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

Using a clinical narrative-aware pre-trained language model for predicting emergency department patient disposition and unscheduled return visits

The increasing prevalence of overcrowding in Emergency Departments (EDs) threatens the effective delivery of urgent healthcare. Mitigation strategies include the deployment of monitoring systems capable of tracking and managing patient disposition to facilitate appropriate and timely care, which subsequently reduces patient revisits, optimizes resource allocation, and enhances patient outcomes. This study used ∼ 250,000 emergency department visit records from Taipei Medical University-Shuang Ho Hospital to develop a natural language processing model using BlueBERT, a biomedical domain-specific pre-trained language model, to predict patient disposition status and unplanned readmissions. Data preprocessing and the integration of both structured and unstructured data were central to our approach. Compared to other models, BlueBERT outperformed due to its pre-training on a diverse range of medical literature, enabling it to better comprehend the specialized terminology, relationships, and context present in ED data. We found that translating Chinese-English clinical narratives into English and textualizing numerical data into categorical representations significantly improved the prediction of patient disposition (AUROC = 0.9014) and 72-hour unscheduled return visits (AUROC = 0.6475). The study concludes that the BlueBERT-based model demonstrated superior prediction capabilities, surpassing the performance of prior patient disposition predictive models, thus offering promising applications in the realm of ED clinical practice.

The inseparability of context and clinical reasoning

Early descriptions of clinical reasoning have described a dual process model that relies on analytical or nonanalytical approaches to develop a working diagnosis. In this classic research, clinical reasoning is portrayed as an individual-driven cognitive process based on gathering information from the patient encounter, forming mental representations that rely on previous experience and engaging developed patterns to drive working diagnoses and management plans. Indeed, approaches to patient safety, as well as teaching and assessing clinical reasoning focus on the individual clinician, often ignoring the complexity of the system surrounding the diagnostic process. More recent theories and evidence portray clinical reasoning as a dynamic collection of processes that takes place among and between persons across clinical settings. Yet, clinical reasoning, taken as both an individual and a system process, is insufficiently supported by theories of cognition based on individual clinicals and lacks the specificity needed to describe the phenomenology of clinical reasoning. In this review, we reinforce that the modern healthcare ecosystem - with its people, processes and technology - is the context in which health care encounters and clinical reasoning take place.

Association of Diagnostic Discrepancy with Length of Stay and Mortality in Congestive Heart Failure Patients Admitted to the Emergency Department

The goal of this study was to analyze diagnostic discrepancies between emergency department (ED) and hospital discharge diagnoses in patients with congestive heart failure admitted to the ED. Using a synthetic dataset from the Department of Veterans Affairs, the patients' primary diagnoses were compared at two levels: diagnostic category and body system. With 12,621 patients and 24,235 admission cases, the study found a 58% mismatch rate at the category level, which was reduced to 30% at the body system level. Diagnostic categories associated with higher levels of mismatch included aplastic anemia, pneumonia, and bacterial infections. In contrast, diagnostic categories associated with lower levels of mismatch included alcohol-related disorders, COVID-19, cardiac dysrhythmias, and gastrointestinal hemorrhage. Further investigation revealed that diagnostic mismatches are associated with longer hospital stays and higher mortality rates. These findings highlight the importance of reducing diagnostic uncertainty, particularly in specific diagnostic categories and body systems, to improve patient care following ED admission.

Insights into diagnostic errors in endocrinology: a prospective, case-based, international study

BACKGROUND

Diagnostic errors in internal medicine are common. While cognitive errors have previously been identified to be the most common contributor to errors, very little is known about errors in specific fields of internal medicine such as endocrinology. This prospective, multicenter study focused on better understanding the causes of diagnostic errors made by general practitioners and internal specialists in the area of endocrinology.

METHODS

From August 2019 until January 2020, 24 physicians completed five endocrine cases on an online platform that simulated the diagnostic process. After each case, the participants had to state and explain why they chose their assumed diagnosis. The data gathering process as well as the participants' explanations were quantitatively and qualitatively analyzed to determine the causes of the errors. The diagnostic processes in correctly and incorrectly solved cases were compared.

RESULTS

Seven different causes of diagnostic error were identified, the most frequent being misidentification (mistaking one diagnosis with a related one or with more frequent and similar diseases) in 23% of the cases. Other causes were faulty context generation (21%) and premature closure (17%). The diagnostic confidence did not differ between correctly and incorrectly solved cases (median 8 out of 10, p = 0.24). However, in incorrectly solved cases, physicians spent less time on the technical findings (such as lab results, imaging) (median 250 s versus 199 s, p < 0.049).

CONCLUSIONS

The causes for errors in endocrine case scenarios are similar to the causes in other fields of internal medicine. Spending more time on technical findings might prevent misdiagnoses in everyday clinical practice.

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.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

INTRODUCTION

Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools.

RESEARCH SIGNIFICANCE

This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

MATERIALS AND METHODS

The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.

RESULTS

Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.

CONCLUSION

AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

Mortality by Admission Diagnosis in Children 1-60 Months of Age Admitted to a Tertiary Care Government Hospital in Malawi

Diagnosis-specific mortality is a measure of pediatric healthcare quality that has been incompletely studied in sub-Saharan African hospitals. Identifying the mortality rates of multiple conditions at the same hospital may allow leaders to better target areas for intervention. In this secondary analysis of routinely collected data, we investigated hospital mortality by admission diagnosis in children aged 1-60 months admitted to a tertiary care government referral hospital in Malawi between October 2017 and June 2020. The mortality rate by diagnosis was calculated as the number of deaths among children admitted with a diagnosis divided by the number of children admitted with the same diagnosis. There were 24,452 admitted children eligible for analysis. Discharge disposition was recorded in 94.2% of patients, and 4.0% (N = 977) died in the hospital. The most frequent diagnoses among admissions and deaths were pneumonia/bronchiolitis, malaria, and sepsis. The highest mortality rates by diagnosis were found in surgical conditions (16.1%; 95% CI: 12.0-20.3), malnutrition (15.8%; 95% CI: 13.6-18.0), and congenital heart disease (14.5%; 95% CI: 9.9-19.2). Diagnoses with the highest mortality rates were alike in their need for significant human and material resources for medical care. Improving mortality in this population will require sustained capacity building in conjunction with targeted quality improvement initiatives against both common and deadly diseases.

Comparison of tomographic reports by radiologists and non-radiologists in trauma and interferences in management in a trauma reference center

OBJECTIVE

diagnostic errors during the interpretation of an imaging test by the physician can lead to increased mortality and length of hospital stay for patients. The rate of divergence in the report given by a radiologist and an Emergency Physicians (EP) can reach over 20%. The objective of this study was to compare the unofficial tomographic reports issued by EP with the official reports issued by radiologists.

METHODS

a cross-sectional study, in which interpretations of the exams (documented in the medical records by the EP) of all patients undergoing computed tomography (CT) of the chest, abdomen or pelvis performed in the emergency room, at an interval of 8 months, were evaluated. These data were compared with the official reports of the radiologist (gold standard).

RESULTS

508 patients were included. The divergence between EP and the radiologist occurred in 27% of the cases. The most common type of divergence was the one not described by the EP, but described by the radiologist. The chance of having divergence in a case of multiple trauma is 4.93 times greater in relation to the case of only blunt trauma in one segment. A statistically relevant difference was also found in the length of stay of patients who had different interpretations of the CT scans.

CONCLUSION

the study found a relatively high divergence rate between the EP report and the official radiologist report. However, less than 4% of these were considered to be clinically relevant, indicating the ability of the EP to interpret it satisfactorily.

Delayed diagnosis of pneumonia in the emergency department: factors associated and prognosis

Introduction

Whether a delayed diagnosis of community-acquired pneumonia (CAP) in the emergency department (ED) is associated with worse outcome is uncertain. We sought factors associated with a delayed diagnosis of CAP in the ED and those associated with in-hospital mortality.

