PARR-30 is a new tool developed by the Nuffield Trust for predicting which patients are most at risk of short-term readmission, designed to be used by acute hospitals.
Using data from the British Medical Journal (BMJ) Open article: Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30), by John Billings and others, this chart shows the number of patients in each of the lower PARR-30 risk bands and the proportion of those patients that were readmitted to hospital within 30 days.
There are significantly more lower risk patients than higher risk patients because less than one tenth of patients are readmitted within 30 days of discharge. We have split the distribution into higher and lower risk bands to make the charts clearer – you can view the chart for the higher risk bands on a separate chart page.
The distribution shown here demonstrates that the model is working, as very few of the patients predicted to have a lower risk of readmission are actually readmitted within 30 days.
For the higher risk patients (risk bands 11 and above), readmission rates ranged from 47.7 per cent to 88.7 per cent in the highest risk band compared to an overall readmissions rate of 12.2 per cent. However, the number of patients in these high risk bands represented only a small share (1.1 per cent) of all patients analysed.
For risk bands 1-10 (shown in the chart above), the risk of readmission within 30 days dropped steadily with decreasing risk score, but the number of patients in each band increased. The two lowest risk bands cover 54.7 per cent of patients with a risk of readmission within 30 days of 7.1 per cent or lower.
The ability to identify patients at high risk of readmission constitutes the first step in any strategy to improve care and services for susceptible patients. The ultimate goal, however, is to couple this ‘case finding’ process with cost-effective interventions that mitigate the risk of readmission and ideally use the ensuing financial savings to help fund the intervention.
Knowledge of the percentage of patients in each risk score band can be useful in determining resources to patients, with more or different types of resources assigned for patients who are most likely to have a hospital admission.
Find out more about our work in this area by visiting the dedicated project page.