The Intersection Of Cognitive Competence And Organizational Support In Early Warning System (Ews) Accuracy: A Multifactorial Analysis In A Regional Hospital Setting

The Intersection Of Cognitive Competence And Organizational Support In Early Warning System (Ews) Accuracy: A Multifactorial Analysis In A Regional Hospital Setting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Intersection Of Cognitive Competence And Organizational Support In Early Warning System (Ews) Accuracy: A Multifactorial Analysis In A Regional Hospital Setting Herna Rinayanti Manurung, Siti Nurmawan Sinaga, Febriana Sari, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8952241/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background : Failure to recognize clinical deterioration remains a global systemic challenge leading to preventable hospital mortality. Aims: This study evaluated the multifactorial determinants predicting the accuracy of Early Warning System (EWS) implementation among clinicians in a regional hospital setting. Methods: An analytical observational study with a cross-sectional design was conducted in 2025 at RSUD Perdagangan, Indonesia, involving 203 health professionals (specialists, general practitioners, midwives, and nurses) using total sampling. Data were collected via validated questionnaires and analyzed using Chi-square tests. Results: Statistical analysis revealed that knowledge (p=0.001), training (p=0.002), attitude (p=0.005), and organizational support (p=0.001) significantly correlate with EWS implementation accuracy. Notably, 77.1% of clinicians failed to implement EWS effectively in environments lacking managerial support. Discussion: These findings highlight that EWS precision is a manifestation of cognitive competence integrated with systemic organizational reinforcement. Conclusion: Bridging the "accuracy gap" requires a paradigm shift from administrative compliance to a robust patient safety ecosystem that integrates structured training and active managerial oversight. Clinical Deterioration Early Warning System Patient Safety Organizational Support Quality of Care INTRODUCTION Failure to recognize clinical deterioration remains a global systemic challenge leading to preventable hospital mortality (S. M. O’Neill et al., 2021 ). Although the Early Warning System (EWS) has been widely adopted as a universal instrument of patient safety, its effectiveness is often hampered by the variability of compliance and scoring precision by frontline healthcare workers (Smith et al., 2014 ). In secondary hospitals with high workloads, such as Handels Hospital, this challenge becomes even more critical due to limited human resources that demand the accuracy of manual assessments without gaps (Okoroafor et al., 2022 ). Therefore, this study aims to dissect the multifactorial determinants that affect the accuracy of EWS implementation to close the gap between clinical protocols and real-life patient-saving outcomes in the field (Ye et al., 2019 ; Huete-Garcia and Rodriguez-Lopez, 2024 ). Theoretically, a clinician's adherence to health protocols such as EWS is not only determined by technical mastery but also by a complex interaction among cognitive, behavioral, and environmental factors within the organization (Mussa, Al-Raimi and Becker, 2019 ; Payne et al., 2024 ). Recent studies (2022–2025) have begun to shift from mere instrument validation to 'Human Factors' analysis, which places health workers' self-efficacy and management support as key pillars of clinical accuracy (Lagreca, Carpagnano and Benvenuto, 2023 ; Piffari et al., 2024 ). However, there is a significant disconnect between patient safety theory and the realities of implementation in resource-constrained regional health facilities (Karikari et al., 2023 ). A synthesis of a wide range of evidence suggests that without precise scoring accuracy, early warning systems will be an administrative artifact that fails to mitigate the risk of patient death effectively(Arnolds et al., 2022 ), (Finlay, Rothman and Smith, 2014 ). The urgency of this research lies in the phenomenon of failure to rescue, which remains a critical indicator of the quality of health services, where delays in clinical response are directly proportional to increases in morbidity and the burden of global health costs (Ahmad et al., 2017 ; Olagnero et al., 2025 ). In the midst of efforts to transform the national health system by 2025, strengthening the early detection capacity of regional referral hospitals is crucial to ensure patient safety and equity, especially in areas with a heavy caseload but limited infrastructure (Owokuhaisa et al., 2024 ; Carmichael et al., 2025 ). By dissecting the determinants of compliance and scoring precision, this study is not only relevant to local policy development in Indonesia but also offers strategic insights for strengthening early warning systems in other developing countries with similar socio-demographic characteristics (Flenady et al., 2020 ; Dwyer et al., 2024 ). Although the Early Warning System (EWS) has become a universal standard, its effectiveness in secondary hospitals is often hampered by 'accuracy gaps' that create a false sense of security in clinical risk management (Wuytack et al., 2017 ; S M O’Neill et al., 2021 ). This study fills a literature gap by evaluating how multifactorial dynamics—particularly the interaction between clinical workload and managerial support—will affect scoring precision in Indonesia's regional order by 2025 (Stein et al., 2023 ; Song et al., 2024 ; Effendi et al., 2025 ). Previous studies (2023–2025) began integrating Human Factors Theory to understand scoring accuracy, but there is a literature gap on how the interaction between extreme workloads and managerial support creates an 'accuracy gap' in manual scoring in rural contexts (Brauner et al., 2018 ). This study fills this gap by evaluating multifactorial determinants of clinical precision amid a resource crisis. Despite the breadth of literature on EWS implementation, there is a critical gap in understanding the accuracy of manual scoring relative to administrative compliance, which often creates a 'false sense of security' in clinical risk management (Leenen and Mondria, 2024 ). Most previous studies have focused on teaching hospital environments in urban centers, thus ignoring multifactorial dynamics—such as the interaction between extreme workloads and organizational support—in regional hospitals with limited resources (Karim Jabr et al., 2025 ; Reich and Knopf, 2025 ). In addition, there has not been an in-depth investigation that integrates the psychosocial determinants of clinicians with the appropriateness of interventions in rural Indonesia by 2025 (Bussmann et al., 2023 ). This study aims to fill this gap by evaluating how systemic and individual factors interact to determine the scoring precision that determines a patient's life or death. This study aims to evaluate the multifactorial determinants of scoring accuracy and clinical adherence to Early Warning System (EWS) protocols in regional hospital settings. We hypothesize that the precision of EWS implementation is not only influenced by individual competencies but is also significantly moderated by organizational support and health workers' self-efficacy in the face of clinical workloads (Fowokan et al., 2022 ; Bussmann et al., 2023 ). By identifying these key predictors, this study is expected to provide a strategic intervention model that improves the accuracy of detecting worsening patient conditions, thereby minimizing preventable clinical risks in secondary health facilities. The novelty of this research lies in the 'Precision in Crisis' evaluation approach, which shifts the focus from mere administrative compliance to clinical accuracy in regional hospitals by 2025. This study makes a unique contribution by integrating the dynamics of Indonesia's local resources into the global patient safety literature, offering empirical evidence on how systemic factors affect the quality of early detection of patient deterioration. In practical terms, these findings will serve as a strategic database for the development of adaptive clinical surveillance protocols that not only strengthen the clinical authority of midwives and nurses but also provide a replicable framework for improving patient safety standards across secondary health facilities worldwide. METHOD Research Design This analytical observational study uses a cross-sectional approach to investigate the relationships between cognitive, behavioral, and organizational determinants and the accuracy of EWS implementation. The knowledge instrument consists of 30 multiple-choice statement items designed to measure cognitive precision relative to the EWS algorithm, with a binary scoring system. The research instrument was adapted from established Early Warning System (EWS) protocols and further refined through a Focus Group Discussion (FGD) involving clinical experts and nursing specialists. This modification was essential to align the instrument with the specific clinical environment of a regional hospital in Indonesia. The final questionnaire consists of 30 items covering definitions, physiological parameters, interpretation of scores, and the roles of healthcare providers (see Supplementary File 1 for the full English version). (Rista et al. , 2025). To ensure data integrity, the total sampling technique was applied to the entire clinical population (N=203), including specialists, general practitioners, midwives, and nurses. Respondents' anonymity is fully guaranteed to minimize social desirability bias . Participants and Sampling The research was conducted at the Trade Hospital in Simalungun Regency, Indonesia. The target population comprises all health workers directly involved in patient care: specialist doctors (n=22), general practitioners (n=11), midwives (n=96), and nurses (n=74). Given the relatively small population and the need to minimize selection bias, a total sampling technique was used, resulting in the entire population of 203 respondents serving as the research sample. The inclusion criteria are active health workers with clinical authority to conduct EWS scoring in inpatient and emergency units. Research Instruments To ensure internal reliability, a knowledge questionnaire comprising 30 statements was administered to a group of nurses with similar characteristics, yielding a Cronbach's Alpha coefficient of 0.001, indicating excellent internal consistency. In addition, the content validity is evaluated by a panel of critical nursing experts to ensure the instrument can capture cognitive precision in line with the latest EWS algorithms by 2025. The data collection instrument consists of two main parts that have been validated: Demographic Data: Includes respondent profiles such as age, gender, educational background, ethnicity, religion, and work experience. EWS Knowledge Questionnaire: This instrument measures the