The Impact of Bed Management Models on Hospital Performance and Patient Flow: A Systematic Review

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Bed management (as a function/service, team, or dedicated system) aims to optimize allocation, turnover, and discharge processes, with potential impact on hospital performance indicators. Aim The aim of this review is to analyse whether the introduction of bed management interventions/models (including dedicated roles, teams, and digital systems) within hospital organizations influences system outcomes, such as length of stay and number of admissions, as well as patient outcomes, including mortality and perceived quality of hospitalization. Materials and Methods Systematic review according to PRISMA (2020). Literature search was conducted in PubMed, CINAHL, and Scopus databases, covering the time period 2005–2025, in English and Italian. Studies conducted in hospital settings with full text available and evaluating explicit bed management interventions/models or the Bed Manager role with measurable organizational outcomes (LOS, ED LOS, bed turnover time, occupancy, diversion/overcrowding, access block) were included. Results Seven studies were included, mainly observational or pre–post in design. In emergency departments, active bed management reduced ED length of stay by up to 98 minutes and overcrowding from 26.6% to 17.9%. A logistics-management program reduced ED evaluation time from 219 to 193 minutes (p < 0.001) across 28,684 admissions and reduced inpatient length of stay by 0.1 days (p < 0.001). In internal medicine, the introduction of a flow/bed manager allowed the absorption of a 22% increase in urgent admissions without increasing average length of stay. Implementation of a Bed Management System improved bed turnover time from 111 to 49 minutes, with reductions in transport times (45→26 min) and housekeeping times (63→49 min). A bed management team supported by a Kanban application reduced overall LOS (5.6→4.9 days; p = 0.001) and complaints related to bed availability (27%→0%). During the pandemic, centralized bed management supported organizational resilience, with mortality stratified by care setting (ICU 29%, wards 10%, intermediate care 4%). Conclusions The included studies suggest that structured bed management models can improve throughput and capacity utilization (LOS, turnover, overcrowding), particularly in high-pressure contexts. Methodological quality is heterogeneous and non-randomized designs prevail; multicenter studies with standardized KPIs and safety measures (readmissions, adverse events) are needed to confirm impact and guide implementation. bed management bed manager patient flow emergency department length of stay hospital performance capacity management Figures Figure 1 INTRODUCTION In recent decades, emergency and urgent care departments have been characterized by recurring organizational problems such as overcrowding, boarding, and congestion, phenomena widely described in the literature (Claret et al., 2015 ; Tampubolon, 2018 ; Howell et al., 2008 ). Among the main determinants of these issues is the progressive reduction in hospital beds, combined with increased care complexity and population aging. Early modelling studies demonstrated the intrinsic instability of hospital bed systems under emergency demand, even at high average occupancy rates (Bagust et al., 1999 ). Hospital beds represent one of the fundamental resources of healthcare systems, and their efficient management constitutes a key indicator of hospital organizational performance. Suboptimal use of bed capacity has been associated with increased waiting times, prolonged emergency department stays, and worsening clinical and organizational outcomes (Forster et al., 2003 ; Howell et al., 2008 ; Healy-Rodriguez et al., 2014 ). In this context, balancing service levels and cost efficiency represents a significant challenge for healthcare leadership (Zhu et al., 2011). To respond to these needs, the figure of the Bed Manager has developed, understood as an organizational function dedicated to patient flow governance and coordinated management of hospital beds. Several studies highlight how the introduction of structured bed management models contributes to improving patient throughput, reducing length of stay, and optimizing bed occupancy ( Meschi et al., 2016; Khanna et al., 2012 ). Organizational studies have identified bed management as a complex coordination function rather than a purely administrative task (Boaden et al., 1999 ). The Bed Manager role is configured as a support function for hospital leadership, aimed at promoting integration between logistics, hospital operational areas, and diagnostic-therapeutic pathways. Qualitative and ethnographic research has described bed management as a situated organizational practice, highlighting its role in negotiating hospital capacity and professional boundaries (Allen, 2015 ). This function can be performed by different professional figures, including nurses, physicians, or multidisciplinary teams, without compromising intervention effectiveness, as demonstrated by numerous observational studies (Howell et al., 2008 ; Healy-Rodriguez et al., 2014 ; Barchielli et al., 2023 ). The bed management role is usually performed by nurse managers (Proudlove et al., 2007 ). Despite growing interest, the literature on bed management remains limited and is mainly characterized by observational and pre–post studies, with marked heterogeneity in outcome indicators. Early contributions date back more than twenty years (Green & Armstrong, 1993 ; 1995 ), but only in recent years has the topic received greater attention in relation to overcrowding and hospital capacity management issues. Changes in bed management policies have been shown to significantly affect emergency department overcrowding and hospital flow dynamics (Claret et al., 2015 ). Emergency department overcrowding and ambulance diversion have long been recognized as system-level problems linked to inpatient bed availability (Olshaker & Rathlev, 2006 ). In light of these considerations, it is necessary to further explore the role of the Bed Manager and systematically evaluate its impact on hospital system performance indicators, such as bed occupancy rates, length of stay, and waiting times for admission, as well as additional measured indicators. METHODS Search strategy The literature search was conducted in PubMed, CINAHL (EBSCOhost), and Scopus databases. Search strings were developed according to PRISMA (2020) recommendations, with the aim of identifying studies analyzing bed management or the Bed Manager role and their impact on hospital organizational outcomes. This review was reported in accordance with the PRISMA 2020 statement (Page et al., 2021 ). Eligibility criteria Inclusion criteria hospital ED/inpatient settings; explicit bed management intervention/model; quantitative organizational outcomes (ED LOS, inpatient LOS, bed turnaround/turnover, crowding/access block, occupancy) and/or patient outcomes; English or Italian language; 2005–2025; full text available. Exclusion criteria non-hospital/outpatient settings; editorials/reviews/protocols; bed management not the main intervention; no measurable outcomes; unavailable full text; languages other than English/Italian. Summary of search strategies The databases searched, search fields, keywords, and applied filters are summarized in Table 1 . [Insert Table 1 here] Table 1 Summary of search strategies and databases searched Database Search fields Main keywords Boolean operators