Dataset Information

Factors predicting patient use of the emergency department: a retrospective cohort study.

ABSTRACT: Many studies have shown the tendency for people without a regular care provider or primary physician to make greater use of emergency departments. We sought to determine the effects of three aspects of care provided by primary physicians (physician specialty, continuity of care and comprehensiveness of care) on their patients' use of the emergency department.Using provincial administrative databases, we created a cohort of 367,315 adults aged 18 years and older. Participants were residents of urban areas of Quebec. Affiliation with a primary physician, the specialty of this physician (i.e., family physician v. specialist), continuity of care (as measured using the Usual Provider Continuity index) and comprehensiveness of care (i.e., number of complete annual examinations) were measured among participants (n = 311,701) who had visited a physician three or more times during a two-year baseline period. We used multivariable negative binomial regression to investigate the relationships between measures of care and the number of visits to emergency departments during a 12-month follow-up period.Among participants under 65 years of age, emergency department use was higher for those not affiliated than for those affiliated with a family physician (incidence rate ratio [IRR] 1.11, 95% confidence interval [CI] 1.05-1.16) or a specialist (IRR 1.10, 95% CI 1.04-1.17). Among patients aged 65 years and older, having a specialist primary physician, as opposed to a family physician, predicted increased use of the emergency department (IRR 1.13, 95% CI 1.09-1.17). Greater continuity of care with a family physician predicted less use of the emergency department only among participants who made 25 or more visits to a physician during the baseline period. Greater continuity of care with a specialist predicted less use of the emergency department overall, particularly among participants with intermediate numbers of multimorbidities and admissions to hospital. Greater comprehensiveness of care by family physicians predicted less use of the emergency department.Efforts to increase the proportion of adults affiliated with a family physician should target older adults, people who visit physicians more frequently and people with multiple comorbidities and admissions to hospital.

SUBMITTER: McCusker J

PROVIDER: S-EPMC3314059 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Factors predicting patient use of the emergency department: a retrospective cohort study.

McCusker Jane J Tousignant Pierre P Borgès Da Silva Roxane R Ciampi Antonio A Lévesque Jean-Frédéric JF Vadeboncoeur Alain A Sanche Steven S

CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne 20120221 6

<h4>Background</h4>Many studies have shown the tendency for people without a regular care provider or primary physician to make greater use of emergency departments. We sought to determine the effects of three aspects of care provided by primary physicians (physician specialty, continuity of care and comprehensiveness of care) on their patients' use of the emergency department.<h4>Methods</h4>Using provincial administrative databases, we created a cohort of 367,315 adults aged 18 years and older ...[more]

PMID: 22353588

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Project description:BackgroundPeripregnancy emergency department use may be common, but data specific to health care systems like that in Canada are lacking. As prior research was limited to livebirths, omitting pregnancies ending in miscarriage or induced abortion, the current study quantified and characterized emergency department use among women in Ontario with a recognized pregnancy.MethodsThis retrospective population-based cohort study included all recognized pregnancies among Ontario residents aged 10-55 years with an estimated date of conception between Apr. 1, 2002, and Mar. 31, 2017. We defined peripregnancy emergency department use as any emergency department visit during pregnancy or within 42 days after pregnancy. We used modified Poisson regression with a robust error variance to generate relative risks (RRs) and 95% confidence intervals (CIs) for the outcome of any peripregnancy emergency department use in association with maternal age, parity, residential income quintile, location of residence, immigrant status, antenatal care provider and number of comorbidities within 120 days before the clinical start of the pregnancy (expressed as total number of Aggregated Diagnosis Groups [ADGs] obtained with the Johns Hopkins Adjusted Clinical Group System). All RRs, except for number of comorbidities, were further adjusted for number of ADGs.ResultsPeripregnancy emergency department use occurred in 1 075 991 (39.4%) of 2 728 236 recognized pregnancies, including 35.8% of livebirths, 47.3% of stillbirths, 73.7% of miscarriages and 84.8% of threatened abortions. A peripregnancy emergency department visit was more likely among women who were less than 25 years of age (adjusted RR 1.16, 95% CI 1.16-1.17), were nulliparous (adjusted RR 1.13, 95% CI 1.13-1.13), resided in the lowest income quintile area (adjusted RR 1.16, 95% CI 1.15-1.16) or in a rural area (adjusted RR 1.50, 95% CI 1.50-1.51), were Canadian-born (adjusted RR 1.22, 95% CI 1.22-1.23), were not seen by an obstetrician (adjusted RR 1.66, 95% CI 1.54-1.80) or had a greater number of ADGs. Emergency department use peaked in the first trimester and in the first week postpartum. Compared to women residing in urban areas, those residing in rural areas had an odds ratio (OR) of 3.44 (95% CI 3.39-3.49) for 3 or more emergency department visits. Women with 3-4 (OR 1.99, 95% CI 1.97-2.01), 5-6 (OR 3.55, 95% CI 3.49-3.61), or 7 or more (OR 7.59, 95% CI 7.39-7.78) prepregnancy comorbidities were more likely to have 3 or more peripregnancy emergency department visits than were those with 2 or fewer comorbidities.InterpretationPeripregnancy emergency department use occurred in nearly 40% of pregnancies, notably in the first trimester and early in the postpartum period. Efforts are needed to streamline rapid access to ambulatory obstetric care during these peak periods, when women are susceptible to miscarriage or a complication after a livebirth.

