Factors predicting a change in diagnosis in patients hospitalised through the emergency room: a prospective observational study

Diagnostic Discrepancies in the Emergency Department: A Retrospective Study

OBJECTIVES

Diagnostic errors contribute substantially to preventable medical errors. Especially, the emergency department (ED) is a high-risk environment. Previous research showed that in 15%-30% of the ED patients, there is a difference between the primary diagnosis assigned by the emergency physician and the discharge diagnosis. This study aimed to determine the number and types of diagnostic discrepancies and to explore factors predicting discrepancies.

METHODS

A retrospective record review was conducted in an academic medical center. The primary diagnosis assigned in the ED was compared with the discharge diagnosis after hospital admission. For each patient, we gathered additional information about the diagnostic process to identify possible predictors of diagnostic discrepancies.

RESULTS

The electronic health records of 200 patients were reviewed. The primary diagnosis assigned in the ED was substantially different from the discharge diagnosis in 16.0%. These diagnostic discrepancies were associated with a higher number of additional diagnostics applied for (2.4 versus 2.0 diagnostics; P = 0.002) and longer stay in the ED (5.9 versus 4.7 hours; P = 0.008).

CONCLUSIONS

A difference between the diagnosis assigned by the emergency physician and the discharge diagnosis was found in almost 1 in 6 patients. The increased number of additional diagnostics and the longer stay at the ED in the group of patients with a diagnostic discrepancy suggests that these cases reflect the more difficult cases. More research should be done on predictive factors of diagnostic discrepancies.

Identifying Acute Aortic Syndrome and Thoracic Aortic Aneurysm from Chest Radiography in the Emergency Department Using Convolutional Neural Network Models

(1) Background: Identifying acute aortic syndrome (AAS) and thoracic aortic aneurysm (TAA) in busy emergency departments (EDs) is crucial due to their life-threatening nature, necessitating timely and accurate diagnosis. (2) Methods: This retrospective case-control study was conducted in the ED of three hospitals. Adult patients visiting the ED between 1 January 2010 and 1 January 2020 with a chief complaint of chest or back pain were enrolled in the study. The collected chest radiography (CXRs) data were divided into training (80%) and testing (20%) datasets. The training dataset was trained by four different convolutional neural network (CNN) models. (3) Results: A total of 1625 patients were enrolled in this study. The InceptionV3 model achieved the highest F1 score of 0.76. (4) Conclusions: Analysis of CXRs using a CNN-based model provides a novel tool for clinicians to interpret ED patients with chest pain and suspected AAS and TAA. The integration of such imaging tools into ED could be considered in the future to enhance the diagnostic workflow for clinically fatal diseases.

Prevalence and Characteristics of Diagnostic Error in Pediatric Critical Care: A Multicenter Study

OBJECTIVES

Effective interventions to prevent diagnostic error among critically ill children should be informed by diagnostic error prevalence and etiologies. We aimed to determine the prevalence and characteristics of diagnostic errors and identify factors associated with error in patients admitted to the PICU.

DESIGN

Multicenter retrospective cohort study using structured medical record review by trained clinicians using the Revised Safer Dx instrument to identify diagnostic error (defined as missed opportunities in diagnosis). Cases with potential errors were further reviewed by four pediatric intensivists who made final consensus determinations of diagnostic error occurrence. Demographic, clinical, clinician, and encounter data were also collected.

SETTING

Four academic tertiary-referral PICUs.

PATIENTS

Eight hundred eighty-two randomly selected patients 0-18 years old who were nonelectively admitted to participating PICUs.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of 882 patient admissions, 13 (1.5%) had a diagnostic error up to 7 days after PICU admission. Infections (46%) and respiratory conditions (23%) were the most common missed diagnoses. One diagnostic error caused harm with a prolonged hospital stay. Common missed diagnostic opportunities included failure to consider the diagnosis despite a suggestive history (69%) and failure to broaden diagnostic testing (69%). Unadjusted analysis identified more diagnostic errors in patients with atypical presentations (23.1% vs 3.6%, p = 0.011), neurologic chief complaints (46.2% vs 18.8%, p = 0.024), admitting intensivists greater than or equal to 45 years old (92.3% vs 65.1%, p = 0.042), admitting intensivists with more service weeks/year (mean 12.8 vs 10.9 wk, p = 0.031), and diagnostic uncertainty on admission (77% vs 25.1%, p < 0.001). Generalized linear mixed models determined that atypical presentation (odds ratio [OR] 4.58; 95% CI, 0.94-17.1) and diagnostic uncertainty on admission (OR 9.67; 95% CI, 2.86-44.0) were significantly associated with diagnostic error.

