Diagnostic agreement between emergency medical service and emergency department physicians, a prospective multicentre study

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Veldhuis, Patrick Gouma, Jeroen Ludikhuize, Prabath Nanayakkara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4102063/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2024 Read the published version in BMC Emergency Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Introduction Early and adequate preliminary diagnosis reduce emergency department (ED) and hospital stay, and may reduce mortality. Several studies demonstrated adequate preliminary diagnosis as stated by emergency medical services (EMS) ranging between 61-77%. Dutch EMS are highly trained, but performance of stating adequate preliminary diagnosis remains unknown. Methods This prospective observational study included 781 patients (>18years), who arrived in the emergency department (ED) by ambulance in two academic hospitals. For each patient, the diagnosis as stated by EMS and the ED physician was obtained and compared. Diagnosis was categorized based on the International Classification of Diseases, 11 th Revision. Results The overall diagnostic agreement was 79% [95%-CI: 76-82%]. Agreement was high for traumatic injuries (94%), neurological emergencies (90%), infectious diseases (84%), cardiovascular (78%), moderate for mental and drug related (71%), gastrointestinal (70%), and low for endocrine and metabolic (50%), and acute internal emergencies (41%). There is no correlation between 28-day mortality, the need for ICU admission or the need for hospital admission with an adequate preliminary diagnosis. Conclusion In the Netherlands, the extent of agreement between EMS diagnosis and ED discharge diagnosis varies between categories. Accuracy is high in diseases with specific observations, e.g., neurological failure, detectable injuries, and electrocardiographic abnormalities. Further studies should use these findings to improve patient outcome. hospital admission deterioration Emergency Department Early Warning Score Figures Figure 1 Introduction The emergency medical services (EMS) and emergency department (ED) staff encounter diverse disease presentations and have a pivotal role as point of entry into the acute care chain. It is known that the correct (suspected) diagnosis by EMS personnel can speed up the diagnostic and therapeutic processes at the ED. [ 1 ] On the other hand, an incorrect preliminary diagnosis from EMS can have negative effects on patient outcomes, leading to increased frequency of intensive care unit (ICU) admissions, prolonged ED and hospital stays, as well as higher mortality rates. [ 2 – 4 ] Therefore, the preliminary diagnosis by EMS should be correct. Previous studies examined the accuracy of preliminary diagnoses made by EMS compared to the discharge diagnoses in the ED. These studies have reported average overall accuracies ranging from 61–77%. [ 5 , 6 ] However, the accuracy varied depending on the type of disease. [ 5 – 8 ] For instance, Koivulahti et al. reported rates of agreement in categories such as mental health and intoxication (86%), cerebral strokes (81%), respiratory emergencies (58%), and infectious diseases (31%). [ 5 ] Similar levels of agreement have been documented for respiratory emergencies in other studies. [ 7 ] However, some categories, such as cerebral strokes and mental health problems, have variable outcomes in terms of diagnostic accuracy in comparable research. [ 6 ] Focused training has been shown to improve the accuracy of EMS in stating adequate diagnosis. [ 5 ] In the Netherlands, EMS nurses receive extensive training and are probably among the most highly trained in the world. Given the pivotal role of preliminary diagnosis in patient outcomes, it is important to ensure its validity within the Dutch context. The purpose of this study is to investigate to what extent EMS nurses are capable in making accurate preliminary diagnosis. Methods Study design and setting This prospective observational study was performed at both locations of the Amsterdam University Medical Centre. From March 11th until October 28th, 2021, all adult patients (> 18years) presented to the ED by ambulance were included. Excluded patients were those with ongoing cardiopulmonary resuscitation and inter-hospital transfers. Dutch EMS system In the Netherlands, each ambulance team consist of an EMS nurse and a driver. EMS nurses hold licenses to administer Advanced Life Support and have completed a foundational nursing program, complemented by specialized training in areas such as emergency medicine, intensive care medicine, or cardiac care medicine, prior to embarking on their EMS traineeship. EMS drivers, on the other hand, undergo training to provide medical support to EMS nurses and to ensure the safe transportation of patients via ambulance. Ethical concerns Ethical approval was received by the Medical Ethical Committee of the AmsterdamUMC (Waiver: W-19_480 # 19.554). Informed consent of participants was waived by the Medical Ethical Committee. Data collection Patients were included on workdays between 10 a.m. and 6 p.m. as at this time a researcher was available, and most patients were presented by ambulance. EMS diagnosis and patient characteristics such as age and sex were included. Also, years of working experience of the EMS nurses was collected. ED discharge diagnosis, the need for hospital admission and ICU admission was obtained from the electronic patient record. Diagnostic agreement Based on the International Classification of Diseases, 11th Revision, the preliminary and ED discharge diagnosis were grouped. Two researchers (PG and LV), grouped the diagnosis based on similarities in terms of symptoms and treatment and consensus was reached. In case more than one diagnosis was reported, the principal diagnosis was used. If a symptom was reported instead of a preliminary diagnosis, we classified this symptom into the most appropriate disease category, see Table 1 . After classification of the EMS and ED diagnosis, they were compared and either concordant or discordant. Similar diagnosis included diagnosis that were either identical diagnosis or more precise diagnosis, i.e., bacterial pneumonia instead of pneumonia. Dissimilar diagnosis were those diagnosis that did not fulfil correct diagnosis criteria. Cohen’s kappa (κ) was used to measure this interrater reliability, interpreting 0.81-1.00 as almost perfect.