Dataset Information

Cancer complaints: The profile of patients from the emergency department of a Brazilian oncology teaching hospital.

ABSTRACT: Background: With the increase in prevalence of cancer in our society, we aim to clarify through primary data use what drives emergency department (ED) utilization among patients with cancer. Methods: This is a cross-sectional study. A direct survey was applied to cancer patients over 277 visits in 2015. Variables including chief complaint for current and last visit, frequency of visits, primary tumor site, and demographics were collected. Results: Pain was the most common complaint, responsible for 40% of visits, followed by constitutional symptoms (17%), and gastrointestinal complaints (11%). Abdominal pain was the single most noted pain type, with 18.4%, and had the highest rate of recurrence. It was followed by back pain, dyspnea, asthenia and fever, accounting for 8.5%, 8.5%, 8.1% and 7%, respectively. Cervical cancer represented 14.8% of patients, followed by breast (11.6%) and lung (7.6%) cancers. The majority of patients visited the emergency department less than once a month. Conclusion: The drivers of emergency department utilization among patients with cancer found through primary use data mostly confirm findings from larger studies with secondary use data. Our research underscores the burden of pain to patients with cancer, as it is the most common complaint leading to ED visits, and generally requires multiple visits. Abdominal pain was more likely to recur than other complaints. Patients could benefit from focused outpatient pain management, and from more research and education targeting cancer-related pain.

SUBMITTER: Batalini F

PROVIDER: S-EPMC6198257 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Cancer complaints: The profile of patients from the emergency department of a Brazilian oncology teaching hospital.

Batalini Felipe F Gomes Millena M I Fábio F Kuwae Flávio F Macanhan Giselle G Pereira Julio L B JLB

F1000Research 20171031

Background: With the increase in prevalence of cancer in our society, we aim to clarify through primary data use what drives emergency department (ED) utilization among patients with cancer. Methods: This is a cross-sectional study. A direct survey was applied to cancer patients over 277 visits in 2015. Variables including chief complaint for current and last visit, frequency of visits, primary tumor site, and demographics were collected. Results: Pain was the most common co ...[more]

PMID: 30410725

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Project description:BackgroundEmergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors.MethodsData on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016-2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital's catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit.ResultsThe number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE's were 8.7%).ConclusionsBecause of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

Project description:Objective:We learn contextual embeddings for emergency department (ED) chief complaints using Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language model, to derive a compact and computationally useful representation for free-text chief complaints. Materials and methods:Retrospective data on 2.1 million adult and pediatric ED visits was obtained from a large healthcare system covering the period of March 2013 to July 2019. A total of 355 497 (16.4%) visits from 65 737 (8.9%) patients were removed for absence of either a structured or unstructured chief complaint. To ensure adequate training set size, chief complaint labels that comprised less than 0.01%, or 1 in 10 000, of all visits were excluded. The cutoff threshold was incremented on a log scale to create seven datasets of decreasing sparsity. The classification task was to predict the provider-assigned label from the free-text chief complaint using BERT, with Long Short-Term Memory (LSTM) and Embeddings from Language Models (ELMo) as baselines. Performance was measured as the Top-k accuracy from k?=?1:5 on a hold-out test set comprising 5% of the samples. The embedding for each free-text chief complaint was extracted as the final 768-dimensional layer of the BERT model and visualized using t-distributed stochastic neighbor embedding (t-SNE). Results:The models achieved increasing performance with datasets of decreasing sparsity, with BERT outperforming both LSTM and ELMo. The BERT model yielded Top-1 accuracies of 0.65 and 0.69, Top-3 accuracies of 0.87 and 0.90, and Top-5 accuracies of 0.92 and 0.94 on datasets comprised of 434 and 188 labels, respectively. Visualization using t-SNE mapped the learned embeddings in a clinically meaningful way, with related concepts embedded close to each other and broader types of chief complaints clustered together. Discussion:Despite the inherent noise in the chief complaint label space, the model was able to learn a rich representation of chief complaints and generate reasonable predictions of their labels. The learned embeddings accurately predict provider-assigned chief complaint labels and map semantically similar chief complaints to nearby points in vector space. Conclusion:Such a model may be used to automatically map free-text chief complaints to structured fields and to assist the development of a standardized, data-driven ontology of chief complaints for healthcare institutions.