CONCLUSIONS

Among critically ill children, 1.5% had a diagnostic error up to 7 days after PICU admission. Diagnostic errors were associated with atypical presentations and diagnostic uncertainty on admission, suggesting possible targets for intervention.

Is language an issue? Accuracy of the German computerized diagnostic decision support system ISABEL and cross-validation with the English counterpart

OBJECTIVES

Existing computerized diagnostic decision support tools (CDDS) accurately return possible differential diagnoses (DDx) based on the clinical information provided. The German versions of the CDDS tools for clinicians (Isabel Pro) and patients (Isabel Symptom Checker) from ISABEL Healthcare have not been validated yet.

METHODS

We entered clinical features of 50 patient vignettes taken from an emergency medical text book and 50 real cases with a confirmed diagnosis derived from the electronic health record (EHR) of a large academic Swiss emergency room into the German versions of Isabel Pro and Isabel Symptom Checker. We analysed the proportion of DDx lists that included the correct diagnosis.

RESULTS

Isabel Pro and Symptom Checker provided the correct diagnosis in 82 and 71 % of the cases, respectively. Overall, the correct diagnosis was ranked in 71 , 61 and 37 % of the cases within the top 20, 10 and 3 of the provided DDx when using Isabel Pro. In general, accuracy was higher with vignettes than ED cases, i.e. listed the correct diagnosis more often (non-significant) and ranked the diagnosis significantly more often within the top 20, 10 and 3. On average, 38 ± 4.5 DDx were provided by Isabel Pro and Symptom Checker.

CONCLUSIONS

The German versions of Isabel achieved a somewhat lower accuracy compared to previous studies of the English version. The accuracy decreases substantially when the position in the suggested DDx list is taken into account. Whether Isabel Pro is accurate enough to improve diagnostic quality in clinical ED routine needs further investigation.

What factors affect team members' evaluation of collaboration in medical teams?

Introduction

Perceived teamwork quality is associated with numerous work-related outcomes, ranging from team effectiveness to job satisfaction. This study explored what situational and stable factors affect the perceived quality of teamwork during a specific team task: when a medical team comprising a senior (supervisor) and a junior (trainee) physician diagnoses a patient.

Methods

During a field study in an emergency department, multisource data describing the patients, the diagnosing physicians, and the context were collected, including physicians' ratings of their teamwork. The relationships between perceived teamwork quality and situational (e.g., workload) and stable (e.g., seniority) factors were estimated in a latent regression model using the structural equation modeling (SEM) approach.

Results

Across the N = 495 patients included, SEM analyses revealed that the patient-specific case clarity and urgency influenced the perceived teamwork quality positively, whereas the work experience of the supervisor influenced the perceived teamwork quality of both supervisor and trainee negatively, albeit to different degrees.

Discussion

Our findings shed light on the complex underpinnings of perceived teamwork quality, a performance-relevant factor that may influence work and organizational effectiveness in healthcare settings.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-022-00665-x.

Impact of subspecialty consultations on diagnosis in the pediatric intensive care unit

OBJECTIVES

Intensivists and subspecialists often collaborate in diagnosing patients in the pediatric intensive care unit (PICU). Our objectives were to characterize critically ill children for whom subspecialty consultations were requested, describe consultation characteristics, and determine consultations' impact on PICU diagnosis.

METHODS

We performed a retrospective study using chart review in a single tertiary referral PICU including children admitted for acute illness. We collected data on patients with and without subspecialty consultations within the first three days of PICU admission and determined changes in PICU clinicians' diagnostic evaluation or treatment after consultations.

RESULTS

PICU clinicians requested 152 subspecialty consultations for 87 of 101 (86%) patients. Consultations were requested equally for assistance in diagnosis (65%) and treatment (66%). Eighteen of 87 (21%) patients with consultations had a change in diagnosis from PICU admission to discharge, 11 (61%) attributed to subspecialty input. Thirty-nine (45%) patients with consultations had additional imaging and/or laboratory testing and 48 (55%) had medication changes and/or a procedure performed immediately after consultation.