[ 9 ] Table 1 – Classification of diseases based on the ICD-11 codes: ICD-11 code Classification 01 Infectious diseases 02–04 Emergencies blood, immune system, neoplasms 05 Endocrine or metabolic emergencies 06, 22 Mental and drug related emergencies 08–09 Neurological emergencies 11 Cardiovascular emergencies 12 Respiratory emergencies 13 Gastrointestinal emergencies 16 Genitourinary emergencies 15, 22 Traumatic injuries 21, 24 Acute internal emergencies ICD-11 = International Classification of diseases, 11th Revision Outcomes The primary outcome of this study was the rate of agreement between preliminary diagnoses made by ambulance staff and physician’s discharge diagnosis made in the ED, expressed as a percentage. Secondary outcome included the association with concordant or discordant diagnosis with the need for hospital admission, ICU admission within 72 hours and 28-day mortality. Statistical analysis All analyses were performed using SPSS Statistics, version 27.0.0 (IBM Corporation, Armonk, NY, USA). Descriptive data were generated for all variables with frequencies, percentages, and mean [Standard deviation (SD)] or median [Interquartile range (IQR)] depending on the distribution of the data. Cases with a missing preliminary or discharge diagnosis were excluded. A p-value < 0.05 was considered statistically significant. To compare accuracies between disease categories, a forest plot was constructed via Excel (Microsoft Excel, 2021), using the observed percentages and its 95%-confidence intervals for the different disease categories. Results Characteristics of study population During inclusion period, a total of 800 patients were screened for eligibility criteria. Of these patients 65 were excluded due to missing diagnosis. This resulted in a study population of 735 patients. The median age was 66 years and 404 (55.0%) were male. Most of the preliminary diagnoses were neurological emergencies (24.4%) and cardiovascular emergencies (17.8%), see Table 2 . Primary outcome Out of the 735 patients included in the study, the preliminary diagnosis was accurate in 584 cases (79.5%). The interrater reliability agreement was almost perfect with a Cohen’s kappa value of 0.95. There was a difference in frequency between disease categories when comparing the population with a concordant preliminary diagnosis and the group with a discordant preliminary diagnosis, see Table 2 . For instance, acute internal emergencies were one of the less frequent disease categories (4.6%) in patients with a concordant preliminary diagnosis but represented the largest percentage (23.8%) in the population with a discordant preliminary diagnosis. Table 2 – Characteristics total study population (n = 735), population with a concordant preliminary diagnosis (n = 584), and population with an discordant preliminary diagnosis (n = 151). Total study population (n = 735) Concordant preliminary diagnosis (n = 584) Discordant preliminary diagnosis (n = 151) P-value Sex, male 404 (55.0%) 314 (53.8%) 90 (59.6%) 0.199 Age (years), median [IQR] 66.0 [51.0–77.0] 65.0 [49.0–76.0] 69.0 [58.0–78.0] 0.179 Residence, home 670 (91.2%) 538 (92.1%) 132 (87.4%) 0.072 Pre-alerted patients by ambulance 524 (71.3%) 413 (70.7%) 111 (73.5%) 0.499 Doctor involved prehospital 359 (48.8%) 267 (45.7%) 92 (60.9%) 0.001 Length working experience EMS nurse (years), median [IQR] 10.0 [4.0-19.5] 10.0 [4.0–19.0] 11.0 [4.0–20.0] 0.294 Disease categories preliminary diagnosis EMS Acute internal emergencies 63 (8.6%) 27 (4.6%) 36 (23.8%) < 0.001 Infectious diseases 113 (15.4%) 96 (16.4%) 17 (11.3%) 0.118 Emergencies blood, immune mechanism, neoplasms 17 (2.3%) 12 (2.1%) 5 (3.3%) 0.364 Endocrine and metabolic emergencies 23 (3.1%) 11 (1.9%) 12 (7.9%) < 0.001 Mental and drug related emergencies 35 (4.8%) 26 (4.5%) 9 (6.0%) 0.440 Neurological emergencies 179 (24.4%) 162 (27.7%) 17 (11.3%) < 0.001 Cardiovascular emergencies 131 (17.8%) 100 (17.1%) 31 (20.5%) 0.330 Respiratory emergencies 9 (1.2%) 8 (1.4%) 1 (0.7%) 0.491 Gastrointestinal emergencies 36 (4.9%) 25 (4.3%) 11 (7.3%) 0.132 Genitourinary emergencies 11 (1.5%) 6 (1.0%) 5 (3.3%) 0.051 Traumatic injuries 118 (16.0%) 111 (19.0%) 7 (4.6%) < 0.001 n = Number, IQR = Inter-quartile range, ED = Emergency department, EMS = Emergency medical services Differences in accuracy between disease categories As shown in Fig. 1 , the highest accuracy of 94% was observed in traumatic injuries, and the lowest in acute internal emergencies (43%). The total accuracy of 79% is also represented with its 95%-confidence interval [77–82%]. For example, infectious diseases show a significant higher accuracy (85% [95%-CI: 78–92%]) compared to endocrine and metabolic emergencies (48% [95%-CI: 27–68%]). Secondary outcome For 706 patients, 28-day mortality data was available. The 28-day mortality was not significantly different between patients with concordant diagnosis (6.6%) versus discordant diagnosis (8.5%), p = 0.429. For 732 patients the need for ICU admission within 72 hours after ED presentation was available. The need for ICU admission was not significantly different between the groups, 7.9% for concordant diagnosis versus 4.6% for discordant