Emergency Department Attendance after Telephone Triage: A Population-Based Data Linkage Study.
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ABSTRACT: Objective
To investigate compliance with telephone helpline advice to attend an emergency department (ED) and the acuity of patients who presented to ED following a call.Data sources/collection methods
In New South Wales (NSW), Australia, 2009-2012, all (1.04 million) calls to a telephone triage service, ED presentations, hospital admissions and death registrations, linked using probabilistic data linkage.Study design
Population-based, observational cohort study measuring ED presentations within 24 hours of a call in patients (1) with dispositions to attend ED (compliance) and (2) low-urgency dispositions (self-referral), triage categories on ED presentation.Principal findings
A total of 66.5 percent of patients were compliant with dispositions to attend an ED. A total of 6.2 percent of patients with low-urgency dispositions self-referred to the ED within 24 hours. After age adjustment, healthdirect compliant patients were significantly less likely (7.8 percent) to receive the least urgent ED triage category compared to the general NSW ED population (16.9 percent).Conclusions
This large population-based data linkage study provides precise estimates of ED attendance following calls to a telephone triage service and details the predictors of ED attendance. Patients who attend an ED compliant with a healthdirect helpline disposition are significantly less likely than the general ED population to receive the lowest urgency triage category on arrival.
SUBMITTER: Gibson A
PROVIDER: S-EPMC5867179 | biostudies-literature | 2018 Apr
REPOSITORIES: biostudies-literature
Publications
Emergency Department Attendance after Telephone Triage: A Population-Based Data Linkage Study.
Gibson Amy A Randall Deborah D Tran Duong T DT Byrne Mary M Lawler Anthony A Havard Alys A Robinson Maureen M Jorm Louisa R LR
Health services research 20170329 2
<h4>Objective</h4>To investigate compliance with telephone helpline advice to attend an emergency department (ED) and the acuity of patients who presented to ED following a call.<h4>Data sources/collection methods</h4>In New South Wales (NSW), Australia, 2009-2012, all (1.04 million) calls to a telephone triage service, ED presentations, hospital admissions and death registrations, linked using probabilistic data linkage.<h4>Study design</h4>Population-based, observational cohort study measuring ...[more]
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Project description:AbstractObjectivesTo describe the effect of multimorbidity on adverse patient centred outcomes in people attending emergency department.DesignPopulation based cohort study.SettingEmergency departments in NHS Lothian in Scotland, from 1 January 2012 to 31 December 2019.ParticipantsAdults (≥18 years) attending emergency departments.Data sourcesLinked data from emergency departments, hospital discharges, and cancer registries, and national mortality data.Main outcome measuresMultimorbidity was defined as at least two conditions from the Elixhauser comorbidity index. Multivariable logistic or linear regression was used to assess associations of multimorbidity with 30 day mortality (primary outcome), hospital admission, reattendance at the emergency department within seven days, and time spent in emergency department (secondary outcomes). Primary analysis was stratified by age (<65 v ≥65 years).Results451 291 people had 1 273 937 attendances to emergency departments during the study period. 43 504 (9.6%) had multimorbidity, and people with multimorbidity were older (median 73 v 43 years), more likely to arrive by emergency ambulance (57.8% v 23.7%), and more likely to be triaged as very urgent (23.5% v 9.2%) than people who do not have multimorbidity. After adjusting for other prognostic covariates, multimorbidity, compared with no multimorbidity, was associated with higher 30 day mortality (8.2% v 1.2%, adjusted odds ratio 1.81 (95% confidence interval (CI) 1.72 to 1.91)), higher rate of hospital admission (60.1% v 20.5%, 1.81 (1.76 to 1.86)), higher reattendance to an emergency department within seven days (7.8% v 3.5%, 1.41 (1.32 to 1.50)), and longer time spent in the department (adjusted coefficient 0.27 h (95% CI 0.26 to 0.27)). The size of associations between multimorbidity and all outcomes were larger in younger patients: for example, the adjusted odds ratio of 30 day mortality was 3.03 (95% CI 2.68 to 3.42) in people younger than 65 years versus 1.61 (95% CI 1.53 to 1.71) in those 65 years or older.ConclusionsAlmost one in ten patients presenting to emergency department had multimorbidity using Elixhauser index conditions. Multimorbidity was strongly associated with adverse outcomes and these associations were stronger in younger people. The increasing prevalence of multimorbidity in the population is likely to exacerbate strain on emergency departments unless practice and policy evolve to meet the growing demand.