Methods

Retrospective study including all inpatients admitted to an ED (Dijon University Hospital, France) from 1 January to 31 December 2019, and hospitalized with a diagnosis of CAP. Patients diagnosed with CAP in the ED (n = 361, early diagnosis) were compared with those diagnosed later, in the hospital ward, after the ED visit (n = 74, delayed diagnosis). Demographic, clinical, biological and radiological data were collected upon admission to the ED, as well as administered therapies and outcomes including in-hospital mortality.

Results

435 inpatients were included: 361 (83%) with an early and 74 (17%) with a delayed diagnosis. The latter less frequently required oxygen (54 vs. 77%; p < 0.001) and were less likely to have a quick-SOFA score ≥ 2 (20 vs. 32%; p = 0.056). Absence of chronic neurocognitive disorders, of dyspnea, and of radiological signs of pneumonia were independently associated with a delayed diagnosis. Patients with a delayed diagnosis less frequently received antibiotics in the ED (34 vs. 75%; p < 0.001). However, a delayed diagnosis was not associated with in-hospital mortality after adjusting on initial severity.

Conclusion

Delayed diagnosis of pneumonia was associated with a less severe clinical presentation, lack of obvious signs of pneumonia on chest X-ray, and delayed antibiotics initiation, but was not associated with worse outcome.

Using routine healthcare data to determine the factors associated with hospital length of stay for hypertensive inpatients in Ghana, 2012-2017

OBJECTIVE

Hospitalisation for hypertension continues to rise in Ghana. It has been revealed that in Ghana, patients hospitalised for hypertension spend between 1 and 91 days on admission. This study therefore sought to estimate the hospital length of stay (LoS) of hypertensive patients and individual or health-related factors that may influence the hospitalisation duration in Ghana.

METHODS

We employed a retrospective study design that used routinely collected health data on hospitalised hypertensive patients in Ghana from the District Health Information Management System database between 2012 and 2017 to model LoS using survival analysis. The cumulative incidence function for discharge stratified by sex was computed. To investigate the factors that influence hospitalisation duration, multivariable Cox regression was used.

RESULTS

Out of a total of 106 372 hypertension admissions, about 72 581 (68.2%) were women. The mean age of the patients was 55.3 (SD=17.5) years. Overall, the median LoS was 3 days with almost 90% of all patients being discharged by the 10th day of admission. Patients admitted in Volta region (HR: 0.89, p<0.001) and Eastern region (HR: 0.96, p=0.002) experienced late discharge as compared with patients admitted in Greater Accra. It was revealed that women (HR: 1.09, p<0.001) were discharged earlier than men. However, having a surgical procedure (HR: 1.07, p<0.001) and having comorbidities such as diabetes (HR: 0.76, p<0.001) and cardiovascular diseases other than hypertension (HR: 0.77, p<0.001) increased the LoS of patients.

CONCLUSION

This study provides the first comprehensive assessment of factors influencing hospitalisation duration of admissions due to hypertension in Ghana. Female sex, all regions except Volta region and Eastern region, experienced early discharge. However, patients with a surgical intervention and comorbidity experienced late discharge.

Diagnostic consistency between admission and discharge of pediatric cases in a tertiary teaching hospital in China

BACKGROUND

Patient-centered, high-quality health care relies on accurate and timely diagnosis. Diagnosis is a complex, error-prone process. Prevention of errors involves understanding the cause of errors. This study investigated diagnostic discordance between admission and discharge in pediatric cases.

METHODS

We retrospectively reviewed the electronic medical records of 5381 pediatric inpatients during 2017-2018 in a tertiary teaching hospital. We analyzed diagnostic consistency by comparing the first 4 digits of admission and discharge ICD-10 codes of the cases and classified them as concordant for "complete and partial match" or discordant for "no match".

RESULTS

Diagnostic discordance was observed in 49.2% with the highest prevalence in infections of the nervous and respiratory systems (Ps < 0.001). Multiple (multivariable) logistic regression analysis predicted a lower risk of diagnostic discordance with older children (aOR, 95%CI: 0.94, 0.93-0.96) and a higher risk with infectious diseases (aOR, 95%CI: 1.49, 1.33-1.66) and admission by resident and attending pediatricians (aOR, 95%CI: 1.41, 1.30-1.54). Discordant cases had a higher rate of antibiotic prescription (OR, 95%CI: 2.09, 1.87-2.33), a longer duration of antibiotic use (P = 0.02), a longer length of hospital stay (P < 0.001), and higher medical expenses (P < 0.001).

CONCLUSIONS

This study denotes a considerably high rate of discordance between admission and discharge diagnoses with an associated higher and longer prescription of antibiotics, a longer length of stay, and higher medical expenses among Chinese pediatric inpatient cases. Infectious diseases were identified as high-risk clinical conditions for discordance. Considering potential diagnostic and coding errors, departmental investigation of preventable diagnostic discordance is suggested for quality health care and preventing potential medicolegal consequences.

Effects of a computerised diagnostic decision support tool on diagnostic quality in emergency departments: study protocol of the DDx-BRO multicentre cluster randomised cross-over trial

INTRODUCTION

Computerised diagnostic decision support systems (CDDS) suggesting differential diagnoses to physicians aim to improve clinical reasoning and diagnostic quality. However, controlled clinical trials investigating their effectiveness and safety are absent and the consequences of its use in clinical practice are unknown. We aim to investigate the effect of CDDS use in the emergency department (ED) on diagnostic quality, workflow, resource consumption and patient outcomes.

METHODS AND ANALYSIS

This is a multicentre, outcome assessor and patient-blinded, cluster-randomised, multiperiod crossover superiority trial. A validated differential diagnosis generator will be implemented in four EDs and randomly allocated to a sequence of six alternating intervention and control periods. During intervention periods, the treating ED physician will be asked to consult the CDDS at least once during diagnostic workup. During control periods, physicians will not have access to the CDDS and diagnostic workup will follow usual clinical care. Key inclusion criteria will be patients' presentation to the ED with either fever, abdominal pain, syncope or a non-specific complaint as chief complaint. The primary outcome is a binary diagnostic quality risk score composed of presence of an unscheduled medical care after discharge, change in diagnosis or death during time of follow-up or an unexpected upscale in care within 24 hours after hospital admission. Time of follow-up is 14 days. At least 1184 patients will be included. Secondary outcomes include length of hospital stay, diagnostics and data regarding CDDS usage, physicians' confidence calibration and diagnostic workflow. Statistical analysis will use general linear mixed modelling methods.

ETHICS AND DISSEMINATION

Approved by the cantonal ethics committee of canton Berne (2022-D0002) and Swissmedic, the Swiss national regulatory authority on medical devices. Study results will be disseminated through peer-reviewed journals, open repositories and the network of investigators and the expert and patients advisory board.

TRIAL REGISTRATION NUMBER

NCT05346523.