cognitive dimension (knowing, understanding, and applying) using 30 multiple-choice statements. The scoring system uses a binary scale: the correct answer is scored 2, and the wrong answer is scored 1. The structure of this instrument is designed to capture the clinician's cognitive precision relative to the EWS scoring algorithm, which is a key predictor of patient clinical deterioration. Data Collection Procedure Data is collected through a combination of primary and secondary sources. Primary data was obtained through the distribution of a structured questionnaire to respondents after obtaining informed consent ( Informed Consent ). The researcher ensures respondents' anonymity to minimize social desirability bias . Secondary data were used to verify the standard operating protocol (SPO) for EWS in place at the hospital, to ensure the instrument's relevance to field practice. Data Analysis Data analysis is carried out systematically using SPSS software: Univariate Analysis: Used to describe the frequency and proportion distribution of each independent variable (multifactorial factor) and dependent variable (EWS accuracy). Bivariate Analysis: The relationships between variables are tested using the Chi-Square test with cross-tabulation . This test was chosen to analyze the significance of differences or relationships between categorical variables at the 95% confidence level (alpha = 0.05). The use of these non-parametric tests provides a solid statistical foundation for concluding the factors that significantly contribute to the accuracy of EWS scoring. RESULTS Analysis of the Relationship of Factors with the Implementation of the Early Warning System (EWS) The results of the Chi-square test confirmed that all independent variables were significantly related to EWS accuracy (p < 0.05). Specifically, knowledge (p=0.001) and management support (p=0.001) emerged as the most determinant predictors. Critical findings show that 77.1% of clinicians fail to implement EWS effectively in an environment with minimal managerial support. In addition, there was a strong correlation between participation in formal training (p=0.002) and clinicians' ability to operate early detection systems accurately. The EWS training variable also showed a significant effect on clinicians' performance (p=0.002). Respondents who had attended the training were more likely to implement EWS well (n=40; 72.7%) than those who had never participated in the training, among whom the majority were in the poor implementation category (n=25; 71.4%). These findings indicate that formal education interventions are an essential factor in operationalizing early warning systems in hospitals. Furthermore, the bivariate test of the attitude variable showed a significant correlation with the application of EWS (p=0.005). Respondents with positive attitudes tended to have reasonable EWS implementation rates (n=38; 76%), while respondents with negative attitudes were more identified in the poor implementation category (n=28; 70%). This suggests that clinicians' internal disposition and acceptance of patient safety protocols contribute to the consistency of clinical behavior. Finally, management support was identified as the most significant factor related to the implementation of EWS, similar to the knowledge variable (p=0.001). Among respondents who felt supported by management, 42 (76.4%) were able to implement EWS well. In contrast, in the group without adequate management support, as many as 27 respondents (77.1%) failed to implement the system properly14. Overall, all independent variables tested showed p-values < 0.05, indicating a significant relationship between knowledge, training, attitudes, and management support and the implementation of EWS at the Handels Hospital in 2025. DISCUSSION Summary of Key Findings This study reveals that the implementation of the Early Warning System (EWS) in the Commercial Hospital is significantly influenced by the convergence of multifactorial determinants, including cognitive, behavioral, and organizational dimensions. All independent variables tested, namely knowledge, training, attitude, and management support, showed a strong and statistically significant relationship with the quality of EWS implementation (p < 0.05). In particular, management support and knowledge level emerged as the main pillars with the highest significance value (p = 0.001), confirming that the effectiveness of early warning systems is highly dependent on the synergy between supportive managerial policies and clinicians' intellectual capacity. Critical phenomena were identified in groups with less knowledge and low management support, where the majority of respondents (66.7% and 77.1%) failed to operate the EWS protocol accurately. These findings provide empirical evidence that failures in the implementation of EWS at the regional hospital level are not isolated incidents but reflect the complex interplay between the limitations of formal education and the weak systemic support infrastructure at the front lines of service delivery. These findings show that EWS accuracy is not merely a manifestation of individual competence but a product of a supportive organizational ecosystem. Management support (p = 0.001) was a key pillar that validated the officer's clinical authority in the field. Without synergy between intellectual capacity and systemic reinforcement, EWS protocols risk becoming 'administrative artifacts' that create a false sense of security in clinical risk management. It expands on the Theory of Planned Behavior by showing that a conducive work environment is a key catalyst for precise clinical behavior (Lee and Vincent, 2021 ). Interpretation and Comparison of Findings These findings show that EWS accuracy is not merely a manifestation of individual competence but a product of a supportive organizational ecosystem. Management support (p = 0.001) was a key pillar that validated the officer's clinical authority in the field. Without synergy between intellectual capacity and systemic reinforcement, EWS protocols risk becoming 'administrative artifacts' that create a false sense of security in clinical risk management. It expands on the Theory of Planned Behavior by showing that a conducive work environment is a key catalyst for precise clinical behavior. In the context of comparative literature, these findings support a global study that states that formal training (p = 0.002) is an absolute determinant in mitigating human error in the recognition of clinical exacerbations. However, this study makes a unique contribution to the existing literature by demonstrating that, in regional hospitals, management support (p = 0.001) has a more decisive influence than individual factors alone, with the absence of systemic support resulting in implementation failure at 77.1%. This aligns with the WHO's patient safety framework, which emphasizes that clinical precision cannot stand alone without a supportive organizational ecosystem, while rejecting the long-held assumption that EWS failures are solely due to individual officer negligence (S. M. O’Neill et al., 2021 ). Implications for Theory and Practice The results of this study make a significant theoretical contribution by strengthening the framework of Human Factors Theory in patient safety management, where clinical accuracy is shown not to be an independent variable but rather a product of a synergistic organizational ecosystem. Theoretically, the finding that management support shows a robust correlation (p = 0.001) expands our understanding of the effectiveness of early warning instruments such as EWS, which are highly dependent on organizational support that validates officers' clinical authority in the field. This confirms that, in the context of regional hospitals, strengthening individual capacity through knowledge (n = 35; 77.8% good implementation) must run in parallel with strengthening systemic support structures to prevent failure to recognize worsening patient conditions ( failure to rescue ). In practical terms, these findings demand a paradigm shift in hospital management, from merely meeting administrative compliance requirements to improving evidence-based clinical precision(Woolnough et al., 2021 ; Radhika and Keepanasseril, 2024 ). The real implication for Handels Hospital and similar institutions is the urgency of transforming the EWS training program from a passive education model to an integrative, continuous training program, given that respondents who have participated in the training show a much higher implementation rate (72.7%) than the unexposed group (28.6%). In addition, as management support proves crucial, hospital leaders must institute a system of routine audits and constructive feedback to ensure that EWS scoring is not just a documentation routine but an accurate, responsive, life-saving instrument for changing patient conditions. Affirmation of Contribution and Novelty (Novelty) This research makes an original contribution to the global patient safety literature by introducing the 'Precision in Crisis' evaluation model in the regional hospital setting in developing countries. The main novelty of this study lies in the empirical evidence that, in low-resource settings, management support (p = 0.001) and formal training (p = 0.002) are more dominant structural determinants than individual demographic characteristics in ensuring the accuracy of EWS scoring. In contrast to previous studies that generally focused on administrative compliance in urban teaching hospitals, this study reveals dynamics in regional hospitals in 2025, where systemic failures in the implementation of EWS of 77.1% in groups without managerial support confirm the existence of a 'accuracy gap' that has been neglected so far. Thus, this paper offers a new perspective that the effectiveness of early warning systems is inseparable from the policy ecosystem that validates clinical action, while providing a replicable framework for improving early detection standards in secondary health facilities at the international level. Research Limitations Despite its significant contribution, the study has some limitations that must be considered in interpreting the results. First, the use of a cross-sectional design limits the ability to draw definitive causal inferences about multifactorial factors when applying the Early Warning System (EWS) (Kim, 2023 ; Hajra, Ghosh and Bhowmik, 2025 ). This means that although there is a statistically significant relationship between management support and scoring precision (p = 0.001), the chronological direction of the relationship cannot be fully ascertained. Second, data collection via self-reported questionnaires may introduce social desirability bias, leading respondents to provide answers that reflect the ideals of clinical protocols rather than their actual daily practices. Finally, although the total sampling technique covers the entire population of the Commercial