diagnosis, p = 0.171. For all 735 patients the need for hospital admission after ED presentation was available. For patients with concordant preliminary diagnosis, 46.9% were admitted and for those with a discordant diagnosis this was 40.4%, p = 0.152. Discussion This is the first Dutch study that examined to what extent the preliminary diagnosis stated by the EMS is in agreement with ED discharge diagnosis. Based on these findings, we conclude that the accuracy of EMS nurses is high for patients with neurological emergencies, traumatic injuries, and those with infectious diseases. As hypothesized, the extent of agreement was higher compared to prior studies, however, the difference was only slightly increased. [ 5 , 6 ] A possible explanation of the high level of agreement, is the high level of education and traineeship for the Dutch EMS, however, another explanation is that many of the EMS nurses were previously working at the ED/ICU. Contrary, a German study, examining diagnostic agreement for EMS emergency physicians, showed a slightly lower level of agreement (64%,) despite their high level of education[ 10 ]. This difference could be attributed to the use of a more specific disease classification (e.g. hypoglycaemia, hypertensive crisis), resulting in a more strict assessment of whether the EMS diagnosis was correct or not. In our perspective, a more comprehensive disease categorisation was more representative considering the objective of the EMS of engaging the appropriate medical expertise for each patient based on their preliminary diagnosis. Furthermore, the variation in accuracies between the different disease categories are in concordance with the suggestion of Koivulahti et al. [ 5 ] They stated that high accuracies are found in categories with specific observations, such as neurological failure, detectable injuries, and electrocardiographic abnormalities. The lower accuracies for endocrine and metabolic and acute internal emergencies might be attributed to the lack of diagnostics and specific symptoms and the requirement for more profound knowledge of diseases including more extensive anamnesis and physical examination. Particularly interesting is the high level of diagnostic agreement for patients with infectious diseases. A previous study in the Netherlands concluded that only in 18% of the cases the EMS recognized (severe) sepsis in a patient. In conclusion, EMS are able to correctly diagnose the disease but find it harder to state the severity of illness. [ 11 ] Strengths and limitations Despite the prospectively collected data in two academic teaching hospitals, this study has several limitations. A limitation pertains to the relatively small sample size compared to the number of disease categories. Therefore, it is expected that the accuracy in these categories is overoptimistic. [ 12 ] Also, patients were only included during working days between 10 a.m. and 6 p.m., while previous studies showed that the accuracy fluctuates over the day and especially comparing day and night. [ 13 , 14 ] However, there is no reason to postulate that the findings during these time-periods would be different. In addition, the ED physician’s discharge diagnosis was considered the gold standard for determining whether the preliminary diagnosis was correct or not. However, previous studies state that physicians can also make wrong conclusions. [ 15 ] Conclusion In the Netherlands, the extent of agreement between EMS diagnosis and ED discharge diagnosis varies between categories. Accuracy is high in diseases with specific observations, e.g., neurological failure, detectable injuries, and electrocardiographic abnormalities. Further studies should investigate how we can use this to reduce time spend at the ED or to improve patient outcome. Abbreviations ED emergency department EMS emergency medical services ICU intensive care unit Declarations Ethics approval statements: This study involves human participants and was approved by the Medical Ethics Committee of AmsterdamUMC Consent for publication: not applicable. Data availability: yes Competing interests: all authors have disclosed that they do not have any conflict of interests with respect to the research, authorship and/or publication of this article. Funding: this study is not funded. Authors’ contributions: Lars I. Veldhuis: conceptualization, methodology, investigation, data curation, writing original draft. Patrick Gouma: investigation, writing original draft. Jeroen Ludikhuize: supervision and reviewing. Prabath Nanayakkara: supervision and reviewing. Acknowledgements: none References Alam, N., et al., Epidemiology, recognition and documentation of sepsis in the pre-hospital setting and associated clinical outcomes: a prospective multicenter study. Acute Med, 2016. 15 (4): p. 168-175. Hautz, W.E., et al., Diagnostic error increases mortality and length of hospital stay in patients presenting through the emergency room. Scand J Trauma Resusc Emerg Med, 2019. 27 (1): p. 54. Bastakoti, M., et al., Discrepancy between emergency department admission diagnosis and hospital discharge diagnosis and its impact on length of stay, up-triage to the intensive care unit, and mortality. Diagnosis (Berl), 2021. 9 (1): p. 107-114. Eames, J., A. Eisenman, and R.J. Schuster, Disagreement between emergency department admission diagnosis and hospital discharge diagnosis: mortality and morbidity. Diagnosis (Berl), 2016. 3 (1): p. 23-30. Koivulahti, O., M. Tommila, and E. Haavisto, The accuracy of preliminary diagnoses made by paramedics - a cross-sectional comparative study. Scand J Trauma Resusc Emerg Med, 2020. 28 (1): p. 70. Purabdollah, M., et al., Comparison of the Agreement and Accuracy Between Paramedic and Hospital Diagnosis. Air Med J, 2022. 41 (2): p. 228-232. Fuller, G.W., et al., The diagnostic accuracy of pre-hospital assessment of acute respiratory failure. Br Paramed J, 2020. 