Publications were retrieved from the Web of Science Core Collection (WoSCC). The search was conducted on March 6, 2026, and covered the period from January 1, 2005, to December 31, 2025. All records were obtained from the Science Citation Index Expanded within WoSCC. The search was performed in the title, author keywords, and abstract fields. Only articles and reviews published in English were included. Because “medication regimen simplification” is neither a standardized indexing term nor a MeSH (Medical Subject Headings) term, the search strategy relied on a combination of free-text terms informed by related expressions reported in previous studies and by the conceptual scope of this study [8,9,26]. These terms included simplification-related expressions for regimens, medications, and therapies, as well as expressions related to reduced medication regimen complexity. The full search strategy is shown in Table 1.

In this study, medication regimen simplification was operationally defined as strategies intended to reduce the complexity of a medication regimen while preserving its therapeutic intent. On the basis of this definition, this bibliometric analysis included publications that explicitly described such strategies as medication regimen simplification or clearly characterized them as a reduction in medication regimen complexity. Interventions such as reduced dosing frequency, consolidated administration times, long-acting formulations, and fixed-dose combinations were included only when they were presented as intended to simplify the regimen or reduce its complexity. Deprescribing was not considered equivalent to medication regimen simplification. Similarly, lower medication regimen complexity scores or simpler medication administration schedules were considered relevant only when they reflected an explicit strategy to simplify the regimen followed by patients or caregivers.

Two researchers independently screened the titles and abstracts of all retrieved records. Full texts of potentially eligible studies were then reviewed independently by the same 2 researchers according to the predefined inclusion and exclusion criteria. Any disagreements were resolved through discussion with a third researcher. Manual checking was performed to identify any remaining duplicate records or overlapping reports of the same study. In addition, the reference lists of included studies were screened manually to identify potentially relevant articles. Interrater agreement between the 2 researchers was assessed using the Cohen κ, which indicated a high level of agreement (κ=0.83).

Bibliometric data were extracted from the included publications, including publication year, country or region, institution, author, journal, keywords, cited references, and research area information, as well as annual publication counts. Before analysis, limited manual corrections were performed to improve the consistency of country, institution, and author information. Institution names were merged when spelling variants, abbreviations, or formatting differences referred to the same organization. Country names were standardized when the WoSCC classification was evidently inconsistent. Author names were standardized only for obvious variants referring to the same individual based on publication context and affiliation information. In addition, the names of the top authors and institutions reported in the main tables were manually cross-checked against the original WoSCC records and affiliation information.

CiteSpace (version 6.4.R1 Advanced) [27] and VOSviewer (version 1.6.20) [28] were used for bibliometric visualization and network analysis. CiteSpace was used to analyze the dual-map overlay of journals, reference cocitation, keyword clustering, and burst detection. VOSviewer was used to visualize collaboration networks among countries and regions, institutions, and authors, as well as keyword co-occurrence. In addition, Bibliometrix in R (version 4.4.1; K-Synth Srl) [29] was used for supplementary visualization of countries and regions and keyword data, and Microsoft Excel 365 was used to present annual publication trends.

For network construction in VOSviewer, the following thresholds were applied: keywords with at least 3 occurrences, countries and regions with at least 5 publications, institutions with at least 3 publications, and authors with at least 2 publications were included. These thresholds were chosen to keep the networks interpretable and reduce noise from low-frequency items [30]. In CiteSpace, the time span was set from 2005 to 2025 with 1 year per slice, and Pathfinder pruning was applied to simplify the network structure and highlight the most meaningful links. Betweenness centrality was calculated by CiteSpace using its default settings and was used to identify important nodes in the networks.

This study was based on publicly available literature and did not involve human or animal participants; thus, no ethics approval was required.

A total of 1466 records were identified. After title and abstract screening, publications without an explicit focus on medication regimen simplification or reduction in medication regimen complexity were excluded. Ultimately, of the 1466 records, 468 (31.9%) publications were included in the final analysis. The study selection process is shown in Figure 1.

Figure 2 illustrates the annual and cumulative number of publications on medication regimen simplification from 2005 to 2025. Annual publication output was low from 2005 to 2008 and then increased from 2009 onward, with fluctuations across years. The cumulative number of publications increased throughout the study period, from 2 in 2005 to 468 in 2025.

Figure 6A shows the reference cocitation clusters. Using CiteSpace with a 1-year time slice from 2005 to 2025, the network included 731 nodes and 1667 links, with a modularity Q of 0.919 and a silhouette score of 0.964. The main labeled clusters included 2-drug regimen (cluster 0), nucleoside or nucleotide reverse transcriptase inhibitors–sparing regimens (cluster 3), single-tablet regimen (cluster 6), tenofovir (cluster 7), nursing homes (cluster 8), and adherence (cluster 9). Figure 6B shows the 12 keywords with the strongest citation bursts. Burst keywords occurring earlier in the study period included “regimens,” “double blind,” “HIV infected patients,” “reverse transcriptase inhibitors,” and “open label,” whereas burst keywords extending into later years included “dual therapy,” “basal insulin,” “fixed ratio combination,” “blood pressure control,” “impact,” and “type 2 diabetes.” The burst keywords “dual therapy” and “regimens” overlapped with the regimen-related cocitation clusters in Figure 6A, including 2-drug regimen (cluster 0), nucleoside or nucleotide reverse transcriptase inhibitors–sparing regimens (cluster 3), and single-tablet regimen (cluster 6).