CONCLUSIONS

Subspecialty consultations were requested during a majority of PICU admissions. Consultations can influence the diagnosis and treatment of critically ill children. Future research should investigate PICU interdisciplinary collaborations, which are essential for teamwork in diagnosis.

Automated identification of diagnostic labelling errors in medicine

Objectives

Identification of diagnostic error is complex and mostly relies on expert ratings, a severely limited procedure. We developed a system that allows to automatically identify diagnostic labelling error from diagnoses coded according to the international classification of diseases (ICD), often available as routine health care data.

Methods

The system developed (index test) was validated against rater based classifications taken from three previous studies of diagnostic labeling error (reference standard). The system compares pairs of diagnoses through calculation of their distance within the ICD taxonomy. Calculation is based on four different algorithms. To assess the concordance between index test and reference standard, we calculated the area under the receiver operating characteristics curve (AUROC) and corresponding confidence intervals. Analysis were conducted overall and separately per algorithm and type of available dataset.

Results

Diagnoses of 1,127 cases were analyzed. Raters previously classified 24.58% of cases as diagnostic labelling errors (ranging from 12.3 to 87.2% in the three datasets). AUROC ranged between 0.821 and 0.837 overall, depending on the algorithm used to calculate the index test (95% CIs ranging from 0.8 to 0.86). Analyzed per type of dataset separately, the highest AUROC was 0.924 (95% CI 0.887–0.962).

Conclusions

The trigger system to automatically identify diagnostic labeling error from routine health care data performs excellent, and is unaffected by the reference standards’ limitations. It is however only applicable to cases with pairs of diagnoses, of which one must be more accurate or otherwise superior than the other, reflecting a prevalent definition of a diagnostic labeling error.

The discrepancy between admission and discharge diagnoses: Underlying factors and potential clinical outcomes in a low socioeconomic country

Objective

The discrepancy between admission and discharge diagnosis can lead to possible adverse patient outcomes. There are gaps in integrated studies, and less is understood about its characteristics and effects. Therefore, this study was conducted to determine the frequency, characteristics, and outcomes of diagnostic discrepancies at admission and discharge.

Design and data sources

This retrospective study reviewed the admitting and discharge diagnoses of adult patients admitted at Aga Khan University Hospital (AKUH), Internal Medicine Department between October 2018 and February 2019. The frequency and outcomes of discrepancies in patient diagnoses were noted among Emergency Department (ED) physician versus admitting physician, admitting physician versus discharge physician, and ED physician versus discharge physician for the full match, partial match, and mismatch diagnoses. The studied outcomes included interdepartmental transfer, Intensive Care Unit (ICU) transfer, in-hospital mortality, readmission within 30 days, and the length of stay. For simplicity, we only analyzed the factors for the discrepancy among ED physicians and discharge physicians.

Results

Out of 537 admissions, there were 25.3–27.2% admissions with full match diagnoses while 18.6–19.4% and 45.3–47.9% had mismatch and partial match diagnoses respectively. The discrepancy resulted in an increased number of interdepartmental transfers (5–5.8%), ICU transfers (5.6–8.7%), in-hospital mortality (8–11%), and readmissions within 30 days in ED (14.4%-16.7%). A statistically significant difference was observed for the ward’s length of stay with the most prolonged stay in partially matched diagnoses (6.3 ± 5.4 days). Among all the factors that were evaluated for the diagnostic discrepancy, older age, multi-morbidities, level of trainee clerking the patient, review by ED faculty, incomplete history, and delay in investigations at ED were associated with significant discrepant diagnoses.

Conclusions

Diagnostic discrepancies are a relevant and significant healthcare problem. Fixed patient or physician characteristics do not readily predict diagnostic discrepancies. To reduce the diagnostic discrepancy, emphasis should be given to good history taking and thorough physical examination. Patients with older age and multi-morbidity should receive significant consideration.

Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study

BACKGROUND

Diagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making.

OBJECTIVE

This study aims to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques.

METHODS

This study plans to use trigger algorithms within EHR data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on whether they meet certain criteria. Samples from both data sets will be validated using medical record reviews, upon which we expect to find a higher number of diagnostic safety events in the trigger-positive subset. Machine learning will be used to evaluate relationships between certain patient factors, provider-care team factors, and system-level risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts.