5 (3): p. 15-22. Green, R.S., et al., Paramedic Recognition of Sepsis in the Prehospital Setting: A Prospective Observational Study. Emerg Med Int, 2016. 2016 : p. 6717261. McHugh, M.L., Interrater reliability: the kappa statistic. Biochem Med (Zagreb), 2012. 22 (3): p. 276-82. Ramadanov, N., et al., Diagnostic Agreement between Prehospital Emergency and In-Hospital Physicians. Emerg Med Int, 2019. 2019 : p. 3769826. Veldhuis, L.I., et al., Recognition of sepsis in the pre-hospital environment one year after intensive traineeship. Eur J Intern Med, 2021. 92 : p. 115-116. Peduzzi, P., et al., A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol, 1996. 49 (12): p. 1373-9. Ramadanov, N., et al., Influence of Time of Mission on Correct Diagnosis by the Prehospital Emergency Physician: A Retrospective Study. Emerg Med Int, 2019. 2019 : p. 3727081. Akerstedt, T. and K.P. Wright, Jr., Sleep Loss and Fatigue in Shift Work and Shift Work Disorder. Sleep Med Clin, 2009. 4 (2): p. 257-271. Hussain, F., et al., Diagnostic error in the emergency department: learning from national patient safety incident report analysis. BMC Emerg Med, 2019. 19 (1): p. 77. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2024 Read the published version in BMC Emergency Medicine → Version 1 posted Editorial decision: Revision requested 17 Apr, 2024 Reviews received at journal 16 Apr, 2024 Reviews received at journal 02 Apr, 2024 Reviewers agreed at journal 27 Mar, 2024 Reviewers agreed at journal 26 Mar, 2024 Reviewers invited by journal 23 Mar, 2024 Editor invited by journal 22 Mar, 2024 Submission checks completed at journal 22 Mar, 2024 Editor assigned by journal 22 Mar, 2024 First submitted to journal 14 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4102063","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283786467,"identity":"0428fe08-ea75-4fc6-9acb-26ce8db9e5a9","order_by":0,"name":"Lars I. 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The vertical axis shows the disease categories with below the total accuracy. The horizontal axis represents the observed accuracy (box) with its 95%-confidence interval (horizontal line), both expressed in percentages.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCI=Confidence interval\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4102063/v1/92292e25743a8b916a3312c5.jpg"},{"id":61594212,"identity":"7c6121d9-df76-444a-b83a-59320d764459","added_by":"auto","created_at":"2024-08-01 16:53:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":571877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4102063/v1/0643525d-2ea6-4e59-a654-7a69cf3cded4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic agreement between emergency medical service and emergency department physicians, a prospective multicentre study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe emergency medical services (EMS) and emergency department (ED) staff encounter diverse disease presentations and have a pivotal role as point of entry into the acute care chain. It is known that the correct (suspected) diagnosis by EMS personnel can speed up the diagnostic and therapeutic processes at the ED. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] On the other hand, an incorrect preliminary diagnosis from EMS can have negative effects on patient outcomes, leading to increased frequency of intensive care unit (ICU) admissions, prolonged ED and hospital stays, as well as higher mortality rates. [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Therefore, the preliminary diagnosis by EMS should be correct.\u003c/p\u003e \u003cp\u003ePrevious studies examined the accuracy of preliminary diagnoses made by EMS compared to the discharge diagnoses in the ED. These studies have reported average overall accuracies ranging from 61\u0026ndash;77%. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] However, the accuracy varied depending on the type of disease. [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] For instance, Koivulahti et al. reported rates of agreement in categories such as mental health and intoxication (86%), cerebral strokes (81%), respiratory emergencies (58%), and infectious diseases (31%). [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Similar levels of agreement have been documented for respiratory emergencies in other studies. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] However, some categories, such as cerebral strokes and mental health problems, have variable outcomes in terms of diagnostic accuracy in comparable research. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFocused training has been shown to improve the accuracy of EMS in stating adequate diagnosis. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] In the Netherlands, EMS nurses receive extensive training and are probably among the most highly trained in the world. Given the pivotal role of preliminary diagnosis in patient outcomes, it is important to ensure its validity within the Dutch context. The purpose of this study is to investigate to what extent EMS nurses are capable in making accurate preliminary diagnosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis prospective observational study was performed at both locations of the Amsterdam University Medical Centre. From March 11th until October 28th, 2021, all adult patients (\u0026gt;\u0026thinsp;18years) presented to the ED by ambulance were included. Excluded patients were those with ongoing cardiopulmonary resuscitation and inter-hospital transfers.