Skip to main content
Download PDF

Frequency and predictors of unspecific medical diagnoses in the emergency department: a prospective observational study

BMC Emergency Medicine volume 22, Article number: 109 (2022) Cite this article

Abstract

Background

Misdiagnosis is a major public health problem, causing increased morbidity and mortality. In the busy setting of an emergency department (ED) patients are diagnosed under difficult circumstances. As a consequence, the ED diagnosis at hospital admittance may often be a descriptive diagnosis, such as “decreased general condition”. Our objective was to determine in how far patients with such an unspecific ED diagnosis differ from patients with a specific ED diagnosis and whether they experience a worse outcome.

Methods

We conducted a prospective observational study in Bern university hospital in Switzerland for all adult non-trauma patients admitted to any internal medicine ward from August 15th 2015 to December 7th 2015. Unspecific ED diagnoses were defined through the clinical classification software for ICD-10 by two outcome assessors. As outcome parameters, we assessed in-hospital mortality and length of hospital stay.

Results

Six hundred eighty six consecutive patients were included. Unspecific diagnoses were identified in 100 (14.6%) of all consultations.

Patients receiving an unspecific diagnosis at ED discharge were significantly more often women (56.0% vs. 43.9%, p = 0.024), presented more often with a non-specific complaint (34% vs. 21%, p = 0.004), were less often demonstrating an abnormal heart rate (5.0% vs. 12.5%, p = 0.03), and less often on antibiotics (32.0% vs. 49.0%, p = 0.002). Apart from these, no studied drug intake, laboratory or clinical data including change in diagnosis was associated significantly with an unspecific diagnosis. Unspecific diagnoses were neither associated with in-hospital mortality in multivariable analysis (OR = 1.74, 95% CI: 0.60–5.04; p = 0.305) adjusted for relevant confounders nor with length of hospital stay (GMR = 0.87, 95% CI: 0.23–3.32; p = 0.840).

Conclusions

Women and patients with non-specific presenting complaints and no abnormal heart rate are at risk of receiving unspecific ED diagnoses that do not allow for targeted treatment, discharge and prognosis. This study did not find an effect of such diagnoses on length of hospital stay nor in-hospital mortality.

Peer Review reports

Background

The practice of emergency medicine is considered “a natural laboratory for the study of error” [1]. In the emergency department (ED), patients are diagnosed under difficult circumstances, including high physician workload, medical urgencies, shift work, nonlinear workflow and overcrowding. All these factors impact medical decision making and cultural and language barriers may further impair communication with the patient. Moreover, particularly complex medical patients often present a diagnostic challenge. Still, it is the task of emergency physicians to assign a preliminary diagnosis to patients who are subsequently hospitalized.

From admission to discharge, the leading clinical diagnosis may change. Such change is often used as an indicator of diagnostic error in the ED [2]. Recently, a German university ED reported a change in diagnosis in 29% of their hospitalized patients [3]. Other studies estimated the rate of diagnostic errors at around 15–30% in contexts such as the ED [4,5,6,7,8]. About 45% of diagnostic errors result in moderate to severe patient harm [4, 9]. In a prospective observational study at a large Swiss university hospital ED, we recently demonstrated a change in diagnosis in ED patients admitted to the internal medicine ward to be associated with a longer hospital stay and substantially higher mortality [10].

One might argue that a change in lead diagnosis is a clinically relevant outcome parameter in itself: when a patient who initially requires hospital-admission improves to be discharged with an identical diagnosis, treatment was most likely adequate. When, however, a change in diagnosis occurs during hospitalization, the patient is at risk of missed, delayed or inadequate initial treatment. This may explain an increase in length of hospital stay, more unscheduled transfers to the intensive care unit, and even higher mortality. It further explains the many well-known effects of diagnostic error, such as medico-legal, economic, and social consequences [4, 11].

However, a change in diagnosis as an indicator of diagnostic error is only assessable in retrospect and not at the point of care. It thus does not help reduce or prevent patient harm. Here, we hypothesize that unspecific (“vague”) ED diagnoses, which include labels such as “generally degraded health status” or “fever of unknown origin”, may have similar effects on treatment and thus on subsequent outcome parameters, but are available in time to identify patients at risk. Unspecific ED diagnoses arguably have little therapeutic or prognostic value and potentially encompass a large variety of underlying diseases. Research into factors associated with such unspecific ED diagnosis and the outcome of patients receiving such unspecific diagnoses is scare, even though they may encompass up to 20–30% of ED patients [12,13,14,15,16,17,18].