Risk factors associated with inappropriate empirical antimicrobial treatment in bloodstream infections. A cohort study

Introduction: Bloodstream infections (BSI) are a major cause of mortality all over the world. Inappropriate empirical antimicrobial treatment (i-EAT) impact on mortality has been largely reported. However, information on related factors for the election of i-EAT in the treatment of BSI in adults is lacking. The aim of the study was the identification of risk-factors associated with the use of i-EAT in BSI. Methods: A retrospective, observational cohort study, from a prospective database was conducted in a 400-bed acute-care teaching hospital including all BSI episodes in adult patients between January and December 2018. The main outcome variable was EAT appropriation. Multivariate analysis using logistic regression was performed. Results: 599 BSI episodes were included, 146 (24%) received i-EAT. Male gender, nosocomial and healthcare-associated acquisition of infection, a high Charlson Comorbidity Index (CCI) score and the isolation of multidrug resistant (MDR) microorganisms were more frequent in the i-EAT group. Adequation to local guidelines' recommendations on EAT resulted in 91% of appropriate empirical antimicrobial treatment (a-EAT). Patients receiving i-EAT presented higher mortality rates at day 14 and 30 when compared to patients with a-EAT (14% vs. 6%, p = 0.002 and 22% vs. 9%, p < 0.001 respectively). In the multivariate analysis, a CCI score ≥3 (OR 1.90 (95% CI 1.16-3.12) p = 0.01) and the isolation of a multidrug resistant (MDR) microorganism (OR 3.79 (95% CI 2.28-6.30), p < 0.001) were found as independent risk factors for i-EAT. In contrast, female gender (OR 0.59 (95% CI 0.35-0.98), p = 0.04), a correct identification of clinical syndrome prior to antibiotics administration (OR 0.26 (95% CI 0.16-0.44), p < 0.001) and adherence to local guidelines (OR 0.22 (95% CI 0.13-0.38), p < 0.001) were identified as protective factors against i-EAT. Conclusion: One quarter of BSI episodes received i-EAT. Some of the i-EAT related factors were unmodifiable (male gender, CCI score ≥3 and isolation of a MDR microorganism) but others (incorrect identification of clinical syndrome before starting EAT or the use of local guidelines for EAT) could be addressed to optimize the use of antimicrobials.

Vestibular syndromes, diagnosis and diagnostic errors in patients with dizziness presenting to the emergency department: a cross-sectional study

OBJECTIVES

We aimed to determine the frequency of vestibular syndromes, diagnoses, diagnostic errors and resources used in patients with dizziness in the emergency department (ED).

DESIGN

Retrospective cross-sectional study.

SETTING

Tertiary referral hospital.

PARTICIPANTS

Adult patients presenting with dizziness.

PRIMARY AND SECONDARY OUTCOME MEASURES

We collected clinical data from the initial ED report from July 2015 to August 2020 and compared them with the follow-up report if available. We calculated the prevalence of vestibular syndromes and stroke prevalence in patients with dizziness. Vestibular syndromes are differentiated in acute (AVS) (eg, stroke, vestibular neuritis), episodic (EVS) (eg, benign paroxysmal positional vertigo, transient ischaemic attack) and chronic (CVS) (eg, persistent postural-perceptual dizziness) vestibular syndrome. We reported the rate of diagnostic errors using the follow-up diagnosis as the reference standard.

RESULTS

We included 1535 patients with dizziness. 19.7% (303) of the patients presented with AVS, 34.7% (533) with EVS, 4.6% (71) with CVS and 40.9% (628) with no or unclassifiable vestibular syndrome. The three most frequent diagnoses were stroke/minor stroke (10.1%, 155), benign paroxysmal positional vertigo (9.8%, 150) and vestibular neuritis (9.6%, 148). Among patients with AVS, 25.4% (77) had stroke. The cause of the dizziness remained unknown in 45.0% (692) and 18.0% received a false diagnosis. There was a follow-up in 662 cases (43.1%) and 58.2% with an initially unknown diagnoses received a final diagnosis. Overall, 69.9% of all 1535 patients with dizziness received neuroimaging (MRI 58.2%, CT 11.6%) in the ED.

CONCLUSIONS

One-fourth of patients with dizziness in the ED presented with AVS with a high prevalence (10%) of vestibular strokes. EVS was more frequent; however, the rate of undiagnosed patients with dizziness and the number of patients receiving neuroimaging were high. Almost half of them still remained without diagnosis and among those diagnosed were often misclassified. Many unclear cases of vertigo could be diagnostically clarified after a follow-up visit.

Expediting Treatment of Trauma Patients in the Emergency Department: Rapid Trauma Evaluation (RTE)

BACKGROUND

Criteria for trauma determination evolves. We developed/evaluated a Rapid Trauma Evaluation (RTE) process for a trauma patient subset not meeting preestablished trauma criteria.

METHODS

Retrospective study (July 2019 - May 2020) for patients either > 65 years with ground level fall within 24 hours or in a motorcycle collision (MCC) arriving by EMS not meeting ACS trauma-criteria. RTE process was immediate evaluation by nurse/EMT, room placement, physician notification, undressing/gowning, vital signs, head-to-toe assessment, upgrade trauma status. Number/type of admissions, discharges, trauma upgrades, LOS obtained via trauma-registry and chart-review. For comparison, historic controls (HC) were used [all patients meeting RTE criteria seen in the ED prior to RTE (Apr- June 2019)].

RESULTS

The RTE cohort (n=755) was 77% falls,23% MCCs, median age 82 [IQR 74-88] years; 42% male-Among falls, 3.2% required a modified-upgrade; 0.7% full-upgrade, 55% admitted [29.4% trauma). HC (n=575) was 92.3% falls, 7.7% MCCs, median age 81 (IQR: 67-88) years, 40.5% males-57.4% admitted (22% trauma). RTE MCC median age 42 (IQR:30-49) years, 84.4% male- 21.9% were upgraded [(6 modified-trauma; 1 full-trauma; 43.8% admitted (85.7% trauma)]. HC MCC median age 29 (IQR: 23-41) years, 95.5% male, 54.5% admitted (75% trauma]. No difference on demographics, admissions or discharges between groups (P>0.05) except HC MCC was younger (P<0.005). RTE median LOS was shorter than HC [203 (IQR: 147-278) minutes vs. 286 (IQR: 205-392) minutes, P<0.001].

CONCLUSIONS

Patients > 65 years with a ground level fall or in a MCC arriving via EMS not meeting ACS trauma criteria may benefit from RTE.

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.

A Novel Use of an Electronic Differential Diagnosis Generator in the Emergency Department Setting

Introduction The ability of electronic differential diagnosis (DDx) tools to generate accurate diagnoses has been well established in simulation and primary care clinical environments. However, the use of such tools has not been well studied in the emergency department (ED). We aimed to characterize the use and perceptions of a DDx tool among emergency medicine (EM) clinicians who were newly provided with access to such a tool. Methods We performed a pilot study investigating the utilization of a DDx tool by clinicians in an ED setting shortly after the tool was introduced. After six months of use, retrospective data were analyzed to characterize the use of the tool among ED clinicians. The clinicians were also surveyed on their perceptions of the use of the tool in the ED setting. Results There were 224 total queries, which were regarding 107 unique patients. The most searched symptoms were related to constitutional, dermatologic, and gastrointestinal complaints whereas symptoms related to toxicology and trauma were less commonly searched. Survey respondents rated the tool favorably, and when not used, reported reasons including forgetting that the tool was available for use, not feeling the need to use the tool, and disruption to workflow. Conclusions Electronic DDx tools may have some utility in assisting ED clinicians in generating a DDx, however, clinician adoption and workflow integration are barriers to their utility.

Pyogenic brain abscess associated with an incidental pulmonary arteriovenous malformation

Pulmonary arteriovenous malformations (PAVMs) are rare and often asymptomatic vascular anomalies that can be associated with serious neurological consequences due to right-to-left shunting. We report a case of a woman in her 80s without substantial medical history who presented with a headache, weakness and personality changes, and was found to have a pyogenic brain abscess requiring emergent neurosurgical evacuation. The abscess grew oral flora, suspected to have reached the brain via an incidentally discovered PAVM. With drainage and antibiotics, the patient achieved a full recovery and the PAVM was embolised. To our knowledge, this is the oldest presentation of a PAVM-associated brain abscess in the published literature. Older patients may present without the typical signs and symptoms of a given illness, which complicates accurate diagnosis and treatment. Primary care physicians can help facilitate timely care and positive clinical outcomes.

A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts

OBJECTIVES

To test a structured electronic health record (EHR) case review process to identify diagnostic errors (DE) and diagnostic process failures (DPFs) in acute care.

