,” 102,561 articles remained for the final analyses. This selection flow is summarized in Figure 1.

Figure 1. Flow diagram of record identification and selection for the bibliometric analysis. The initial dataset comprised all WoS records indexed in the emergency medicine category between 1980 and 2024 (n = 161,842). The final analytic dataset retained only records classified as “Article” (n = 102,561); nonarticle document types were excluded from the main analyses, while overlapping WoS tags (e.g. book chapter, proceedings paper, and retracted publication) were flagged descriptively within the retained set

Within the retained article set, some records also carried overlapping WoS labels such as book chapter or proceedings paper; these were flagged and reported descriptively but were not excluded because the primary inclusion criterion was the WoS document type “Article.” Records flagged as retracted publications were also identified and counted separately, but were retained in the dataset. Because only a single bibliographic database was used, no cross database de duplication was required.

Data extraction and classification

From each eligible record, we extracted publication year, source title (journal), country/region and institutional affiliations, author names, document type, abstract, author keywords, Keywords Plus, total citation count in WoS, and cited references.

Study designs were classified using a rule based text mining algorithm applied to concatenated title, abstract, and keyword fields. Articles were assigned hierarchically to one mutually exclusive category: randomized controlled trials; diagnostic/prognostic accuracy studies; health services and systems research; educational and simulation studies; methodological/ validation/scores; qualitative or mixed methods studies; observational clinical studies (cohort or cross sectional); and other/not classified. If an article matched more than one study design category, it was assigned to the highest priority category in the predefined hierarchy. The full keyword dictionary used for study design classification is provided in Supplementary Table 2.

Conceptual domains were assigned using a similar rule based approach applied to the same combined text field. Articles were classified into one dominant domain: trauma and injury; cardiovascular and chest pain; resuscitation and cardiac arrest; infectious disease and sepsis; respiratory emergencies and chronic obstructive pulmonary disease (COPD)/asthma; pediatric EM; imaging and point of care ultrasound (POCUS); digital health and artificial intelligence (AI); disaster, prehospital and emergency medical services (EMS); and other/miscellaneous. The detailed term lists are provided in Supplementary Table 3.

To strengthen confidence in the rule based classification, we performed an internal validation on a manually reviewed sample drawn from the final dataset. A computer generated validation sample was selected using decade stratified random sampling, with additional oversampling from records initially assigned to “other/ not classified” categories. Two independent reviewers manually assigned study design and conceptual domain labels based on title, abstract, author keywords, and Keywords Plus using the same operational framework as in the main analysis. Inter rater agreement was assessed using percentage agreement and Cohen’s kappa, and discordant cases were resolved by consensus. The final consensus labels were then used as the manual reference standard for comparison with the rule based algorithm.

Quality assessment

Because this was a bibliometric analysis of published records rather than a systematic review of primary clinical studies, we did not perform article level methodological quality or risk of bias assessments. The focus was on describing the structure, volume, distribution, and thematic content of the literature indexed in the WoS Category “EM.”

Data synthesis and analysis

Descriptive statistics were used to summarize publication counts by year, decade, study design category, conceptual domain, country or region, institution, journal, and author. Categorical variables were described using frequencies and percentages, and time trends in annual publication output were visualized with bar charts.

Bibliometric network analyses were performed in VOSviewer (version 1.6.20; Centre for Science and Technology Studies, Leiden University, Leiden, the Netherlands) using full counting. Journal co citation networks were analyzed at the source level, author co authorship networks at the author level, and keyword co occurrence networks at the author keyword level. Lower bounds on citation counts, document counts, or keyword occurrences were applied to improve map interpretability, and overlay visualization based on average publication year was used for the keyword map.

For transparency and reproducibility, the main VOSviewer preprocessing decisions and parameter settings are summarized in Supplementary Table 4. Default VOSviewer normalization and clustering settings were retained. Journal co citation analysis used a minimum threshold of 200 citations per source, after which the top 90 sources were selected. Author co authorship analysis used minimum thresholds of 15 documents and 100 citations per author, with the top 100 authors selected. Keyword co occurrence analysis used author keywords with a minimum threshold of 20 occurrences, after which the top 100 keywords were selected for the overlay map. No external thesaurus file or manual term merging procedure was applied for keyword cleaning, and author names were analyzed as indexed in WoS/VOSviewer without additional external author disambiguation procedures. All preprocessing, rule-based classification, and tabulation were performed using Python (version 3.13; Python Software Foundation, Wilmington, Delaware, USA) and Microsoft Excel (Microsoft Corporation, Redmond, Washington, USA). Network maps were exported as high resolution images for inclusion in the manuscript.