This study thus aims to explore the following research questions:

  1. 1.

    What is the frequency of unspecific ED diagnoses among patients admitted to the internal medicine ward?

  2. 2.

    Do patients with unspecific ED diagnoses differ from patients with specific ED diagnoses in clinical aspects (e.g., age, gender, presenting complaint, severity of medical problem)?

  3. 3.

    Do patients with unspecific diagnoses experience a worse outcome than patients with specific diagnoses?

Methods

Study design, setting, and ethical approval

This is a secondary analysis of a dataset obtained during the cDx study, a prospective observational study following patients admitted to hospital through the ED [2]. This study found that patients who experienced a change in diagnosis from admittance to discharge had a significantly longer length of hospital stay and a significantly higher mortality than those who did not [10].

The study took place in the ED of the Bern University Hospital and included all non-trauma patients older than 18 years admitted to any internal medicine ward (IM) through the ED. The ED at Bern University hospital, a tertiary care center, is a self-contained, interdisciplinary unit that employs around 45 physicians and 120 nurses, and sees more than 45,000 patients per year, of which around 30% are admitted to the hospital [19]. The department of internal medicine cares for over 4,000 in-patient cases per year. The final ED diagnosis is made by the treating ED team, which consists of an attending physician (board certified in internal medicine and emergency medicine) and a resident. The patient left the ED with an ED diagnosis that is based on available previous medical records and the details of the actual presentation. Further details on study settings are reported in previous publications [2, 10, 20].

Patient data were collected during usual ED care and in the internal medicine ward. The Bern ethics committee registered the study as a quality evaluation study under Kantonale Ethikkomission Bern BASEC number 197/15 and waived the need for informed consent.

Inclusion/Exclusion

All patients ≥ 18 years of age admitted to any internal medicine ward (IM) at Bern University Hospital via the ED during the five months study period were included.

Exclusion criteria were patients admitted to the IM ward for palliative or social care as primary purpose, because diagnostic workup and treatment are different in this specific patient population. Also, patients with surgical main problems (e.g., fractures) admitted to the IM ward because of age or comorbidities were excluded for similar reasons. The third group of excluded patients were patients transferred to our tertiary hospital with a diagnosis established externally, because we assumed diagnostic workup in our ED not to be comparable to other patients. Furthermore, patients with incomplete information on the potential confounding variables were excluded. For an overview of the inclusion and exclusion procedure, see the study flowchart (Fig. 1).

Fig. 1
figure 1

Flowchart

Full size image

Data collection & extraction

The following data were collected from the hospital’s electronic patient documentation systems (E-Care, ED 2.1.3.0, Turnhout Belgium and i-pdos_Prod_ODA 7.10.1.2): patient characteristics (date of birth, sex, date and time of presentation, prescribed medication, insurance status), encounter characteristics (Swiss triage scale, ED admittance via resuscitation bay, first measured blood pressure and heart rate, the total diagnostic resources), consultation characteristics (creatinine, sodium, hemoglobin, leucocytes, hemoglobin) and outcome data(length of stay in the ED and hospital, discharge diagnoses from the ED and IM, and in-hospital mortality). All patients admitted to the ED were triaged by registered nurses using the Swiss triage scale, a five-level triage scale with high inter-rater and intra-rater reliability. Chief complaints, objective parameters (vital signs), and key questions are used to stratify the risk: life-threatening emergencies requiring immediate care, urgent conditions requiring medical evaluation within 20 min, semiurgent conditions, requiring medical evaluation within 2 h, nonurgent conditions and follow-ups [21].

Diagnoses according to the ICD-10 classification

Patient records were reviewed to establish the corresponding final diagnosis according to the 10th International Classification of Diseases and Health Related Problems (ICD-10) [22] by two outcome assessors, experienced in internal and emergency medicine. In cases where a specific organ-related ICD-diagnosis could not be established, the presenting clinical symptom was coded (e.g., “decreased general condition” was attributed to R53). Because ED diagnoses are often descriptive, specific coding rules were established (supporting information S1 Table). For example, sepsis was coded under the corresponding infection (e.g., “sepsis due to pneumonia” was coded as “pneumonia”).

One hundred randomly selected patients were independently classified by both raters to assess their rater agreement (kappa = 0.96). The remaining cases were then classified by one rater.