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Project description:OBJECTIVE:To investigate the appropriateness of cases presenting to the emergency department (ED) following ambulance-based secondary telephone triage. DESIGN:A pragmatic retrospective cohort analysis of all the planned and unplanned ED presentations within 48 hours of a secondary telephone triage. SETTING:The secondary telephone triage service, called the Referral Service, and the hospitals were located in metropolitan Melbourne, Australia and operated 24 hours a day, servicing 4.25 million people. The Referral Service provides an in-depth secondary triage of cases classified as low acuity when calling the Australian emergency telephone number. POPULATION:Cases triaged by the Referral Service between September 2009 and June 2012 were linked to ED and hospital admission records (N=44,523). Planned ED presentations were cases referred to the ED following the secondary triage, unplanned ED presentations were cases that presented despite being referred to alternative care pathways. MAIN OUTCOME MEASURES:Appropriateness was measured using an ED suitability definition and hospital admission rates. These were compared with mean population data which consisted of all of the ED presentations for the state (termed the 'average Victorian ED presentation'). RESULTS:Planned ED presentations were more likely to be ED suitable than unplanned ED presentations (OR 1.62; 95% CI 1.5 to 1.7; p<0.001) and the average Victorian ED presentation (OR 1.85; 95% CI 1.01 to 3.4; p=0.046). They were also more likely to be admitted to the hospital than the unplanned ED presentation (OR 1.5; 95% CI 1.4 to 1.6; p<0.001) and the average Victorian ED presentation (OR 2.3, 95% CI 2.24 to 2.33; p<0.001). Just under 15% of cases diverted away from the emergency care pathways presented in the ED (unplanned ED attendances), and 9.5% of all the alternative care pathway cases were classified as ED suitable and 6.5% were admitted to hospital. CONCLUSIONS:Secondary telephone triage was able to appropriately identify many ED suitable cases, and while most cases referred to alternative care pathways did not present in the ED. Further research is required to establish that these were not inappropriately triaged away from the emergency care pathways.
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Project description:OBJECTIVE:To use linked administrative datasets to assess factors associated with emergency department (ED) presentation and psychiatric readmission in three distinctive time intervals after the index psychiatric admission. DESIGN:A retrospective data-linkage study. SETTING:Cohort study using four linked government minimum datasets including acute hospital care from July 2005 to June 2012 in New South Wales, Australia. PARTICIPANTS:People who were alive and aged ?18 years on 1 July 2005 and who had their index admission to a psychiatric ward from 1 July 2007 to 30 June 2010. OUTCOME MEASURES:ORs of factors associated with psychiatric admission and ED presentation were calculated for three intervals: 0-1?month, 2-5 months and 6-24 months after index separation. RESULTS:Index admission was identified in 35?056 individuals (51% -males) with a median age of 42 years. A total of 12?826 (37%) individuals had at least one ED presentation in the 24 months after index admission. Of those, 3608 (28%) presented within 0-1?month, 6350 (50%) within 2-5 months and 10?294 (80%) within 6-24 months after index admission. A total of 14?153 (40%) individuals had at least one psychiatric readmission in the first 24 months. Of those, 6808 (48%) were admitted within 0-1?month, 6433 (45%) within 2-5 months and 7649 (54%) within 6-24 months after index admission. Principal diagnoses and length of stay at index admission, sociodemographic factors, Charlson Comorbidity Index score, drug and alcohol comorbidity, intellectual disability and other inpatient service use were significantly associated with ED presentations and psychiatric readmissions, and these relationships varied somewhat over the intervals studied. CONCLUSION:Social determinants of service use, drug and alcohol intervention, addressing needs of individuals with intellectual disability and recovery-oriented whole-person approaches at index admission are key areas for investment to improve trajectories after index admission.