RESULTS

This federally funded study was approved by the institutional review board of 2 academic medical centers with affiliated community hospitals. Trigger queries are being developed at both organizations, and sample cohorts will be labeled using the triggers. Machine learning techniques such as association rule mining, chi-square automated interaction detection, and classification and regression trees will be used to discover important variables that could be incorporated within future clinical decision support systems to help identify and reduce risks that contribute to diagnostic errors.

CONCLUSIONS

The use of large EHR data sets and machine learning to investigate risk factors (related to the patient, provider-care team, and system-level) in the diagnostic process may help create future mechanisms for monitoring diagnostic safety.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/24642.

Diagnostic Errors Induced by a Wrong a Priori Diagnosis: A Prospective Randomized Simulator-Based Trial

Preventive strategies against diagnostic errors require the knowledge of underlying mechanisms. We examined the effects of a wrong a priori diagnosis on diagnostic accuracy of a focussed assessment in an acute myocardial infarction scenario. One-hundred-and-fifty-six medical students (cohort 1) were randomized to three study arms differing in the a priori diagnosis revealed: no diagnosis (control group), myocardial infarction (correct diagnosis group), and pulmonary embolism (wrong diagnosis group). Forty-four physicians (cohort 2) were randomized to the control group and the wrong diagnosis group. Primary endpoint was the participants’ final presumptive diagnosis. Among students, the correct diagnosis of an acute myocardial infarction was made by 48/52 (92%) in the control group, 49/52 (94%) in the correct diagnosis group, and 14/52 (27%) in the wrong diagnosis group (p < 0.001 vs. both other groups). Among physicians, the correct diagnosis was made by 20/21 (95%) in the control group and 15/23 (65%) in the wrong diagnosis group (p = 0.023). In the wrong diagnosis group, 31/52 (60%) students and 6/23 (19%) physicians indicated their initially given wrong a priori diagnosis pulmonary embolism as final diagnosis. A wrong a priori diagnosis significantly increases the likelihood of a diagnostic error during a subsequent patient encounter.

Study protocol for a prospective, double-blinded, observational study investigating the diagnostic accuracy of an app-based diagnostic health care application in an emergency room setting: the eRadaR trial

INTRODUCTION

Occurrence of inaccurate or delayed diagnoses is a significant concern in patient care, particularly in emergency medicine, where decision making is often constrained by high throughput and inaccurate admission diagnoses. Artificial intelligence-based diagnostic decision support system have been developed to enhance clinical performance by suggesting differential diagnoses to a given case, based on an integrated medical knowledge base and machine learning techniques. The purpose of the study is to evaluate the diagnostic accuracy of Ada, an app-based diagnostic tool and the impact on patient outcome.

METHODS AND ANALYSIS

The eRadaR trial is a prospective, double-blinded study with patients presenting to the emergency room (ER) with abdominal pain. At initial contact in the ER, a structured interview will be performed using the Ada-App and both, patients and attending physicians, will be blinded to the proposed diagnosis lists until trial completion. Throughout the study, clinical data relating to diagnostic findings and types of therapy will be obtained and the follow-up until day 90 will comprise occurrence of complications and overall survival of patients. The primary efficacy of the trial is defined by the percentage of correct diagnoses suggested by Ada compared with the final discharge diagnosis. Further, accuracy and timing of diagnosis will be compared with decision making of classical doctor-patient interaction. Secondary objectives are complications, length of hospital stay and overall survival.

ETHICS AND DISSEMINATION

Ethical approval was received by the independent ethics committee (IEC) of the Goethe-University Frankfurt on 9 April 2020 including the patient information material and informed consent form. All protocol amendments must be reported to and adapted by the IEC. The results from this study will be submitted to peer-reviewed journals and reported at suitable national and international meetings.

TRIAL REGISTRATION NUMBER

DRKS00019098.

Discrepancy between Admission and Discharge Diagnoses in Central Serbia: Analysis by the Groups of International Classification of Diseases, 10th Revision

Background

Repeated research while using the same methodology can be useful and it can enable relevant conclusions in the same health care system. The aim of our study was to perform comparative analysis of the agreement between admission and discharge diagnostic groups in period 2014-2017 with period 2006-2013 in the Clinical Center of Kragujevac, Serbia.

