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

Comparison of Two Symptom Checkers (Ada and Symptoma) in the Emergency Department: Randomized, Crossover, Head-to-Head, Double-Blinded Study

BACKGROUND

Emergency departments (EDs) are frequently overcrowded and increasingly used by nonurgent patients. Symptom checkers (SCs) offer on-demand access to disease suggestions and recommended actions, potentially improving overall patient flow. Contrary to the increasing use of SCs, there is a lack of supporting evidence based on direct patient use.

OBJECTIVE

This study aimed to compare the diagnostic accuracy, safety, usability, and acceptance of 2 SCs, Ada and Symptoma.

METHODS

A randomized, crossover, head-to-head, double-blinded study including consecutive adult patients presenting to the ED at University Hospital Erlangen. Patients completed both SCs, Ada and Symptoma. The primary outcome was the diagnostic accuracy of SCs. In total, 6 blinded independent expert raters classified diagnostic concordance of SC suggestions with the final discharge diagnosis as (1) identical, (2) plausible, or (3) diagnostically different. SC suggestions per patient were additionally classified as safe or potentially life-threatening, and the concordance of Ada's and physician-based triage category was assessed. Secondary outcomes were SC usability (5-point Likert-scale: 1=very easy to use to 5=very difficult to use) and SC acceptance net promoter score (NPS).

RESULTS

A total of 450 patients completed the study between April and November 2021. The most common chief complaint was chest pain (160/437, 37%). The identical diagnosis was ranked first (or within the top 5 diagnoses) by Ada and Symptoma in 14% (59/437; 27%, 117/437) and 4% (16/437; 13%, 55/437) of patients, respectively. An identical or plausible diagnosis was ranked first (or within the top 5 diagnoses) by Ada and Symptoma in 58% (253/437; 75%, 329/437) and 38% (164/437; 64%, 281/437) of patients, respectively. Ada and Symptoma did not suggest potentially life-threatening diagnoses in 13% (56/437) and 14% (61/437) of patients, respectively. Ada correctly triaged, undertriaged, and overtriaged 34% (149/437), 13% (58/437), and 53% (230/437) of patients, respectively. A total of 88% (385/437) and 78% (342/437) of participants rated Ada and Symptoma as very easy or easy to use, respectively. Ada's NPS was -34 (55% [239/437] detractors; 21% [93/437] promoters) and Symptoma's NPS was -47 (63% [275/437] detractors and 16% [70/437]) promoters.

CONCLUSIONS

Ada demonstrated a higher diagnostic accuracy than Symptoma, and substantially more patients would recommend Ada and assessed Ada as easy to use. The high number of unrecognized potentially life-threatening diagnoses by both SCs and inappropriate triage advice by Ada was alarming. Overall, the trustworthiness of SC recommendations appears questionable. SC authorization should necessitate rigorous clinical evaluation studies to prevent misdiagnoses, fatal triage advice, and misuse of scarce medical resources.

TRIAL REGISTRATION

German Register of Clinical Trials DRKS00024830; https://drks.de/search/en/trial/DRKS00024830.

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

INTRODUCTION

Early and adequate preliminary diagnosis reduce emergency department (ED) and hospital stay and may reduce mortality. Several studies demonstrated adequate preliminary diagnosis as stated by emergency medical services (EMS) ranging between 61 and 77%. Dutch EMS are highly trained, but performance of stating adequate preliminary diagnosis remains unknown.

METHODS

This prospective observational study included 781 patients (> 18years), who arrived in the emergency department (ED) by ambulance in two academic hospitals. For each patient, the diagnosis as stated by EMS and the ED physician was obtained and compared. Diagnosis was categorized based on the International Classification of Diseases, 11th Revision.

RESULTS

The overall diagnostic agreement was 79% [95%-CI: 76-82%]. Agreement was high for traumatic injuries (94%), neurological emergencies (90%), infectious diseases (84%), cardiovascular (78%), moderate for mental and drug related (71%), gastrointestinal (70%), and low for endocrine and metabolic (50%), and acute internal emergencies (41%). There is no correlation between 28-day mortality, the need for ICU admission or the need for hospital admission with an adequate preliminary diagnosis.

CONCLUSION

In the Netherlands, the extent of agreement between EMS diagnosis and ED discharge diagnosis varies between categories. Accuracy is high in diseases with specific observations, e.g., neurological failure, detectable injuries, and electrocardiographic abnormalities. Further studies should use these findings to improve patient outcome.

The Clinical Emergency Data Registry: Structure, Use, and Limitations for Research

The Clinical Emergency Data Registry (CEDR) is a qualified clinical data registry that collects data from participating emergency departments (EDs) in the United States for quality measurement, improvement, and reporting purposes. This article aims to provide an overview of the data collection and validation process, describe the existing data structure and elements, and explain the potential opportunities and limitations for ongoing and future research use. CEDR data are primarily collected for quality reporting purposes and are obtained from diverse sources, including electronic health records and billing data that are de-identified and stored in a secure, centralized database. The CEDR data structure is organized around clinical episodes, which contain multiple data elements that are standardized using common data elements and are mapped to established terminologies to enable interoperability and data sharing. The data elements include patient demographics, clinical characteristics, diagnostic and treatment procedures, and outcomes. Key limitations include the limited generalizability due to the selective nature of participating EDs and the limited validation and completeness of data elements not currently used for quality reporting purposes, including demographic data. Nonetheless, CEDR holds great potential for ongoing and future research in emergency medicine due to its large-volume, longitudinal, near real-time, clinical data. In 2021, the American College of Emergency Physicians authorized the transition from CEDR to the Emergency Medicine Data Institute, which will catalyze investments in improved data quality and completeness for research to advance emergency care.

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.

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.

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.

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.

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.

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.