ICD codes were grouped into diagnostic categories taken from the clinical classification software (CCS) for ICD-10 [23]. The software groups diseases by ICD 10 code in 18 different groups such as “cardio-circulatory diseases” or “infectious diseases”.

Classification of diagnoses

All diagnoses classified as “residual codes” or “symptoms; signs, ill-defined conditions and factors influencing health status” by the CCS (categories 17 and 18) were summarized as unspecific diagnoses, including labels such as “generally degraded health status” or “fever of unknown origin.”

Outcomes

The primary outcome was in-hospital mortality. Secondary outcome was length of stay in hospital.

Potential confounders

The following variables were considered as potential confounders:

  1. i.

    Sociodemographic parameter including age, gender and insurance status

  2. ii.

    Drug intake, i.e., antihypertensive, diuretic, antidiabetic, antiepileptic, psycholeptic, antibiotic, or antithrombotic therapy

  3. iii.

    Acute consultation characteristics, i.e., triage scale, resuscitation bay use, heart rate deviation (below 50 bpm or above 110 bpm) and reduced blood pressure below 90 mmHg [24].

  4. iv.

    Additional laboratory characteristics known to be associated with poor outcome(sodium, creatinine, hemoglobin, and leucocyte count) [25].

  5. v.

    Diagnostic ED resources measured in tax points as the sum of radiological, laboratory and physician’s work. Each service is assigned a certain number of tax points depending on the time required, the difficulty and the infrastructure required. This is used for standardization for billing/accounting reasons in the Swiss healthcare system. 1 tax point roughly is the equivalent to 1 Swiss Franc, varying from time and region.

  6. vi.

    Specificity of the presenting complaint: presenting complaints, for which no evidence-based management protocols for the ED exist [26], were coded by two independent raters as described previously as unspecific, so called non-specific complaint (NSC) [27], or as specific otherwise.

Sample size motivation

The in-hospital mortality in our cohort in the specific diagnosis group and the proportion of unspecific diagnoses of patients admitted to the hospital ward from the ED were estimated to be 4.3% and 21% respectively[10] (in analogy to Wogan et al. studying hospital admissions from the ED) [15]. Furthermore, we assumed a strong impact of an unspecific diagnosis because of the strong impact of a discrepancy between the ED and IM ward discharge diagnosis, which was found to result in an OR of 2.4 [10]. Assuming these numbers, 644 consultations would be sufficient to detect an OR for mortality of 2.4 between specific vs. unspecific ED discharge diagnosis with a power of 70% and a significance level of 0.05.

Statistical analysis

Data was analysed in Stata® 16.1 (StataCorp, The College Station, Texas, USA).

Baseline characteristics are presented as numbers and percentage or median and interquartile range (IQR) using descriptive statistics as appropriate. Groups of patients with unspecific and specific diagnoses are compared regarding presentation, ED and hospital outcome with Mann–Whitney U tests or Chi-square tests as applicable.

A stepwise backward logistic (respectively linear regression) analysis (maintaining all predictors with a p < 0.1) based on all potential confounders described above was used to control the association of an unspecific diagnosis and in-hospital mortality (or length of hospital stay) for confounding using Stata’s – stepwise – command.

The effect sizes of the logistic regression are presented as odds ratio and 95% confidence interval (CI). As length of hospital stay (los) was not normally distributed, it was ln-transformed. The coefficients and 95% CI of the linear regression model to predict ln-los were exponentiated, thus, corresponding to the geometric mean ratio (GMR) accompanied by the 95% CI.

Results

A total of 14,187 non-trauma medical patients presented to the ED during the study period, of which 894 patients were admitted to an IM ward (Fig. 1). From those, 183 (20.4%) were excluded because they met our exclusion criteria, 25 (3.5%) had incomplete data on any of the studied confounder and were therefore excluded too, leaving 686 consultations for analysis.

In total, 100 (14.6%) of the 686 included patients received an unspecific diagnosis in the ED at hospital admission, of which 76 (11.1% of all) were classified in CCS category 17 (“symptoms; signs; and ill-defined conditions and factors influencing health status”) and the remainder 24 (3.5% of all) in CCS categories 18 (“residual codes”).

Table 1 summarizes the ED diagnoses patients received at hospital admittance by their respective CCS category. The most common specific categories were diseases of the circulatory (18.2%), respiratory (15.6%), and digestive system (12.1%).

