Abstract
Emergency readmission is defined within the NHS as an emergency admission to hospital within 30 days of discharge. Excess readmissions are undesirable in terms of care quality and efficiency; yet, despite financial incentives for improvement, reports of increasing readmission rates continue. There is evidence that pharmacist intervention can prevent medication errors, discrepancies and adverse drug events; which can each contribute to readmission. The purpose of the work in this thesis was to develop a model based on routinely collected prescription information to enable the pharmacy team to estimate readmission risk in the clinical setting, thereby facilitating appropriate prioritisation of potentially preventative intervention.A multiple logistic regression model for estimating readmission risk using routinely recorded prescription information among patients discharged home from the medical short stay units of one NHS Trust was developed, and survival analysis was undertaken to characterise readmission behaviour in relation to the predictors.
The readmission rate was 18% (220/1240). Readmission risk increased with increasing age and polypharmacy: each additional medicine prescribed increased the odds of readmission within 30 days by eight per cent and each additional year of age increased the odds of readmission within 30 days by two per cent. Each additional medicine prescribed decreased the time to readmission by seven per cent and each additional year of age decreased the time to readmission by one per cent. Over one-third of readmissions occurred within one week (73/200) and more than half (114/200) occurred within two weeks, supporting that identification of those at risk and intervention to prevent readmission should be provided promptly. The predictive model developed is suitable for application on admission and could therefore enable clinicians to identify the patients most likely to require intervention to prevent readmission before they are discharged home from hospital, thereby maximising the time available to organise and/or provide the necessary support. Although the logistic regression model improved accuracy by 36% compared to indiscriminate intervention whilst identifying 70% of patients who would be readmitted, it had relatively weak discriminative capability (c-statistic 0.637). It may be the case that clinical intuition is as effective for predicting readmission and further research should be undertaken to confirm whether this is the case.
| Date of Award | 2023 |
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| Original language | English |
| Supervisor | Barbara Conway (Co-Supervisor) & John Stephenson (Co-Supervisor) |