As shown in Figure 6C, the dual-map overlay reveals the knowledge flow between disciplines in the field of medication regimen simplification. The main citation paths originated from journals in the medicine, medical, and clinical area and extended to journals in the molecular, biology, and genetics area and the health, nursing, and medicine area. The most prominent paths are shown in green.

Figure 8B shows the thematic evolution of keywords across 3 periods: 2005 to 2016, 2017 to 2021, and 2022 to 2025. From 2005 to 2016, the main keywords included “hypertension,” “adherence,” “toxicity,” “fixed-dose combination,” “antiretroviral therapy,” “nevirapine,” “medication adherence,” “polypharmacy,” “treatment,” and “safety.” From 2017 to 2021, the main keywords included “HIV,” “adherence,” “medication adherence,” “safety,” “pharmacokinetics,” “type 2 diabetes,” “hypertension,” and “long-term care.” From 2022 to 2025, the main keywords included “HIV,” “meta-analysis,” “bictegravir,” “type 2 diabetes,” “adherence,” “medication adherence,” “glucagon-like peptide-1 receptor agonists,” and “asthma.” Links between periods are shown for several keywords, including “adherence,” “medication adherence,” “HIV,” “type 2 diabetes,” and “safety.”

This study provides a bibliometric overview of research on medication regimen simplification from 2005 to 2025. The findings show increasing publication output over time, with research activity concentrated in a limited number of countries, institutions, and author groups, whereas the intellectual structure and evolving keyword patterns indicate a field centered on regimen simplification, adherence, and disease-specific applications.

At the country level, research on medication regimen simplification was dominated by a limited number of contributors. The United States ranked first in both publication output and total citations, possibly reflecting not only the size of its research community and funding environment [31,32] but also broader factors that influence bibliometric visibility, including English-language publication and database coverage. At the institutional level, Monash University and the University of Sydney were among the leading contributors, and 50% (5/10) of the most productive institutions were from Australia. Monash University’s prominent contribution may reflect both its sustained research on medication use, medication safety, healthy aging, and aged care [33] and the broader Australian policy and practice focus on polypharmacy and medication management in older adults [34,35]. Notably, several productive institutions were hospitals, aged care providers, or industry-affiliated institutions, suggesting that medication regimen simplification is often closely linked to clinical practice and service delivery. This interpretation is supported by aged care simplification studies, including the Medication Regimen Simplification Guide for Residential Aged Care and related Australian care-based research [16,36], as well as by HIV simplification studies and treatment switch trials conducted by hospital-based and industry-affiliated teams [37,38]. Meanwhile, institution-level productivity may also be influenced by organizational structures, affiliation patterns, and database indexing practices and, therefore, should be interpreted with caution. At the collaboration level, collaboration patterns were similarly concentrated. International links were most evident between North America and Europe, with additional connections involving East Asia and Oceania, whereas institutional and author networks were organized into several clusters. This pattern suggests that the field has been shaped by a limited number of active research groups and broader cross-regional and cross-institutional collaboration may be needed to expand the evidence base across different health systems and care settings.

In this study, keyword burst analysis was used to identify terms receiving rapidly increasing attention over specific periods, whereas the interpretation of broader research hot spots was based on their consistency with cocitation structure, keyword clustering, and thematic evolution. A notable feature of medication regimen simplification is its extension across multiple disease areas and care settings. The complementary analyses suggest that medication regimen simplification has developed from an initially focused literature centered largely on antiretroviral therapy, fixed-dose combinations, and treatment-related toxicity into a broader area that also encompasses chronic disease management, long-term care, and medication regimen complexity. Within this broader development, HIV remained the most consistent research direction, with persistent signals from clusters related to “2-drug regimen,” “NRTI-sparing regimens,” and “single-tablet regimen,” together with thematic evolution results including “antiretroviral therapy,” “HIV,” and “bictegravir.” This continuity is likely related to the long-term nature of HIV treatment and the need to reduce regimen burden while maintaining adherence and efficacy [39,40]. Beyond HIV, the field has expanded into other long-term treatment settings, particularly diabetes and cardiovascular disease [41-43], with increasing attention to “type 2 diabetes,” “heart failure,” “blood pressure control,” “basal insulin,” and “glucagon-like peptide-1 receptor agonists.” However, this expansion remains uneven across disease areas. HIV, diabetes, and selected cardiovascular topics were more prominently represented in the keyword and clustering analyses, whereas depression and Alzheimer disease were present in the keywords but did not form independent clusters or clearly defined thematic pathways. It may reflect variation in dominant research priorities across disease areas, with some fields historically placing greater emphasis on efficacy or survival-oriented outcomes rather than focusing on adherence or simplification [44,45]. It may also reflect the greater difficulty evaluating regimen simplification in mental health disorders and neurodegenerative diseases, where treatment and medication management are often individualized and shaped by cognitive impairment and caregiving involvement [46,47]. Further research should examine regimen simplification more explicitly in mental health, cognitive impairment, and multi-morbidity to clarify the indications, implementation, and long-term outcomes.