Hospital (N = 203), the generalizability of the results of this study must be carefully assessed at the national level, given the variability in infrastructure and policies across different regional hospitals4. Nevertheless, these limitations do not diminish the internal validity of these findings as a crucial portrait of the urgency of clinical precision in secondary health facilities. Suggestions for Future Research Based on the findings and limitations that have been described, future research needs to consider the use of longitudinal study designs to validate the consistency of the accuracy of the implementation of the Early Warning System (EWS) after being given periodic training interventions, to understand knowledge retention and long-term changes in clinical behavior. Given that training is a significant determinant (p = 0.002), further investigation through a mixed-methods approach is strongly recommended to explore in depth the psychological and sociocultural barriers that clinicians face when scoring under extreme workload pressures. In addition, future research should test the effectiveness of integrating digital technologies or artificial intelligence-based clinical decision support systems in regional hospitals to mitigate human error in manual score calculations (Fawzi, 2023 ; Premalatha et al., 2025 ). Exploration on a broader scale, involving multiple hospitals nationwide, is also needed to strengthen the generalizability of EWS success-predictor models across heterogeneous health systems (Anitha et al., 2024 ). Social and Ethical Implications The findings of this study carry profound ethical implications for patient safety standards, where systemic failures in implementing the Early Warning System (EWS) are not merely technical problems but health justice issues. The extreme significance of the management support variable (p = 0.001) underscores the ethical dimension that responsibility for patient safety should not be placed entirely on the shoulders of individual clinicians on the front lines. When managerial support is inadequate, the risk of system failure increases to 77.1%, which is ethically a form of organizational neglect of the risk of preventable deaths . Socially, it emphasizes that every patient in a secondary health facility has a moral right to accurate early detection. Therefore, ensuring scoring accuracy through increased knowledge (n = 35; 77.8% exemplary implementation) and strengthening managerial commitment is an urgent ethical mandate to ensure that regional hospitals can provide equal and dignified health protection for all levels of society. Although it provides crucial insights, this study has limitations due to its cross-sectional design, which cannot definitively determine the temporal direction of causal relationships. The use of self-reported questionnaires also opens the door to respondents' subjective biases whe n reporting their compliance with clinical protocols (Van Den Bergh and Walentynowicz, 2016 ). Finally, generalizing results on a national scale must be done carefully, given the variability in infrastructure across regional hospitals in Indonesia. Implications for Nursing Management These findings recommend that hospital leaders not only focus on one-way technical training, but also institute a system of routine clinical audits and constructive feedback. The 'EWS Champions' strategy or unit leaders who actively provide managerial support in the field have proven to be a crucial determinant in mitigating the risk of failure to rescue . CONCLUSION This study concludes that the effectiveness of implementing the Early Warning System (EWS) as a life-saving instrument in regional hospitals is determined mainly by the convergence of cognitive, behavioral, and organizational support determinants. Clinical knowledge and management support were identified as the most crucial predictors of the precision of clinical scoring. These findings show that without adequate managerial support and ongoing education, early warning systems fail to function optimally, potentially posing clinical risks to patients. Based on these findings, regional hospitals must make a paradigm shift from simply fulfilling administrative compliance to strengthening evidence-based clinical precision. Transforming the training program from a passive method to continuous, integrative education is highly recommended. Ensuring scoring accuracy through managerial support is not only an operational necessity but also an ethical mandate to ensure safety and fairness for every patient in a secondary health facility. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. Administrative approval and ethical clearance for the study were obtained from the Management and Director of RSUD Perdagangan , Simalungun Regency, Indonesia. The study was approved by the Internal Review Board of STIKes Mitra Husada Medan and received administrative approval from RSUD Perdagangan. Informed consent was obtained from all individual participants prior to data collection. Consent for publication Not applicable. The study does not contain any individual person’s data in any form (including individual details, images, or videos). Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no external funding. Authors' contributions H.M. conceptualized and designed the study, coordinated the data collection, and drafted the initial manuscript. S.N.S. provided methodological guidance and conducted the statistical analysis using SPSS. F.S. and I.S.S. participated in the data collection process and performed the literature review. N.Z. assisted in data validation and the preparation of research instruments. R.H.G contributed to the critical revision of the manuscript for important intellectual content and supervised the research project. All authors reviewed and approved the final manuscript. Acknowledgements The authors would like to express their sincere gratitude to the Management and Director of RSUD Perdagangan for their invaluable support, permission, and cooperation throughout the data collection process. Special appreciation is extended to all participating clinicians for their time and dedication. This research has been approved by the Chair of the Research Ethics Committee of STIKes Mitra Husada Medan, Dr. Herna Rinayanti Manurung, S.Tr.Keb., Bd., M.Kes. Informed consent was obtained from all participants involved in this study. References Ahmad, T. et al. (2017) “Use of failure-to-resue to identify international variation in postoperative care in low-, middle- and high-income countries: A 7-day cohort study of elective surgery,” British Journal of Anaesthesia , 119(2), pp. 258–266. Available at: https://doi.org/10.1093/bja/aex185. Anitha, D. et al. (2024) “Advancements in early warning systems and human factors in lightweight secured iomt ecosystems for healthcare 4.0,” in Social Innovations in Education, Environment, and Healthcare . Department of Information Technology, Muthayammal Engineering College, Namakkal, India: IGI Global, pp. 318–338. Available at: https://doi.org/10.4018/979-8-3693-2569-8.ch016. Arnolds, D.E. et al. (2022) “Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients,” BMC Pregnancy and Childbirth , 22(1). Available at: https://doi.org/10.1186/s12884-022-04631-0. Van Den Bergh, O. and Walentynowicz, M. (2016) “Accuracy and bias in retrospective symptom reporting,” Current Opinion in Psychiatry , 29(5), pp. 302–308. Available at: https://doi.org/10.1097/YCO.0000000000000267. Brauner, P. et al. (2018) “The interaction of causal attribution of performance and compliance with decision support systems in cyber-physical production systems - an empirical study using a business simulation game,” in C. W. and F. L.E. (eds.) Advances in Intelligent Systems and Computing . Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany: Springer Verlag, pp. 11–23. Available at: https://doi.org/10.1007/978-3-319-60486-2_2. Bussmann, S. et al. (2023) “Screening and assessment for cancer patients’ psychosocial support needs: Development and validation of the psychosocial risk questionnaire,” European Journal of Oncology Nursing , 63. Available at: https://doi.org/10.1016/j.ejon.2022.102240. Carmichael, H.L. et al. (2025) “Optimizing patient care and hospital operations through interhospital admission transfers: The load balancing protocol,” Journal of Hospital Medicine [Preprint]. Available at: https://doi.org/10.1002/jhm.70174. Dwyer, T. et al. (2024) “A theoretical framework for identifying sociocultural factors that influence nurses’ compliance with early warning systems for acute clinical deterioration: A cross-sectional survey,” International Journal of Nursing Studies , 158. Available at: https://doi.org/10.1016/j.ijnurstu.2024.104846. Effendi, M.S. et al. (2025) “Beyond Workload: Uncovering the Link Between Supervisor Support, Work–Life Balance, and Lecturer Productivity,” Education Sciences , 15(9). Available at: https://doi.org/10.3390/educsci15091122. Fawzi, S. (2023) “A Review of the Role of ChatGPT for Clinical Decision Support Systems,” in 5th Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023 - Proceedings . Nile University, Medical Informatics Research Group, Center for Informatics Science Information Technology and Computer Science School, Cairo, Egypt: Institute of Electrical and Electronics Engineers Inc., pp. 439–442. Available at: https://doi.org/10.1109/NILES59815.2023.10296668. Finlay, G.D., Rothman, M.J. and Smith, R.A. (2014) “Measuring the modified early warning score and the Rothman Index: Advantages of utilizing the electronic medical record in an early warning system,” Journal of Hospital Medicine , 9(2), pp. 116–119. Available at: https://doi.org/10.1002/jhm.2132. Flenady, T. et al. (2020) “Developing a sociocultural framework of compliance: An exploration of factors related to the use of early warning systems among acute care clinicians,” BMC Health Services Research , 20(1). Available at: https://doi.org/10.1186/s12913-020-05615-6. Fowokan, A. et al. (2022) “Evaluation of a Health Care Worker Training Intervention to Improve the Early Diagnosis and Referral of Childhood Cancers in Ghana: A Qualitative Descriptive Study,” JCO global oncology , 8, p. e2200151. Available at: https://doi.org/10.1200/GO.22.00151. Hajra, A., Ghosh, B. and Bhowmik, S. (2025) “Cross-sectional studies,” in Translational Cardiology . Department of Internal Medicine, Bringham and Women’s Hospital/Harvard Medical School, Boston, MA, United States: Elsevier, pp. 221–225. Available at: https://doi.org/10.1016/B978-0-323-91790-2.00099-X. Huete-Garcia, A. and Rodriguez-Lopez, S. (2024) “Development and implementation of an artificial intelligence–enhanced care model to improve patient safety in hospital wards in Spain,” Acute and Critical Care , 39(4), pp. 488–498. Available at: https://doi.org/10.4266/acc.2024.00759. Karikari, A.K. et al. (2023) “Understanding the challenges and coping mechanisms adopted by nursing