Methods

The 5% simple, random sample was made from the basic set of all hospital reports from Clinical Centre Kragujevac, Serbia, in the period 01.01. 2014 - 31.12. 2017 (n=10228). The first four digits of ICD-10 codes at admission and discharge were compared for agreement. We used discharge diagnosis as a "golden standard". Statistical analysis was performed using Cohen's Kappa statistic.

Results

In the period 2014-2017, agreement between diagnosis among the most ICD10 groups increased in comparison with the period 2006-2013. Disagreements between diagnosis in the period 2014-2017 in comparation with period 2006-2013 was associated with increased length of stay in the hospital (7.5 vs. 9.1 days, P<0.01), patients were younger (54 vs 49.6 yr, P<0.01), number of males declined (26.3% vs 16.2%, P<0.05), kappa value decreased in XV ICD10 group and XI ICD10 group and kappa value increased in XIV ICD10 group.

Conclusion

Agreement between admission and discharge diagnosis among the most ICD10 diagnostic groups increased. Introduction of a new web application has increased the quality of data, but interpreting it requires the skill of researchers. Further research should identify modifiable causes of discrepancy between admission and discharge diagnoses.

Diagnostic error increases mortality and length of hospital stay in patients presenting through the emergency room

BACKGROUND

Diagnostic errors occur frequently, especially in the emergency room. Estimates about the consequences of diagnostic error vary widely and little is known about the factors predicting error. Our objectives thus was to determine the rate of discrepancy between diagnoses at hospital admission and discharge in patients presenting through the emergency room, the discrepancies' consequences, and factors predicting them.

METHODS

Prospective observational clinical study combined with a survey in a University-affiliated tertiary care hospital. Patients' hospital discharge diagnosis was compared with the diagnosis at hospital admittance through the emergency room and classified as similar or discrepant according to a predefined scheme by two independent expert raters. Generalized linear mixed-effects models were used to estimate the effect of diagnostic discrepancy on mortality and length of hospital stay and to determine whether characteristics of patients, diagnosing physicians, and context predicted diagnostic discrepancy.

RESULTS

755 consecutive patients (322 [42.7%] female; mean age 65.14 years) were included. The discharge diagnosis differed substantially from the admittance diagnosis in 12.3% of cases. Diagnostic discrepancy was associated with a longer hospital stay (mean 10.29 vs. 6.90 days; Cohen's d 0.47; 95% confidence interval 0.26 to 0.70; P = 0.002) and increased patient mortality (8 (8.60%) vs. 25(3.78%); OR 2.40; 95% CI 1.05 to 5.5 P = 0.038). A factor available at admittance that predicted diagnostic discrepancy was the diagnosing physician's assessment that the patient presented atypically for the diagnosis assigned (OR 3.04; 95% CI 1.33-6.96; P = 0.009).

CONCLUSIONS

Diagnostic discrepancies are a relevant healthcare problem in patients admitted through the emergency room because they occur in every ninth patient and are associated with increased in-hospital mortality. Discrepancies are not readily predictable by fixed patient or physician characteristics; attention should focus on context.

TRIAL REGISTRATION

https://bmjopen.bmj.com/content/6/5/e011585.

Patient safety culture in a university hospital emergency department in Switzerland - a survey study

Aim of the study: Poor safety culture, bad teamwork, non-functional inter-departmental working relationships and increased cognitive demands are associated with higher amounts of adverse events in hospitals. To improve patient safety, one of the first steps is to assess safety culture among health care providers in an institution. Considering the sparsity of studies addressing patient safety culture in Europe and Switzerland, the aim of the present study was to assess patient safety culture in the emergency department of a University Hospital in Switzerland. Methods: We employed the Hospital Survey On Patient Safety Culture, developed by the U.S. Agency for Healthcare Research and Quality. 140 questionnaires were distributed to nurses and physicians. Two weeks after the first questionnaire, we performed a sensitization campaign addressed to health care providers, and then repeated the survey. We calculated composite scores for each question category and percentages of positive responses for each dimension. For group comparisons such as possible differences relating to education and duration of employment and to compare results of the first and second survey we used T-tests. The results were compared to other published surveys outside of Switzerland. Results: Particularly positive assessments were found for the categories "nonpunitive response to errors", "teamwork within units", "supervisor/manager expectations and actions promoting patient safety" and, compared to other hospitals, also "staffing". The lowest average percent positive responses were found in the categories "frequencies of reported event", "teamwork across units" and "handoffs and transitions". Nurses and health care personnel with a longer employment history had an overall more negative assessment of patient safety culture, when compared to physicians and personnel with a shorter duration of employment, respectively. Conclusions: The present study has identified strengths and potential weaknesses in the safety culture of a large university hospital emergency department in Switzerland. The results provide opportunities for improvement of patient safety in particular in the reporting of adverse events, in interaction across units and patient transitions. Furthermore, as we employed a standardized self-assessment tool similar to previously published studies, the work contributes to the establishment of a benchmark for hospital safety culture at the national, European and international level.