Project description:BACKGROUND:Nonspecific complaint (NSC) is a common presenting complaint in the emergency setting, especially in the elderly population. Individual studies have shown that it is associated with significant morbidity and mortality. This prognostic systematic review draws a synthesis of reported outcomes for patients presenting with NSC and compares them with outcomes for patients presenting with a specific complaint. METHODS:We conducted a literature search for publications, abstracts and conference presentations from Ovid, Scopus and Web of Science for the past 20?years. Studies were included which treated adult patients presenting to the Emergency Medical Services or Emergency Department with NSC. 2599 studies were screened for eligibility and quality was assessed using the SIGN assessment for bias tool. We excluded any low-quality studies, resulting in nine studies for quantitative analysis. We analysed the included studies for in-hospital mortality, triage category, emergency department length of stay, admission rate, hospital length of stay, intensive care admissions and re-visitation rate and compared outcomes to patients presenting with specific complaints (SC), where data were available. We grouped discharge diagnoses by ICD-10 category. RESULTS:We found that patients presenting with NSC were mostly older adults. Mortality for patients with NSC was significantly increased compared to patients presenting with SC [OR 2.50 (95% CI 1.40-4.47)]. They were triaged as urgent less often than SC patients [OR 2.12 (95% CI 1.08-4.16)]. Emergency department length of stay was increased in two out of three studies. Hospital length of stay was increased by 1-3?days. Admission rates were high in most studies, 55 to 84%, and increased in comparison to patients with SC [OR 3.86 (95% CI 1.76-8.47)]. These patients seemed to require more resources than patients with SC. The number for intensive care admissions did not seem to be increased. Data were insufficient to make conclusions regarding re-visitation rates. Discharge diagnoses were spread throughout the ICD-10 main chapters, infections being the most prevalent. CONCLUSIONS:Patients with NSC have a high risk of mortality and their care in the Emergency Department requires more time and resources than for patients with SC. We suggest that NSC should be considered a major emergency presentation.

Project description:ObjectivesVeteran persons living with dementia (PLWDs) have high acute care utilization. We aim to understand why PLWDs seek care in the emergency department (ED) and how their utilization differs from older Veterans with no dementia diagnosis. We demonstrate the use of a novel national chief complaint data set in the Veteran Affairs Health Care System.MethodsThis was a retrospective observational study of ED users 65 years or older as of FY2017. The primary outcome is presence of one or more ED visits in FYs 2017-2018 using a logistic regression model controlling for dementia and other variables. Secondary outcomes include counts of ED visits by disposition, Emergency Severity Index, chief complaints defined by a natural language processing program, and ED encounter diagnoses defined by primary International Statistical Classification of Diseases, Tenth Revision (ICD-10-CM) code.ResultsOur cohort of Veterans comprised 3,115,263 patients. Of those, 255,372 (8.2%) had a diagnosis of dementia. Logistic regression modeling demonstrated that dementia is a significant predictor of ED use (p < 0.0001), with PLWDs more likely to have an ED visit (odds ratio 1.96, 95% confidence interval 1.94-1.98). PLWDs were admitted at higher rates when accounting for age and acuity. Chief complaints that were more common among PLWDs included falls (6.7% dementia vs. 3.3% without dementia), weakness (3.6% vs. 2.2%), and abnormal mental state (2.2% vs. 0.4%). ICD-10-CM codes were largely similar between the two groups.ConclusionsOur results reinforce that the ED is a common access point for Veterans with dementia. These patients require special consideration as they are more likely to visit the ED and be admitted. Our use of a novel national chief complaint data set suggests that they more commonly present with certain geriatric syndromes and nonspecific complaints. Further work is needed to determine whether these would warrant targeted interventions to improve quality of acute care.

Dataset Information

Cancer complaints: The profile of patients from the emergency department of a Brazilian oncology teaching hospital.

Publications

Cancer complaints: The profile of patients from the emergency department of a Brazilian oncology teaching hospital.

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