staff in managing critically-ill patients at district hospitals in the Ashanti Region, Ghana,” International Journal of Africa Nursing Sciences , 18(November 2022), p. 100515. Available at: https://doi.org/10.1016/j.ijans.2022.100515. Karim Jabr, M. et al. (2025) “The Impact of Workload and Emotional Stress on Nursing Care Quality in Baghdad Hospitals: A Cross-Sectional Study,” International Journal of Body, Mind and Culture , 12(7), pp. 55–59. Available at: https://doi.org/10.61838/ijbmc.v12i7.1035. Kim, J.H. (2023) “Cross-sectional study: The role of observation in epidemiological studies,” in Statistical Approaches for Epidemiology: From Concept to Application . Department of Epidemiology, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong: Springer International Publishing, pp. 19–42. Available at: https://doi.org/10.1007/978-3-031-41784-9_2. Lagreca, D., Carpagnano, L.F. and Benvenuto, M. (2023) “What skills and competences profiles for the Patient Safety Manager?,” Mecosan , (126), pp. 151–183. Available at: https://doi.org/10.3280/MESA2023-126OA17290. Lee, S. and Vincent, C. (2021) “Analysis and Evaluation of the Theory of Planned Behavior,” Advances in Nursing Science , 44(4), pp. E127–E140. Available at: https://doi.org/10.1097/ANS.0000000000000369. Leenen, J.P. and Mondria, C.L. (2024) “Variation in nurses’ compliance with an Early Warning Score protocol: A retrospective cohort study,” Heliyon , 10(16). Available at: https://doi.org/10.1016/j.heliyon.2024.e36147. Mussa, C.C., Al-Raimi, A. and Becker, E.A. (2019) “Predicting respiratory therapists’ intentions to use the modified early warning score by using an enhanced technology acceptance model,” Respiratory Care , 64(4), pp. 416–424. Available at: https://doi.org/10.4187/respcare.06428. O’Neill, S. M. et al. (2021) “Why do healthcare professionals fail to escalate as per the early warning system (EWS) protocol? A qualitative evidence synthesis of the barriers and facilitators of escalation,” BMC Emergency Medicine , 21(1), pp. 1–19. Available at: https://doi.org/10.1186/s12873-021-00403-9. O’Neill, S M et al. (2021) “Why do healthcare professionals fail to escalate as per the early warning system (EWS) protocol? A qualitative evidence synthesis of the barriers and facilitators of escalation,” BMC Emergency Medicine , 21(1). Available at: https://doi.org/10.1186/s12873-021-00403-9. Okoroafor, S.C. et al. (2022) “Assessing the staffing needs for primary health care centers in Cross River State, Nigeria: a workload indicators of staffing needs study,” Human Resources for Health , 19(1), pp. 1–11. Available at: https://doi.org/10.1186/s12960-021-00648-2. Olagnero, J.M. et al. (2025) “Failure to rescue: a useful quality-of-care indicator to integrate into the Italian healthcare context?,” Assistenza infermieristica e ricerca : AIR , 44(3), pp. 117–121. Available at: https://doi.org/10.1702/4564.45637. Owokuhaisa, J. et al. (2024) “Barriers and Facilitators to Screening for Kidney Disease Among Older Adults with Hypertension and Diabetes in Mbarara Southwestern Uganda: Healthcare Providers’ Perspective,” Journal of Multidisciplinary Healthcare , 17, pp. 855–865. Available at: https://doi.org/10.2147/JMDH.S451533. Payne, V.L. et al. (2024) “Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record,” Journal of the American Medical Informatics Association , 31(6), pp. 1331–1340. Available at: https://doi.org/10.1093/jamia/ocae089. Piffari, C. et al. (2024) “Human Factors in Healthcare operations: A Case Study in Italian Emergency Rooms,” IFAC-PapersOnLine , 58(19), pp. 706–711. Available at: https://doi.org/10.1016/j.ifacol.2024.09.280. Premalatha, R. et al. (2025) “AI-Powered Clinical Decision Support Systems: Real-Time Assistance for Enhanced Patient Care,” in Synthesis Lectures on Computer Science . Department of Economics, VELS Institute of Science, Technology and Advanced Studies, Chennai, India: Springer Nature, pp. 47–55. Available at: https://doi.org/10.1007/978-3-031-93673-9_5. Radhika, A.G. and Keepanasseril, A. (2024) “Developing a culture of evidence-based practice in gynecology and obstetrics,” Clinical Epidemiology and Global Health , 26. Available at: https://doi.org/10.1016/j.cegh.2024.101509. Reich, L. and Knopf, K.B. (2025) “The Oncology Drug Shortages and Its Impact on Community Hospitals,” Cancer journal (Sudbury, Mass.) , 31(5). Available at: https://doi.org/10.1097/PPO.0000000000000794. Rista, P. et al. (2025) "Level of nurses' knowledge of the Early Warning Score (EWS) in the internal medicine ward of Santa Elisabeth Hospital, Medan in 2025." STIKes Santa Elisabeth Medan. Smith, M.E.B. et al. (2014) “Early warning system scores for clinical deterioration in hospitalized patients: A systematic review,” Annals of the American Thoracic Society , 11(9), pp. 1454–1465. Available at: https://doi.org/10.1513/AnnalsATS.201403-102OC. Song, Y. et al. (2024) “Predicting nursing workload in digestive wards based on machine learning: A prospective study,” BMC Nursing , 23(1). Available at: https://doi.org/10.1186/s12912-024-02570-z. Stein, D.T. et al. (2023) “Attributes of funding flows and quality of maternal health services in a mixed provider payment system: A cross-sectional survey of 108 healthcare providers in Indonesia,” World Medical and Health Policy , 15(2), pp. 179–193. Available at: https://doi.org/10.1002/wmh3.545. Woolnough, T. et al. (2021) “Principles of Evidence-Based Management of Distal Radius Fractures,” in Distal Radius Fractures: Evidence-Based Management . Division of Orthopaedic Surgery, McMaster University, Center for Evidence Based Orthopaedics, Hamilton, ON, Canada: Elsevier, pp. 1–12. Available at: https://doi.org/10.1016/B978-0-323-75764-5.00008-1. Wuytack, F. et al. (2017) “The effectiveness of physiologically based early warning or track and trigger systems after triage in adult patients presenting to emergency departments: A systematic review,” BMC Emergency Medicine , 17(1). Available at: https://doi.org/10.1186/s12873-017-0148-z. Ye, C. et al. (2019) “A real-time early warning system for monitoring inpatient mortality risk: Prospective study using electronic medical record data,” Journal of Medical Internet Research , 21(7). Available at: https://doi.org/10.296/13719. Additional Declarations No competing interests reported. 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M. O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although the Early Warning System (EWS) has been widely adopted as a universal instrument of patient safety, its effectiveness is often hampered by the variability of compliance and scoring precision by frontline healthcare workers (Smith et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In secondary hospitals with high workloads, such as Handels Hospital, this challenge becomes even more critical due to limited human resources that demand the accuracy of manual assessments without gaps (Okoroafor et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, this study aims to dissect the multifactorial determinants that affect the accuracy of EWS implementation to close the gap between clinical protocols and real-life patient-saving outcomes in the field (Ye et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huete-Garcia and Rodriguez-Lopez, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Theoretically, a clinician's adherence to health protocols such as EWS is not only determined by technical mastery but also by a complex interaction among cognitive, behavioral, and environmental factors within the organization (Mussa, Al-Raimi and Becker, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Payne et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent studies (2022\u0026ndash;2025) have begun to shift from mere instrument validation to 'Human Factors' analysis, which places health workers' self-efficacy and management support as key pillars of clinical accuracy (Lagreca, Carpagnano and Benvenuto, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Piffari et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, there is a significant disconnect between patient safety theory and the realities of implementation in resource-constrained regional health facilities (Karikari et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A synthesis of a wide range of evidence suggests that without precise scoring accuracy, early warning systems will be an administrative artifact that fails to mitigate the risk of patient death effectively(Arnolds et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), (Finlay, Rothman and Smith, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe urgency of this research lies in the phenomenon of failure to rescue, which remains a critical indicator of the quality of health services, where delays in clinical response are directly proportional to increases in morbidity and the burden of global health costs (Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Olagnero et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the midst of efforts to transform the national health system by 2025, strengthening the early detection capacity of regional referral hospitals is crucial to ensure patient safety and equity, especially in areas with a heavy caseload but limited infrastructure (Owokuhaisa et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Carmichael et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By dissecting the determinants of compliance and scoring precision, this study is not only relevant to local policy development in Indonesia but also offers strategic insights for strengthening early warning systems in other developing countries with similar socio-demographic characteristics (Flenady et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dwyer et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the Early Warning System (EWS) has become a universal standard, its effectiveness in secondary hospitals is often hampered by 'accuracy gaps' that create a false sense of security in clinical risk management (Wuytack et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; S M O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study fills a literature gap by evaluating how multifactorial dynamics\u0026mdash;particularly the interaction between clinical workload and managerial support\u0026mdash;will affect scoring precision in Indonesia's regional order by 2025 (Stein et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Effendi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Previous studies (2023\u0026ndash;2025) began integrating Human Factors Theory to understand scoring accuracy, but there is a literature gap on how the interaction between extreme workloads and managerial support creates an 'accuracy gap' in manual scoring in rural contexts (Brauner et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study fills this gap by evaluating multifactorial determinants of clinical precision amid a resource crisis.