Optimizing Patient Care in the Emergency Department: Insights from Automated Alert Systems and Triage Strategies


  • Baha Hamdi Alkahlout
  • Shabbir Ahmad

Received Date: 08.08.2023 Accepted Date: 09.08.2023 Glob Emerg Crit Care 2023;2(2):71-76

Over the past three decades, various scoring systems have emerged in clinical practice, but evidence primarily originates from ward contexts, yielding conflicting data on their impact, including mortality and referrals. Although early intervention benefits emergency department (ED) patients, the effectiveness of early warning systems remains uncertain due to insufficient evidence. This systematic review aims to comprehensively analyze current literature, exploring the integration of automated alerts and novel triage methods in the ED. Thorough database searches (e.g., MEDLINE/PubMed, Cochrane Library) identified relevant articles meeting specific criteria: adults (≥18 years), randomized controlled trials, observational, and comparative studies. Inclusion focused on English-language, human-participant studies; exclusions involved pediatric, case, non-English studies, and abstract-only content. Initially, 260 studies underwent screening based on titles and abstracts, with 218 papers excluded. Subsequently, 42 papers underwent full-text assessment, eliminating articles not meeting the criteria. Ultimately, only four studies were deemed suitable for final data collection and inclusion in the review. The integration of technology-assisted decision support, combined triage approaches, and standardized assessment tools shows promise in enhancing patient outcomes and optimizing resource utilization. Nonetheless, the studies’ limitations highlight the importance of robust research methods and a considerate implementation process. Advancing emergency care necessitates a harmonious balance between innovation and evidence-based practice to ensure the highest quality of patient well-being.

Keywords: Early warning score, mortality, triage methods, vital signs


Physiological scoring systems such as the early warning score (EWS) play a vital role in quantifying the degree of deviation of physiological parameters from their normal values by consolidating them into a single numerical measure. These scores are extensively used in emergency departments (EDs) and general wards to promptly detect critically ill and deteriorating patients, facilitating early intervention and escalation of care [1].

Over the past three decades, several different scoring systems have been developed and implemented in clinical practice [2]. However, most of the supporting evidence comes from the ward setting, leading to conflicting data on the effectiveness of these scores in improving patient care, such as reduced mortality rates or increased referrals/admissions to intensive care facilities. Despite the benefits of early care for many conditions in the ED [3-5], the benefit of EWS in this setting remains uncertain due to a lack of sufficient evidence. One of the reasons for the limited evidence could be that most EWS were originally developed based on ward data sets, which might not fully capture the unique challenges of the ED environment. ED patients often present with acute and rapidly resolved physiological disturbances (e.g., supraventricular tachycardia), making the application of ward-based scoring systems challenging [6,7]. For example, diseases that are normally mild can suddenly become very aggressive and potentially fatal for patients [8]. Recognizing these patients early in the ED is crucial. Therefore, EWS systems are of great importance in EDs.

In response to these concerns, the UK-based Royal College of Physicians developed the national early warning score (NEWS) and its updated version, NEWS2, to enhance the detection of patients at risk for deterioration, thereby triggering timely escalation of care [9,10]. Both NEWS and NEWS2 have demonstrated superior performance over other EWS in predicting mortality and clinical deterioration, leading to widespread adoption in hospitals and EDs in the UK and beyond [11,12].

However, EWS have faced criticism for their failure to consider chronic hypoxia in their scoring systems, particularly in patients with chronic pulmonary diseases. Current scoring systems often trigger similar scores for decreased oxygen saturations (SpO2) regardless of patient age or disease chronicity, potentially resulting in higher scores for patients with chronic lung conditions. National guidelines for patients with chronic lung disease recommend lower SpO2 target levels (88-92%) during oxygen therapy, which fall below the trigger thresholds of most EWS, including NEWS [11]. This could lead to an overestimation of physiological disturbance in patients with chronic lung disease.

To address these concerns, the NEWS2 was introduced in 2017, incorporating a new SpO2 scoring scale specifically tailored to patients with or at risk of chronic lung disease and type II respiratory failure [9]. Some studies have indicated that a NEWS2 score ≥5 effectively identifies patients at a higher risk of death, potentially benefiting from intensive care unit (ICU) admission (sensitivity for in-hospital mortality: 84.5%, and for ICU admission: 83.4%) [13].

However, despite being an updated version of NEWS, some studies have reported lower success rates for NEWS2 in discriminating adverse outcomes, such as inpatient mortality, unanticipated ICU admission, or cardiac arrest within the first 24 hours of admission, when compared to NEWS [14].

