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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