\u003c/p\u003e \u003cp\u003eDespite the breadth of literature on EWS implementation, there is a critical gap in understanding the accuracy of manual scoring relative to administrative compliance, which often creates a 'false sense of security' in clinical risk management (Leenen and Mondria, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Most previous studies have focused on teaching hospital environments in urban centers, thus ignoring multifactorial dynamics\u0026mdash;such as the interaction between extreme workloads and organizational support\u0026mdash;in regional hospitals with limited resources (Karim Jabr et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Reich and Knopf, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, there has not been an in-depth investigation that integrates the psychosocial determinants of clinicians with the appropriateness of interventions in rural Indonesia by 2025 (Bussmann et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study aims to fill this gap by evaluating how systemic and individual factors interact to determine the scoring precision that determines a patient's life or death.\u003c/p\u003e \u003cp\u003e This study aims to evaluate the multifactorial determinants of scoring accuracy and clinical adherence to Early Warning System (EWS) protocols in regional hospital settings. We hypothesize that the precision of EWS implementation is not only influenced by individual competencies but is also significantly moderated by organizational support and health workers' self-efficacy in the face of clinical workloads (Fowokan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bussmann et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By identifying these key predictors, this study is expected to provide a strategic intervention model that improves the accuracy of detecting worsening patient conditions, thereby minimizing preventable clinical risks in secondary health facilities.\u003c/p\u003e \u003cp\u003e The novelty of this research lies in the 'Precision in Crisis' evaluation approach, which shifts the focus from mere administrative compliance to clinical accuracy in regional hospitals by 2025. This study makes a unique contribution by integrating the dynamics of Indonesia's local resources into the global patient safety literature, offering empirical evidence on how systemic factors affect the quality of early detection of patient deterioration. In practical terms, these findings will serve as a strategic database for the development of adaptive clinical surveillance protocols that not only strengthen the clinical authority of midwives and nurses but also provide a replicable framework for improving patient safety standards across secondary health facilities worldwide.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003e\u003cstrong\u003eResearch Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analytical observational study uses \u003cem\u003ea cross-sectional approach\u0026nbsp;\u003c/em\u003eto investigate the relationships between cognitive, behavioral, and organizational determinants and the accuracy of EWS implementation. The knowledge instrument consists of 30 multiple-choice statement items designed to measure cognitive precision relative to the EWS algorithm, with a binary scoring system. The research instrument was adapted from established Early Warning System (EWS) protocols and further refined through a Focus Group Discussion (FGD) involving clinical experts and nursing specialists. This modification was essential to align the instrument with the specific clinical environment of a regional hospital in Indonesia. The final questionnaire consists of 30 items covering definitions, physiological parameters, interpretation of scores, and the roles of healthcare providers (see Supplementary File 1 for the full English version). (Rista \u003cem\u003eet al.\u003c/em\u003e, 2025). To ensure data integrity, \u003cem\u003ethe total sampling technique\u003c/em\u003e was applied to the entire clinical population (N=203), including specialists, general practitioners, midwives, and nurses. Respondents\u0026apos; anonymity is fully guaranteed to minimize \u003cem\u003esocial desirability bias\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants and Sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was conducted at the Trade Hospital in Simalungun Regency, Indonesia. The target population comprises all health workers directly involved in patient care: specialist doctors (n=22), general practitioners (n=11), midwives (n=96), and nurses (n=74). Given the relatively small population and the need to minimize selection bias, a total sampling technique was used, resulting in the entire population of 203 respondents serving as the research sample. The inclusion criteria are active health workers with clinical authority to conduct EWS scoring in inpatient and emergency units.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure internal reliability, a knowledge questionnaire comprising 30 statements was administered to a group of nurses with similar characteristics, yielding a Cronbach\u0026apos;s Alpha coefficient of 0.001, indicating excellent internal consistency. In addition, the content validity is evaluated by a panel of critical nursing experts to ensure the instrument can capture cognitive precision in line with the latest EWS algorithms by 2025. The data collection instrument consists of two main parts that have been validated:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eDemographic Data: Includes respondent profiles such as age, gender, educational background, ethnicity, religion, and work experience.\u003c/li\u003e\n \u003cli\u003eEWS Knowledge Questionnaire: This instrument measures the cognitive dimension (knowing, understanding, and applying) using 30 multiple-choice statements. The scoring system uses a binary scale: the correct answer is scored 2, and the wrong answer is scored 1. The structure of this instrument is designed to capture the clinician\u0026apos;s cognitive precision relative to the EWS scoring algorithm, which is a key predictor of patient clinical deterioration.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is collected through a combination of primary and secondary sources. Primary data was obtained through the distribution of a structured questionnaire to respondents after obtaining informed consent (\u003cem\u003eInformed Consent\u003c/em\u003e). The researcher ensures respondents\u0026apos; anonymity to minimize \u003cem\u003esocial desirability bias\u003c/em\u003e. Secondary data were used to verify the standard operating protocol (SPO) for EWS in place at the hospital, to ensure the instrument\u0026apos;s relevance to field practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis is carried out systematically using SPSS software:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eUnivariate Analysis: Used to describe the frequency and proportion distribution of each independent variable (multifactorial factor) and dependent variable (EWS accuracy).\u003c/li\u003e\n \u003cli\u003eBivariate Analysis: The relationships between variables are tested using the Chi-Square test with \u003cem\u003ecross-tabulation\u003c/em\u003e. This test was chosen to analyze the significance of differences or relationships between categorical variables at the 95% confidence level (alpha = 0.05). The use of these non-parametric tests provides a solid statistical foundation for concluding the factors that significantly contribute to the accuracy of EWS scoring.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eAnalysis of the Relationship of Factors with the Implementation of the Early Warning System (EWS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the Chi-square test confirmed that all independent variables were significantly related to EWS accuracy (p \u0026lt; 0.05). Specifically, knowledge (p=0.001) and management support (p=0.001) emerged as the most determinant predictors. Critical findings show that 77.1% of clinicians fail to implement EWS effectively in an environment with minimal managerial support. In addition, there was a strong correlation between participation in formal training (p=0.002) and clinicians\u0026apos; ability to operate early detection systems accurately.\u003c/p\u003e\n\u003cp\u003eThe EWS training variable also showed a significant effect on clinicians\u0026apos; performance (p=0.002). Respondents who had attended the training were more likely to implement EWS well (n=40; 72.7%) than those who had never participated in the training, among whom the majority were in the poor implementation category (n=25; 71.4%). These findings indicate that formal education interventions are an essential factor in operationalizing early warning systems in hospitals.\u003c/p\u003e\n\u003cp\u003eFurthermore, the bivariate test of the attitude variable showed a significant correlation with the application of EWS (p=0.005). Respondents with positive attitudes tended to have reasonable EWS implementation rates (n=38; 76%), while respondents with negative attitudes were more identified in the poor implementation category (n=28; 70%). This suggests that clinicians\u0026apos; internal disposition and acceptance of patient safety protocols contribute to the consistency of clinical behavior.\u003c/p\u003e\n\u003cp\u003eFinally, management support was identified as the most significant factor related to the implementation of EWS, similar to the knowledge variable (p=0.001). Among respondents who felt supported by management, 42 (76.4%) were able to implement EWS well. In contrast, in the group without adequate management support, as many as 27 respondents (77.1%) failed to implement the system properly14. Overall, all independent variables tested showed p-values \u0026lt; 0.05, indicating a significant relationship between knowledge, training, attitudes, and management support and the implementation of EWS at the Handels Hospital in 2025.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Key Findings\u003c/h2\u003e \u003cp\u003eThis study reveals that the implementation of \u003cem\u003ethe Early Warning System\u003c/em\u003e (EWS) in the Commercial Hospital is significantly influenced by the convergence of multifactorial determinants, including cognitive, behavioral, and organizational dimensions. All independent variables tested, namely knowledge, training, attitude, and management support, showed a strong and statistically significant relationship with the quality of EWS implementation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In particular, management support and knowledge level emerged as the main pillars with the highest significance value (p\u0026thinsp;=\u0026thinsp;0.001), confirming that the effectiveness of early warning systems is highly dependent on the synergy between supportive managerial policies and clinicians' intellectual capacity. Critical phenomena were identified in groups with less knowledge and low management support, where the majority of respondents (66.7% and 77.1%) failed to operate the EWS protocol accurately. These findings provide empirical evidence that failures in the implementation of EWS at the regional hospital level are not isolated incidents but reflect the complex interplay between the limitations of formal education and the weak systemic support infrastructure at the front lines of service delivery.