Given these uncertainties, this systematic review aims to comprehensively assess and synthesize the current body of literature surrounding the integration of automated alert systems and innovative triage strategies in EDs.


This systematic review adheres to the recommendations provided in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines [15]. The primary objective of this review is to comprehensively analyze and consolidate existing literature on the incorporation of automated alert systems and inventive triage strategies within the ED. To achieve this goal, the study question was framed using the population, exposure (intervention), control group, and outcome framework, ensuring a predetermined and well-defined methodology for the investigation [16].

Search Strategy

A comprehensive literature search was conducted across multiple electronic databases, including MEDLINE/PubMed, PubMed Central, Google Scholar, and the Cochrane Library, to identify relevant articles for this study. The search terms encompassed EWSs “in conjunction with terms related to the” ED acute medical unit in adult populations. Additionally, extensive searches were performed in reference lists and relevant journals to ensure the inclusion of all pertinent papers. Two independent reviewers executed the search and selected articles for further assessment. Furthermore, the references of the initially retrieved papers were manually examined to uncover any additional research that might have been overlooked during the initial search.

Inclusion and Exclusion Criteria

We included studies that met the following inclusion criteria: adult patients aged 18 years and above, randomized controlled trials, observational studies, and comparative studies. Only studies involving human participants and published in the English language were considered for inclusion.

However, the review excluded studies that fell under the following criteria: pediatric populations, individual case studies, studies presented in the form of letters, responses to letters or comments, articles published in languages other than English, and studies with only abstracts available and lacking full-text content.

Risk-of-Bias Assessment

In this systematic review, we utilized the “risk of bias in non-randomised studies-of interventions (ROBINS-I)” tool to assess the ROBINS included in our analysis [17]. Two independent reviewers conducted the evaluation of each study, and any discrepancies were resolved through discussion or consultation with a third reviewer when necessary.

For each included study, the reviewers assessed bias across the various domains outlined in the ROBINS-I tool. They carefully examined the study design, participant selection methods, intervention classification, deviations from intended interventions, outcome measurement procedures, and potential selective reporting of results. Each domain was evaluated to determine the level of bias in the study.

Following the assessments, an overall judgment of the risk of bias for each study was assigned based on the findings from individual domains.


Following the initial search across PubMed, Google Scholar, and the Cochrane Library, a substantial pool of 2.937 studies was identified. However, an automated screening tool flagged 2.615 studies as ineligible based on predefined criteria.

A total of 260 studies underwent the initial title and the abstract screening process, and of these, 218 papers were excluded from further consideration. The remaining 42 papers underwent full-text evaluation, where articles that did not align with the topic or meet the exclusion criteria were removed. Only 4 studies emerged as suitable candidates for the final data collection and inclusion in the review (Figure 1, Table 1) [18-21].


The synthesis of the reviewed studies offers a comprehensive exploration into the intricate landscape of optimizing patient care within the ED by integrating automated alert systems and innovative triage strategies. These studies collectively unravel multifaceted insights that underscore the potential benefits, operational challenges, and ethical considerations associated with these transformative approaches. By delving into the intricacies of patient assessment, monitoring, and timely interventions, these studies contribute to the evolving paradigm of emergency care.

In the midst of the hectic and unpredictable clinical atmosphere of the ED, an automated EWS becomes a vital ally and safety net, providing crucial assistance to patients in these dynamic and bustling healthcare hubs [22-25].

Writing a clinical trial varies with respect to the audience it is intended for [26]. This consideration is essential in ensuring effective communication and tailored dissemination of study results to different stakeholders.

In Alam et al. [18] study, correlations between NEWS and patient outcomes were significant across various time points, encompassing 30-day mortality, hospital admission, and length of stay. Although bearing a moderate level of bias and operational challenges, the study’s findings indicate that NEWS holds potential value in the ED-distinct from its role as a triage system-by offering continuous monitoring throughout patients’ ED and hospital stay. While the study design is commendable, limitations in sample size and operational aspects warrant consideration.

The concept of leveraging technology-driven solutions, as observed in studies examining automated alert systems, introduces a promising avenue for proactive patient management. The integration of an automated decision support system coupled with the NEWS, as demonstrated in Howard et al. [21] showcases tangible improvements in patient outcomes, such as reduced adjusted hospital mortality and length of stay. This finding aligns with emerging evidence that underscores the role of EWSs in identifying patients at risk of decompensation. However, the presence of moderate bias in the study warrants a measured interpretation of the results and prompts a critical assessment of potential confounders that may influence the observed outcomes.