METHODS

We adapted validated tools (Safer Dx, Diagnostic Error Evaluation Research [DEER] Taxonomy) to assess the diagnostic process during the hospital encounter and categorized 13 postulated e-triggers. We created two test cohorts of all preventable cases (n=28) and an equal number of randomly sampled non-preventable cases (n=28) from 365 adult general medicine patients who expired and underwent our institution's mortality case review process. After excluding patients with a length of stay of more than one month, each case was reviewed by two blinded clinicians trained in our process and by an expert panel. Inter-rater reliability was assessed. We compared the frequency of DE contributing to death in both cohorts, as well as mean DPFs and e-triggers for DE positive and negative cases within each cohort.

RESULTS

Twenty-seven (96.4%) preventable and 24 (85.7%) non-preventable cases underwent our review process. Inter-rater reliability was moderate between individual reviewers (Cohen's kappa 0.41) and substantial with the expert panel (Cohen's kappa 0.74). The frequency of DE contributing to death was significantly higher for the preventable compared to the non-preventable cohort (56% vs. 17%, OR 6.25 [1.68, 23.27], p<0.01). Mean DPFs and e-triggers were significantly and non-significantly higher for DE positive compared to DE negative cases in each cohort, respectively.

CONCLUSIONS

We observed substantial agreement among final consensus and expert panel reviews using our structured EHR case review process. DEs contributing to death associated with DPFs were identified in institutionally designated preventable and non-preventable cases. While e-triggers may be useful for discriminating DE positive from DE negative cases, larger studies are required for validation. Our approach has potential to augment institutional mortality case review processes with respect to DE surveillance.

Contributors to Diagnostic Error or Delay in the Acute Care Setting: A Survey of Clinical Stakeholders

Diagnostic error or delay (DEOD) is common in the acute care setting and results in poor patient outcomes. Many factors contribute to DEOD, but little is known about how contributors may differ across acute care areas and professional roles. As part of a sequential exploratory mixed methods research study, we surveyed acute care clinical stakeholders about the frequency with which different factors contribute to DEOD. Survey respondents could also propose solutions in open text fields. N = 220 clinical stakeholders completed the survey. Care Team Interactions, Systems and Process, Patient, Provider, and Cognitive factors were perceived to contribute to DEOD with similar frequency. Organization and Infrastructure factors were perceived to contribute to DEOD significantly less often. Responses did not vary across acute care setting. Physicians perceived Cognitive factors to contribute to DEOD more frequently compared to those in other roles. Commonly proposed solutions included: technological solutions, organization level fixes, ensuring staff know and are encouraged to work to the full scope of their role, and cultivating a culture of collaboration and respect. Multiple factors contribute to DEOD with similar frequency across acute care areas, suggesting the need for a multi-pronged approach that can be applied across acute care areas.

Cognitive biases encountered by physicians in the emergency room

Background

Diagnostic errors constitute an important medical safety problem that needs improvement, and their frequency and severity are high in emergency room settings. Previous studies have suggested that diagnostic errors occur in 0.6-12% of first-time patients in the emergency room and that one or more cognitive factors are involved in 96% of these cases. This study aimed to identify the types of cognitive biases experienced by physicians in emergency rooms in Japan.

Methods

We conducted a questionnaire survey using Nikkei Medical Online (Internet) from January 21 to January 31, 2019. Of the 159,519 physicians registered with Nikkei Medical Online when the survey was administered, those who volunteered their most memorable diagnostic error cases in the emergency room participated in the study. EZR was used for the statistical analyses.

Results

A total of 387 physicians were included. The most common cognitive biases were overconfidence (22.5%), confirmation (21.2%), availability (12.4%), and anchoring (11.4%). Of the error cases, the top five most common initial diagnoses were upper gastrointestinal disease (22.7%), trauma (14.7%), cardiovascular disease (10.9%), respiratory disease (7.5%), and primary headache (6.5%). The corresponding final diagnoses for these errors were intestinal obstruction or peritonitis (27.3%), overlooked traumas (47.4%), other cardiovascular diseases (66.7%), cardiovascular disease (41.4%), and stroke (80%), respectively.

Conclusions

A comparison of the initial and final diagnoses of cases with diagnostic errors shows that there were more cases with diagnostic errors caused by overlooking another disease in the same organ or a disease in a closely related organ.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-022-00708-3.

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.

Patient recalls associated with resident-to-attending radiology report discrepancies: predictive factors for risky discrepancies

BACKGROUND

This study aimed to identify predictive factors for risky discrepancies in the emergency department (ED) by analyzing patient recalls associated with resident-to-attending radiology report discrepancies (RRDs).

RESULTS

This retrospective study analyzed 759 RRDs in computed tomography (CT) and magnetic resonance imaging and their outcomes from 2013 to 2021. After excluding 73 patients lost to follow-up, we included 686 records in the final analysis. Risky discrepancies were defined as RRDs resulting in (1) inpatient management (hospitalization) and (2) adverse outcomes (delayed operations, 30-day in-hospital mortality, or intensive care unit admission). Predictors of risky discrepancies were assessed using multivariable logistic regression analysis. The overall RRD rate was 0.4% (759 of 171,419). Of 686 eligible patients, 21.4% (147 of 686) received inpatient management, and 6.0% (41 of 686) experienced adverse outcomes. RRDs with neurological diseases were associated with the highest ED revisit rate (79.4%, 81 of 102) but not with risky RRDs. Predictive factors of inpatient management were critical finding (odds ratio [OR], 5.60; p < 0.001), CT examination (OR, 3.93; p = 0.01), digestive diseases (OR, 2.54; p < 0.001), and late finalized report (OR, 1.65; p = 0.02). Digestive diseases (OR, 6.14; p = 0.006) were identified as the only significant predictor of adverse outcomes.

CONCLUSIONS

Risky RRDs were associated with several factors, including CT examination, digestive diseases, and late finalized reports, as well as critical image findings. This knowledge could aid in determining the priority of discrepancies for the appropriate management of RRDs.

Analysis of Variation Between Diagnosis at Admission vs Discharge and Clinical Outcomes Among Adults With Possible Bacteremia

IMPORTANCE

Misdiagnosis of infection is among the most commonly made diagnostic errors and is associated with increased morbidity and mortality. Little is known about how often misdiagnosed site of infection occurs and its association with clinical outcomes.

OBJECTIVES

To evaluate the discrepancy between admission and discharge site of infection diagnoses among patients with suspected bacteremia, to explore factors associated with discrepant diagnoses, and to evaluate the association with clinical outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used electronic records of 1477 adult patients who were admitted to the hospital for suspected bacteremia from April 1, 2019, to May 31, 2020, and who had blood cultures taken at the emergency department at Haga Teaching Hospital, The Hague, the Netherlands. Suspected infection sites were classified into 8 categories at admission and discharge. Misdiagnosed site was defined as a discrepancy between the suspected site of infection at admission and at discharge.

MAIN OUTCOMES AND MEASURES

Clinical outcomes were 30-day mortality, intensive care unit admission, length of hospital stay, and antibiotic use, analyzed with logistic and linear regression. Risk factors for misdiagnosed site were determined using regression analysis.

RESULTS

A total of 1477 patients (820 [55.5%] male; median [IQR] age, 68 [56-78] years) were analyzed. The rate of misdiagnosed site of infection was 11.6% (171 of 1477); 3.1% of all patients (46 of 1477) ultimately had no infection. No association was found between misdiagnosis and 30-day mortality (adjusted odds ratio [aOR], 0.8; 95% CI, 0.3-1.9; P = .60), intensive care unit admission (aOR, 1.3; 95% CI, 0.6-3.0; P = .54), and hospital length of stay (adjusted increase of stay, 15.5%; 95% CI, -3.1% to 37.7%; P = .11). Misdiagnosed site was associated with receiving broad-spectrum antibiotics (aOR, 4.0; 95% CI, 1.8-8.8; P < .001). Older age, dementia, a positive urine sediment test result without urinary symptoms, and suspicion of an intravascular, central nervous system, or bone and joint infection were risk factors for misdiagnosed site of infection.

CONCLUSIONS AND RELEVANCE

In this cohort study, misdiagnosed site of infection occurred in 1 of 9 patients and was not associated with worse short-term clinical outcomes. Clinicians should be aware of risk factors associated with misdiagnosed site of infection and potential inappropriate antibiotic use.

Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department

Introduction

One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare.

Aim of the study

Our objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting.

Material and methods

We assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDi-mer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed.

Results

The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model.

Conclusions

Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.

LOw-dose CT Or Lung UltraSonography versus standard of care based-strategies for the diagnosis of pneumonia in the elderly: protocol for a multicentre randomised controlled trial (OCTOPLUS)

INTRODUCTION

Pneumonia is a leading cause of mortality and a common indication for antibiotic in elderly patients. However, its diagnosis is often inaccurate. We aim to compare the diagnostic accuracy, the clinical and cost outcomes and the use of antibiotics associated with three imaging strategies in patients >65 years old with suspected pneumonia in the emergency room (ER): chest X-ray (CXR, standard of care), low-dose CT scan (LDCT) or lung ultrasonography (LUS).

METHODS AND ANALYSIS

This is a multicentre randomised superiority clinical trial with three parallel arms. Patients will be allocated in the ER to a diagnostic strategy based on either CXR, LDCT or LUS. All three imaging modalities will be performed but the results of two of them will be masked during 5 days to the patients, the physicians in charge of the patients and the investigators according to random allocation. The primary objective is to compare the accuracy of LDCT versus CXR-based strategies. As secondary objectives, antibiotics prescription, clinical and cost outcomes will be compared, and the same analyses repeated to compare the LUS and CXR strategies. The reference diagnosis will be established a posteriori by a panel of experts. Based on a previous study, we expect an improvement of 16% of the accuracy of pneumonia diagnosis using LDCT instead of CXR. Under this assumption, and accounting for 10% of drop-out, the enrolment of 495 patients is needed to prove the superiority of LDCT over CRX (alpha error=0.05, beta error=0.10).

ETHICS AND DISSEMINATION

Ethical approval: CER Geneva 2019-01288.

TRIAL REGISTRATION NUMBER

NCT04978116.

Electronic diagnostic support in emergency physician triage: a qualitative study (Preprint)

Background

Not thinking of a diagnosis is a leading cause of diagnostic error in the emergency department, resulting in delayed treatment, morbidity, and excess mortality. Electronic differential diagnostic support (EDS) results in small but significant reductions in diagnostic error. However, the uptake of EDS by clinicians is limited.

Objective

We sought to understand physician perceptions and barriers to the uptake of EDS within the emergency department triage process.

Methods

We conducted a qualitative study using a research associate to rapidly prototype an embedded EDS into the emergency department triage process. Physicians involved in the triage assessment of a busy emergency department were provided the output of an EDS based on the triage complaint by an embedded researcher to simulate an automated system that would draw from the electronic medical record. Physicians were interviewed immediately after their experience. Verbatim transcripts were analyzed by a team using open and axial coding, informed by direct content analysis.

Results

In all, 4 themes emerged from 14 interviews: (1) the quality of the EDS was inferred from the scope and prioritization of the diagnoses present in the EDS differential; (2) the trust of the EDS was linked to varied beliefs around the diagnostic process and potential for bias; (3) clinicians foresaw more benefit to EDS use for colleagues and trainees rather than themselves; and (4) clinicians felt strongly that EDS output should not be included in the patient record.

Conclusions

The adoption of an EDS into an emergency department triage process will require a system that provides diagnostic suggestions appropriate for the scope and context of the emergency department triage process, transparency of system design, and affordances for clinician beliefs about the diagnostic process and addresses clinician concern around including EDS output in the patient record.

Checklists to reduce diagnostic error: a systematic review of the literature using a human factors framework

OBJECTIVES

To apply a human factors framework to understand whether checklists reduce clinical diagnostic error have (1) gaps in composition; and (2) components that may be more likely to reduce errors.

DESIGN

Systematic review.

DATA SOURCES

PubMed, EMBASE, Scopus and Web of Science were searched through 15 February 2022.

ELIGIBILITY CRITERIA

Any article that included a clinical checklist aimed at improving the diagnostic process. Checklists were defined as any structured guide intended to elicit additional thinking regarding diagnosis.

DATA EXTRACTION AND SYNTHESIS

Two authors independently reviewed and selected articles based on eligibility criteria. Each extracted unique checklist was independently characterised according to the well-established human factors framework: Systems Engineering Initiative for Patient Safety 2.0 (SEIPS 2.0). If reported, checklist efficacy in reducing diagnostic error (eg, diagnostic accuracy, number of errors or any patient-related outcomes) was outlined. Risk of study bias was independently evaluated using standardised quality assessment tools in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

RESULTS

A total of 30 articles containing 25 unique checklists were included. Checklists were characterised within the SEIPS 2.0 framework as follows: Work Systems subcomponents of Tasks (n=13), Persons (n=2) and Internal Environment (n=3); Processes subcomponents of Cognitive (n=20) and Social and Behavioural (n=2); and Outcomes subcomponents of Professional (n=2). Other subcomponents, such as External Environment or Patient outcomes, were not addressed. Fourteen checklists examined effect on diagnostic outcomes: seven demonstrated improvement, six were without improvement and one demonstrated mixed results. Importantly, Tasks-oriented studies more often demonstrated error reduction (n=5/7) than those addressing the Cognitive process (n=4/10).

CONCLUSIONS

Most diagnostic checklists incorporated few human factors components. Checklists addressing the SEIPS 2.0 Tasks subcomponent were more often associated with a reduction in diagnostic errors. Studies examining less explored subcomponents and emphasis on Tasks, rather than the Cognitive subcomponents, may be warranted to prevent diagnostic errors.

Crossing Academic Boundaries for Diagnostic Safety: 10 Complex Challenges and Potential Solutions From Clinical Perspectives and High-Reliability Organizing Principles

OBJECTIVE

We apply the high-reliability organization (HRO) paradigm to the diagnostic process, outlining challenges to enacting HRO principles in diagnosis and offering solutions for how diagnostic process stakeholders can overcome these barriers.

BACKGROUND

Evidence shows that healthcare is starting to organize for higher reliability by employing various principles and practices of HRO. These hold promise for enhancing safer care, but there has been little consideration of the challenges that clinicians and healthcare systems face while enacting HRO principles in the diagnostic process. To effectively deploy the HRO perspective, these barriers must be seriously considered.

METHOD

We review key principles of the HRO paradigm, the diagnostic errors and harms that potentially can be prevented by its enactment, the challenges that clinicians and healthcare systems face in executing various principles and practices, and possible solutions that clinicians and organizational leaders can take to overcome these challenges and barriers.

RESULTS

Our analyses reveal multiple challenges including the inherent diagnostic uncertainty; the lack of diagnosis-focused performance feedback; the fact that diagnosis is often a solo, rather than team, activity; the tendency to simplify the diagnostic process; and professional and institutional status hierarchies. But these challenges are not insurmountable-there are strategies and solutions available to overcome them.

CONCLUSION

The HRO lens offers some important ideas for how the safety of the diagnostic process can be improved.

APPLICATION

The ideas proposed here can be enacted by both individual clinicians and healthcare leaders; both are necessary for making systematic progress in enhancing diagnostic performance.