\u003c/p\u003e \u003cp\u003eThese findings show that EWS accuracy is not merely a manifestation of individual competence but a product of a supportive organizational ecosystem. Management support (p\u0026thinsp;=\u0026thinsp;0.001) was a key pillar that validated the officer's clinical authority in the field. Without synergy between intellectual capacity and systemic reinforcement, EWS protocols risk becoming 'administrative artifacts' that create a false sense of security in clinical risk management. It expands \u003cem\u003eon the Theory of Planned Behavior\u003c/em\u003e by showing that a conducive work environment is a key catalyst for precise clinical behavior (Lee and Vincent, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation and Comparison of Findings\u003c/h2\u003e \u003cp\u003eThese findings show that EWS accuracy is not merely a manifestation of individual competence but a product of a supportive organizational ecosystem. Management support (p\u0026thinsp;=\u0026thinsp;0.001) was a key pillar that validated the officer's clinical authority in the field. Without synergy between intellectual capacity and systemic reinforcement, EWS protocols risk becoming 'administrative artifacts' that create a false sense of security in clinical risk management. It expands \u003cem\u003eon the Theory of Planned Behavior\u003c/em\u003e by showing that a conducive work environment is a key catalyst for precise clinical behavior. In the context of comparative literature, these findings support a global study that states that formal training (p\u0026thinsp;=\u0026thinsp;0.002) is an absolute determinant in mitigating human error in the recognition of clinical exacerbations. However, this study makes a unique contribution to the existing literature by demonstrating that, in regional hospitals, management support (p\u0026thinsp;=\u0026thinsp;0.001) has a more decisive influence than individual factors alone, with the absence of systemic support resulting in implementation failure at 77.1%. This aligns with the WHO's patient safety framework, which emphasizes that clinical precision cannot stand alone without a supportive organizational ecosystem, while rejecting the long-held assumption that EWS failures are solely due to individual officer negligence (S. M. O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Theory and Practice\u003c/h2\u003e \u003cp\u003eThe results of this study make a significant theoretical contribution by strengthening the framework of Human Factors Theory in patient safety management, where clinical accuracy is shown not to be an independent variable but rather a product of a synergistic organizational ecosystem. Theoretically, the finding that management support shows a robust correlation (p\u0026thinsp;=\u0026thinsp;0.001) expands our understanding of the effectiveness of early warning instruments such as EWS, which are highly dependent on organizational support that validates officers' clinical authority in the field. This confirms that, in the context of regional hospitals, strengthening individual capacity through knowledge (n\u0026thinsp;=\u0026thinsp;35; 77.8% good implementation) must run in parallel with strengthening systemic support structures to prevent failure to recognize worsening patient conditions (\u003cem\u003efailure to rescue\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eIn practical terms, these findings demand a paradigm shift in hospital management, from merely meeting administrative compliance requirements to improving evidence-based clinical precision(Woolnough et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Radhika and Keepanasseril, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The real implication for Handels Hospital and similar institutions is the urgency of transforming the EWS training program from a passive education model to an integrative, continuous training program, given that respondents who have participated in the training show a much higher implementation rate (72.7%) than the unexposed group (28.6%). In addition, as management support proves crucial, hospital leaders must institute a system of routine audits and constructive feedback to ensure that EWS scoring is not just a documentation routine but an accurate, responsive, life-saving instrument for changing patient conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAffirmation of Contribution and Novelty (Novelty)\u003c/h2\u003e \u003cp\u003eThis research makes an original contribution to the global patient safety literature by introducing the 'Precision in Crisis' evaluation model in the regional hospital setting in developing countries. The main novelty of this study lies in the empirical evidence that, in low-resource settings, management support (p\u0026thinsp;=\u0026thinsp;0.001) and formal training (p\u0026thinsp;=\u0026thinsp;0.002) are more dominant structural determinants than individual demographic characteristics in ensuring the accuracy of EWS scoring. In contrast to previous studies that generally focused on administrative compliance in urban teaching hospitals, this study reveals dynamics in regional hospitals in 2025, where systemic failures in the implementation of EWS of 77.1% in groups without managerial support confirm the existence of a 'accuracy gap' that has been neglected so far. Thus, this paper offers a new perspective that the effectiveness of early warning systems is inseparable from the policy ecosystem that validates clinical action, while providing a replicable framework for improving early detection standards in secondary health facilities at the international level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eResearch Limitations\u003c/h2\u003e \u003cp\u003eDespite its significant contribution, the study has some limitations that must be considered in interpreting the results. First, the use of \u003cem\u003ea cross-sectional design limits the ability to draw definitive causal inferences about multifactorial factors when applying the Early Warning System\u003c/em\u003e (EWS) (Kim, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hajra, Ghosh and Bhowmik, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This means that although there is a statistically significant relationship between management support and scoring precision (p\u0026thinsp;=\u0026thinsp;0.001), the chronological direction of the relationship cannot be fully ascertained. Second, data collection via self-reported questionnaires may introduce social desirability bias, leading respondents to provide answers that reflect the ideals of clinical protocols rather than their actual daily practices. Finally, although the \u003cem\u003etotal sampling technique\u003c/em\u003e covers the entire population of the Commercial Hospital (N\u0026thinsp;=\u0026thinsp;203), the generalizability of the results of this study must be carefully assessed at the national level, given the variability in infrastructure and policies across different regional hospitals4. Nevertheless, these limitations do not diminish the internal validity of these findings as a crucial portrait of the urgency of clinical precision in secondary health facilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSuggestions for Future Research\u003c/h2\u003e \u003cp\u003eBased on the findings and limitations that have been described, future research needs to consider the use of longitudinal study designs to validate the consistency of the accuracy of the \u003cem\u003eimplementation of the Early Warning System\u003c/em\u003e (EWS) after being given periodic training interventions, to understand knowledge retention and long-term changes in clinical behavior. Given that training is a significant determinant (p\u0026thinsp;=\u0026thinsp;0.002), further investigation through \u003cem\u003ea mixed-methods\u003c/em\u003e approach is strongly recommended to explore in depth the psychological and sociocultural barriers that clinicians face when scoring under extreme workload pressures. In addition, future research should test the effectiveness of integrating digital technologies or artificial intelligence-based clinical decision support systems in regional hospitals to mitigate human error in manual score calculations (Fawzi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Premalatha et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Exploration on a broader scale, involving multiple hospitals nationwide, is also needed to strengthen the generalizability of EWS success-predictor models across heterogeneous health systems (Anitha et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSocial and Ethical Implications\u003c/h2\u003e \u003cp\u003e The findings of this study carry profound ethical implications for patient safety standards, where systemic failures in implementing the Early Warning System (EWS) are not merely technical problems but health justice issues. The extreme significance of the management support variable (p\u0026thinsp;=\u0026thinsp;0.001) underscores the ethical dimension that responsibility for patient safety should not be placed entirely on the shoulders of individual clinicians on the front lines. When managerial support is inadequate, the risk of system failure increases to 77.1%, which is ethically a form of organizational neglect of the risk of \u003cem\u003epreventable deaths\u003c/em\u003e. Socially, it emphasizes that every patient in a secondary health facility has a moral right to accurate early detection. Therefore, ensuring scoring accuracy through increased knowledge (n\u0026thinsp;=\u0026thinsp;35; 77.8% exemplary implementation) and strengthening managerial commitment is an urgent ethical mandate to ensure that regional hospitals can provide equal and dignified health protection for all levels of society.