Applied filters Time period PubMed Title/Abstract bed management; bed manager; hospital; emergency department; length of stay; overcrowding OR / AND Full text; English or Italian language 2005–2025 CINAHL (EBSCOhost) Title (TI) / Abstract (AB) bed management; bed manager; hospital; inpatient; patient flow; bed occupancy OR / AND Full text; peer-reviewed; English or Italian language 2005–2025 Scopus TITLE-ABS-KEY bed management; bed manager; hospital; acute care; boarding; throughput OR / AND Article; Journal; English or Italian language 2005–2025 Complete search strings PubMed ("bed management"[Title/Abstract] OR "bed manager"[Title/Abstract] OR "bed managers"[Title/Abstract]) AND (hospital*[Title/Abstract] OR inpatient*[Title/Abstract] OR ward*[Title/Abstract] OR "acute care"[Title/Abstract] OR "emergency department"[Title/Abstract]) AND ("length of stay"[Title/Abstract] OR LOS[Title/Abstract] OR throughput[Title/Abstract] OR boarding[Title/Abstract] OR overcrowding[Title/Abstract] OR "bed occupancy"[Title/Abstract]) CINAHL (EBSCOhost) (TI ("bed management" OR "bed manager" OR "bed managers") OR AB ("bed management" OR "bed manager" OR "bed managers")) AND (TI (hospital* OR inpatient* OR ward* OR "acute care" OR "emergency department") OR AB (hospital* OR inpatient* OR ward* OR "acute care" OR "emergency department")) AND (TI ("length of stay" OR LOS OR throughput OR boarding OR overcrowding OR "bed occupancy") OR AB ("length of stay" OR LOS OR throughput OR boarding OR overcrowding OR "bed occupancy")) Scopus TITLE-ABS-KEY("bed management" OR "bed manager" OR "bed managers") AND TITLE-ABS-KEY(hospital OR hospitals OR inpatient OR ward OR "acute care" OR "emergency department") AND TITLE-ABS-KEY("length of stay" OR LOS OR throughput OR boarding OR overcrowding OR "bed occupancy") AND PUBYEAR > 2004 Data selection and extraction The last database search was performed on January 11, 2026. All retrieved records were imported into reference management software (Zotero version 7.0.30), where automatic and manual removal of duplicates was performed prior to selection. Titles and abstracts were independently screened by two reviewers (SA and RM). The full texts of potentially eligible studies were independently assessed, and discrepancies were resolved through discussion until consensus was reached. Data were extracted using a standardized data extraction form, which included study design, setting, sample characteristics, bed management intervention, and reported outcomes. Data synthesis The methodological quality and risk of bias of the included studies were assessed using the Joanna Briggs Institute (JBI) critical appraisal tools appropriate for each study design. Due to heterogeneity in study designs, interventions, and outcome measures, a meta-analysis was not feasible; therefore, findings were synthesized narratively. The study selection process is illustrated in Fig. 1 . The main characteristics and outcomes of the studies included in the review are summarized in Table 2 . [Insert Table 2 here] Table 2 Studies included in the review. Study (author, year) Title Study design Setting and sample size Bed management role/structure Bed management intervention/model Outcomes and key results Howell et al., 2008 Active bed management by hospitalists and emergency department throughput Pre–post quasi-experimental study University hospital emergency department (USA). Process metrics; patient-level N not reported / not applicable (analysis across two pre/post periods). Hospitalist physician with an operational mandate for active bed allocation and admission management (in collaboration with the ED). Active bed management (proactive management of beds and admissions from the ED to inpatient wards). ED LOS for admitted patients reduced: 458→360 min (− 98 min). Overcrowding time reduced: 26.6%→17.9%. Healy-Rodriguez et al., 2014 Impact of a logistics management program on admitted boarders in the emergency department Pre–post organizational intervention study Emergency department (USA). Sample: 28,684 admissions during the study period. Dedicated logistics/management team for boarded patients (professional profile not uniquely specified). Logistics Management Program to coordinate patient flow, transfers, and placement of admitted patients (focus on boarders). ED evaluation time: 219→193 min (p < 0.001). Inpatient LOS: −0.1 days (p < 0.001). Meschi et al., 2016 A case study from Parma Hospital, Italy: management model to cope with ED overload Observational organizational case study Parma Hospital (Italy): Internal Medicine and ED overload management. Process metrics; patient-level N not reported / not applicable. Bed manager/flow manager unit activated to manage overload (integrated with Internal Medicine). Organizational model with a short-stay ('come’n’go') unit activated through a bed management unit to support patient flow and turnover. Absorption of a + 22% increase in urgent ED admissions without an increase in LOS (indicator of system capacity/flexibility). Tortorella et al., 2013 Improving bed turnover time with a bed management system Quality improvement before–after study Inpatient cancer center (USA). Process metrics; patient-level N not reported / not applicable. Multidisciplinary Bed Management Team (not a single professional role) with organizational governance and use of a BMS. Implementation of a Bed Management System (BMS) plus workflow redesign (ADT–housekeeping–transport) and monitoring of operational KPIs. Bed turnover time: 111→49 min. Housekeeping turnaround: 63→49 min. Transport turnaround: 45→26 min. DBN compliance: 20%→~90%. Rocha et al., 2018 Bed management team with Kanban web-based application Before–after implementation study General hospital (~ 300 beds), Brazil. Sample: 67,878 patients analysed (pre/post). Dedicated bed management team (physician, nurse, and two social workers). Web-based Kanban application for managing bed availability, assignments, and flow coordination. Overall LOS: 5.6→4.9 days (p = 0.001). ICU LOS: 6.0→2.0 days (p = 0.001). Bed-availability complaints: 27%→0%. Barchielli et al., 2023 The Function of Bed Management in Pandemic Times: the COVID-19 experience in Tuscany Descriptive observational study (pandemic context) Tuscany region / hospital network (Italy). Sample: 7,098 hospitalized patients. Centralized bed management unit/service integrated with executive leadership and regional information systems. Centralized capacity management (acute, sub-intensive, and intensive beds) and dynamic bed reallocation during COVID-19 waves. Mortality by care setting: ICU 29%, wards 10%, intermediate care 4% (outcomes described in an emergency context). Khanna et al., 2012 Early discharge and its effect on emergency department length of stay and access block Multi-hospital observational study 23 public hospitals (Australia). Period: 913 days. Sample: administrative datasets (daily volumes). Patient flow/bed availability function (professional role not specified); focus on discharge timing and occupancy. Analysis of misalignment between discharge peaks and admission peaks and its association with access block and ED LOS. Category 5 vs Category 1: ED LOS 6.2 h vs 5.5 h; access block 229/day vs ~ 169/day ( + ~ 60/day); mean peak occupancy 103% (> 13% vs Category 1). Discussion The present systematic review highlights that bed management, understood as an organizational function (dedicated teams, information tools and/or roles with a mandate to coordinate patient flows), is associated with measurable improvements in several indicators of hospital