Assessing left ventricular systolic function by emergency physician using point of care echocardiography compared to expert: systematic review and meta-analysis

Assessing left ventricular systolic function (LVSF) by echocardiography assists in the diagnosis and management of a diverse range of patients presenting to the emergency department (ED). We evaluated the agreement between ED-based clinician sonographers and apriori-defined expert sonographers. We conducted a systematic review and meta-analysis based on Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. We searched Medline, EMBASE, Cochrane, ClinicalTrials.gov, TRIP and Google Scholar for eligible studies from inception to February 2021. Risk of bias was evaluated using Quality Assessment Tool for Diagnostic Accuracy Studies-2 tool. The level of agreement between clinician and expert sonographers was measured using kappa, sensitivity, specificity, positive and negative likelihood ratio statistics using random-effects models. Twelve studies were included (1131 patients, 1229 scans and 159 clinician sonographers). Significant heterogeneity was identified in patient selection, methods of assessment of LVSF, reference standards and statistical methods for assessing agreement. The overall quality of studies was low, with most being small, single centre convenience samples. A meta-analysis including seven studies (786 scans) where visual estimation method was used by clinician sonographers demonstrated simple Kappa of 0.68 [95% confidence interval (CI), 0.57-0.79], and sensitivity, specificity, positive and negative likelihood ratio of 89% (95% CI, 80-94%), 85% (95% CI, 80-89%), 5.98 (95% CI, 4.13-8.68) and 0.13 (95% CI, 0.06-0.24), respectively, between clinician sonographer and expert sonographer for normal/abnormal LVSF. The weighted kappa for five studies (429 scans) was 0.70 (95% CI, 0.61-0.80) for normal/reduced/severely reduced LVSF. There is substantial agreement between ED-based clinician sonographers and expert sonographers for assessing LVSF using visual estimation and ranking it as normal/reduced, or normal/reduced/severely reduced, in patients presenting to ED.

Diagnostic errors: Under-appreciated, under-reported and under-researched

Diagnostic errors, were given relatively little attention, compared with the effort invested in treatment errors. However, erroneous diagnoses continue to be quite prevalent (10%-15% in every setting investigated), and are often associated with substantial patient harm including increased mortality and frequent permanent disability. Physicians may not be aware of the fact that despite the wide availability of sophisticated diagnostic imaging and new and sensitive tests, diagnosis remains far from infallible, because of a complex interplay of physician, patient and illness factors. Research devoted to misdiagnosis remains difficult to perform and insufficient in scope, but the search for the optimal means to improve diagnostic accuracy continues. Newly achieved insights regarding diagnostic errors are presented, and essential system and individual approaches to improve diagnostic accuracy are proposed.

Incidence of diagnostic errors in unplanned hospitalized patients using an automated medical history-taking system with differential diagnosis generator: retrospective observational study (Preprint)

Background

Automated medical history–taking systems that generate differential diagnosis lists have been suggested to contribute to improved diagnostic accuracy. However, the effect of these systems on diagnostic errors in clinical practice remains unknown.

Objective

This study aimed to assess the incidence of diagnostic errors in an outpatient department, where an artificial intelligence (AI)–driven automated medical history–taking system that generates differential diagnosis lists was implemented in clinical practice.

Methods

We conducted a retrospective observational study using data from a community hospital in Japan. We included patients aged 20 years and older who used an AI-driven, automated medical history–taking system that generates differential diagnosis lists in the outpatient department of internal medicine for whom the index visit was between July 1, 2019, and June 30, 2020, followed by unplanned hospitalization within 14 days. The primary endpoint was the incidence of diagnostic errors, which were detected using the Revised Safer Dx Instrument by at least two independent reviewers. To evaluate the effect of differential diagnosis lists from the AI system on the incidence of diagnostic errors, we compared the incidence of these errors between a group where the AI system generated the final diagnosis in the differential diagnosis list and a group where the AI system did not generate the final diagnosis in the list; the Fisher exact test was used for comparison between these groups. For cases with confirmed diagnostic errors, further review was conducted to identify the contributing factors of these errors via discussion among three reviewers, using the Safer Dx Process Breakdown Supplement as a reference.

Results

A total of 146 patients were analyzed. A final diagnosis was confirmed for 138 patients and was observed in the differential diagnosis list from the AI system for 69 patients. Diagnostic errors occurred in 16 out of 146 patients (11.0%, 95% CI 6.4%-17.2%). Although statistically insignificant, the incidence of diagnostic errors was lower in cases where the final diagnosis was included in the differential diagnosis list from the AI system than in cases where the final diagnosis was not included in the list (7.2% vs 15.9%, P=.18).

Conclusions

The incidence of diagnostic errors among patients in the outpatient department of internal medicine who used an automated medical history–taking system that generates differential diagnosis lists seemed to be lower than the previously reported incidence of diagnostic errors. This result suggests that the implementation of an automated medical history–taking system that generates differential diagnosis lists could be beneficial for diagnostic safety in the outpatient department of internal medicine.

Organisational determinants and consequences of diagnostic discrepancy in two large patient groups in the emergency departments: a national study of consecutive episodes between 2008 and 2016

Background

Diagnostic discrepancy (DD) is a common phenomenon in healthcare, but little is known about its organisational determinants and consequences. Thus, the aim of the study was to evaluate this among selected emergency department (ED) patients.

Method

We conducted an observational study including all consecutive ED patients (hip fracture or erysipelas) in the Danish healthcare sector admitted between 2008 and 2016. DD was defined as a discrepancy between discharge and admission diagnoses. Episode and department statistics were retrieved from Danish registers. We conducted a survey among all 21 Danish EDs to gather information about organisational determinants. To estimate the results while adjusting for episode- and department-level heterogeneity, we used mixed effect models of ED organisational determinants and 30-day readmission, 30-day mortality and episode costs (2018-DKK) of DDs.

Results

DD was observed in 2308 (3.3%) of 69,928 hip fracture episodes and 3206 (8.5%) of 37,558 erysipelas episodes. The main organisational determinant of DD was senior physicians (nonspecific medical specialty) being employed at the ED (hip fracture: odds ratio (OR) 2.74, 95% confidence interval (CI) 2.15–3.51; erysipelas: OR 3.29, 95% CI 2.65–4.07). However, 24-h presence of senior physicians (nonspecific medical specialty) (hip fracture) and availability of external senior physicians (specific medical specialty) (both groups) were negatively associated with DD. DD was associated with increased 30-day readmission (hip fracture, mean 9.45% vs 13.76%, OR 1.46, 95% CI 1.28–1.66, p < 0.001) and episode costs (hip fracture, 61,681 DKK vs 109,860 DKK, log cost 0.58, 95% CI 0.53–0.63, p < 0.001; erysipelas, mean 20,818 DKK vs 56,329 DKK, log cost 0.97, 95% CI 0.92–1.02, p < 0.001) compared with episodes without DD.

Conclusion

DD was found to have a negative impact on two out of three study outcomes, and particular organisational characteristics seem to be associated with DD. Yet, the complexity of organisations and settings warrant further studies into these associations.

Diagnostic Error in the Critically Ill: A Hidden Epidemic?

Epidemiologic studies of diagnostic error in the intensive care unit (ICU) consist mostly of descriptive autopsy series. In these studies, rates of diagnostic errors are approximately 5% to 10%. Recently validated methods for retrospectively measuring error have expanded our understanding of the scope of the problem. These alternative measurement strategies have yielded similar estimates for the frequency of diagnostic error in the ICU. Although there is a fair understanding of the frequency of errors, further research is needed to better define the risk factors for diagnostic error in the ICU.

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.

Differential diagnosis checklists reduce diagnostic error differentially: A randomised experiment

INTRODUCTION

Wrong and missed diagnoses contribute substantially to medical error. Can a prompt to generate alternative diagnoses (prompt) or a differential diagnosis checklist (DDXC) increase diagnostic accuracy? How do these interventions affect the diagnostic process and self-monitoring?

METHODS

Advanced medical students (N = 90) were randomly assigned to one of four conditions to complete six computer-based patient cases: group 1 (prompt) was instructed to write down all diagnoses they considered while acquiring diagnostic test results and to finally rank them. Groups 2 and 3 received the same instruction plus a list of 17 differential diagnoses for the chief complaint of the patient. For half of the cases, the DDXC contained the correct diagnosis (DDXC+), and for the other half, it did not (DDXC-; counterbalanced). Group 4 (control) was only instructed to indicate their final diagnosis. Mixed-effects models were used to analyse results.