Results

Search, selection, and descriptive characteristics

The search of the WoS Core Collection using the WoS Category “EM” for the period January 1, 1980–December 31, 2024 identified 161,842 records. After restricting the document type to “Article,” 102,561 articles remained and constituted the final dataset for all analyses [Figure 1]. Within this set, 1989 records were also indexed as book chapters, 3472 as proceedings papers, and 141 were flagged as retracted publications in WoS.

Annual publication counts increased gradually during the 1980s and 1990s, followed by steep growth after 2000 and further acceleration in the 2010–2019 and 2020–2024 periods [Figure 2]. The ten most cited articles were predominantly influential methodological or clinical works in trauma, pain measurement, fracture healing, resuscitation, appendicitis scoring, and burn severity assessment, published mainly in Injury, Annals of EM, Resuscitation, and Academic EM [Table 1].

Figure 2. Annual number of emergency medicine‑related publications indexed in Web of Science (1980–2024). Bars represent the yearly number of articles included in the final dataset (N = 102,561)

Table 1. Ten most cited articles in the final analytic dataset indexed in the Web of Science category “emergency medicine”

Across the full dataset, health services and systems research accounted for the largest share of publications (36.6%), followed by diagnostic/prognostic accuracy studies (8.0%), educational and simulation studies (7.1%), randomized controlled trials (6.6%), observational clinical studies (6.4%), methodological/validation work (5.5%), and qualitative or mixed methods studies (0.4%). Nearly one third of articles (29.4%) remained in an “other/not classified” category [Table 2].

Table 2. Distribution of study designs and main study types by decade (1980–2024)

Internal validation of the rule based classification

In the validation sample (n = 240), inter rater agreement between the two manual reviewers was 67.9% (κ = 0.474) for study design classification and 72.1% (κ = 0.648) for conceptual domain assignment. Agreement between the rule based algorithm and the final manual reference standard after consensus adjudication was 75.8% (κ = 0.638) for study design and 86.3% (κ =0.828) for the conceptual domain. Most discordant cases reflected ambiguous study design wording, overlap between adjacent conceptual domains, or sparse metadata in earlier records. A detailed summary is provided in Supplementary Table 5.

Schematic maps and temporal trends

The journal co citation network revealed a concentrated core of sources, with general EM and trauma journals forming a central cluster, flanked by resuscitation/critical care and pediatric emergency care clusters [Figure 3]. The author co authorship map showed several large multicenter collaboration networks with highly productive researchers occupying central positions within dense clusters [Figure 4]. The author keyword co occurrence overlay map demonstrated a temporal shift in research focus: older topics such as trauma, resuscitation, prehospital care, and traditional cardiovascular emergencies appeared as blue–green nodes, whereas more recent terms related to POCUS, COVID 19, and selected digital health related topics appeared as yellow nodes [Figure 5].

Figure 3. Journal co‑citation network of emergency medicine‑related literature. Nodes represent journals and edges represent co‑citation links between journals. Node size is proportional to the total number of citations, and colors indicate clusters of closely related journals identified by the VOSviewer clustering algorithm

Figure 4. Author co‑authorship network in emergency medicine research. Nodes represent authors and edges represent co‑authorship links. Node size reflects the number of publications attributed to each author, and colors indicate collaboration clusters identified by the VOSviewer clustering algorithm

Figure 5. Keyword co‑occurrence overlay map showing established and emerging topics. Nodes represent author keywords and Keywords Plus terms, and edges represent co‑occurrence links between terms. Node size is proportional to the frequency of occurrence, and node color indicates the average publication year, with blue–green nodes corresponding to older, established topics and yellow nodes representing more recent, emerging topics

Tabulated summary of study designs and conceptual domains

When stratified by decade, the contribution of study designs changed substantially over time [Table 2]. In the 1980s and 1990s, a large proportion of publications fell into the “other/not classified” category, reflecting heterogeneous early work together with limited metadata completeness and methodological standardization. From 2000 onward, health services and systems research increased steadily, accounting for 34.7% of articles in 2000–2009, 39.2% in 2010–2019, and 43.6% in 2020–2024. Diagnostic/prognostic accuracy studies also increased from 5.9% to 11.6% over the same period, while qualitative and mixed methods studies remained rare across all decades.

Conceptually, the literature clustered into a limited number of dominant domains [Table 3]. Trauma and injury (23.6%), pediatric EM (16.0%), resuscitation and cardiac arrest (14.4%), and cardiovascular and chest pain research (12.8%) together accounted for more than half of all articles. Smaller domains included infectious disease and sepsis (4.5%), respiratory emergencies and COPD/ asthma (2.8%), disaster/prehospital/EMS (2.6%), imaging and POCUS (2.5%), and digital health and AI (0.4%). The “other/miscellaneous” domain (20.2%) comprised diverse low frequency topics.