performance, particularly in high-pressure care settings such as emergency departments and acute care wards. These findings are consistent with previous evidence showing that high inpatient bed occupancy is associated with reduced throughput and prolonged emergency department length of stay, even outside adult acute care settings (Hillier et al., 2009 ). In particular, the included studies show favourable effects on throughput, length of stay (LOS), bed turnover time and, more generally, on the system’s capacity to absorb demand by reducing bottlenecks and delays along the patient pathway (Howell et al., 2008 ; Healy-Rodriguez et al., 2014 ; Tortorella et al., 2013 ; Rocha et al., 2018 ). A first robust finding concerns the impact of bed management on admission processes from the emergency department. The “active bed management” model described by Howell et al. ( 2008 ) demonstrates that a structured intervention for proactive bed management, with clear responsibilities and clinical–organizational oversight, can substantially reduce emergency department length of stay and the proportion of overcrowding. Consistently, the introduction of a logistics–management programme targeting the management of boarded patients resulted in a significant reduction in emergency department evaluation times and a modest, yet meaningful, improvement in overall inpatient length of stay (Healy-Rodriguez et al., 2014 ). Together, these findings suggest that bed management acts as an interface lever between the emergency department and inpatient wards, reducing allocation delays and improving synchronisation between discharge, bed cleaning and internal transfers. A second key domain relates to the optimisation of inpatient internal processes, including bed availability, cleaning, transport services and the updating of information systems. The study by Tortorella et al. ( 2013 ) shows how the implementation of a Bed Management System (BMS), combined with workflow redesign and the monitoring of process metrics, can dramatically reduce bed turnover time and improve housekeeping and transport turnaround times. This finding is particularly relevant because it identifies a concrete operational mechanism: the performance of bed management depends not only on “coordination”, but also on the availability of real-time data and the alignment of multiple operational units (admission/transfer/discharge, housekeeping and transport). Similarly, Rocha et al. ( 2018 ) report that a bed management team supported by a web-based Kanban digital tool is associated with a significant reduction in length of stay and a marked decrease in complaints related to bed availability, reinforcing the notion that information systems and visual management tools can translate into measurable organizational benefits. The review also shows that the effectiveness of bed management is not limited to the emergency department, but can support the resilience of acute care wards in the presence of increased demand. In internal medicine, the introduction of a model incorporating a flow/bed manager enabled the management of an increase in urgent admissions without worsening length of stay, suggesting an improvement in the system’s “absorptive” capacity through flow coordination and management of care transitions (Meschi et al., 2016). Moreover, the multi-hospital analysis by Khanna et al. ( 2012 ) demonstrates that discharge timing and occupancy levels are closely associated with access block and emergency department length of stay, providing a quantitative rationale for the role of bed management in governing the discharge–admission cycle and preventing congestion (Khanna et al., 2012 ). Although not a strictly “bed manager–centric” intervention, the study supports causal plausibility: when discharges and bed availability are not managed in a coordinated manner, downstream flow KPIs deteriorate. From an organizational resilience perspective, Barchielli et al. ( 2023 ) show that during the COVID-19 pandemic, centralized governance of capacity and bed reconversion contributed to the management of large volumes of admissions and the rapid reorganization of care settings. This finding underscores that bed management can play a strategic role even in crisis situations, acting as a central coordination hub between logistics, clinical activity and executive leadership. Despite these promising results, the overall interpretation must take into account important methodological limitations. Most included studies are observational or pre–post in design, often single-centre, and therefore subject to bias from co-interventions, temporal trends and concurrent organizational changes (Howell et al., 2008 ; Healy-Rodriguez et al., 2014 ; Tortorella et al., 2013 ; Rocha et al., 2018 ). A recent systematic review highlighted the heterogeneity of research designs and modelling approaches in inpatient bed management studies, limiting comparability across settings (He et al., 2019 ). In addition, outcome definitions (LOS, turnaround time, occupancy, access block) and measurement methods are heterogeneous, limiting comparability and precluding a robust meta-analysis. Overall, the review suggests that the most effective interventions are not those based on a single “isolated” role, but rather those combining: (i) clear governance and accountability, (ii) real-time information and visual tools, (iii) integration with discharge–cleaning–transfer processes, and (iv) multiprofessional collaboration (Tortorella et al., 2013 ; Rocha et al., 2018 ; Howell et al., 2008 ). Future studies should adopt multicentre comparative designs and include not only efficiency KPIs (LOS, turnover, boarding), but also safety outcomes (readmissions, adverse events), patient-reported outcomes and economic analyses, in order to more precisely define which bed management “configuration” achieves the best balance between efficiency, quality and sustainability (Khanna et al., 2012 ; Healy-Rodriguez et al., 2014 ; Barchielli et al., 2023 ). Conclusions This systematic review indicates that bed management exerts a positive organizational impact on hospital outcomes, particularly in terms of reducing length of stay, improving bed turnover and optimising patient flows. These benefits are especially evident in high-complexity contexts, such as emergency departments and acute medical wards. The findings support the adoption of bed management as a strategic function within healthcare organizations, not necessarily tied to a single professional role, but integrated into organizational decision-making processes and information systems. In this sense, bed management can be considered a key tool of clinical and organizational governance, capable of contributing to the sustainability of healthcare systems. However, the available evidence remains limited by the predominance of observational studies and the lack of robust experimental designs. Further multicentre, prospective and controlled studies are therefore required to systematically evaluate the impact of bed management not only on efficiency indicators, but also on clinical outcomes, patient safety and staff satisfaction. In conclusion, bed management emerges as an essential component of modern hospital management. Investing in the development of structured organizational models, in the training of the professionals involved and in integration with information systems represents a promising strategy to address current and future challenges faced by healthcare systems. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The data supporting the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work did not receive any funding. Authors’ contributions SA conceived the study, conducted the literature search, performed data extraction and synthesis, and drafted the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Allen D. Inside bed management: ethnographic insights from the vantage point of UK hospital nurses. Sociol Health Illn. 2015;37(3):370–84. 10.1111/1467-9566.12195 . Bagust A, Place M, Posnett JW. Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. BMJ. 1999;319(7203):155–8. 10.1136/bmj.319.7203.155 . Barchielli C, Vainieri M, Seghieri C, Salutini E, Zoppi P. The Function of Bed Management in Pandemic Times—A Case Study of Reaction Time and Bed Reconversion. Int J Environ Res Public Health. 2023;20(12):6179. 10.3390/ijerph20126179 . Boaden R, Proudlove N, Wilson M. An exploratory study of bed management. J Manage Med. 1999;13(4–5):234–50. 10.1108/02689239910292945 . Claret PG, Bobbia X, et al. Consequences for overcrowding in the emergency room of a change in bed management policy on available in-hospital beds. Aust Health Rev. 2015;40(4):466–72. 10.1071/AH15088 . Forster AJ, Stiell I, Wells G, Lee AJ, van Walraven C. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med. 2003;10(2):127–33. 10.1197/aemj.10.2.127 . Green J, Armstrong D. Controlling the bed state: negotiating hospital organisation. Sociol Health Illn. 1993;15(3):337–52. 10.1111/1467-9566.ep10490544 . Green J, Armstrong D. Bed managers and the crisis in emergency admissions. Sociol Rev. 1995;43(4):743–62. 10.1111/j.1467-954X.1995.tb00717.x . He L, Madathil SC, Oberoi A, Servis G, Khasawneh MT. A systematic review of research design and modeling techniques in inpatient bed management. Comput Ind Eng. 2019;127:451–66. 10.1016/j.cie.2018.11.032 . Healy-Rodriguez MA, Freer C, Pontiggia L, Wilson R, Metraux S, Lord L. Impact of a logistics management program on admitted patient boarders within an emergency department. J Emerg Nurs. 2014;40(2):138–45. 10.1016/j.jen.2012.12.008 . Hillier DF, Parry GJ, Shannon MW, Stack AM. The effect of hospital bed occupancy on throughput in the pediatric emergency department. Ann Emerg Med. 2009;53(6):767–e7763. 10.1016/j.annemergmed.2008.11.024 . Howell E, Bessman E, Kravet SJ, Kolodner K, Marshall R, Wright SM. Active bed management by hospitalists and emergency department throughput. Ann Intern Med. 2008;149(11):804–11. 10.7326/0003-4819-149-11-200812020-00006 . Khanna S, Boyle J, Good N, Lind J. Early discharge and its effect on ED length of stay and access block. Stud Health Technol Inform. 2012;178:92–8. PMID:22797025. Olshaker JS, Rathlev NK. Emergency department overcrowding and ambulance diversion: the impact and potential solutions. Emerg Med Clin North Am. 2006;24(4):831–43. 10.1016/j.emc.2006.06.004 . Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. (2021) ‘The PRISMA 2020 statement: an updated guideline for reporting systematic reviews’, BMJ , 372, p. n71. 10.1136/bmj.n71 Proudlove NC, Boaden R, Jorgensen J. Developing bed managers: the why and the how. J Nurs Adm Manag. 2007;15(1):34–42. 10.1111/j.1365-2834.2006.00633.x . Rocha HAL, Santos AKLDC, Alcântara ACC, Lima CSSDC, Rocha SGMO, Cardoso RM, Cremonin JR. Bed management team with Kanban web-based application. Int J Qual Health Care. 2018;30(9):708–14. 10.1093/intqhc/mzy108 . Saghafian S, Austin G, Traub SJ. Operations research/management contributions to emergency department patient flow optimization: review and research prospects. IIE Trans Healthc Syst Eng. 2015;5(2):101–23. 10.1080/19488300.2015.1017676 . Tampubolon L and Pujiyanto. Bed Management Strategy for Overcrowding at the Emergency Department: A Systematic Review. KnE Life Sci. 2018;4:50–9. Tortorella F, Ukanowicz D, Douglas-Ntagha P, Ray R, Triller M. Improving bed turnover time with a bed management system. J Nurs Adm. 2013;43(1):37–43. 10.1097/NNA.0b013e3182785fe7 . Zhu Z. Impact of different discharge patterns on bed occupancy rate and bed waiting time: a simulation approach. J Med Eng Technol. 2011;35(6–7):338–43. 10.3109/03091902.2011.595528 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8641313","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":579888439,"identity":"443a628c-198c-4c2f-a11f-6ae881ea11b7","order_by":0,"name":"Altavilla Salvatore","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie2PsQrCMBCGUw7SJdg1pVpfoSIUB9FXEQSnDkIncRDxAVx9DZfMSoaOrgEXq+CsiGLBwZyTU6ObYL7h54b7uPsJsVh+ERcjalV1OjsdrGJUAFDhDMcIFfqZQl4K5TgaFW/mrS9syJk3l8fRNelUKYF8r0oULgECph/jahBva6KvH6PNZlJ2RgLxF9hFkXjrC9AKo0GZUtdXClTqm+yW+mJiViIJlJ+0Eq2S2DkLaVYaEuIWKg2VpIEjMkbB0CXMpgfVe7TDcJMtz4UYdz13lh9K678D7JWfriPO/Ztti8Vi+Ruebbk8AcQ6dSwAAAAASUVORK5CYII=","orcid":"","institution":"Policlinico di Bari University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Altavilla","middleName":"","lastName":"Salvatore","suffix":""}],"badges":[],"createdAt":"2026-01-19 16:10:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8641313/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8641313/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101214311,"identity":"19dc8445-051f-4f11-885f-15dcfbdab66c","added_by":"auto","created_at":"2026-01-27 10:34:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":880944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 flow diagram of study selection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8641313/v1/ca3db5f0368dd9170ce93a46.png"},{"id":101214424,"identity":"7a47381c-cd08-416e-a9ec-07f203bbd730","added_by":"auto","created_at":"2026-01-27 10:34:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1545497,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8641313/v1/7578a765-ce72-43c1-8101-78330c818fa0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Bed Management Models on Hospital Performance and Patient Flow: A Systematic Review","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn recent decades, emergency and urgent care departments have been characterized by recurring organizational problems such as overcrowding, boarding, and congestion, phenomena widely described in the literature (Claret et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tampubolon, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Howell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Among the main determinants of these issues is the progressive reduction in hospital beds, combined with increased care complexity and population aging. Early modelling studies demonstrated the intrinsic instability of hospital bed systems under emergency demand, even at high average occupancy rates (Bagust et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHospital beds represent one of the fundamental resources of healthcare systems, and their efficient management constitutes a key indicator of hospital organizational performance. Suboptimal use of bed capacity has been associated with increased waiting times, prolonged emergency department stays, and worsening clinical and organizational outcomes (Forster et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Howell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Healy-Rodriguez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this context, balancing service levels and cost efficiency represents a significant challenge for healthcare leadership (Zhu et al., 2011).