\u003c/p\u003e \u003cp\u003eAlthough it provides crucial insights, this study has limitations due to its cross-sectional design, which cannot definitively determine the temporal direction of causal relationships. The use \u003cem\u003eof self-reported questionnaires also opens the door to respondents' subjective biases whe\u003c/em\u003en reporting their compliance with clinical protocols (Van Den Bergh and Walentynowicz, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Finally, generalizing results on a national scale must be done carefully, given the variability in infrastructure across regional hospitals in Indonesia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Nursing Management\u003c/h2\u003e \u003cp\u003eThese findings recommend that hospital leaders not only focus on one-way technical training, but also institute a system of routine clinical audits and constructive feedback. The 'EWS Champions' strategy or unit leaders who actively provide managerial support in the field have proven to be a crucial determinant in mitigating the risk of \u003cem\u003efailure to rescue\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study concludes that the effectiveness of implementing the \u003cem\u003eEarly Warning System\u003c/em\u003e (EWS) as a life-saving instrument in regional hospitals is determined mainly by the convergence of cognitive, behavioral, and organizational support determinants. Clinical knowledge and management support were identified as the most crucial predictors of the precision of clinical scoring. These findings show that without adequate managerial support and ongoing education, early warning systems fail to function optimally, potentially posing clinical risks to patients. Based on these findings, regional hospitals must make a paradigm shift from simply fulfilling administrative compliance to strengthening evidence-based clinical precision. Transforming the training program from a passive method to continuous, integrative education is highly recommended. Ensuring scoring accuracy through managerial support is not only an operational necessity but also an ethical mandate to ensure safety and fairness for every patient in a secondary health facility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. Administrative approval and ethical clearance for the study were obtained from the Management and Director of \u003cstrong\u003eRSUD Perdagangan\u003c/strong\u003e, Simalungun Regency, Indonesia. The study was approved by the Internal Review Board of STIKes Mitra Husada Medan and received administrative approval from RSUD Perdagangan. Informed consent was obtained from all individual participants prior to data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable. The study does not contain any individual person\u0026rsquo;s data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH.M.\u003c/strong\u003e conceptualized and designed the study, coordinated the data collection, and drafted the initial manuscript. \u003cstrong\u003eS.N.S.\u003c/strong\u003e provided methodological guidance and conducted the statistical analysis using SPSS. \u003cstrong\u003eF.S.\u003c/strong\u003e and \u003cstrong\u003eI.S.S.\u003c/strong\u003e participated in the data collection process and performed the literature review. \u003cstrong\u003eN.Z.\u003c/strong\u003e assisted in data validation and the preparation of research instruments. \u003cstrong\u003eR.H.G\u003c/strong\u003e contributed to the critical revision of the manuscript for important intellectual content and supervised the research project. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to the Management and Director of \u003cstrong\u003eRSUD Perdagangan\u003c/strong\u003e for their invaluable support, permission, and cooperation throughout the data collection process. Special appreciation is extended to all participating clinicians for their time and dedication. This research has been approved by the Chair of the Research Ethics Committee of STIKes Mitra Husada Medan, Dr. Herna Rinayanti Manurung, S.Tr.Keb., Bd., M.Kes. Informed consent was obtained from all participants involved in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, T. \u003cem\u003eet al.\u003c/em\u003e (2017) \u0026ldquo;Use of failure-to-resue to identify international variation in postoperative care in low-, middle- and high-income countries: A 7-day cohort study of elective surgery,\u0026rdquo; \u003cem\u003eBritish Journal of Anaesthesia\u003c/em\u003e, 119(2), pp. 258\u0026ndash;266. Available at: https://doi.org/10.1093/bja/aex185. \u003c/li\u003e\n\u003cli\u003eAnitha, D. \u003cem\u003eet al.\u003c/em\u003e (2024) \u0026ldquo;Advancements in early warning systems and human factors in lightweight secured iomt ecosystems for healthcare 4.0,\u0026rdquo; in \u003cem\u003eSocial Innovations in Education, Environment, and Healthcare\u003c/em\u003e. Department of Information Technology, Muthayammal Engineering College, Namakkal, India: IGI Global, pp. 318\u0026ndash;338. Available at: https://doi.org/10.4018/979-8-3693-2569-8.ch016.\u003c/li\u003e\n\u003cli\u003eArnolds, D.E. \u003cem\u003eet al.\u003c/em\u003e (2022) \u0026ldquo;Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients,\u0026rdquo; \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, 22(1). Available at: https://doi.org/10.1186/s12884-022-04631-0.\u003c/li\u003e\n\u003cli\u003eVan Den Bergh, O. and Walentynowicz, M. (2016) \u0026ldquo;Accuracy and bias in retrospective symptom reporting,\u0026rdquo; \u003cem\u003eCurrent Opinion in Psychiatry\u003c/em\u003e, 29(5), pp. 302\u0026ndash;308. Available at: https://doi.org/10.1097/YCO.0000000000000267.\u003c/li\u003e\n\u003cli\u003eBrauner, P. \u003cem\u003eet al.\u003c/em\u003e (2018) \u0026ldquo;The interaction of causal attribution of performance and compliance with decision support systems in cyber-physical production systems - an empirical study using a business simulation game,\u0026rdquo; in C. W. and F. L.E. (eds.) \u003cem\u003eAdvances in Intelligent Systems and Computing\u003c/em\u003e. Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany: Springer Verlag, pp. 11\u0026ndash;23. Available at: https://doi.org/10.1007/978-3-319-60486-2_2.\u003c/li\u003e\n\u003cli\u003eBussmann, S. \u003cem\u003eet al.\u003c/em\u003e (2023) \u0026ldquo;Screening and assessment for cancer patients\u0026rsquo; psychosocial support needs: Development and validation of the psychosocial risk questionnaire,\u0026rdquo; \u003cem\u003eEuropean Journal of Oncology Nursing\u003c/em\u003e, 63. Available at: https://doi.org/10.1016/j.ejon.2022.102240.\u003c/li\u003e\n\u003cli\u003eCarmichael, H.L. \u003cem\u003eet al.\u003c/em\u003e (2025) \u0026ldquo;Optimizing patient care and hospital operations through interhospital admission transfers: The load balancing protocol,\u0026rdquo; \u003cem\u003eJournal of Hospital Medicine\u003c/em\u003e [Preprint]. Available at: https://doi.org/10.1002/jhm.70174.\u003c/li\u003e\n\u003cli\u003eDwyer, T. \u003cem\u003eet al.\u003c/em\u003e (2024) \u0026ldquo;A theoretical framework for identifying sociocultural factors that influence nurses\u0026rsquo; compliance with early warning systems for acute clinical deterioration: A cross-sectional survey,\u0026rdquo; \u003cem\u003eInternational Journal of Nursing Studies\u003c/em\u003e, 158. Available at: https://doi.org/10.1016/j.ijnurstu.2024.104846.\u003c/li\u003e\n\u003cli\u003eEffendi, M.S. \u003cem\u003eet al.\u003c/em\u003e (2025) \u0026ldquo;Beyond Workload: Uncovering the Link Between Supervisor Support, Work\u0026ndash;Life Balance, and Lecturer Productivity,\u0026rdquo; \u003cem\u003eEducation Sciences\u003c/em\u003e, 15(9). Available at: https://doi.org/10.3390/educsci15091122.\u003c/li\u003e\n\u003cli\u003eFawzi, S. (2023) \u0026ldquo;A Review of the Role of ChatGPT for Clinical Decision Support Systems,\u0026rdquo; in \u003cem\u003e5th Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023 - Proceedings\u003c/em\u003e. Nile University, Medical Informatics Research Group, Center for Informatics Science Information Technology and Computer Science School, Cairo, Egypt: Institute of Electrical and Electronics Engineers Inc., pp. 439\u0026ndash;442. Available at: https://doi.org/10.1109/NILES59815.2023.10296668.\u003c/li\u003e\n\u003cli\u003eFinlay, G.D., Rothman, M.J. and Smith, R.A. (2014) \u0026ldquo;Measuring the modified early warning score and the Rothman Index: Advantages of utilizing the electronic medical record in an early warning system,\u0026rdquo; \u003cem\u003eJournal of Hospital Medicine\u003c/em\u003e, 9(2), pp. 116\u0026ndash;119. Available at: https://doi.org/10.1002/jhm.2132.\u003c/li\u003e\n\u003cli\u003eFlenady, T. \u003cem\u003eet al.\u003c/em\u003e (2020) \u0026ldquo;Developing a sociocultural framework of compliance: An exploration of factors related to the use of early warning systems among acute care clinicians,\u0026rdquo; \u003cem\u003eBMC Health Services Research\u003c/em\u003e, 20(1). Available at: https://doi.org/10.1186/s12913-020-05615-6.\u003c/li\u003e\n\u003cli\u003eFowokan, A. \u003cem\u003eet al.\u003c/em\u003e (2022) \u0026ldquo;Evaluation of a Health Care Worker Training Intervention to Improve the Early Diagnosis and Referral of Childhood Cancers in Ghana: A Qualitative Descriptive Study,\u0026rdquo; \u003cem\u003eJCO global oncology\u003c/em\u003e, 8, p. e2200151. Available at: https://doi.org/10.1200/GO.22.00151.\u003c/li\u003e\n\u003cli\u003eHajra, A., Ghosh, B. and Bhowmik, S. (2025) \u0026ldquo;Cross-sectional studies,\u0026rdquo; in \u003cem\u003eTranslational Cardiology\u003c/em\u003e. Department of Internal Medicine, Bringham and Women\u0026rsquo;s Hospital/Harvard Medical School, Boston, MA, United States: Elsevier, pp. 221\u0026ndash;225. Available at: https://doi.org/10.1016/B978-0-323-91790-2.00099-X.\u003c/li\u003e\n\u003cli\u003eHuete-Garcia, A. and Rodriguez-Lopez, S. (2024) \u0026ldquo;Development and implementation of an artificial intelligence\u0026ndash;enhanced care model to improve patient safety in hospital wards in Spain,\u0026rdquo; \u003cem\u003eAcute and Critical Care\u003c/em\u003e, 39(4), pp. 488\u0026ndash;498. Available at: https://doi.org/10.4266/acc.2024.00759.\u003c/li\u003e\n\u003cli\u003eKarikari, A.K. \u003cem\u003eet al.\u003c/em\u003e (2023) \u0026ldquo;Understanding the challenges and coping mechanisms adopted by nursing staff in managing critically-ill patients at district hospitals in the Ashanti Region, Ghana,\u0026rdquo; \u003cem\u003eInternational Journal of Africa Nursing Sciences\u003c/em\u003e, 18(November 2022), p. 100515. Available at: https://doi.org/10.1016/j.ijans.2022.100515.\u003c/li\u003e\n\u003cli\u003eKarim Jabr, M. \u003cem\u003eet al.\u003c/em\u003e (2025) \u0026ldquo;The Impact of Workload and Emotional Stress on Nursing Care Quality in Baghdad Hospitals: A Cross-Sectional Study,\u0026rdquo; \u003cem\u003eInternational Journal of Body, Mind and Culture\u003c/em\u003e, 12(7), pp. 55\u0026ndash;59. Available at: https://doi.org/10.61838/ijbmc.v12i7.1035.\u003c/li\u003e\n\u003cli\u003eKim, J.H. (2023) \u0026ldquo;Cross-sectional study: The role of observation in epidemiological studies,\u0026rdquo; in \u003cem\u003eStatistical Approaches for Epidemiology: From Concept to Application\u003c/em\u003e. Department of Epidemiology, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong: Springer International Publishing, pp. 19\u0026ndash;42. Available at: https://doi.org/10.1007/978-3-031-41784-9_2.\u003c/li\u003e\n\u003cli\u003eLagreca, D., Carpagnano, L.F. and Benvenuto, M. (2023) \u0026ldquo;What skills and competences profiles for the Patient Safety Manager?,\u0026rdquo; \u003cem\u003eMecosan\u003c/em\u003e, (126), pp. 151\u0026ndash;183. Available at: https://doi.org/10.3280/MESA2023-126OA17290.