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A prospective audit of bed utilisation and delays in care across London
Milka Marinova1, Lauren McKenzie Bell2, Simon Watkin3
1Clinical Research Fellow at NIHR CLAHRC North West London, Imperial College, London, UK,
Background:
A prospective bed utilisation census of acute London hospitals using an established Day of Care Survey (DoCS), which quantified adult patients not meeting criteria for in-hospital care.
Methods:
Twenty-three hospitals were surveyed over two weeks in October/November 2017 using supervised trained hospital staff. Pairs of staff visited wards, reviewed all patients and identified those not meeting inpatient care criteria, recording reasons for delay. Patient demographics, length of stay (LOS), ward specialty and delay reasons were collected.
Results:
Overall - In total, 8,656 in-patients were studied (overall occupancy 96%, range 82-117%): 800 definite discharges were excluded, leaving 7,856 patients for analysis; seven hospitals had °100% occupancy; 1,919/7,856 patients (24%, range 12-43%) did not meet criteria; 56% of patients were over 70 years; five hospitals had higher number of patients <70yo 56% patients had LOS 0˛7days. Delayed patients - Number of delayed patients increased with age, but three hospitals had more patients <70yo; 53% had LOS˛14 days and 47% LOS<14 days; 13 hospitals had greater/equal number of patients in ˛14 days LOS. For delayed patients in ˛14 days group, most were within seven days of admission (627 ˛7days, 393 8-14 days). In total 34% (range 11-54%) of delays were related to acute hospital reasons (AHR) and 61% (range 46-83%) to wider system reasons (WSR). Eight common themes accounted for 67% of recorded reasons and were equally split between AHR and WSR.
Conclusion:
Data showed high occupancy levels with insufficient discharges. This study suggests policies selecting age and/or LOS alone as cut offs to tackle delays in care may miss a large proportion of patients requiring more timely interventions. Adopting a proactive thematic approach to improvement using the top eight delay reasons provides an obvious opportunity to reduce delays while noting the inter site variation. All metrics analysed emphasized the need for informed local data to help support local change.