\u003c/p\u003e \u003cp\u003eTo respond to these needs, the figure of the Bed Manager has developed, understood as an organizational function dedicated to patient flow governance and coordinated management of hospital beds. Several studies highlight how the introduction of structured bed management models contributes to improving patient throughput, reducing length of stay, and optimizing bed occupancy ( Meschi et al., 2016; Khanna et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Organizational studies have identified bed management as a complex coordination function rather than a purely administrative task (Boaden et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Bed Manager role is configured as a support function for hospital leadership, aimed at promoting integration between logistics, hospital operational areas, and diagnostic-therapeutic pathways. Qualitative and ethnographic research has described bed management as a situated organizational practice, highlighting its role in negotiating hospital capacity and professional boundaries (Allen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This function can be performed by different professional figures, including nurses, physicians, or multidisciplinary teams, without compromising intervention effectiveness, as demonstrated by numerous observational studies (Howell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Healy-Rodriguez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Barchielli et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The bed management role is usually performed by nurse managers (Proudlove et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing interest, the literature on bed management remains limited and is mainly characterized by observational and pre\u0026ndash;post studies, with marked heterogeneity in outcome indicators. Early contributions date back more than twenty years (Green \u0026amp; Armstrong, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), but only in recent years has the topic received greater attention in relation to overcrowding and hospital capacity management issues. Changes in bed management policies have been shown to significantly affect emergency department overcrowding and hospital flow dynamics (Claret et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Emergency department overcrowding and ambulance diversion have long been recognized as system-level problems linked to inpatient bed availability (Olshaker \u0026amp; Rathlev, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn light of these considerations, it is necessary to further explore the role of the Bed Manager and systematically evaluate its impact on hospital system performance indicators, such as bed occupancy rates, length of stay, and waiting times for admission, as well as additional measured indicators.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch strategy\u003c/h2\u003e \u003cp\u003eThe literature search was conducted in PubMed, CINAHL (EBSCOhost), and Scopus databases. Search strings were developed according to PRISMA (2020) recommendations, with the aim of identifying studies analyzing bed management or the Bed Manager role and their impact on hospital organizational outcomes. This review was reported in accordance with the PRISMA 2020 statement (Page et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eInclusion criteria\u003c/strong\u003e \u003cp\u003ehospital ED/inpatient settings; explicit bed management intervention/model; quantitative organizational outcomes (ED LOS, inpatient LOS, bed turnaround/turnover, crowding/access block, occupancy) and/or patient outcomes; English or Italian language; 2005\u0026ndash;2025; full text available.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion criteria\u003c/strong\u003e \u003cp\u003enon-hospital/outpatient settings; editorials/reviews/protocols; bed management not the main intervention; no measurable outcomes; unavailable full text; languages other than English/Italian.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eSummary of search strategies\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eThe databases searched, search fields, keywords, and applied filters are summarized in\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of search strategies and databases searched\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSearch fields\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain keywords\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBoolean operators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApplied filters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTime period\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePubMed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle/Abstract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebed management; bed manager; hospital; emergency department; length of stay; overcrowding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR / AND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull text; English or Italian language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCINAHL (EBSCOhost)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle (TI) / Abstract (AB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebed management; bed manager; hospital; inpatient; patient flow; bed occupancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR / AND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull text; peer-reviewed; English or Italian language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScopus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTITLE-ABS-KEY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebed management; bed manager; hospital; acute care; boarding; throughput\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR / AND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArticle; Journal; English or Italian language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2005\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eComplete search strings\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePubMed\u003c/h2\u003e \u003cp\u003e(\"bed management\"[Title/Abstract] OR \"bed manager\"[Title/Abstract] OR \"bed managers\"[Title/Abstract]) AND (hospital*[Title/Abstract] OR inpatient*[Title/Abstract] OR ward*[Title/Abstract] OR \"acute care\"[Title/Abstract] OR \"emergency department\"[Title/Abstract]) AND (\"length of stay\"[Title/Abstract] OR LOS[Title/Abstract] OR throughput[Title/Abstract] OR boarding[Title/Abstract] OR overcrowding[Title/Abstract] OR \"bed occupancy\"[Title/Abstract])\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCINAHL (EBSCOhost)\u003c/h2\u003e \u003cp\u003e(TI (\"bed management\" OR \"bed manager\" OR \"bed managers\") OR AB (\"bed management\" OR \"bed manager\" OR \"bed managers\")) AND (TI (hospital* OR inpatient* OR ward* OR \"acute care\" OR \"emergency department\") OR AB (hospital* OR inpatient* OR ward* OR \"acute care\" OR \"emergency department\")) AND (TI (\"length of stay\" OR LOS OR throughput OR boarding OR overcrowding OR \"bed occupancy\") OR AB (\"length of stay\" OR LOS OR throughput OR boarding OR overcrowding OR \"bed occupancy\"))\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eScopus\u003c/h3\u003e\n\u003cp\u003eTITLE-ABS-KEY(\"bed management\" OR \"bed manager\" OR \"bed managers\") AND TITLE-ABS-KEY(hospital OR hospitals OR inpatient OR ward OR \"acute care\" OR \"emergency department\") AND TITLE-ABS-KEY(\"length of stay\" OR LOS OR throughput OR boarding OR overcrowding OR \"bed occupancy\") AND PUBYEAR\u0026thinsp;\u0026gt;\u0026thinsp;2004\u003c/p\u003e\n\u003ch3\u003eData selection and extraction\u003c/h3\u003e\n\u003cp\u003eThe last database search was performed on January 11, 2026. All retrieved records were imported into reference management software (Zotero version 7.0.30), where automatic and manual removal of duplicates was performed prior to selection. Titles and abstracts were independently screened by two reviewers (SA and RM). The full texts of potentially eligible studies were independently assessed, and discrepancies were resolved through discussion until consensus was reached. Data were extracted using a standardized data extraction form, which included study design, setting, sample characteristics, bed management intervention, and reported outcomes.