Table 3. Conceptual domains and topic clusters of emergency medicine research (meso‑ and micro‑level)

Synthesis of findings and conceptual model

Overall, the results depict a rapidly growing but structurally layered research field, dominated by observational clinical studies and health services and systems research, particularly in trauma, resuscitation, pediatric care, and cardiovascular emergencies. Randomized trials, diagnostic/prognostic accuracy studies, educational interventions, methodological work, and digital health related research expanded after 2000, but still represented a relatively small proportion of the overall literature.

Discussion

This bibliometric analysis of 102,561 articles indexed in the WoS Category “EM” between 1980 and 2024 provides a broad overview of how the field has evolved over 45 years. Overall, EM research output increased markedly, with annual publication counts accelerating particularly after 2000, in line with previous journal and country specific bibliometric studies.[6,9,10] This quantitative expansion was accompanied by a progressive diversification of study designs and topics.

Across the dataset, health services and systems research constituted the single largest study type category, reflecting sustained interest in emergency care delivery, quality improvement, crowding, and health policy in acute care settings.[6,11] Classic conceptual work on ED crowding and input–throughput–output models appears to have provided a durable framework for much of this literature.[9,12] In contrast, randomized controlled trials (RCTs) accounted for only a small minority of publications, and qualitative research remained rare despite a modest increase in recent decades, consistent with earlier reports that RCTs represent roughly 5% of global EM studies and that qualitative methods are still emerging in this field.[8,13] Thus, the overall evidence base in EM is dominated by observational and health services and systems research, with comparatively fewer experimental or in depth qualitative investigations.

In terms of subject matter, trauma and resuscitation have historically been the most prominent domains, with cardiac arrest, cardiopulmonary resuscitation and trauma systems repeatedly appearing among the most cited and co occurring topics.[8,10,14] Other major clinical themes included acute cardiovascular care, sepsis, and pediatric emergencies. More recently, newer domains such as digital health, POCUS, COVID 19 related topics, and AI have gained visibility in EM literature, particularly in the last decade, while disaster, humanitarian, and global EM research also suggest a widening geographical and thematic scope of the specialty.[15,16]

Several factors help explain the observed expansion and shifting profile of EM research over the past four decades. The sustained increase in publication output parallels the formal establishment and growth of EM as an academic specialty, with the proliferation of dedicated departments, journals and funding streams in many countries.[6,8,10] As EM matured, research agendas broadened from primarily descriptive clinical reports to encompass systems, policy, and methodological questions, which is reflected in the rising share of health services and systems research in our dataset.[6,11] In this context, influential conceptual models of ED crowding and input–throughput–output dynamics appear to have anchored much of the subsequent work on access block, patient flow, and quality improvement.[9,12]

The relatively large “other/not classified” category in the earlier decades should be interpreted cautiously. Based on re examination of the dataset, this pattern appears to reflect a combination of historical metadata sparsity, evolving terminology, and genuine heterogeneity in early EM research. In the 1980–1989 subset, only 24.0% of records contained an abstract, while author keywords and keywords plus were essentially absent, meaning that most records from this period could be classified only from the title text. By contrast, records from recent years were far more information rich, with abstracts and keyword fields available for the great majority of articles. Accordingly, the high residual category in the earlier period likely reflects not only the limitations inherent to rule based text classification when metadata are sparse but also the fact that early EM scholarship was more formative, descriptive, and methodologically heterogeneous, and therefore less easily mapped onto later, more standardized categories.

The relative scarcity of RCTs and qualitative studies in EM likely reflects ethical, logistical, and structural constraints. Emergency care often involves time critical decisions, unstable patients, and limited opportunity for conventional consent processes, which complicates the conduct of RCTs and favors observational designs that can leverage routinely collected data.[8,17,18] Similarly, qualitative approaches have historically been underused in a specialty that has prioritized biomedical and quantitative paradigms, despite growing recognition that questions related to communication, equity, and clinician cognition may be better addressed with qualitative or mixed methods designs.[13,19] Recent increases in both RCTs and qualitative work suggest gradual methodological diversification, but our findings indicate that the field’s evidence base remains heavily skewed towards observational and health services and systems research. Internal validation of the rule based classification further suggested that discordant cases were concentrated in borderline distinctions between adjacent study design and conceptual domain categories, rather than being randomly distributed across the dataset.