\u003c/li\u003e\n\u003cli\u003eLee, S. and Vincent, C. (2021) \u0026ldquo;Analysis and Evaluation of the Theory of Planned Behavior,\u0026rdquo; \u003cem\u003eAdvances in Nursing Science\u003c/em\u003e, 44(4), pp. E127\u0026ndash;E140. Available at: https://doi.org/10.1097/ANS.0000000000000369.\u003c/li\u003e\n\u003cli\u003eLeenen, J.P. and Mondria, C.L. (2024) \u0026ldquo;Variation in nurses\u0026rsquo; compliance with an Early Warning Score protocol: A retrospective cohort study,\u0026rdquo; \u003cem\u003eHeliyon\u003c/em\u003e, 10(16). Available at: https://doi.org/10.1016/j.heliyon.2024.e36147.\u003c/li\u003e\n\u003cli\u003eMussa, C.C., Al-Raimi, A. and Becker, E.A. (2019) \u0026ldquo;Predicting respiratory therapists\u0026rsquo; intentions to use the modified early warning score by using an enhanced technology acceptance model,\u0026rdquo; \u003cem\u003eRespiratory Care\u003c/em\u003e, 64(4), pp. 416\u0026ndash;424. Available at: https://doi.org/10.4187/respcare.06428.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Neill, S. M. \u003cem\u003eet al.\u003c/em\u003e (2021) \u0026ldquo;Why do healthcare professionals fail to escalate as per the early warning system (EWS) protocol? A qualitative evidence synthesis of the barriers and facilitators of escalation,\u0026rdquo; \u003cem\u003eBMC Emergency Medicine\u003c/em\u003e, 21(1), pp. 1\u0026ndash;19. Available at: https://doi.org/10.1186/s12873-021-00403-9.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Neill, S M \u003cem\u003eet al.\u003c/em\u003e (2021) \u0026ldquo;Why do healthcare professionals fail to escalate as per the early warning system (EWS) protocol? A qualitative evidence synthesis of the barriers and facilitators of escalation,\u0026rdquo; \u003cem\u003eBMC Emergency Medicine\u003c/em\u003e, 21(1). Available at: https://doi.org/10.1186/s12873-021-00403-9.\u003c/li\u003e\n\u003cli\u003eOkoroafor, S.C. \u003cem\u003eet al.\u003c/em\u003e (2022) \u0026ldquo;Assessing the staffing needs for primary health care centers in Cross River State, Nigeria: a workload indicators of staffing needs study,\u0026rdquo; \u003cem\u003eHuman Resources for Health\u003c/em\u003e, 19(1), pp. 1\u0026ndash;11. Available at: https://doi.org/10.1186/s12960-021-00648-2.\u003c/li\u003e\n\u003cli\u003eOlagnero, J.M. \u003cem\u003eet al.\u003c/em\u003e (2025) \u0026ldquo;Failure to rescue: a useful quality-of-care indicator to integrate into the Italian healthcare context?,\u0026rdquo; \u003cem\u003eAssistenza infermieristica e ricerca : AIR\u003c/em\u003e, 44(3), pp. 117\u0026ndash;121. Available at: https://doi.org/10.1702/4564.45637.\u003c/li\u003e\n\u003cli\u003eOwokuhaisa, J. \u003cem\u003eet al.\u003c/em\u003e (2024) \u0026ldquo;Barriers and Facilitators to Screening for Kidney Disease Among Older Adults with Hypertension and Diabetes in Mbarara Southwestern Uganda: Healthcare Providers\u0026rsquo; Perspective,\u0026rdquo; \u003cem\u003eJournal of Multidisciplinary Healthcare\u003c/em\u003e, 17, pp. 855\u0026ndash;865. Available at: https://doi.org/10.2147/JMDH.S451533.\u003c/li\u003e\n\u003cli\u003ePayne, V.L. \u003cem\u003eet al.\u003c/em\u003e (2024) \u0026ldquo;Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record,\u0026rdquo; \u003cem\u003eJournal of the American Medical Informatics Association\u003c/em\u003e, 31(6), pp. 1331\u0026ndash;1340. Available at: https://doi.org/10.1093/jamia/ocae089.\u003c/li\u003e\n\u003cli\u003ePiffari, C. \u003cem\u003eet al.\u003c/em\u003e (2024) \u0026ldquo;Human Factors in Healthcare operations: A Case Study in Italian Emergency Rooms,\u0026rdquo; \u003cem\u003eIFAC-PapersOnLine\u003c/em\u003e, 58(19), pp. 706\u0026ndash;711. Available at: https://doi.org/10.1016/j.ifacol.2024.09.280.\u003c/li\u003e\n\u003cli\u003ePremalatha, R. \u003cem\u003eet al.\u003c/em\u003e (2025) \u0026ldquo;AI-Powered Clinical Decision Support Systems: Real-Time Assistance for Enhanced Patient Care,\u0026rdquo; in \u003cem\u003eSynthesis Lectures on Computer Science\u003c/em\u003e. Department of Economics, VELS Institute of Science, Technology and Advanced Studies, Chennai, India: Springer Nature, pp. 47\u0026ndash;55. Available at: https://doi.org/10.1007/978-3-031-93673-9_5.\u003c/li\u003e\n\u003cli\u003eRadhika, A.G. and Keepanasseril, A. (2024) \u0026ldquo;Developing a culture of evidence-based practice in gynecology and obstetrics,\u0026rdquo; \u003cem\u003eClinical Epidemiology and Global Health\u003c/em\u003e, 26. Available at: https://doi.org/10.1016/j.cegh.2024.101509.\u003c/li\u003e\n\u003cli\u003eReich, L. and Knopf, K.B. (2025) \u0026ldquo;The Oncology Drug Shortages and Its Impact on Community Hospitals,\u0026rdquo; \u003cem\u003eCancer journal (Sudbury, Mass.)\u003c/em\u003e, 31(5). Available at: https://doi.org/10.1097/PPO.0000000000000794.\u003c/li\u003e\n\u003cli\u003eRista, P. \u003cem\u003eet al.\u003c/em\u003e (2025) \u0026quot;Level of nurses\u0026apos; knowledge of the Early Warning Score (EWS) in the internal medicine ward of Santa Elisabeth Hospital, Medan in 2025.\u0026quot; STIKes Santa Elisabeth Medan.\u003c/li\u003e\n\u003cli\u003eSmith, M.E.B. \u003cem\u003eet al.\u003c/em\u003e (2014) \u0026ldquo;Early warning system scores for clinical deterioration in hospitalized patients: A systematic review,\u0026rdquo; \u003cem\u003eAnnals of the American Thoracic Society\u003c/em\u003e, 11(9), pp. 1454\u0026ndash;1465. Available at: https://doi.org/10.1513/AnnalsATS.201403-102OC.\u003c/li\u003e\n\u003cli\u003eSong, Y. \u003cem\u003eet al.\u003c/em\u003e (2024) \u0026ldquo;Predicting nursing workload in digestive wards based on machine learning: A prospective study,\u0026rdquo; \u003cem\u003eBMC Nursing\u003c/em\u003e, 23(1). Available at: https://doi.org/10.1186/s12912-024-02570-z.\u003c/li\u003e\n\u003cli\u003eStein, D.T. \u003cem\u003eet al.\u003c/em\u003e (2023) \u0026ldquo;Attributes of funding flows and quality of maternal health services in a mixed provider payment system: A cross-sectional survey of 108 healthcare providers in Indonesia,\u0026rdquo; \u003cem\u003eWorld Medical and Health Policy\u003c/em\u003e, 15(2), pp. 179\u0026ndash;193. Available at: https://doi.org/10.1002/wmh3.545.\u003c/li\u003e\n\u003cli\u003eWoolnough, T. \u003cem\u003eet al.\u003c/em\u003e (2021) \u0026ldquo;Principles of Evidence-Based Management of Distal Radius Fractures,\u0026rdquo; in \u003cem\u003eDistal Radius Fractures: Evidence-Based Management\u003c/em\u003e. Division of Orthopaedic Surgery, McMaster University, Center for Evidence Based Orthopaedics, Hamilton, ON, Canada: Elsevier, pp. 1\u0026ndash;12. Available at: https://doi.org/10.1016/B978-0-323-75764-5.00008-1.\u003c/li\u003e\n\u003cli\u003eWuytack, F. \u003cem\u003eet al.\u003c/em\u003e (2017) \u0026ldquo;The effectiveness of physiologically based early warning or track and trigger systems after triage in adult patients presenting to emergency departments: A systematic review,\u0026rdquo; \u003cem\u003eBMC Emergency Medicine\u003c/em\u003e, 17(1). Available at: https://doi.org/10.1186/s12873-017-0148-z.\u003c/li\u003e\n\u003cli\u003eYe, C. \u003cem\u003eet al.\u003c/em\u003e (2019) \u0026ldquo;A real-time early warning system for monitoring inpatient mortality risk: Prospective study using electronic medical record data,\u0026rdquo; \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e, 21(7). Available at: https://doi.org/10.296/13719.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clinical Deterioration, Early Warning System, Patient Safety, Organizational Support, Quality of Care","lastPublishedDoi":"10.21203/rs.3.rs-8952241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8952241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Failure to recognize clinical deterioration remains a global systemic challenge leading to preventable hospital mortality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims:\u003c/strong\u003e This study evaluated the multifactorial determinants predicting the accuracy of Early Warning System (EWS) implementation among clinicians in a regional hospital setting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eAn analytical observational study with a cross-sectional design was conducted in 2025 at RSUD Perdagangan, Indonesia, involving 203 health professionals (specialists, general practitioners, midwives, and nurses) using total sampling. Data were collected via validated questionnaires and analyzed using Chi-square tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Statistical analysis revealed that knowledge (p=0.001), training (p=0.002), attitude (p=0.005), and organizational support (p=0.001) significantly correlate with EWS implementation accuracy. Notably, 77.1% of clinicians failed to implement EWS effectively in environments lacking managerial support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion:\u003c/strong\u003e These findings highlight that EWS precision is a manifestation of cognitive competence integrated with systemic organizational reinforcement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Bridging the \"accuracy gap\" requires a paradigm shift from administrative compliance to a robust patient safety ecosystem that integrates structured training and active managerial oversight.\u003c/p\u003e","manuscriptTitle":"The Intersection Of Cognitive Competence And Organizational Support In Early Warning System (Ews) Accuracy: A Multifactorial Analysis In A Regional Hospital Setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-17 14:48:24","doi":"10.21203/rs.3.rs-8952241/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-06T15:37:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T07:18:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T07:50:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41898569298823140986772143537009959474","date":"2026-04-17T03:05:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263880986367502748707381215670656533952","date":"2026-04-17T00:42:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270873974060365638431947052177734372677","date":"2026-04-09T23:31:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T09:56:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T08:45:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-15T18:25:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T08:26:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-03-11T05:59:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2aeaf59-e4b0-49cb-b40f-8aa6e80055e3","owner":[],"postedDate":"April 17th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-06T15:37:01+00:00","index":77,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T14:48:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-17 14:48:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8952241","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8952241","identity":"rs-8952241","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}