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData synthesis\u003c/h2\u003e \u003cp\u003eThe methodological quality and risk of bias of the included studies were assessed using the Joanna Briggs Institute (JBI) critical appraisal tools appropriate for each study design.\u003c/p\u003e \u003cp\u003eDue to heterogeneity in study designs, interventions, and outcome measures, a meta-analysis was not feasible; therefore, findings were synthesized narratively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe study selection process is illustrated in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe main characteristics and outcomes of the studies included in the review are summarized in\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudies included in the review.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy (author, year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSetting and sample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBed management role/structure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBed management intervention/model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOutcomes and key results\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHowell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive bed management by hospitalists and emergency department throughput\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre\u0026ndash;post quasi-experimental study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUniversity hospital emergency department (USA). Process metrics; patient-level N not reported / not applicable (analysis across two pre/post periods).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHospitalist physician with an operational mandate for active bed allocation and admission management (in collaboration with the ED).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eActive bed management (proactive management of beds and admissions from the ED to inpatient wards).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eED LOS for admitted patients reduced: 458\u0026rarr;360 min (\u0026minus;\u0026thinsp;98 min). Overcrowding time reduced: 26.6%\u0026rarr;17.9%.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealy-Rodriguez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact of a logistics management program on admitted boarders in the emergency department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre\u0026ndash;post organizational intervention study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmergency department (USA). Sample: 28,684 admissions during the study period.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDedicated logistics/management team for boarded patients (professional profile not uniquely specified).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLogistics Management Program to coordinate patient flow, transfers, and placement of admitted patients (focus on boarders).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eED evaluation time: 219\u0026rarr;193 min (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Inpatient LOS: \u0026minus;0.1 days (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeschi et al., 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA case study from Parma Hospital, Italy: management model to cope with ED overload\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservational organizational case study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParma Hospital (Italy): Internal Medicine and ED overload management. Process metrics; patient-level N not reported / not applicable.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBed manager/flow manager unit activated to manage overload (integrated with Internal Medicine).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrganizational model with a short-stay ('come\u0026rsquo;n\u0026rsquo;go') unit activated through a bed management unit to support patient flow and turnover.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAbsorption of a\u0026thinsp;+\u0026thinsp;22% increase in urgent ED admissions without an increase in LOS (indicator of system capacity/flexibility).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTortorella et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproving bed turnover time with a bed management system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuality improvement before\u0026ndash;after study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInpatient cancer center (USA). Process metrics; patient-level N not reported / not applicable.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultidisciplinary Bed Management Team (not a single professional role) with organizational governance and use of a BMS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImplementation of a Bed Management System (BMS) plus workflow redesign (ADT\u0026ndash;housekeeping\u0026ndash;transport) and monitoring of operational KPIs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBed turnover time: 111\u0026rarr;49 min. Housekeeping turnaround: 63\u0026rarr;49 min. Transport turnaround: 45\u0026rarr;26 min. DBN compliance: 20%\u0026rarr;~90%.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRocha et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBed management team with Kanban web-based application\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBefore\u0026ndash;after implementation study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral hospital (~\u0026thinsp;300 beds), Brazil. Sample: 67,878 patients analysed (pre/post).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDedicated bed management team (physician, nurse, and two social workers).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeb-based Kanban application for managing bed availability, assignments, and flow coordination.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall LOS: 5.6\u0026rarr;4.9 days (p\u0026thinsp;=\u0026thinsp;0.001). ICU LOS: 6.0\u0026rarr;2.0 days (p\u0026thinsp;=\u0026thinsp;0.001). Bed-availability complaints: 27%\u0026rarr;0%.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarchielli et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Function of Bed Management in Pandemic Times: the COVID-19 experience in Tuscany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescriptive observational study (pandemic context)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTuscany region / hospital network (Italy). Sample: 7,098 hospitalized patients.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentralized bed management unit/service integrated with executive leadership and regional information systems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCentralized capacity management (acute, sub-intensive, and intensive beds) and dynamic bed reallocation during COVID-19 waves.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMortality by care setting: ICU 29%, wards 10%, intermediate care 4% (outcomes described in an emergency context).