Table 1 Summary of diagnoses patients received in the emergency department, according to the clinical classification system (CCS)
Full size table

The relation/the flow between presenting chief complaint (specific vs. nonspecific), ED diagnosis (specific vs. unspecific) and hospital discharge diagnosis (change in diagnosis vs. idem diagnosis as ED diagnosis) is demonstrated in Fig. 2.

Fig. 2
figure 2

Relation between presenting chief complaint, ED diagnosis, and hospital discharge diagnosis. Abbreviations: cDx, Hospital Discharge Diagnosis; ED, Emergency Department; NSC, Non-Specific Complaint; SC, specific complaint; SED, Specific ED Diagnosis; UED, Unspecific ED Diagnosis

Full size image

Table 2 provides a comparison of patients receiving unspecific vs. specific diagnoses. Patients receiving an unspecific diagnosis at ED discharge were significantly more often women (56.0% vs. 43.9%, p = 0.024), presented more often with a NSC (34% vs. 21%, p = 0.004), were less often demonstrating a heart rate deviation (5.0% vs. 12.5%, p = 0.03), and less often on antibiotics on admission to the IM ward (32.0% vs. 49.0%, p = 0.002). Furthermore, their median length of stay in the ED was longer (6.75 vs. 6.2 h, p = 0.036). Apart from these, no studied drug intake, laboratory or clinical data was significantly associated with an unspecific diagnosis.

Table 2 Comparison of patients with unspecific versus specific ED diagnoses
Full size table

Unspecific diagnoses were not associated with in-hospital death in multivariable analysis (OR = 1.74, 95% CI: 0.60–5.04; p = 0.305), adjusted for age, medical intake (antihypertensive, antithrombotic, and psycholeptic therapy), need for resuscitation bay, blood pressure deviation (< 90 mmHg) as well as hemoglobin level (Table 3). Length of hospital stay was not significantly associated with unspecific diagnoses in backward regression analysis (GMR = 0.87, 95% CI: 0.23–3.32; p = 0.840).

Table 3 Multivariable analysis; stepwise backward logistic regression
Full size table

Discussion

Every seventh patients (14.6%) left the ED for the IM ward with an unspecific ED diagnosis. Females and patients presenting with a NSC were more likely to receive an unspecific diagnosis. In contrast, consultations with an abnormal heart rate (< 50 or > 110 bpm) and patients prescribed an antibiotic therapy in hospital had lower odds for an unspecific diagnosis.

Our estimate of the frequency of unspecific ED diagnosis is at the lower end of the scale of published estimates. A nationally representative study in the United States looking at diagnoses of patients presenting with the three most common ED chief complaints, chest pain, abdominal pain, and headache in ambulatory patients found that 37% of patients were discharged from the ED with a symptomatic instead of a specific pathological diagnosis [12]. Similarly, unspecific diagnoses (“signs and symptoms”) accounted for over one-third of ED discharge diagnoses of outpatients in a single academic center [28], and over 20% in an Icelandic investigation [17]. In a large Danish cohort study of patients brought to the hospital in an ambulance dispatched after emergency calls, unspecific diagnoses accounted for one-third of patients [14]. Analyzing an US American sample, Wogan has pointed out that 23% of patients admitted to a hospital ward form the ED did not have a causative diagnosis [15]. In elderly patients in France, this proportion even reaches one third [16].

A reason for the difference between these and our estimate may be that several countries limit the time spent in the ED to a maximum, e.g. 4 h. Patients may afterwards be transferred to a medical investigation unit for another maximum of e.g. 24 h before finally deciding on admission. Our ED, like many other European EDs, combines these two functions within a single unit, and allows for certain patients to stay in the ED until the diagnostic/therapeutic process has advanced (median in our sample: approx. 7 h) possibly yielding/ allowing for a more definite/specific diagnosis. This may explain the comparatively lower rate of unspecific ED diagnoses found here. Furthermore, the effect of a tertiary care center with the corresponding diagnostic possibilities might have had an influence on the lower rate of unspecific ED diagnoses.