\u003c/p\u003e \u003cp\u003eDutch EMS system\u003c/p\u003e \u003cp\u003eIn the Netherlands, each ambulance team consist of an EMS nurse and a driver. EMS nurses hold licenses to administer Advanced Life Support and have completed a foundational nursing program, complemented by specialized training in areas such as emergency medicine, intensive care medicine, or cardiac care medicine, prior to embarking on their EMS traineeship. EMS drivers, on the other hand, undergo training to provide medical support to EMS nurses and to ensure the safe transportation of patients via ambulance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEthical concerns\u003c/h2\u003e \u003cp\u003eEthical approval was received by the Medical Ethical Committee of the AmsterdamUMC (Waiver: W-19_480 # 19.554). Informed consent of participants was waived by the Medical Ethical Committee.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003ePatients were included on workdays between 10 a.m. and 6 p.m. as at this time a researcher was available, and most patients were presented by ambulance. EMS diagnosis and patient characteristics such as age and sex were included. Also, years of working experience of the EMS nurses was collected. ED discharge diagnosis, the need for hospital admission and ICU admission was obtained from the electronic patient record.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic agreement\u003c/h2\u003e \u003cp\u003eBased on the International Classification of Diseases, 11th Revision, the preliminary and ED discharge diagnosis were grouped. Two researchers (PG and LV), grouped the diagnosis based on similarities in terms of symptoms and treatment and consensus was reached. In case more than one diagnosis was reported, the principal diagnosis was used. If a symptom was reported instead of a preliminary diagnosis, we classified this symptom into the most appropriate disease category, see Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAfter classification of the EMS and ED diagnosis, they were compared and either concordant or discordant. Similar diagnosis included diagnosis that were either identical diagnosis or more precise diagnosis, i.e., bacterial pneumonia instead of pneumonia. Dissimilar diagnosis were those diagnosis that did not fulfil correct diagnosis criteria. Cohen\u0026rsquo;s kappa (κ) was used to measure this interrater reliability, interpreting 0.81-1.00 as almost perfect.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Classification of diseases based on the ICD-11 codes:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICD-11 code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfectious diseases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e02\u0026ndash;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmergencies blood, immune system, neoplasms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndocrine or metabolic emergencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e06, 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMental and drug related emergencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e08\u0026ndash;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurological emergencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiovascular emergencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory emergencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGastrointestinal emergencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenitourinary emergencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15, 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraumatic injuries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21, 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute internal emergencies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eICD-11\u0026thinsp;=\u0026thinsp;International Classification of diseases, 11th Revision\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome of this study was the rate of agreement between preliminary diagnoses made by ambulance staff and physician\u0026rsquo;s discharge diagnosis made in the ED, expressed as a percentage.\u003c/p\u003e \u003cp\u003eSecondary outcome included the association with concordant or discordant diagnosis with the need for hospital admission, ICU admission within 72 hours and 28-day mortality.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using SPSS Statistics, version 27.0.0 (IBM Corporation, Armonk, NY, USA). Descriptive data were generated for all variables with frequencies, percentages, and mean [Standard deviation (SD)] or median [Interquartile range (IQR)] depending on the distribution of the data. Cases with a missing preliminary or discharge diagnosis were excluded. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eTo compare accuracies between disease categories, a forest plot was constructed via Excel (Microsoft Excel, 2021), using the observed percentages and its 95%-confidence intervals for the different disease categories.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study population\u003c/h2\u003e \u003cp\u003eDuring inclusion period, a total of 800 patients were screened for eligibility criteria. Of these patients 65 were excluded due to missing diagnosis. This resulted in a study population of 735 patients. The median age was 66 years and 404 (55.0%) were male. Most of the preliminary diagnoses were neurological emergencies (24.4%) and cardiovascular emergencies (17.8%), see Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrimary outcome\u003c/h2\u003e \u003cp\u003eOut of the 735 patients included in the study, the preliminary diagnosis was accurate in 584 cases (79.5%). The interrater reliability agreement was almost perfect with a Cohen\u0026rsquo;s kappa value of 0.95. There was a difference in frequency between disease categories when comparing the population with a concordant preliminary diagnosis and the group with a discordant preliminary diagnosis, see Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For instance, acute internal emergencies were one of the less frequent disease categories (4.6%) in patients with a concordant preliminary diagnosis but represented the largest percentage (23.8%) in the population with a discordant preliminary diagnosis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Characteristics total study population (n\u0026thinsp;=\u0026thinsp;735), population with a concordant preliminary diagnosis (n\u0026thinsp;=\u0026thinsp;584), and population with an discordant preliminary diagnosis (n\u0026thinsp;=\u0026thinsp;151).