| S-EPMC5855390 | biostudies-literature
Project description:Objectives:The aim of this study was to develop machine learning (ML) and initial nursing assessment (INA)-based emergency department (ED) triage to predict adverse clinical outcome. Methods:The retrospective study included ED visits between January 2016 and December 2017 that resulted in either intensive care unit admission or emergency room death. We trained four classifiers using logistic regression and a deep learning model on INA and low dimensional (LD) INA, logistic regression on the Korea Triage and acuity scale (KTAS) and Sequential Related Organ Failure Assessment (SOFA). We varied the outcome ratio for external validation. Finally, variables of importance were identified using the random forest model's information gain. The four most influential variables were used for LD modeling for efficiency. Results:A total of 86,304 patient visits were included, with an overall outcome rate of 3.5%. The area under the curve (AUC) values for the KTAS model were 76.8 (74.9-78.6) with logistic regression and 74.0 (72.1-75.9) for the SOFA model, while the AUC values of the INA model were 87.2 (85.9-88.6) and 87.6 (86.3-88.9) with logistic regression and deep learning, suggesting that the ML and INA-based triage system result more accurately predicted the outcomes. The AUC values for the LD model were 81.2 (79.4-82.9) and 80.7 (78.9-82.5) for logistic regression and deep learning, respectively. Conclusions:We developed an ML and INA-based triage system for EDs. The novel system was able to predict clinical outcomes more accurately than existing triage systems, KTAS and SOFA.
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Project description:IntroductionThe American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.MethodsUsing the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.ResultsOf the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.ConclusionExcluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.
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Project description:BackgroundEmergency department (ED) crowding is associated with negative health outcomes, patient dissatisfaction, and longer length of stay (LOS). The addition of advanced diagnostic imaging (ADI), namely CT, ultrasound (U/S), and MRI to ED encounter work up is a predictor of longer length of stay. Earlier and improved prediction of patients' need for advanced imaging may improve overall ED efficiency. The aim of the study was to detect the association between ADI utilization and the structured and unstructured information immediately available during ED triage, and to develop and validate models to predict utilization of ADI during an ED encounter.MethodsWe used the United States National Hospital Ambulatory Medical Care Survey data from 2009 to 2014 to examine which sociodemographic and clinical factors immediately available at ED triage were associated with the utilization of CT, U/S, MRI, and multiple ADI during a patient's ED stay. We used natural language processing (NLP) topic modeling to incorporate free-text reason for visit data available at time of ED triage in addition to other structured patient data to predict the use of ADI using multivariable logistic regression models.ResultsAmong the 139,150 adult ED visits from a national probability sample of hospitals across the U.S, 21.9% resulted in ADI use, including 16.8% who had a CT, 3.6% who had an ultrasound, 0.4% who had an MRI, and 1.2% of the population who had multiple types of ADI. The c-statistic of the predictive models was greater than or equal to 0.78 for all imaging outcomes, and the addition of text-based reason for visit information improved the accuracy of all predictive models.ConclusionsPatient information immediately available during ED triage can accurately predict the eventual use of advanced diagnostic imaging during an ED visit. Such models have the potential to be incorporated into the ED triage workflow in order to more rapidly identify patients who may require advanced imaging during their ED stay and assist with medical decision-making.