Patients presenting with a NSC were more likely to receive an unspecific diagnosis in our study. Several studies have found an increased mortality in patients with NSC [29,30,31,32]. Indeed, patients presenting to the ED with NSC present a challenge to the treating physician, and are at an increased risk of misdiagnosis, admission to hospital, prolonged stay in hospital and even mortality [27, 29,30,31,32,33]. It has thus been hypothesized, that a low quality ED diagnosis in this patient group may be a factor contributing to impaired outcome. Our results do not suggest so. Patients with an unspecific diagnosis at ED discharge were not found to have an increased risk of in-hospital mortality in multivariable analyses adjusted for relevant confounders nor an increased length of stay. It thus remains an open question for further research, how non-specific complaints worsen outcome of patients hospitalized [27, 30, 32, 34] if they do not do so through the quality of ED diagnoses.

In a previous study, we found a change in diagnosis in ED patients admitted to the internal medicine ward in 12.3% of patients, associated with a longer hospital stay and higher mortality [10]. We initially hypothesized that an unspecific ED discharge diagnosis should be associated with a change in diagnosis when comparing ED and hospital discharge diagnoses. Surprisingly, this was not the case: The rate of change in diagnosis in this study was not significantly different in patients with a specific diagnosis vs. unspecific diagnosis (12.5% vs. 13%, p = 0.880; Table 2). Instead, we found that just as NSC do not necessarily lead to an unspecific ED diagnosis, only a small portion of those with an unspecific ED diagnosis experience a change in their final diagnosis when discharged from the hospital (Fig. 2). This finding implies that at least some patients admitted to the hospital with an unspecific diagnosis from the ED are discharged from the hospital with no improvement in the specificity of their diagnosis, a finding that we did not expect.

We do not know the reason for that lack of improvement in diagnosis specificity. One could hypothesise that when patients admitted to the hospital from the ED improve in their health over the next few days, hospitalists have little reason to revisit (and potentially change) the initial ED diagnosis. Maybe such a change in diagnosis is only triggered by a lack of patient improvement on the ward.

Next to unspecific presenting complaints, female gender was associated with receiving an unspecific diagnosis. Similarly, in patients presenting with NSC to another Swiss ED, diagnoses were more often missed in women [33], a phenomenon also known for acute coronary syndrome [35], and psychiatric diseases [36]. This may be attributable to a less specific presentation of some diseases in female patients (e.g. urinary tract infection, coronary artery disease), or even due to a bias towards psychogenic explanations for women [35].

Some authors conclude older patients to be more difficult to diagnose, perhaps because some symptoms may be underestimated or clinically less pronounced, attributed to aging in general, or because some of the diagnostic criteria might not be applicable to the elderly [4, 6]. Interestingly, in this study, no association between age and an unspecific ED diagnosis could be detected.

As a last finding, diagnostic resource consumption in the ED was not statistically different in patients receiving an unspecific versus a specific diagnosis. This finding implies a comparable diagnostic effort invested in both groups, which is somewhat surprising, given that ED physicians tend to order more diagnostic tests in cases about which they are uncertain [37]. The finding that physicians did not invest more diagnostic effort in patients eventually receiving a vague diagnosis calls the calibration of their confidence to actual diagnostic performance into question [38].

Sir William Osler stated that “Medicine is a science of uncertainty, and an art of probability” [39]. The uncertainty inherent in medical diagnosis is easily masked by disease labels [40]. It may be worthwhile to actually communicate our level of certainty in our diagnosis [40], as case-related confidence has previously been shown to be associated with the actual accuracy of medical diagnoses [41,42,43,44]. Our findings raise the question whether this association holds for patients with non-specific complaints and those receiving unspecific ED diagnoses eventually.

Limitations

Our study is limited to a single university center, albeit one of the largest of its kind in Switzerland. Thus, the generalizability of our findings to other populations and other ED settings remains unknown. Because we only included patients hospitalized to IM, we cannot rule out that our inclusion criteria introduced selection bias. This study did not include patients with a non-specific surgical problems. However, these problems are rare, because most presenting complaints are only identified as surgical if they can be diagnosed as a specifically addressable surgical problem. The CI for some of the analyses was rather wide, this might imply that the population is too small to make a more precise estimate. Furthermore, the sample size was small, thus the power might not have been enough to detect a smaller strength of association between non-specific diagnosis and in-hospital mortality than calculated. We also cannot expand our findings to the outpatient-population. Furthermore, we did not include information on 4-week mortality, but only on in-hospital mortality.