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal study population (n\u0026thinsp;=\u0026thinsp;735)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConcordant preliminary diagnosis (n\u0026thinsp;=\u0026thinsp;584)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiscordant preliminary diagnosis (n\u0026thinsp;=\u0026thinsp;151)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e404 (55.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.0 [51.0\u0026ndash;77.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.0 [49.0\u0026ndash;76.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.0 [58.0\u0026ndash;78.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence, home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e670 (91.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e538 (92.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (87.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-alerted patients by ambulance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e524 (71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e413 (70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor involved prehospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength working experience EMS nurse (years), median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 [4.0-19.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0 [4.0\u0026ndash;19.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 [4.0\u0026ndash;20.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDisease categories preliminary diagnosis EMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute internal emergencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergencies blood, immune\u003c/p\u003e \u003cp\u003emechanism, neoplasms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocrine and metabolic emergencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental and drug related emergencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological emergencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular emergencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory emergencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal emergencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenitourinary emergencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraumatic injuries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003en\u0026thinsp;=\u0026thinsp;Number, IQR\u0026thinsp;=\u0026thinsp;Inter-quartile range, ED\u0026thinsp;=\u0026thinsp;Emergency department, EMS\u0026thinsp;=\u0026thinsp;Emergency medical services\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eDifferences in accuracy between disease categories\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the highest accuracy of 94% was observed in traumatic injuries, and the lowest in acute internal emergencies (43%). The total accuracy of 79% is also represented with its 95%-confidence interval [77\u0026ndash;82%]. For example, infectious diseases show a significant higher accuracy (85% [95%-CI: 78\u0026ndash;92%]) compared to endocrine and metabolic emergencies (48% [95%-CI: 27\u0026ndash;68%]).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eSecondary outcome\u003c/h2\u003e \u003cp\u003eFor 706 patients, 28-day mortality data was available. The 28-day mortality was not significantly different between patients with concordant diagnosis (6.6%) versus discordant diagnosis (8.5%), p\u0026thinsp;=\u0026thinsp;0.429.\u003c/p\u003e \u003cp\u003eFor 732 patients the need for ICU admission within 72 hours after ED presentation was available. The need for ICU admission was not significantly different between the groups, 7.9% for concordant diagnosis versus 4.6% for discordant diagnosis, p\u0026thinsp;=\u0026thinsp;0.171.\u003c/p\u003e \u003cp\u003eFor all 735 patients the need for hospital admission after ED presentation was available. For patients with concordant preliminary diagnosis, 46.9% were admitted and for those with a discordant diagnosis this was 40.4%, p\u0026thinsp;=\u0026thinsp;0.152.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first Dutch study that examined to what extent the preliminary diagnosis stated by the EMS is in agreement with ED discharge diagnosis. Based on these findings, we conclude that the accuracy of EMS nurses is high for patients with neurological emergencies, traumatic injuries, and those with infectious diseases. As hypothesized, the extent of agreement was higher compared to prior studies, however, the difference was only slightly increased. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] A possible explanation of the high level of agreement, is the high level of education and traineeship for the Dutch EMS, however, another explanation is that many of the EMS nurses were previously working at the ED/ICU.\u003c/p\u003e \u003cp\u003eContrary, a German study, examining diagnostic agreement for EMS emergency physicians, showed a slightly lower level of agreement (64%,) despite their high level of education[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This difference could be attributed to the use of a more specific disease classification (e.g. hypoglycaemia, hypertensive crisis), resulting in a more strict assessment of whether the EMS diagnosis was correct or not. In our perspective, a more comprehensive disease categorisation was more representative considering the objective of the EMS of engaging the appropriate medical expertise for each patient based on their preliminary diagnosis.