Dataset Information

Emergency Department Attendance after Telephone Triage: A Population-Based Data Linkage Study.

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

ACCESS DATA

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]

PMID: 28369871

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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.

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.

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.

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.

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.

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.

Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Dataset Information

Emergency Department Attendance after Telephone Triage: A Population-Based Data Linkage Study.

Objective

Data sources/collection methods

Study design

Principal findings

Conclusions

Publications

Emergency Department Attendance after Telephone Triage: A Population-Based Data Linkage Study.

Similar Datasets

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