Conclusion

In this prospective, observational study, an unspecific ED diagnosis was more often made in females and patients presenting with a NSC. Consultations with an abnormal heart rate and patients on antibiotics had lower odds for an unspecific diagnosis. Unspecific ED diagnoses were not associated with impaired patient outcomes. Thus, unspecific diagnoses seem not be the primary factor driving in-house mortality and length of stay in patients presenting with NSC. More research is needed to uncover the complex relation between NSC, unspecific diagnoses and patient outcomes.

Availability of data and materials

Data contain potentially identifying or sensitive patient information. Data used in this study are available upon reasonable request from the corresponding author at the Emergency Department of the University Hospital Bern, Switzerland to researchers eligible under Swiss legislation to work with codified personal health care data. Eligibility will be determined by Cantonal ethics committee Bern.

Abbreviations

BPM:

Beats Per Minute

CCS:

Clinical Classification Software for ICD-10

CI:

Confidence Interval

ED:

Emergency Department

GMR:

Geometric Mean Ratio

ICD-10:

10th International Classification of Diseases and Health Related Problems

IM:

Internal Medicine

IQR:

Interquartile Range

LOS:

Length of Stay

NSC:

Non-Specific Complaint

OR:

Odds Ratio

References

  1. Croskerry P, Sinclair D. Emergency medicine: A practice prone to error? CJEM. 2001;3(04):271–6.

    Article  CAS  PubMed  Google Scholar 

  2. Hautz SC, Schuler L, Kämmer JE, et al. Factors predicting a change in diagnosis in patients hospitalised through the emergency room: a prospective observational study. BMJ Open. 2016;6(5):e011585.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Dormann H, Diesch K, Ganslandt T, et al. Numerical parameters and quality indicators in a medical emergency department. Dtsch Arztebl Int. 2010;107(15):261–7.

    PubMed  PubMed Central  Google Scholar 

  4. Tudela P, Carreres A, Ballester M. Diagnostic errors in emergency departments. Med Clin Barc. 2017;149(4):170–5.

    Article  PubMed  Google Scholar 

  5. Heuer JF, Gruschka D, Crozier TA, et al. Accuracy of prehospital diagnoses by emergency physicians: comparison with discharge diagnosis. Eur J Emerg Med. 2012;19(5):292–6.

    Article  PubMed  Google Scholar 

  6. Ong TJ, Ariathianto Y, Sinnappu R, et al. Lower rates of appropriate initial diagnosis in older emergency department patients associated with hospital length of stay. Australas J Ageing. 2015;34(2):121–6.

    Article  PubMed  Google Scholar 

  7. Klasco RS, Wolfe RE, Wong M, et al. Assessing the rates of error and adverse events in the ED. Am J Emerg Med. 2015;33(12):1786–9.

    Article  PubMed  Google Scholar 

  8. Caterino JM, Stevenson KB. Disagreement between emergency physician and inpatient physician diagnosis of infection in older adults admitted from the emergency department. Acad Emerg Med. 2012;19(8):908–15.

    Article  PubMed  Google Scholar 

  9. Okafor N, Payne VL, Chathampally Y, et al. Using voluntary reports from physicians to learn from diagnostic errors in emergency medicine. Emerg Med J. 2016;33(4):245–52.

    Article  PubMed  Google Scholar 

  10. Hautz WE, Kämmer JE, Hautz SC, 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):27–54.

    Article  Google Scholar 

  11. Zhang E, Hung SC, Wu CH, et al. Adverse event and error of unexpected life-threatening events within 24hours of ED admission. Am J Emerg Med. 2017;35(3):479–83.

    Article  CAS  PubMed  Google Scholar 

  12. Wen LS, Espinola JA, Kosowsky JM, et al. Do emergency department patients receive a pathological diagnosis? A nationally-representative sample. West J Emerg Med. 2015;16(1):50–4.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Dijkstra R, Wensing M, Thomas R, et al. The relationship between organisational characteristics and the effects of clinical guidelines on medical performance in hospitals, a meta-analysis. BMC Health Serv Res. 2006;6(1):53.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Christensen EF, Larsen TM, Jensen FB, et al. Diagnosis and mortality in prehospital emergency patients transported to hospital: a population-based and registry-based cohort study. BMJ Open. 2016;6(7):e011558.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wogan JM. ED follow-up: A comparison of admission and discharge diagnoses. Am J Emerg Med. 2001;19(3):249–51.

