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Take a look at a selection of our recent media coverage:
6th January 2022
Triage nurse assessment based on clinical judgement alone, of whether a patient should be admitted after visiting an emergency department (ED), has been shown to be far better than several scoring systems. This was the finding of a study by a team from UOC Pronto Soccorso e Medicina d’Urgenza, Milan, Italy.
Overcrowding in an ED leads to an increased waiting time and some evidence shows that reducing overcrowding is linked with better clinical outcomes. Consequently, ED staff require some form of rapid assessment of patients to ensure appropriate disposition. Although several scoring tools such as the Ambulatory (AMB score), the Glasgow Admission Prediction (GAP) and the Sydney Triage to Admission Risk Tool (START) have good predictive accuracy, such tools have not yet proven their worth. However, an alternative to the use of assessment tools would be for nurses to use their clinical judgement but a recent systematic review concluded that ‘triage nurse prediction of disposition is not accurate enough to expedite admission for ED patients.‘
Nevertheless, there is currently a lack of data comparing individual scoring tools with triage nurse assessment and for the present study, the Italian team decided to compare these existing tools with the clinical judgement of nurses in predicting hospital admission.
They conducted a prospective, single-centre, observational study at a tertiary teaching hospital which has around 70,000 adult ED visits each year. For the study, the triage nurse calculated a patient’s AMB, GAP and START scores and estimated the probability of admission according to their clinical judgement using a 0 to 100 scale. Though the nurses collected the data to calculate each score, this was determined by the investigator team so that the nurses were blinded to the final score. Their own assessments were dichotomised for the purposes of analysis, with a greater than 50% estimated probability, used to define a prediction of admission. The primary outcome of the study was hospital admission and receiver operating characteristic curves were generated for the accuracy of predictions and the area under the curve (AUC) for each tool compared.
A total of 1710 patients with a median age of 54 years (49.3% male) visited the hospital ED and were included in the analysis and among whom, 310 (18%) were subsequently admitted from the ED.
The AUCs were 0.77, 0.72, 0.61 and 0.86 for the AMB, GAP, START and triage nurse clinical assessment respectively and the nurses’ clinical judgement was significantly higher than the AUC of all the other tools and for all comparisons (p < 0.0001).
In a separate analysis, age, years of experience as a nurse, years of experience as an ED nurse and years of performing triage were found not be related to the nurse’s ability to predict triage and hence regression analysis of this data was not undertaken.
Commenting on their findings, the authors noted how this was the first study which directly compared the currently available tools and they concluded that while clinical judgement was subjective, it still provided good predictive accuracy and was superior to any of the other tools available.
16th November 2021
A model which accurately predicts delayed transfer of care (DTOC) has been developed with only eight pieces of data routinely collected from patients upon admission to hospital. This was the finding of a retrospective study by a team from University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, UK. Within a hospital setting it is necessary to ensure not only that patients receive appropriate clinical care but that they can be discharged either back home or to another setting, in a timely manner. A DTOC has become associated with the term ‘bed blocking” and a symbol of inefficiency within the national health service, occurring when a medically fit person is unable to go home or to another clinical setting and thus still occupies a hospital bed. In fact, national data for England shows that in February 2020 there were 155,700 total delayed days, of which 103,000 were in acute care, amounting to 5,370 people delayed per day. Furthermore, a report from the Department of Health in the UK estimated that in 2014-15 the cost due to discharge delay among patients over 65 years of age was £820 million.
With such enormous costs associated with DTOC, the Stoke-on-Trent team, sought to explore whether it was possible to identify the specific risk factors associated with DTOC among those patients admitted to hospital following attendance at an emergency department. They hypothesised that the capacity to predict which patients were more likely to experience a delayed transfer could enable earlier discharge planning.
The team turned to routinely collected data within the hospital including age, gender, ethnicity, national early warning score (NEWS), arrival by ambulance, the Glasgow admission prediction (GAP) score and an index of multiple deprivation (IMD) for their DTOC analysis. Using data on all adult patients admitted through the emergency department between January 2018 and December 2020, the team randomised these patients into a training and a validation dataset. Using the above and other variables, the team created a predictive model that included only statistically significant variables. The final model was assessed using the area under the receiver operating curve (AUC).
There were a total of 132,311 admissions over the 3-year period which were available for the delayed transfer of care analysis. The cohort had an overall mean age of 63 years (52% female) and over 90% were Caucasian. Initially, 10 variables were included in the predictive model, of which eight remained statistically significant: age, gender, ethnicity, GAP score, IMD, NEWS, arrival by ambulance, admitted in the last 12 months. Using all eight variables, the predictive DTOC model achieved a sensitivity of 0.77 (95% CI 0.75 – 0.78) and a specificity of 0.70 (95% CI 0.69 – 0.70) with an overall accuracy of 70%.
In their discussion, the authors noted that for example, patients arriving by ambulance were 13 times more likely to experience a DTOC. From a policy perspective, they suggested that use of the model would enable targeting of potential delayed patients for more proactive support.
They concluded that future studies should examine the potential effect of other factors and which together with machine learning, could improve the accuracy of prediction.
Davy A et al. A predictive model for identifying patients at risk of delayed transfer of care: a retrospective, cross-sectional study of routinely collected data. Int J Qual Health Care 2021