A central challenge in this field is no longer whether medication regimen simplification is feasible in principle but whether it can be implemented and sustained in routine care. Existing studies have focused mainly on populations with long-term medication use and high treatment burden, particularly older adults, people receiving community care, and those in long-term care settings [23,48,49]. In these populations, implementation depends not only on the regimen itself but also on cognitive and functional status, caregiver involvement, and the level of existing medication support [8,23,50]. Furthermore, simplification may also remain a clinical and pharmaceutical balancing process. Its real-world application requires consideration of effectiveness, safety, adherence, dosage form, dosing frequency, drug interactions, and pharmacokinetic constraints [8,13]. In this sense, regimen simplification is better understood as part of comprehensive medication management than as a simple act of discontinuing or switching treatment. Additionally, system-level factors may further shape implementation. In routine practice, medication simplification strategies may be limited by unclear workflows, insufficient multidisciplinary collaboration, and poor integration into everyday care processes [51,52]. These barriers may be more prominent in long-term care and community settings, where decision-making often depends on coordination among physicians, pharmacists, nurses, and caregivers [8]. These findings suggest that the future development of this field may depend not only on identifying effective simplification strategies but also on understanding how they can be embedded, coordinated, and sustained across real-world care settings.

The wider implementation of medication regimen simplification is likely to depend on policy and system conditions that support its integration into routine care. Reimbursement and reporting requirements may facilitate this process by providing institutional support for service delivery. For example, in the United States, Medicare Part D requires sponsors to establish medication therapy management programs and incorporate them into plan benefit structures and annual reporting requirements [53]. Quality measurement is another key element to routine implementation [54]. Although existing medication-related indicators can support accountability for medication use and safety [55,56], they seldom capture whether regimens have become simpler, whether simplification recommendations have been implemented, or whether treatment burden has been reduced. Therefore, more targeted indicators of regimen complexity, implementation, and treatment burden may be needed. Furthermore, implementation is also likely to depend on clearer professional roles and better workflow integration. In real-world practice, simplification is rarely a single prescription event but, rather, an ongoing process of assessment, decision-making, implementation, and follow-up. Clearer role allocation across physicians, pharmacists, and nurses may improve implementation [8,23,52]. In this context, the formalization of pharmacist prescribing pathways in Australia may support a more structured role for pharmacists in simplification-related care [57]. Additionally, digital support should be considered within the same implementation framework. Its value is likely to depend less on stand-alone tools than on the integration of complexity assessment, simplification prompts, and follow-up monitoring into existing workflows. Previous studies suggest that electronic decision support is more likely to be scalable when combined with routine structured medication review in long-term care [58,59]. Therefore, a more feasible approach may be to embed simplification-related functions within established care processes, including electronic health records, postdischarge medication reconciliation, and medication review in long-term care and chronic disease follow-up. Beyond reviews that focus on prescribing decisions or regimen optimization in specific patient groups [60,61], this study highlights a broader implementation-oriented perspective by linking bibliometric patterns to policy conditions for routine care.

Several limitations should be considered when interpreting the findings. First, this analysis was restricted to English-language publications indexed in the WoSCC. Therefore, relevant studies from other databases or in other languages may not have been captured, which could affect the completeness of the research landscape reported. Second, because medication regimen simplification is not a standardized indexing term, the search strategy relied on free-text terms and an operational definition, which may have resulted in the exclusion of conceptually relevant studies using different terminology. Third, bibliometric indicators describe patterns of publication, citation, and thematic structure, and recently published articles may have been underestimated because of citation lag. In addition, despite software-assisted standardization and manual checking, residual errors in author and institutional name disambiguation may have remained. Such errors can be reduced but are difficult to eliminate completely in bibliometric datasets, particularly for common surnames and institutions with multiple name variants, which could have affected the identification of prolific authors or institutions and the collaboration network analysis.

This bibliometric analysis indicates a clear increase in publications on medication regimen simplification over the past 2 decades, particularly in recent years. Quantitative mapping further indicated that the literature has concentrated mainly on HIV, diabetes, and cardiovascular-related management, whereas some areas such as mental health and cognitive disorders remain comparatively underrepresented. Overall, the field is expanding, but its thematic development is still uneven. Future studies should further clarify the definition and scope of medication regimen simplification, pay more attention to underexplored diseases and care settings, and strengthen the evidence on implementation and patient-relevant outcomes. Cross-national and cross-system research will also be important for developing quality indicators, implementation pathways, and policy frameworks to support the wider use of medication regimen simplification in real-world practice.