RESULTS

Students using a DDXC that contained the correct diagnosis had better diagnostic accuracy, mean (standard deviation), 0.75 (0.44), compared to controls without a checklist, 0.49 (0.50), P < 0.001, but those using a DDXC that did not contain the correct diagnosis did slightly worse, 0.43 (0.50), P = 0.602. The number and relevance of diagnostic tests acquired were not affected by condition, nor was self-monitoring. However, participants spent more time on a case in the DDXC-, 4:20 min (2:36), P ≤ 0.001, and DDXC+ condition, 3:52 min (2:09), than in the control condition, 2:59 min (1:44), P ≤ 0.001.

DISCUSSION

Being provided a list of possible diagnoses improves diagnostic accuracy compared with a prompt to create a differential diagnosis list, if the provided list contains the correct diagnosis. However, being provided a diagnosis list without the correct diagnosis did not improve and might have slightly reduced diagnostic accuracy. Interventions neither affected information gathering nor self-monitoring.

Thresholds, rules and defensive strategies: how physicians learn from their prior diagnosis-related experiences

Background Health professionals are encouraged to learn from their errors. Determining how primary care physicians (PCPs) react to a case, in which their original diagnosis differed from the final outcome, could provide new insights on how they learn from experiences. We explored how PCPs altered their diagnostic evaluation of future patients after cases where the originally assumed diagnosis turned out to be wrong. Methods We asked German PCPs to complete an online survey where they described how the patient concerned originally presented, the subsequent course of events and whether they would change their diagnostic work-up of future patients. Qualitative methods were used to analyze narrative text obtained by this survey. Results A total of 29 PCPs submitted cases, most of which were ultimately found to be more severe than originally assumed. PCPs (n = 27) reflected on changes to their subsequent clinical decisions in the form of general maxims (n = 20) or more specific rules (n = 11). Most changes would have resulted in a lower threshold for investigations, referral and/or a more extensive collection of diagnostic information. PCPs decided not only to listen more often to their intuition (gut feelings), but to also practice more analytical reasoning. Participants felt the need for change of practice even if no clinical standards had been violated in the diagnosis of that case. Some decided to resort to defensive strategies in the future. Conclusions We describe mechanisms by which physicians calibrate their decision thresholds, as well as their cognitive mode (intuitive vs. analytical). PCPs reported the need for change in clinical practice despite the absence of error in some cases.

Impaired kidney function at ED admission: a comparison of bleeding complications of patients with different oral anticoagulants

Background

Up to a fourth of patients at emergency department (ED) presentation suffer from acute deterioration of renal function, which is an important risk factor for bleeding events in patients on oral anticoagulation therapy. We hypothesized that outcomes of patients, bleeding characteristics, therapy, and outcome differ between direct oral anticoagulants (DOACs) and vitamin-K antagonists (VKAs).

Methods

All anticoagulated patients older than 17 years with an impaired kidney function treated for an acute haemorrhage in a large Swiss university ED from 01.06.2012 to 01.07.2017 were included in this retrospective cohort study. Patient, treatment, and bleeding characteristics as well as outcomes (length of stay ED, intensive care unit and in-hospital admission, ED resource consumption, in-hospital mortality) were compared between patients on DOAC or VKA anticoagulant.

Results

In total, 158 patients on DOAC and 419 patients on VKA with acute bleeding and impaired renal function were included. The renal function in patients on VKA was significantly worse compared to patients on DOAC (VKA: median 141 μmol/L vs. DOAC 132 μmol/L, p = 0.002). Patients on DOAC presented with a smaller number of intracranial bleeding compared to VKA (14.6% DOAC vs. 22.4% VKA, p = 0.036). DOAC patients needed more emergency endoscopies (15.8% DOAC vs, 9.1% VKA, p = 0.020) but less interventional emergency therapies to stop the bleeding (13.9% DOAC vs. 22.2% VKA, p = 0.027). Investigated outcomes did not differ significantly between the two groups.

Conclusions

DOAC patients were found to have a smaller proportional incidence of intracranial bleedings, needed more emergency endoscopies but less often interventional therapy compared to patients on VKA. Adapted treatment algorithms are a potential target to improve care in patients with DOAC.

Diagnostic delay of acute mitral regurgitation during the coronavirus disease 2019 pandemic: a case report

BACKGROUND

Diagnostic errors or delays can cause serious consequences for patient safety, especially in the emergency department. Anchoring bias is one of the major factors leading to diagnostic error. During the coronavirus disease 2019 (COVID-19) pandemic, the high probability of COVID-19 in febrile patients could be a major cause of anchoring bias leading to diagnostic error. In addition, certain evaluations such as auscultation are difficult to perform on a casual basis due to the increased risk of contact infection, which lead to inadequate assessment of the patients with valvular disease. Acute mitral regurgitation (MR) could be a fatal disease in the emergency department, especially if there is a diagnostic error or delay in diagnosis. It is often reported that diagnosis can be difficult even though there is no treatment other than emergent surgery. The diagnosis of acute MR has become more difficult because coronavirus disease 2019 (COVID-19) pandemic could affect our daily practice especially in febrile patients. We report a case of a diagnostic delay of a febrile patient because of anchoring bias during the COVID-19 pandemic.

CASE PRESENTATION

A 45-year-old man presented to the emergency department complaining of acute dyspnea and fever. Based on vital signs and computed tomography of the chest, acute pneumonia due to COVID-19 was suspected. Auscultation was avoided because of facility rule based on concern of contact infection. After admission to the intensive care unit, Doppler echocardiography revealed acute mitral regurgitation, and transesophageal echocardiography revealed mitral valve tendon rupture. After confirming the negative result for the polymerase chain reaction of severe acute respiratory syndrome coronavirus 2, mitral valvuloplasty was performed on the third day after admission. The patient was discharged 14 days after admission without complications.

CONCLUSIONS

In COVID-19 pandemic, anchoring bias suspecting COVID-19 among febrile patients becomes a strong heuristic factor. A thorough history and physical examination is still important in febrile patients presenting with dyspnea to ensure the correct diagnosis of acute mitral regurgitation.

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

OBJECTIVES

Published discrepancy rates between emergency department (ED) and hospital discharge (HD) diagnoses vary widely (from 6.5 to 75.6%). The goal of this study was to determine the extent of diagnostic discrepancy and its impact on length of hospital stay (LOS), up-triage to the intensive care unit (ICU) and in-hospital mortality.

METHODS

A retrospective chart review of adult patients admitted from the ED to a hospitalist service at a tertiary hospital was performed. The ED and HD diagnoses were compared and classified as concordant, discordant, or symptom diagnoses according to predefined criteria. Logistic regression analysis was conducted to examine the associations of diagnostic discordance with in-hospital mortality and up-triage to the ICU. A linear regression model was used for the length of stay.

RESULTS

Of the 636 patients whose records were reviewed, 418 (217 [51.9%] women, with a mean age of 64.1 years) were included. Overall, 318 patients (76%) had concordant diagnoses, while 91 (21.77%) had discordant diagnoses. Only 9 patients (2.15%) had symptom diagnoses. A discordant diagnosis was associated with increased mortality (OR: 3.64; 95% CI: 1.026-12.91; p=0.045) and up-triage to the ICU (OR: 5.51; 95% CI: 2.43-12.5; p<0.001). The median LOS was significantly greater for patients with discordant diagnoses (7 days) than for those with concordant diagnoses (4.7 days) (p=0.004). Symptom diagnosis did not affect the mortality or ICU up-triage.

CONCLUSIONS

One in five hospitalized patients had discordant HD and admission diagnoses. This diagnostic discrepancy was associated with significant impacts on patient morbidity and mortality.

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 Uncertainty in Dyspneic Patients with Cancer in the Emergency Department

OBJECTIVE

Dyspnea is the second most common symptom experienced by the approximately 4.5 million patients with cancer presenting to emergency departments (ED) each year. Distinguishing pneumonia, the most common reason for presentation, from other causes of dyspnea is challenging. This report characterizes the diagnostic uncertainty in patients with dyspnea and pneumonia presenting to an ED by establishing the rates of co-diagnosis, co-treatment, and misdiagnosis.

