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Society for Acute Medicine Benchmarking Audit 2019 (SAMBA19): Trends in Acute Medical Care

Introduction: The eighth Society for Acute Medicine Benchmarking Audit (SAMBA19) took place on Thursday 27th June 2019. SAMBA gives a broad picture of acute medical care in the UK and allows individual units to compare their performance against their peers.

Method: All UK hospitals were invited to participate. Unit and patient level were collected. Data were analysed against published Clinical Quality indicators (CQI) and standards. This was the biggest SAMBA to date, with data from 7170 patients across 142 units in 140 hospitals.

Results: 84.5% of patients had an Early Warning Score measured within 30 minutes of arrival in hospital (SAMBA18 84.1%), 90.4% of patients were seen by a competent clinical decision maker within four hours of arrival in hospital (SAMBA18 91.4 %) and 68.6% of patients were seen by a consultant within the timeframe standard (SAMBA18 62.7%). Ambulatory Emergency Care is provided in 99.3% of hospitals. 61.8% of patients are initially seen in the Emergency Department (ED). Since SAMBA18 death rates and planned discharge rates, while the use of NEWS2 increased from 2.5% to 59.2% of hospitals.

Conclusion: SAMBA19 highlighted the evolving complexity of acute medical pathways for patients. The challenge now is to increase sample frequency, assess the impact of SAMBA open a broader debate to define optimal CQIs.

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Can we predict Acute Medical readmissions using the BOOST tool? A retrospective case note review

Abstract

Background: Readmissions within 30-days of hospital discharge are a problem. The aim was to determine if the Better Outcomes for Older Adults through Safe Transitions (BOOST) risk assessment tool was applicable within the UK.

Methods: Patients over 65 readmitted were identified retrospectively via a casenote review. BOOST assessment was applied with 1 point for each risk factor.

Results: 324 patients were readmitted (mean age 77 years) with a median of 7 days between discharge and readmission. The median BOOST score was 3 (IQR 2-4) with polypharmacy evident in 88% and prior hospitalisation in 70%. The tool correctly predicted 90% of readmissions using two or more risk factors and 99.1% if one risk factor was included.

Conclusion: The BOOST assessment tool appears appropriate in predicting readmissions however further analysis is required to determine its precision.

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