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Project description:Emergency departments (ED) are complex, triage is a main task in the ED to prioritize patient with limited medical resources who need them most. Machine learning (ML) based ED triage tool, Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable ML framework with single center. We aimed to develop SERP with 3 Korean multicenter cohorts based on common data model (CDM) without data sharing and compare performance with inter-hospital validation design. This retrospective cohort study included all adult emergency visit patients of 3 hospitals in Korea from 2016 to 2017. We adopted CDM for the standardized multicenter research. The outcome of interest was 2-day mortality after the patients' ED visit. We developed each hospital SERP using interpretable ML framework and validated inter-hospital wisely. We accessed the performance of each hospital's score based on some metrics considering data imbalance strategy. The study population for each hospital included 87,670, 83,363 and 54,423 ED visits from 2016 to 2017. The 2-day mortality rate were 0.51%, 0.56% and 0.65%. Validation results showed accurate for inter hospital validation which has at least AUROC of 0.899 (0.858-0.940). We developed multicenter based Interpretable ML model using CDM for 2-day mortality prediction and executed Inter-hospital external validation which showed enough high accuracy.
| S-EPMC10954621 | biostudies-literature
Project description:ImportanceWhile machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage.ObjectivesTo examine the performance of machine learning approaches to predict clinical outcomes and disposition in children in the ED and to compare their performance with conventional triage approaches.Design, setting, and participantsPrognostic study of ED data from the National Hospital Ambulatory Medical Care Survey from January 1, 2007, through December 31, 2015. A nationally representative sample of 52 037 children aged 18 years or younger who presented to the ED were included. Data analysis was performed in August 2018.Main outcomes and measuresThe outcomes were critical care (admission to an intensive care unit and/or in-hospital death) and hospitalization (direct hospital admission or transfer). In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning-based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. In the test set (the remaining 30% of the sample), we measured the models' prediction performance by computing C statistics, prospective prediction results, and decision curves. These machine learning models were built for each outcome and compared with the reference model using the conventional triage classification information.ResultsOf 52 037 eligible ED visits by children (median [interquartile range] age, 6 [2-14] years; 24 929 [48.0%] female), 163 (0.3%) had the critical care outcome and 2352 (4.5%) had the hospitalization outcome. For the critical care prediction, all machine learning approaches had higher discriminative ability compared with the reference model, although the difference was not statistically significant (eg, C statistics of 0.85 [95% CI, 0.78-0.92] for the deep neural network vs 0.78 [95% CI, 0.71-0.85] for the reference; P = .16), and lower number of undertriaged critically ill children in the conventional triage levels 3 to 5 (urgent to nonurgent). For the hospitalization prediction, all machine learning approaches had significantly higher discrimination ability (eg, C statistic, 0.80 [95% CI, 0.78-0.81] for the deep neural network vs 0.73 [95% CI, 0.71-0.75] for the reference; P < .001) and fewer overtriaged children who did not require inpatient management in the conventional triage levels 1 to 3 (immediate to urgent). The decision curve analysis demonstrated a greater net benefit of machine learning models over ranges of clinical thresholds.Conclusions and relevanceMachine learning-based triage had better discrimination ability to predict clinical outcomes and disposition, with reduction in undertriaging critically ill children and overtriaging children who are less ill.