The emergence of digital health and AI as visible themes in EM research reflects a broader technological shift. Telemedicine and remote care models, particularly highlighted during and after the COVID 19 period, have been adopted to improve access and throughput in EDs.[15,20] In parallel, AI and machine–learning applications are being explored for triage, diagnostic support, and operational forecasting, aligning well with the data intensive, time pressured nature of emergency care.[15,16,21,22] However, many of these studies are still in early development or validation phases, and their real world impact on patient outcomes and system performance remains to be established. Finally, our co authorship and co citation patterns suggest that EM research is structured into partially siloed subfields (e.g., trauma/resuscitation vs. health policy/ health services and systems research), with limited but growing international and cross disciplinary collaboration, particularly between high income and low and middle income settings.[8,10] This imbalance was also evident in the affiliation profile of the dataset: among 177,549 classifiable country/region address occurrences, 154,227 (86.9%) originated from high income economies, whereas only 23,322 (13.1%) came from low and middle income economies overall, including just 821 (0.5%) from low income economies. From a regional perspective, Türkiye contributed a modest but increasingly visible share of the literature in our dataset. Articles with at least one Türkiye affiliated address accounted for approximately 5.7% of all records overall, with the annual proportion rising to around 7%– 8% in more recent years, suggesting growing integration of Türkiye into the global EM research landscape.

Limitations

Several limitations should be acknowledged. Restricting the analysis to WoS means that some emergency relevant articles indexed only in other databases (e.g., Scopus, regional indexes) may have been missed, and the predominance of English language journals in WoS introduces a language and publication type bias that likely underrepresents research from non English speaking regions.[8] In addition, because the dataset was defined using the WoS Category “EM,” the findings should be interpreted as reflecting the literature indexed within this category rather than a manually curated set of core EM journals. Our focus on articles also excluded other influential document types such as conference abstracts, editorials, and grey literature. The text mining approach used to classify study designs and conceptual domains, although systematically applied and internally validated, remains imperfect and may have misclassified some borderline articles, particularly in records with sparse metadata or overlapping terminology. As with all citation based bibliometrics, our indicators reflect patterns of visibility and influence rather than methodological quality, and are sensitive to article age and field specific citation norms. Nevertheless, these constraints are typical of large scale bibliometric work, and the convergent patterns observed across multiple analytic approaches suggest that our main conclusions about the trajectory and structure of EM research are robust.

Conclusion

Over the past 45 years, EM has evolved into a mature academic discipline with a rapidly expanding and increasingly diversified research portfolio. Our analysis shows that publication volume has risen sharply, especially since 2000, with health services and systems research emerging as the dominant study type and trauma, resuscitation, acute cardiovascular care, and pediatric emergencies remaining core thematic pillars.[6,9-12] At the same time, newer domains such as digital health, POCUS, COVID 19–related topics, and AI have gained visibility only in the last decade, indicating a gradual but still nascent technological shift in the field.[15,16,21,22] Overall, the evidence base in EM is substantial but methodologically imbalanced, with observational and health services and systems research far outnumbering randomized trials and qualitative or mixed methods work.[8,13,17-19]

These findings have several implications for future research agendas. To support more robust, practice changing guidance, greater investment is needed in interventional, implementation, and qualitative studies, particularly in underrepresented areas such as mental health emergencies, injury prevention, geriatric emergency care, and care in low resource settings.[6,8,10] Strengthening international and cross disciplinary collaborations will also be essential to ensure that emerging tools – especially AI driven decision support and digital care models – are rigorously evaluated, equitably implemented and aligned with the needs of diverse patient populations.[8,15,16,21,22] In parallel, EM journals may help reduce geographic imbalance by broadening editorial board and peer review representation, adopting more context sensitive evaluation of research from resource constrained settings, and creating mentorship or developmental review pathways for authors from underrepresented regions. By addressing these gaps, the EM community can build a more methodologically diverse, globally inclusive, and clinically impactful research ecosystem.

Supplementary Table 1. Journals indexed in the web of science category “emergency medicine” at the time of search, with publication counts in the study dataset

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Supplementary Table 2. Keyword dictionary for study design classification

Supplementary Table 3. Keyword dictionary for conceptual domain assignment

Supplementary Table 4. VOSviewer preprocessing and parameter settings for network visualizations

Supplementary Table 5. Internal validation of the rule‑based classification algorithm

How to cite this article: Aykut A, Günsoy E, Yıldırım C, Öncül MV. Emergency medicine literature in the Web of Science category “emergency medicine”: A bibliometric analysis (1980–2024). Turk J Emerg Med 2026;26:330-9.

Not applicable. This study used only publicly available and anonymized bibliographic data; therefore, ethics committee approval was not required according to institutional policies.

AA: Conceptualization (lead); Methodology (lead); Formal analysis (lead); Data curation (lead); Writing – original draft (lead); Visualization (lead).
EG: Supervision (lead); Validation (lead); Writing – review and editing (equal); Project administration (lead).
CY: Methodology (supporting); Data curation (supporting); Writing – review and editing (equal).
MVÖ: Visualization (supporting); Writing – review and editing (equal).

None Declared.

None.

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