Non-specific complaints at emergency department presentation result in unclear diagnoses and lengthened hospitalization: a prospective observational study

Background

Up to 20% of patients admitted to an emergency department present with non-specific complaints. Retrospective studies suggest an increased risk of misdiagnosis and hospital admission for these patients, but prospective comparisons of the outcomes of emergency patients with non-specific complaints versus specific complaints are lacking.

Methods

All consecutive patients ≥18 years of age admitted to any internal medicine ward at Bern University Hospital via the emergency department from August 15th 2015 to December 7th 2015 were prospectively included and followed up upon. Patients with non-specific complaints were compared against those with specific complaints regarding the quality of their emergency department diagnosis, length of hospital stay and in-hospital mortality.

Results

Seven hundred and-eleven patients, 165 (23.21%) with non-specific complaints and 546 with specific complaints, were included in this study. No differences between patient groups regarding age, gender or initial severity of the medical problem (deducted from triage category and treatment in a resuscitation bay) were found. Patients with non-specific complaints received more unspecific diagnoses (30.3% vs. 23.1%, p = 0.001, OR = 1.82 [95% CI 1.159–2.899]), were hospitalized significantly longer (Median = 6.51 (IQR = 5.85) vs. 5.22 (5.83) days, p = 0.025, d = 0.2) but did not have a higher mortality than patients with specific complaints (7.3% vs. 3.7%, p = 0.087, OR 1.922 [95% CI 0.909–4.065]).

Conclusions

Non-specific complaints in patients admitted to an emergency department result in low-quality diagnoses and lengthened hospitalization, despite the patients being comparable to patients with specific complaints at admission.

Now you see me: a pragmatic cohort study comparing first and final radiological diagnoses in the emergency department

OBJECTIVES

To (1) compare timely but preliminary and definitive but delayed radiological reports in a large urban level 1 trauma centre, (2) assess the clinical significance of their differences and (3) identify clinical predictors of such differences.

DESIGN, SETTING AND PARTICIPANTS

We performed a retrospective record review for all 2914 patients who presented to our university affiliated emergency department (ED) during a 6-week period. In those that underwent radiological imaging, we compared the patients' discharge letter from the ED to the definitive radiological report. All identified discrepancies were assessed regarding their clinical significance by trained raters, independent and in duplicate. A binary logistic regression was performed to calculate the likelihood of discrepancies based on readily available clinical data.

RESULTS

1522 patients had radiographic examinations performed. Rater agreement on the clinical significance of identified discrepancies was substantial (kappa=0.86). We found an overall discrepancy rate of 20.35% of which about one-third (7.48% overall) are clinically relevant. A logistic regression identified patients' age, the imaging modality and the anatomic region under investigation to be predictive of future discrepancies.

CONCLUSIONS

Discrepancies between radiological diagnoses in the ED are frequent and readily available clinical factors predict their likelihood. Emergency physicians should reconsider their discharge diagnosis especially in older patients undergoing CT scans of more than one anatomic region.

Accuracy of acute burns diagnosis made using smartphones and tablets: a questionnaire-based study among medical experts

BACKGROUND

Remote assistance for burns by medical experts can support nurses and general physicians in emergency care with diagnostic and management advice. Previous studies indicate a high diagnostic accuracy based on images viewed on a computer screen, but whether image-based analysis by experts using handheld devices is accurate remains to be determined.

METHOD

A review of patient data from eight emergency centres in the Western Cape, South Africa, revealed 10 typical cases of burns commonly seen in children and adults. A web-based questionnaire was created with 51 images of burns representing those cases. Burns specialists from two countries (South Africa and Sweden (n = 8 and 7 respectively)) and emergency medicine specialists from South Africa (n = 11) were contacted by email and asked to assess each burn's total body surface area (TBSA) and depth using a smartphone or tablet. The accuracy and inter-rater reliability of the assessments were measured using intraclass correlation coefficients (ICC), both for all cases aggregated and for paediatric and adult burn cases separately. Eight participants repeated the questionnaire on a computer and intra-rater reliability was calculated.