| S-EPMC6484561 | biostudies-literature
Project description:BackgroundUse of emergency department (ED) care globally seems to be increasing at a faster rate than population growth (Baker, House of Commons Library. Accident and Emergency Statistics, Demand, Performance, 2017). In the UK there has been a reported 16% rise in emergency admissions over the past 5 years. Estimates that between 11 and 40% of ED attendances are non-urgent, with 11% of patients being discharged from the ED without treatment (NHS Digital 2017), and a further 44% require no follow-up treatment (NHS Digital, Hospital Accident and Emergency Activity 2016-17, 2019) is cited as evidence that these patients did not require this level of care. The solution to not using the most appropriate point in the system has traditionally been seen as a knowledge problem, requiring, improved sign-posting and information to enable people to self-manage or use health care management for minor ailments. However research about help-seeking behaviour suggests that the problem may not be an informational one. A considerable literature points to help seeking as a social process influenced by a range of contingencies and contextual factors including the way in which lay people influence health care utilisation (Giebel et al. BMJ Open 9:1, 2019). Personal communities comprise a variety of active and significant social ties which have potential to influence individual capacity to seek help. Here we extend and unpack further influencing decisions about seeking formal health care with reference to how they are shaped and informed by and within personal social networks.MethodsWe undertook a personal network mapping and qualitative interview-based study to look at, problematize and understand attendance for non-urgent problems. We used network analysis and methods to map and characterise the personal communities of people seeking help from ED for minor ailments and semi-structured interviews with 40 people attending a single ED and associated GP hub providing equivalent care. Interviews were built around an ego network mapping activity and a topic guide structured to explore attender's narratives about why they had visited the ED. This ego network activity uses a diagram consisting of three concentric circles (Fiori et al. J Gerontol B-Psychol 62: 322-30, 2007), representing closest social network members (in the centre) and those at further distance. Participants were initially presented with one of these diagrams and asked to write names of people or resources that had played a role in their attendance and the interviewer probed the interviewee to discuss the actions, input and value of the people and services that supported the visit to the ED.ResultsWe analysed number and type of network connections and undertook a thematic analysis to identify how imagined and actual network members and influences were implicated in ED attendance. The network maps created during the interviews were examined and a typology of networks was developed and used to distinguish different types of networks informed by our reading of the data, and a Network Typology Scoring Tool, a measure of frequency of contact and relationship type in networks.ConclusionsOur study suggests that faced with acute minor illness or injury people's networks narrow: they do not (and perhaps cannot) mobilise their imagined care network because the resources or connections may not be there or are difficult to engage. In addition we identified important system drivers of behaviour, notably that these patients are often directed to the ED by 'professional influencers' including health services staff.
| S-EPMC7504825 | biostudies-literature
Project description:BackgroundTelephone triage has been established in many countries as a response to the challenge of non-urgent use of out-of-hours primary care services. However, limited evidence is available regarding the effect of training interventions on clinicians' telephone consultation skills and patient outcomes.MethodsThis was a pragmatic randomized controlled educational intervention for telephone triage nurses in 59 Norwegian out-of-hours general practitioners' (GPs) cooperatives, serving 59% of the Norwegian population. Computer-generated randomization was performed at the level of out-of-hours GP cooperatives, stratified by the population size. Thirty-two out-of-hours GP cooperatives were randomized to intervention. One cooperative did not accept the invitation to participate in the educational programme, leaving 31 cooperatives in the intervention group. The intervention comprised a 90-minute e-learning course and 90-minute group discussion about respiratory tract infections (RTIs), telephone communication skills and local practices. We aimed to assess the effect of the intervention on out-of-hours attendance and describe the distribution of RTIs between out-of-hours GP cooperatives and list-holding GPs. The outcome was the difference in the number of doctor's consultations per 1000 inhabitants between the intervention and control groups during the winter months before and after the intervention. A negative binomial regression model was used for the statistical analyses. The model was adjusted for the number of nurses who had participated in the e-learning course, the population size and patients' age groups, with the out-of-hours GP cooperatives defined as clusters.ResultsThe regression showed that the intervention did not change the number of consultations for RTIs between the two groups of out-of-hours GP cooperatives (incidence rate ratio 0.99, 95% confidence interval 0.91-1.07). The winter season's out-of-hours patient population was younger and had a higher proportion of RTIs than the patient population in the list-holding GP offices. Laryngitis, sore throat, and pneumonia were the most common diagnoses during the out-of-hours primary care service.ConclusionsThe intervention did not influence the out-of-hours attendance. This finding may be due to the intervention's limited scope and the intention-to-treat design. Changing a population's out-of-hours attendance is complicated and needs to be targeted at several organizational levels.
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