\u003c/p\u003e \u003cp\u003eFurthermore, the variation in accuracies between the different disease categories are in concordance with the suggestion of Koivulahti et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] They stated that high accuracies are found in categories with specific observations, such as neurological failure, detectable injuries, and electrocardiographic abnormalities. The lower accuracies for endocrine and metabolic and acute internal emergencies might be attributed to the lack of diagnostics and specific symptoms and the requirement for more profound knowledge of diseases including more extensive anamnesis and physical examination.\u003c/p\u003e \u003cp\u003eParticularly interesting is the high level of diagnostic agreement for patients with infectious diseases. A previous study in the Netherlands concluded that only in 18% of the cases the EMS recognized (severe) sepsis in a patient. In conclusion, EMS are able to correctly diagnose the disease but find it harder to state the severity of illness. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eDespite the prospectively collected data in two academic teaching hospitals, this study has several limitations. A limitation pertains to the relatively small sample size compared to the number of disease categories. Therefore, it is expected that the accuracy in these categories is overoptimistic. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Also, patients were only included during working days between 10 a.m. and 6 p.m., while previous studies showed that the accuracy fluctuates over the day and especially comparing day and night. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] However, there is no reason to postulate that the findings during these time-periods would be different. In addition, the ED physician\u0026rsquo;s discharge diagnosis was considered the gold standard for determining whether the preliminary diagnosis was correct or not. However, previous studies state that physicians can also make wrong conclusions. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the Netherlands, the extent of agreement between EMS diagnosis and ED discharge diagnosis varies between categories. Accuracy is high in diseases with specific observations, e.g., neurological failure, detectable injuries, and electrocardiographic abnormalities. Further studies should investigate how we can use this to reduce time spend at the ED or to improve patient outcome.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eED \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;emergency department\u003c/p\u003e\n\u003cp\u003eEMS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;emergency medical services\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; intensive care unit\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval statements: This study involves human participants and was approved by the Medical Ethics Committee of AmsterdamUMC\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u0026nbsp;not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability: yes\u003c/p\u003e\n\u003cp\u003eCompeting interests: all authors have disclosed that they do not have any conflict of interests with respect to the research, authorship and/or publication of this article.\u003c/p\u003e\n\u003cp\u003eFunding: this study is not funded.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLars I. Veldhuis: conceptualization, methodology, investigation, data curation, writing original draft. Patrick Gouma: investigation, writing original draft.\u003cbr\u003e\u0026nbsp;Jeroen Ludikhuize: supervision and reviewing.\u003cbr\u003e\u0026nbsp;Prabath Nanayakkara: supervision and reviewing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements: none\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlam, N., et al., \u003cem\u003eEpidemiology, recognition and documentation of sepsis in the pre-hospital setting and associated clinical outcomes: a prospective multicenter study.\u003c/em\u003e Acute Med, 2016. \u003cstrong\u003e15\u003c/strong\u003e(4): p. 168-175.\u003c/li\u003e\n\u003cli\u003eHautz, W.E., et al., \u003cem\u003eDiagnostic error increases mortality and length of hospital stay in patients presenting through the emergency room.\u003c/em\u003e Scand J Trauma Resusc Emerg Med, 2019. \u003cstrong\u003e27\u003c/strong\u003e(1): p. 54.\u003c/li\u003e\n\u003cli\u003eBastakoti, M., et al., \u003cem\u003eDiscrepancy between emergency department admission diagnosis and hospital discharge diagnosis and its impact on length of stay, up-triage to the intensive care unit, and mortality.\u003c/em\u003e Diagnosis (Berl), 2021. \u003cstrong\u003e9\u003c/strong\u003e(1): p. 107-114.\u003c/li\u003e\n\u003cli\u003eEames, J., A. Eisenman, and R.J. Schuster, \u003cem\u003eDisagreement between emergency department admission diagnosis and hospital discharge diagnosis: mortality and morbidity.\u003c/em\u003e Diagnosis (Berl), 2016. \u003cstrong\u003e3\u003c/strong\u003e(1): p. 23-30.\u003c/li\u003e\n\u003cli\u003eKoivulahti, O., M. Tommila, and E. Haavisto, \u003cem\u003eThe accuracy of preliminary diagnoses made by paramedics - a cross-sectional comparative study.\u003c/em\u003e Scand J Trauma Resusc Emerg Med, 2020. \u003cstrong\u003e28\u003c/strong\u003e(1): p. 70.\u003c/li\u003e\n\u003cli\u003ePurabdollah, M., et al., \u003cem\u003eComparison of the Agreement and Accuracy Between Paramedic and Hospital Diagnosis.\u003c/em\u003e Air Med J, 2022. \u003cstrong\u003e41\u003c/strong\u003e(2): p. 228-232.\u003c/li\u003e\n\u003cli\u003eFuller, G.W., et al., \u003cem\u003eThe diagnostic accuracy of pre-hospital assessment of acute respiratory failure.