Project description:BackgroundUnplanned visits to the emergency department after total joint arthroplasty are far more common than unplanned readmissions. Our objectives were to characterize the prevalence of presentation to an emergency department for any reason after total joint arthroplasty and to identify risk factors for such visits.MethodsUsing health administrative databases, we conducted a population-based retrospective cohort study of adults (19-89 yr of age) who received their first primary elective total hip arthroplasty (THA) or total knee arthroplasty (TKA) procedure for arthritis between April 2011 and March 2016 in Ontario. We made univariate comparisons between patients who presented to the emergency department within 30 days of surgery and those who did not in. We determined differences in use of health care services between groups by comparing the change in use in the year before and after surgery between patients who presented to the emergency department and those who did not. We developed logistic regression models for the occurrence of an emergency department visit using backward variable elimination.ResultsWe identified 42 273 total hip recipients and 70 725 total knee recipients, of whom 5640 (13.3%) and 11 224 (15.9%), respectively, presented to the emergency department within 30 days of surgery. Fewer than 1% of these patients required admission, and nearly half (45%) went to a different institution from where they had their surgery. Among both THA and TKA recipients, patients who presented to the emergency department had a net increase in their median annual health care costs (THA: $501, TKA: $682), compared to a net decrease for the cohort as a whole. Factors associated with increased risk of an emergency visit included increased patient age, male sex, rural residence and various comorbidities. Predictive regression models showed poor discriminative ability for both THA (C-statistic 0.57) and TKA (C-statistic 0.58) recipients.InterpretationOne in 7 patients presented to the emergency department within 30 days of THA or TKA. Some may conceivably have been managed remotely, and very few required readmission. There is a crucial need for strategies to minimize these events.

Project description:Study objectiveTo assess the association of patient and organisational factors with emergency department length of stay (ED-LOS) in elderly ED patients (226565 years old) and in younger patients (<65 years old).MethodsA retrospective cohort study of internal medicine patients visiting the emergency department between September 1st 2010 and August 31st 2011 was performed. All emergency department visits by internal medicine patients 226565 years old and a random sample of internal medicine patients <65 years old were included. Organisational factors were defined as non-medical factors. ED-LOS is defined as the time between ED arrival and ED discharge or admission. Prolonged ED-LOS is defined as ≥75th percentile of ED-LOS in the study population, which was 208 minutes.ResultsData on 1782 emergency department visits by elderly patients and 597 emergency department visits by younger patients were analysed. Prolonged ED-LOS in elderly patients was associated with three organisational factors: >1 consultation during the emergency department visit (odds ratio (OR) 3.2, 95% confidence interval (CI) 2.3-4.3), a higher number of diagnostic tests (OR 1.2, 95% CI 1.16-1.33) and evaluation by a medical student or non-trainee resident compared with a medical specialist (OR 4.2, 95% CI 2.0-8.8 and OR 2.3, 95% CI 1.4-3.9). In younger patients, prolonged ED-LOS was associated with >1 consultation (OR 2.6, 95% CI 1.4-4.6). Factors associated with shorter ED-LOS were arrival during nights or weekends as well as a high urgency level in elderly patients and self-referral in younger patients.ConclusionOrganisational factors, such as a higher number of consultations and tests in the emergency department and a lower seniority of the physician, were the main aspects associated with prolonged ED-LOS in elderly patients. Optimisation of the organisation and coordination of emergency care is important to accommodate the needs of the continuously growing number of elderly patients in a better way.

Project description:BackgroundWithin the trauma system, the emergency department (ED) is the hospital's first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED.ObjectiveThe aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED.MethodsWe used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED.ResultsOur proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320).ConclusionsOur proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.

Dataset Information

Factors predicting patient use of the emergency department: a retrospective cohort study.

Publications

Factors predicting patient use of the emergency department: a retrospective cohort study.

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