    Article  CAS  PubMed  Google Scholar 

  16. Onen F, Abidi H, Savoye L, et al. Emergency hospitalization in the elderly in a French university hospital: medical and social conditions and crisis factors precipitating admissions and outcome at discharge. Aging Milano. 2001;13(6):421–9.

    CAS  PubMed  Google Scholar 

  17. Gunnarsdottir OS, Rafnsson V. Seven-year evolution of discharge diagnoses of emergency department users. Eur J Emerg Med. 2007;14(4):193–8.

    Article  PubMed  Google Scholar 

  18. Owens PL, Barrett ML, Gibson TB, et al. Emergency department care in the United States: a profile of national data sources. Ann Emerg Med. 2010;56(2):150–65.

    Article  PubMed  Google Scholar 

  19. Exadaktylos AHW. Emergency medicine in Switzerland. ICU Manag. 2015;15:160–2.

    Google Scholar 

  20. Müller M, Schechter CB, Hautz WE, et al. The development and validation of a resource consumption score of an emergency department consultation. PLoS ONE. 2021;16(2):e0247244.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Rutschmann OT, Hugli OW, Marti C, et al. Reliability of the revised Swiss Emergency Triage Scale: a computer simulation study. Eur J Emerg Med. 2018;25(4):264–9.

    Article  PubMed  Google Scholar 

  22. World Health Organization. International statistical classification of diseases and related health problems 10th revision [internet]. [cited 29 Apr 2017]. Available: http://apps.who.int/classifications/icd10/browse/2016/en.

  23. Agency for Healthcare Research and Quality (AHRQ). Clinical Classification Software (CCS) dor ICD-10-CM/PCS.

  24. Ljunggren M, Castren M, Nordberg M, et al. The association between vital signs and mortality in a retrospective cohort study of an unselected emergency department population. Scand J Trauma Resusc Emerg Med. 2016;24:21.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Knaus WA, Zimmerman JE, Wagner DP, et al. APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit Care Med. 1981;9(8):591–7.

    Article  CAS  PubMed  Google Scholar 

  26. Nemec M, Koller MT, Nickel CH, et al. Patients presenting to the emergency department with non-specific complaints: The Basel Non-specific Complaints (BANC) Study. Acad Emerg Med. 2010;17(3):284–92.

    Article  PubMed  Google Scholar 

  27. Sauter TC, Capaldo G, Hoffmann M, et al. Non-specific complaints at emergency department presentation result in unclear diagnoses and lengthened hospitalization: a prospective observational study. Scand J Trauma Resusc Emerg Med. 2018;26(60):1–7.

    Google Scholar 

  28. Slovis BH, McCarthy DM, Nord G, et al. Identifying emergency department symptom-based diagnoses with the unified medical language system. West J Emerg Med. 2019;20(6):910–7.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Karakoumis J, Nickel CH, Kirsch M, et al. Emergency presentations with nonspecific complaints - the burden of morbidity and the spectrum of underlying disease: Nonspecific complaints and underlying disease. Med Baltim. 2015;94(26):e840.

    Article  Google Scholar 

  30. Djarv T, Castren M, Martenson L, et al. Decreased general condition in the emergency department: high in-hospital mortality and a broad range of discharge diagnoses. Eur J Emerg Med. 2015;22(4):241–6.

    Article  PubMed  Google Scholar 

  31. Safwenberg U, Terent A, Lind L. The Emergency Department presenting complaint as predictor of in-hospital fatality. Eur J Emerg Med. 2007;14(6):324–31.

    Article  PubMed  Google Scholar 

  32. Kemp K, Mertanen R, Laaperi M, et al. Nonspecific complaints in the emergency department - a systematic review. Scand J Trauma Resusc Emerg Med. 2020;28(1):6.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Peng A, Rohacek M, Ackermann S, et al. The proportion of correct diagnoses is low in emergency patients with nonspecific complaints presenting to the emergency department. Swiss Med Wkly. 2015;145:w14121.

    PubMed  Google Scholar 

  34. Wachelder JJ, Stassen PM, Hubens LP, et al. Elderly emergency patients presenting with non-specific complaints: characteristics and outcomes. PLoS ONE. 2017;12(11):e0188954.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol Biol Sci Med Sci. 2001;56(3):M146–56.

    Article  CAS  Google Scholar 

  36. Lewis R, Lamdan RM, Wald D, et al. Gender bias in the diagnosis of a geriatric standardized patient: a potential confounding variable. Acad Psychiatry. 2006;30(5):392–6.