METHODS

Visits by individuals ≥18 years old with cancer who presented with a complaint of dyspnea were identified using the National Hospital Ambulatory Medical Care Survey between 2012-2014 and analyzed for rates of co-diagnosis, co-treatment (treatment or diagnosis for >1 of pneumonia, chronic obstructive pulmonary disease [COPD], and heart failure), and misdiagnosis of pneumonia. Additionally, we assessed rates of diagnostic uncertainty (co-diagnosis, co-treatment, or a lone diagnosis of dyspnea not otherwise specified [NOS]).

RESULTS

Among dyspneic cancer visits (1,593,930), 15.2% (95% confidence interval [CI], 11.1-20.5%) were diagnosed with pneumonia, 22.5% (95% CI, 16.7-29.7%) with COPD, and 7.4% (95% CI 4.7-11.4%) with heart failure. Dyspnea NOS was diagnosed in 32.3% (95% CI, 25.7-39.7%) of visits and as the only diagnosis in 23.1% (95% CI, 16.3-31.6%) of all visits. Co-diagnosis occurred in 4.0% (95% CI, 2.0-7.6%) of dyspneic adults with cancer and co-treatment in 12.1% (95% CI, 7.5-18.9%). Agreement between emergency physician and inpatient documentation for presence of pneumonia was 57.7% (95% CI, 37.0-76.1%).

CONCLUSION

Diagnostic uncertainty remains a significant concern in patients with cancer presenting to the ED with dyspnea. Clinical uncertainty among dyspneic patients results in both misdiagnosis and under-treatment of patients with pneumonia and cancer.

The Study of Incidence and Characteristics of Patients with Eye-Related Chief Complaints at the Emergency Department of Thammasat University Hospital

Background

Patients with eye-related chief complaints could be diagnosed not only with eye diseases but also with noneye diseases. This study determines rates and characteristics of patients with eye-related chief complaints at the Emergency Department of Thammasat University Hospital.

Methods

The study design is a descriptive retrospective observational study of patients with eye-related chief complaints at the Emergency Department of Thammasat University Hospital in 2017. Demographic data, diagnosis, management, consultation, and disposition were recorded by chart review. Categorical data were reported by percentage.

Results

Of the 52081 patients, 704 (1.3%) presented with eye problems. 60% of the patients were males. Patients were classified into three groups which are traumatic eye disease, nontraumatic eye disease, and noneye disease. 75.9% of the patients suffered traumatic injuries. The most common diagnoses of the traumatic eye injuries were foreign bodies at the cornea and conjunctiva and minor trauma to the conjunctiva. The most common mechanisms were foreign bodies in the eyes, cuts, or pierces. The most common causes of the injuries were from metals and housewares. The most common nontraumatic eye diagnoses were conjunctivitis and corneal ulcer. The most common noneye diagnoses were exposure of healthcare providers to secretions from patients, angioedema, and hypertensive crisis.

Conclusions

Most of the patients who came to the ER with chief complaints of the eyes could be treated by doctors in the emergency room without consulting ophthalmologists. Chief complaints of the eyes could be the leading symptoms of many organ systems. Emergency physicians should be differentially diagnosed to cover neurologic, cardiovascular, and immunologic problems.

Think Twice: Effects on Diagnostic Accuracy of Returning to the Case to Reflect Upon the Initial Diagnosis

PURPOSE

Diagnostic errors have been attributed to failure to sufficiently reflect on initial diagnoses. However, evidence of the benefits of reflection is conflicting. This study examined whether reflection upon initial diagnoses on difficult cases improved diagnostic accuracy and whether reflection triggered by confrontation with case evidence was more beneficial than simply revising initial diagnoses.

METHOD

Participants were physicians in Bern, Switzerland, registered for the 2018 Swiss internal medicine certification exam. They diagnosed written clinical cases, providing an initial diagnosis by following the same instructions and returning to the case to provide a final diagnosis. The latter required different types of reflection depending on the physician's experimental condition: return without instructions, identify confirmatory evidence, identify contradictory evidence, or identify both confirmatory and contradictory evidence. The authors examined diagnostic accuracy scores (range 0-1) as a function of diagnostic phase and reflection type.

RESULTS

One hundred and sixty-seven physicians participated. Diagnostic accuracy scores did not significantly differ between the 4 groups of physicians in the initial (I) or the final (F) diagnostic phase (mean [95% CI]: return without instructions, I: 0.21 [0.17, 0.26], F: 0.23 [0.18, 0.28]; confirmatory evidence, I: 0.24 [0.19, 0.29], F: 0.31 [0.25, 0.37]; contradictory evidence, I: 0.22 [0.17, 0.26], F: 0.26 [0.22, 0.30]; confirmatory and contradictory evidence, I: 0.19 [0.15, 0.23], F: 0.25 [0.20, 0.31]). Regardless of type of reflection employed while revising the case, accuracy increased significantly between initial and final diagnosis, I: 0.22 (0.19, 0.24) vs F: 0.26 (0.24, 0.29); P < .001.

CONCLUSIONS

Physicians' diagnostic accuracy improved after reflecting upon initial diagnoses provided for difficult cases, independently of the evidence searched for while reflecting. The findings support the importance attributed to reflection in clinical teaching. Future research should investigate whether revising the case can become more beneficial by triggering additional reflection.

Operational measurement of diagnostic safety: state of the science

Reducing the incidence of diagnostic errors is increasingly a priority for government, professional, and philanthropic organizations. Several obstacles to measurement of diagnostic safety have hampered progress toward this goal. Although a coordinated national strategy to measure diagnostic safety remains an aspirational goal, recent research has yielded practical guidance for healthcare organizations to start using measurement to enhance diagnostic safety. This paper, concurrently published as an Issue Brief by the Agency for Healthcare Research and Quality, issues a "call to action" for healthcare organizations to begin measurement efforts using data sources currently available to them. Our aims are to outline the state of the science and provide practical recommendations for organizations to start identifying and learning from diagnostic errors. Whether by strategically leveraging current resources or building additional capacity for data gathering, nearly all organizations can begin their journeys to measure and reduce preventable diagnostic harm.

The accuracy of preliminary diagnoses made by paramedics - a cross-sectional comparative study

Background

Clinical decision-making skills of paramedics have been emphasized because of the growing complexity of emergency medicine nursing. A preliminary diagnosis made by a paramedic has an essential role in directing the subsequent care. An accurate preliminary diagnosis improves the patient’s outcome. The research in this area is relatively scarce and there are no previous studies in Finland describing the accuracy of preliminary diagnoses made by paramedics. The aim of this study was to evaluate whether paramedics are making accurate preliminary diagnoses for the patients they are transporting to hospital. In addition, the aim was to describe the variables related to an accurate preliminary diagnosis.

Methods

A cross-sectional comparative approach was used and conducted through a questionnaire to gather data from the paramedics. A total of 71 paramedics participated in the study and 378 patient cases were included. The paramedics were asked to describe the basic information of a case, to state their preliminary diagnosis, and give their own educational background. The accuracy of the paramedic’s preliminary diagnosis was compared with the discharge diagnosis of the ED physicians retrieved from hospital’s patient records. Logistic regression analysis and a binomial test were used to test the statistical significance.

Results

The agreement between the paramedics’ preliminary diagnosis vs. hospital diagnosis was 70% (n = 261). Diagnostic accuracy varied according to the medical condition from mental diseases and intoxication (86%, p = 0,000), cerebral strokes (81%, p = 0,007) to infections (31% p = 0,029). The educational background of a bachelor-degree-level paramedic (p = 0,016, 95% Cl 1,7-139,6) and a good self-assessment value (p = 0,003, 95% Cl 1,2-2,7) were related to making a correct diagnosis.

Conclusions

Paramedics are able to determine preliminary diagnoses at satisfactory level. The relationship between educational background and diagnostic accuracy suggests that there is a definitive need for a specific pre-hospital nursing education.