Triage is a crucial step in emergency care, evaluating the urgency of a patient’s clinical state. Various triage scales facilitate this evaluation [27-31]. Although generally displaying moderate to good validity [32], these scales may encounter challenges in terms of interrater reliability [27]. Additionally, while effective for prioritization, they might not serve as continuous monitoring tools in the ED [33]. This drawback could potentially result in undetected patient deterioration, especially during extended waiting periods.

The interplay between different triage strategies, notably the combination of the manchester triage system and the EWS, in McCabe et al. [19] study elucidates the potential to address the challenges posed by overcrowded EDs. While the study suggests positive effects on patient categorization and waiting times, the underlying operational complexities and the presence of serious bias necessitate caution in extrapolating the findings. This study underscores the intricate balance between appropriate prioritization, efficient resource utilization, and the need for a experienced clinical staff.

Direct comparisons between traditional triage scales and EWSs, as conducted in the Schinkel et al. [20] study, highlight the potential superiority of EWS in recognizing patients in need of urgent care. The robust performance of the modified EWS in predicting the need for hospital admission and 30-day all-cause mortality challenges the conventional reliance on patient complaints as the basis for triage decisions. This study contributes to the growing body of literature advocating for standardized assessment tools that can enhance the accuracy and efficiency of patient prioritization. However, the presence of serious bias underscores the importance of future investigations with rigorous designs and methodological robustness.

Among these insightful findings, it is imperative to acknowledge the limitations that underpin the studies. Operational challenges, inherent biases, and relatively low patient numbers underscore the complexities of conducting research in a dynamic and fast-paced ED environment. While these studies provide valuable glimpses into the potential benefits of integrated approaches, they also emphasize the need for comprehensive evaluation and contextual understanding.

In conclusion, the amalgamation of these studies paints a nuanced portrait of optimizing patient care within the ED by merging automated alert systems and innovative triage strategies. The integration of technology-assisted decision support, combined triage methodologies, and standardized assessment tools holds promise for enhancing patient outcomes and streamlining resource allocation. However, the studies’ limitations underscore the necessity of robust research methodologies and a thoughtful approach to implementation. The evolution of emergency care necessitates a concerted effort to balance innovation with evidence-based practicewhile striving to ensure the highest quality of care for patients in their most vulnerable moments.


Peer-review: Externally peer-reviewed.

Authorship Contributions

Concept: B.H.A., S.A., Design: B.H.A., S.A., Data Collection or Processing: B.H.A., S.A., Analysis or Interpretation: B.H.A., S.A., Literature Search: B.H.A., S.A., Writing: B.H.A., S.A.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study received no financial support.