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKhanna et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly discharge and its effect on emergency department length of stay and access block\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMulti-hospital observational study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 public hospitals (Australia). Period: 913 days. Sample: administrative datasets (daily volumes).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatient flow/bed availability function (professional role not specified); focus on discharge timing and occupancy.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnalysis of misalignment between discharge peaks and admission peaks and its association with access block and ED LOS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCategory 5 vs Category 1: ED LOS 6.2 h vs 5.5 h; access block 229/day vs\u0026thinsp;~\u0026thinsp;169/day (\u0026thinsp;+\u0026thinsp;~\u0026thinsp;60/day); mean peak occupancy 103% (\u0026gt;\u0026thinsp;13% vs Category 1).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present systematic review highlights that bed management, understood as an organizational function (dedicated teams, information tools and/or roles with a mandate to coordinate patient flows), is associated with measurable improvements in several indicators of hospital performance, particularly in high-pressure care settings such as emergency departments and acute care wards. These findings are consistent with previous evidence showing that high inpatient bed occupancy is associated with reduced throughput and prolonged emergency department length of stay, even outside adult acute care settings (Hillier et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In particular, the included studies show favourable effects on throughput, length of stay (LOS), bed turnover time and, more generally, on the system\u0026rsquo;s capacity to absorb demand by reducing bottlenecks and delays along the patient pathway (Howell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Healy-Rodriguez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tortorella et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rocha et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA first robust finding concerns the impact of bed management on admission processes from the emergency department. The \u0026ldquo;active bed management\u0026rdquo; model described by Howell et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) demonstrates that a structured intervention for proactive bed management, with clear responsibilities and clinical\u0026ndash;organizational oversight, can substantially reduce emergency department length of stay and the proportion of overcrowding. Consistently, the introduction of a logistics\u0026ndash;management programme targeting the management of boarded patients resulted in a significant reduction in emergency department evaluation times and a modest, yet meaningful, improvement in overall inpatient length of stay (Healy-Rodriguez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Together, these findings suggest that bed management acts as an interface lever between the emergency department and inpatient wards, reducing allocation delays and improving synchronisation between discharge, bed cleaning and internal transfers.\u003c/p\u003e \u003cp\u003eA second key domain relates to the optimisation of inpatient internal processes, including bed availability, cleaning, transport services and the updating of information systems. The study by Tortorella et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) shows how the implementation of a Bed Management System (BMS), combined with workflow redesign and the monitoring of process metrics, can dramatically reduce bed turnover time and improve housekeeping and transport turnaround times. This finding is particularly relevant because it identifies a concrete operational mechanism: the performance of bed management depends not only on \u0026ldquo;coordination\u0026rdquo;, but also on the availability of real-time data and the alignment of multiple operational units (admission/transfer/discharge, housekeeping and transport). Similarly, Rocha et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) report that a bed management team supported by a web-based Kanban digital tool is associated with a significant reduction in length of stay and a marked decrease in complaints related to bed availability, reinforcing the notion that information systems and visual management tools can translate into measurable organizational benefits.\u003c/p\u003e \u003cp\u003eThe review also shows that the effectiveness of bed management is not limited to the emergency department, but can support the resilience of acute care wards in the presence of increased demand. In internal medicine, the introduction of a model incorporating a flow/bed manager enabled the management of an increase in urgent admissions without worsening length of stay, suggesting an improvement in the system\u0026rsquo;s \u0026ldquo;absorptive\u0026rdquo; capacity through flow coordination and management of care transitions (Meschi et al., 2016). Moreover, the multi-hospital analysis by Khanna et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) demonstrates that discharge timing and occupancy levels are closely associated with access block and emergency department length of stay, providing a quantitative rationale for the role of bed management in governing the discharge\u0026ndash;admission cycle and preventing congestion (Khanna et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although not a strictly \u0026ldquo;bed manager\u0026ndash;centric\u0026rdquo; intervention, the study supports causal plausibility: when discharges and bed availability are not managed in a coordinated manner, downstream flow KPIs deteriorate.\u003c/p\u003e \u003cp\u003eFrom an organizational resilience perspective, Barchielli et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) show that during the COVID-19 pandemic, centralized governance of capacity and bed reconversion contributed to the management of large volumes of admissions and the rapid reorganization of care settings. This finding underscores that bed management can play a strategic role even in crisis situations, acting as a central coordination hub between logistics, clinical activity and executive leadership.\u003c/p\u003e \u003cp\u003eDespite these promising results, the overall interpretation must take into account important methodological limitations. Most included studies are observational or pre\u0026ndash;post in design, often single-centre, and therefore subject to bias from co-interventions, temporal trends and concurrent organizational changes (Howell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Healy-Rodriguez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tortorella et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rocha et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A recent systematic review highlighted the heterogeneity of research designs and modelling approaches in inpatient bed management studies, limiting comparability across settings (He et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, outcome definitions (LOS, turnaround time, occupancy, access block) and measurement methods are heterogeneous, limiting comparability and precluding a robust meta-analysis.