    Article  PubMed  Google Scholar 

  37. Hautz WE, Sauter TC, Hautz SC, et al. What determines diagnostic resource consumption in emergency medicine: patients, physicians or context? Emerg Med J. 2020;37(9):546–51.

    Article  PubMed  Google Scholar 

  38. Zwaan L, Hautz WE. Bridging the gap between uncertainty, confidence and diagnostic accuracy: calibration is key. BMJ Qual Saf. 2019;28(5):352–5.

    Article  PubMed  Google Scholar 

  39. Bean RBBW. Sir William Osler: Aphorisms from His Bedside Teachings and Writings. New York: H. Schuman; 1950.

    Google Scholar 

  40. Morgenstern J. “Communicating diagnostic uncertainty”, First 10EM blog.

  41. Hautz SC, Oberholzer DL, Freytag J, et al. An observational study of self-monitoring in ad hoc health care teams. BMC Med Educ. 2020;20(1):201.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Hautz WE, Schubert S, Schauber SK, et al. Accuracy of self-monitoring: Does experience, ability or case difficulty matter? Med Educ. 2019;53(7):735–44.

    Article  PubMed  Google Scholar 

  43. Kämmer JE, Hautz WE, März M. Self-monitoring accuracy does not increase throughout undergraduate medical education. Med Educ. 2020;54(4):320–7.

    Article  PubMed  Google Scholar 

  44. Pusic MV, Chiaramonte R, Gladding S, et al. Accuracy of self-monitoring during learning of radiograph interpretation. Med Educ. 2015;49(8):838–46.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Non applicable.

Funding

This study was partially funded by a grant from the Clinical Trials Unit and the Director of Research at Inselspital Bern to Hautz WE and by financial support from the “Mittelbauvereinigung” of the University of Bern to Hautz WE and Hautz SC. Both funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decisions to submit the manuscript for publication.

MM was funded by the Bangerter Foundation and the Swiss Academy of Medical Sciences through the "Young Talents in Clinical Research" (grant TCR 14/17).

Author information

Author notes

  1. Martin Müller and Wolf E. Hautz contributed equally to this work.

Authors and Affiliations

  1. Department of Emergency Medicine, Inselspital, University Hospital, University of Bern, 3010, Bern, Switzerland

    Tanja Birrenbach, Michele Hoffmann, Stefanie C. Hautz, Juliane E. Kämmer, Aristomenis K. Exadaktylos, Thomas C. Sauter, Martin Müller & Wolf E. Hautz

  2. Faculty of Medicine, Centre for Health Sciences Education, University of Oslo, Oslo, Norway

    Tanja Birrenbach

Authors
  1. Tanja Birrenbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michele Hoffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefanie C. Hautz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juliane E. Kämmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aristomenis K. Exadaktylos
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thomas C. Sauter
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Martin Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wolf E. Hautz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TB: conception, design, acquisition, analysis, interpretation, drafting and revising. MH: acquisition, interpretation. SCH: conception, design, interpretation, revising. JEK: conception, design, acquisition, interpretation, revising. AK: conception, design. TCS: conception, design, acquisition, interpretation. WEH: conception, design, acquisition, analysis, interpretation, drafting and revising. MM: conception, design, acquisition, analysis, interpretation, drafting and revising. All authors have approved the submitted version. All authors have agreed both to be personally accountable for their own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Corresponding author

Correspondence to Tanja Birrenbach.

Ethics declarations

Ethics approval and consent to participate

The Bern ethics committee registered the study as a quality evaluation study under Kantonale Ethikkomission Bern BASEC number 197/15 and waived the need for informed consent. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Non applicable.

Competing interests

TCS holds an endowed professorship that is financially supported by the Touring Club Switzerland. The founder has no influence on the research agenda, results or the decision to publish. WEH has received speaker fees from the AO Foundation Zürich and research funding from Mundipharma Medical Basel. All authors have nothing to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: S1 Table.

Specific ICD-10 coding rules.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Birrenbach, T., Hoffmann, M., Hautz, S.C. et al. Frequency and predictors of unspecific medical diagnoses in the emergency department: a prospective observational study. BMC Emerg Med 22, 109 (2022). https://doi.org/10.1186/s12873-022-00665-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12873-022-00665-x

Keywords

BMC Emergency Medicine

ISSN: 1471-227X

Contact us