  1. Prytherch DR, Smith GB, Schmidt PE, Featherstone PI. ViEWS--towards a national early warning score for detecting adult inpatient deterioration. Resuscitation. 2010;81:932-7.
  2. Lee A, Bishop G, Hillman KM, Daffurn K. The Medical Emergency Team. Anaesth Intensive Care. 1995;23:183-6.
  3. Seymour CW, Gesten F, Prescott HC, Friedrich ME, Iwashyna TJ, Phillips GS, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med. 2017;376:2235-44.
  4. Ferrer R, Martin-Loeches I, Phillips G, Osborn TM, Townsend S, Dellinger RP, et al. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. Critical Care Medicine. 2014;42:1749-55.
  5. Soar J, Nolan JP, Böttiger BW, Perkins GD, Lott C, Carli P, et al. European Resuscitation Council Guidelines for Resuscitation 2015: section 3. Adult advanced life support. Resuscitation. 2015;95:100-47.
  6. Rees JE, Mann C. Use of the patient at risk scores in the emergency department: a preliminary study. Emerg Med J. 2004;21:698-9.
  7. Bruijns SR, Wallis LA, Burch VC. A prospective evaluation of the Cape triage score in the emergency department of an urban public hospital in South Africa. Emerg Med J. 2008;25:398-402.
  8. Yigit Y, Haddad M, Elmoheen A, Shogaa MR, Tawel R, Mohamed YK, et al. Can COVID-19 Cause flare-ups of acute hepatitis B? An atypical presentation of COVID-19 with acute hepatitis B. Case Rep Infect Dis. 2021;2021:8818678.
  9. Royal College of Physicians (London). National Early Warning Score (NEWS) 2: standardising the assessment of acute-illness severity in the NHS - Updated report of a working party, 2017. Available from: URL:
  10. Royal College of Physicians (London). National Early Warning Score (NEWS Standardising the assessment of acute illness severity in the NHS - Report of a working party. London: Royal College of Physicians (London), 2012.
  11. Nannan Panday RS, Minderhoud TC, Alam N, Nanayakkara PWB. Prognostic value of early warning scores in the emergency department (ED) and acute medical unit (AMU): a narrative review. Eur J Intern Med. 2017;45:20-31.
  12. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The ability of the national early warning score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation. 2013;84:465-70.
  13. Fernando SM, Fox-Robichaud AE, Rochwerg B, Cardinal P, Seely AJE, Perry JJ, et al. Prognostic accuracy of the hamilton early warning score (HEWS) and the national early warning score 2 (NEWS2) among hospitalized patients assessed by a rapid response team. Crit Care. 2019;23:60.
  14. Pimentel MAF, Redfern OC, Gerry S, Collins GS, Malycha J, Prytherch D, et al. A comparison of the ability of the national early warning score and the national early warning score 2 to identify patients at risk of in-hospital mortality: a multi-centre database study. Resuscitation. 2019;134:147-56.
  15. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
  16. Morgan RL, Whaley P, Thayer KA, Schünemann HJ. Identifying the PECO: a framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int. 2018;121:1027-31.
  17. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919.
  18. Alam N, Vegting IL, Houben E, van Berkel B, Vaughan L, Kramer MH, et al. Exploring the performance of the national early warning score (NEWS) in a European emergency department. Resuscitation. 2015;90:111-5.
  19. McCabe C, O’Brien M, Quirke MB. The introduction of the early warning score in the emergency department: a retrospective cohort study. Int Emerg Nurs. 2019;45:31-5.
  20. Schinkel M, Bergsma L, Veldhuis LI, Ridderikhof ML, Holleman F. Comparing complaint-based triage scales and early warning scores for emergency department triage. Emerg Med J. 2022;39:691-6.
  21. Howard C, Amspoker AB, Morgan CK, Kuo D, Esquivel A, Rosen T, et al. Implementation of automated early warning decision support to detect acute decompensation in the emergency department improves hospital mortality. BMJ Open Qual. 2022;11:e001653.
  22. Bashir K, Arshad W, Azad AM, Alfalahi S, Kodumayil A, Elmoheen A. Acceptability and feasibility of mini clinical evaluation exercise (Mini-CEX) in the busy emergency department. Open Access Emerg Med. 2021;13:481-6.
  23. Courtney DM, Neumar RW, Venkatesh AK, Kaji AH, Cairns CB, Lavonas E, et al. Unique characteristics of emergency care research: scope, populations, and infrastructure. Acad Emerg Med. 2009;16:990-4.
  24. Roland D, Coats TJ. An early warning? Universal risk scoring in emergency medicine. Emerg Med J. 2011;28:263.
  25. Yigit Y, Yilmaz S, Ozbek AE, Karakayali O, Cetin B, Halhalli HC. Can platelet indices reduce negative appendectomy rates? Cureus. 2019;11:e4293.
  26. Bhatti JA, Janjua NZ, Akhtar U, Azad A. Writing the introduction and methods of a controlled trial. J Pak Med Assoc. 2014;64:720-2.
  27. Mélot C. To score or not to score during triage in the emergency department? Intensive Care Med. 2015;41:1135-7.
  28. Mirhaghi A, Heydari A, Mazlom R, Ebrahimi M. The reliability of the Canadian triage and acuity scale: meta-analysis. N Am J Med Sci. 2015;7:299-305.
  29. Parenti N, Reggiani ML, Iannone P, Percudani D, Dowding D. A systematic review on the validity and reliability of an emergency department triage scale, the Manchester Triage System. Int J Nurs Stud. 2014;51:1062-9.
  30. Gottschalk SB, Wood D, DeVries S, Wallis LA, Bruijns S; Cape Triage Group. The cape triage score: a new triage system South Africa. Proposal from the Cape Triage Group. Emerg Med J. 2006;23:149-53.
  31. Widgren BR, Jourak M. Medical emergency triage and treatment system (METTS): a new protocol in primary triage and secondary priority decision in emergency medicine. J Emerg Med. 2011;40:623-8.
  32. Zachariasse JM, Seiger N, Rood PP, Alves CF, Freitas P, Smit FJ, et al. Validity of the manchester triage system in emergency care: a prospective observational study. PLoS One. 2017;12:e0170811.
  33. Jo S, Jeong T, Jin YH, Lee JB, Yoon J, Park B. ED crowding is associated with inpatient mortality among critically ill patients admitted via the ED: post hoc analysis from a retrospective study. Am J Emerg Med. 2015;33:1725-31.