\u003c/p\u003e \u003cp\u003eOverall, the review suggests that the most effective interventions are not those based on a single \u0026ldquo;isolated\u0026rdquo; role, but rather those combining: (i) clear governance and accountability, (ii) real-time information and visual tools, (iii) integration with discharge\u0026ndash;cleaning\u0026ndash;transfer processes, and (iv) multiprofessional collaboration (Tortorella et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rocha et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Howell et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Future studies should adopt multicentre comparative designs and include not only efficiency KPIs (LOS, turnover, boarding), but also safety outcomes (readmissions, adverse events), patient-reported outcomes and economic analyses, in order to more precisely define which bed management \u0026ldquo;configuration\u0026rdquo; achieves the best balance between efficiency, quality and sustainability (Khanna et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Healy-Rodriguez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Barchielli et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis systematic review indicates that bed management exerts a positive organizational impact on hospital outcomes, particularly in terms of reducing length of stay, improving bed turnover and optimising patient flows. These benefits are especially evident in high-complexity contexts, such as emergency departments and acute medical wards.\u003c/p\u003e \u003cp\u003eThe findings support the adoption of bed management as a strategic function within healthcare organizations, not necessarily tied to a single professional role, but integrated into organizational decision-making processes and information systems. In this sense, bed management can be considered a key tool of clinical and organizational governance, capable of contributing to the sustainability of healthcare systems.\u003c/p\u003e \u003cp\u003eHowever, the available evidence remains limited by the predominance of observational studies and the lack of robust experimental designs. Further multicentre, prospective and controlled studies are therefore required to systematically evaluate the impact of bed management not only on efficiency indicators, but also on clinical outcomes, patient safety and staff satisfaction.\u003c/p\u003e \u003cp\u003eIn conclusion, bed management emerges as an essential component of modern hospital management. Investing in the development of structured organizational models, in the training of the professionals involved and in integration with information systems represents a promising strategy to address current and future challenges faced by healthcare systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Availability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eThis work did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Authors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eSA conceived the study, conducted the literature search, performed data extraction and synthesis, and drafted the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Acknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllen D. Inside bed management: ethnographic insights from the vantage point of UK hospital nurses. Sociol Health Illn. 2015;37(3):370\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1467-9566.12195\u003c/span\u003e\u003cspan address=\"10.1111/1467-9566.12195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagust A, Place M, Posnett JW. Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. 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J Med Eng Technol. 2011;35(6\u0026ndash;7):338\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/03091902.2011.595528\u003c/span\u003e\u003cspan address=\"10.3109/03091902.2011.595528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"bed management, bed manager, patient flow, emergency department, length of stay, hospital performance, capacity management","lastPublishedDoi":"10.21203/rs.3.rs-8641313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8641313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003ePressure on emergency departments and inpatient wards, combined with reduced capacity and variability in patient flows, makes bed management a key management priority. Bed management (as a function/service, team, or dedicated system) aims to optimize allocation, turnover, and discharge processes, with potential impact on hospital performance indicators.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThe aim of this review is to analyse whether the introduction of bed management interventions/models (including dedicated roles, teams, and digital systems) within hospital organizations influences system outcomes, such as length of stay and number of admissions, as well as patient outcomes, including mortality and perceived quality of hospitalization.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eSystematic review according to PRISMA (2020). Literature search was conducted in PubMed, CINAHL, and Scopus databases, covering the time period 2005\u0026ndash;2025, in English and Italian. Studies conducted in hospital settings with full text available and evaluating explicit bed management interventions/models or the Bed Manager role with measurable organizational outcomes (LOS, ED LOS, bed turnover time, occupancy, diversion/overcrowding, access block) were included.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSeven studies were included, mainly observational or pre\u0026ndash;post in design. In emergency departments, active bed management reduced ED length of stay by up to 98 minutes and overcrowding from 26.6% to 17.9%. A logistics-management program reduced ED evaluation time from 219 to 193 minutes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) across 28,684 admissions and reduced inpatient length of stay by 0.1 days (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In internal medicine, the introduction of a flow/bed manager allowed the absorption of a 22% increase in urgent admissions without increasing average length of stay. Implementation of a Bed Management System improved bed turnover time from 111 to 49 minutes, with reductions in transport times (45\u0026rarr;26 min) and housekeeping times (63\u0026rarr;49 min). A bed management team supported by a Kanban application reduced overall LOS (5.6\u0026rarr;4.9 days; p\u0026thinsp;=\u0026thinsp;0.001) and complaints related to bed availability (27%\u0026rarr;0%). During the pandemic, centralized bed management supported organizational resilience, with mortality stratified by care setting (ICU 29%, wards 10%, intermediate care 4%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe included studies suggest that structured bed management models can improve throughput and capacity utilization (LOS, turnover, overcrowding), particularly in high-pressure contexts. Methodological quality is heterogeneous and non-randomized designs prevail; multicenter studies with standardized KPIs and safety measures (readmissions, adverse events) are needed to confirm impact and guide implementation.\u003c/p\u003e","manuscriptTitle":"The Impact of Bed Management Models on Hospital Performance and Patient Flow: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 10:27:31","doi":"10.21203/rs.3.rs-8641313/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10dd7507-706a-4ba2-9de9-972d905b0cef","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T10:27:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 10:27:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8641313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8641313","identity":"rs-8641313","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}