\u003c/em\u003e Br Paramed J, 2020. \u003cstrong\u003e5\u003c/strong\u003e(3): p. 15-22.\u003c/li\u003e\n\u003cli\u003eGreen, R.S., et al., \u003cem\u003eParamedic Recognition of Sepsis in the Prehospital Setting: A Prospective Observational Study.\u003c/em\u003e Emerg Med Int, 2016. \u003cstrong\u003e2016\u003c/strong\u003e: p. 6717261.\u003c/li\u003e\n\u003cli\u003eMcHugh, M.L., \u003cem\u003eInterrater reliability: the kappa statistic.\u003c/em\u003e Biochem Med (Zagreb), 2012. \u003cstrong\u003e22\u003c/strong\u003e(3): p. 276-82.\u003c/li\u003e\n\u003cli\u003eRamadanov, N., et al., \u003cem\u003eDiagnostic Agreement between Prehospital Emergency and In-Hospital Physicians.\u003c/em\u003e Emerg Med Int, 2019. \u003cstrong\u003e2019\u003c/strong\u003e: p. 3769826.\u003c/li\u003e\n\u003cli\u003eVeldhuis, L.I., et al., \u003cem\u003eRecognition of sepsis in the pre-hospital environment one year after intensive traineeship.\u003c/em\u003e Eur J Intern Med, 2021. \u003cstrong\u003e92\u003c/strong\u003e: p. 115-116.\u003c/li\u003e\n\u003cli\u003ePeduzzi, P., et al., \u003cem\u003eA simulation study of the number of events per variable in logistic regression analysis.\u003c/em\u003e J Clin Epidemiol, 1996. \u003cstrong\u003e49\u003c/strong\u003e(12): p. 1373-9.\u003c/li\u003e\n\u003cli\u003eRamadanov, N., et al., \u003cem\u003eInfluence of Time of Mission on Correct Diagnosis by the Prehospital Emergency Physician: A Retrospective Study.\u003c/em\u003e Emerg Med Int, 2019. \u003cstrong\u003e2019\u003c/strong\u003e: p. 3727081.\u003c/li\u003e\n\u003cli\u003eAkerstedt, T. and K.P. Wright, Jr., \u003cem\u003eSleep Loss and Fatigue in Shift Work and Shift Work Disorder.\u003c/em\u003e Sleep Med Clin, 2009. \u003cstrong\u003e4\u003c/strong\u003e(2): p. 257-271.\u003c/li\u003e\n\u003cli\u003eHussain, F., et al., \u003cem\u003eDiagnostic error in the emergency department: learning from national patient safety incident report analysis.\u003c/em\u003e BMC Emerg Med, 2019. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 77.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hospital admission, deterioration, Emergency Department, Early Warning Score","lastPublishedDoi":"10.21203/rs.3.rs-4102063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4102063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction\u003c/p\u003e\n\u003cp\u003eEarly and adequate preliminary diagnosis reduce emergency department (ED) and hospital stay, and may reduce mortality. Several studies demonstrated adequate preliminary diagnosis as stated by emergency medical services (EMS) ranging between 61-77%. Dutch EMS are highly trained, but performance of stating adequate preliminary diagnosis remains unknown.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eThis prospective observational study included 781 patients (\u0026gt;18years), who arrived in the emergency department (ED) by ambulance in two academic hospitals. For each patient, the diagnosis as stated by EMS and the ED physician was obtained and compared. Diagnosis was categorized based on the International Classification of Diseases, 11\u003csup\u003eth\u003c/sup\u003e Revision.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eThe overall diagnostic agreement was 79% [95%-CI: 76-82%]. Agreement was high for traumatic injuries (94%), neurological emergencies (90%), infectious diseases (84%), cardiovascular (78%), moderate for mental and drug related (71%), gastrointestinal (70%), and low for endocrine and metabolic (50%), and acute internal emergencies (41%). There is no correlation between 28-day mortality, the need for ICU admission or the need for hospital admission with an adequate preliminary diagnosis.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eIn the Netherlands, the extent of agreement between EMS diagnosis and ED discharge diagnosis varies between categories. Accuracy is high in diseases with specific observations, e.g., neurological failure, detectable injuries, and electrocardiographic abnormalities. Further studies should use these findings to improve patient outcome.\u003c/p\u003e","manuscriptTitle":"Diagnostic agreement between emergency medical service and emergency department physicians, a prospective multicentre study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 17:28:04","doi":"10.21203/rs.3.rs-4102063/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-17T04:20:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-16T08:03:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-02T08:43:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28799f79-1a7e-4b7a-a309-fe05add0cd3b","date":"2024-03-27T12:34:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c9abf15d-e8df-4471-8fb0-432860707b19","date":"2024-03-26T08:51:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-23T20:44:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-22T08:41:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-22T08:39:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-22T08:39:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2024-03-14T15:29:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9783b07c-6259-40ed-9480-b852c3f2004d","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T16:14:08+00:00","versionOfRecord":{"articleIdentity":"rs-4102063","link":"https://doi.org/10.1186/s12873-024-01041-7","journal":{"identity":"bmc-emergency-medicine","isVorOnly":false,"title":"BMC Emergency Medicine"},"publishedOn":"2024-07-18 16:04:51","publishedOnDateReadable":"July 18th, 2024"},"versionCreatedAt":"2024-03-27 17:28:04","video":"","vorDoi":"10.1186/s12873-024-01041-7","vorDoiUrl":"https://doi.org/10.1186/s12873-024-01041-7","workflowStages":[]},"version":"v1","identity":"rs-4102063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4102063","identity":"rs-4102063","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}