RESULTS

The assessments of TBSA are of high accuracy all specialists aggregated (ICC = 0.82 overall and 0.81 for both child and adult cases separately) and remain high for all three participant groups separately. The burn depth assessments have low accuracy all specialists aggregated, with ICCs of 0.53 overall, 0.61 for child and 0.46 for adult cases. The most accurate assessments of depth are among South African burns specialists (reaching acceptable for child cases); the other two groups' ICCs are low in all instances. Computer-based assessments were similar to those made on handheld devices.

CONCLUSION

As was the case for computer-based studies, burns images viewed on handheld devices may be a suitable means of seeking expert advice even with limited additional information when it comes to burn size but less so in the case of burn depth. Familiarity with the type of cases presented could facilitate image-based diagnosis of depth.

Improving patient safety through better teamwork: how effective are different methods of simulation debriefing? Protocol for a pragmatic, prospective and randomised study

INTRODUCTION

Medical errors have an incidence of 9% and may lead to worse patient outcome. Teamwork training has the capacity to significantly reduce medical errors and therefore improve patient outcome. One common framework for teamwork training is crisis resource management, adapted from aviation and usually trained in simulation settings. Debriefing after simulation is thought to be crucial to learning teamwork-related concepts and behaviours but it remains unclear how best to debrief these aspects. Furthermore, teamwork-training sessions and studies examining education effects on undergraduates are rare. The study aims to evaluate the effects of two teamwork-focused debriefings on team performance after an extensive medical student teamwork training.

METHODS AND ANALYSES

A prospective experimental study has been designed to compare a well-established three-phase debriefing method (gather-analyse-summarise; the GAS method) to a newly developed and more structured debriefing approach that extends the GAS method with TeamTAG (teamwork techniques analysis grid). TeamTAG is a cognitive aid listing preselected teamwork principles and descriptions of behavioural anchors that serve as observable patterns of teamwork and is supposed to help structure teamwork-focused debriefing. Both debriefing methods will be tested during an emergency room teamwork-training simulation comprising six emergency medicine cases faced by 35 final-year medical students in teams of five. Teams will be randomised into the two debriefing conditions. Team performance during simulation and the number of principles discussed during debriefing will be evaluated. Learning opportunities, helpfulness and feasibility will be rated by participants and instructors. Analyses will include descriptive, inferential and explorative statistics.

ETHICS AND DISSEMINATION

The study protocol was approved by the institutional office for data protection and the ethics committee of Charité Medical School Berlin and registered under EA2/172/16. All students will participate voluntarily and will sign an informed consent after receiving written and oral information about the study. Results will be published.

A simulated night shift in the emergency room increases students' self-efficacy independent of role taking over during simulation

BACKGROUND

Junior doctors do not feel well prepared when they start into postgraduate training. High self-efficacy however is linked to better clinical performance and may thus improve patient care. What factors affect self-efficacy is currently unknown. We conducted a simulated night shift in an emergency room (ER) with final-year medical students to identify factors contributing to their self-efficacy and thus inform simulation training in the ER.

METHODS

We simulated a night in the ER using best educational practice including multi-source feedback, simulated patients and vicarious learning with 30 participants. Students underwent 7 prototypic cases in groups of 5 in different roles (leader, member and observer). Feeling of preparedness was measured at baseline and 5 days after the event. After every case students recorded their confidence dependent of their role during simulation and evaluated the case.

RESULTS

Thirty students participated, 18 (60 %) completed all surveys. At baseline students feel unconfident (Mean -0.34). Feeling of preparedness increases significantly at follow up (Mean 0.66, p = 0.001, d = 1.86). Confidence after simulation is independent of the role during simulation (F(2,52) = 0.123, p = 0.884). Observers in a simulation can estimate leader's confidence independent of their own (r = 0.188, p = 0.32) while team members cannot (r = 0.61, p < 0.001).

CONCLUSIONS

Simulation improves self-efficacy. The improvement of self-efficacy is independent of the role taken during simulation. As a consequence, groups can include observers as participants without impairing their increase in self-efficacy, providing a convenient way for educators to increase simulation efficiency. Different roles can furthermore be included into multi-source peer-feedback.