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Increasing patient flow through neurosurgical critical care: the Leeds Improvement Method
James Meacock1, Soumya Mukherjee2, Asim Sheikh2
1Neurosciences, Leeds Teaching Hospitals NHS Trust, Leeds, UK James.Meacock@nhs.net.
Abstract:
At Leeds General Infirmary, a busy tertiary centre for neurosurgery, there has been little visibility of the step-down status of the patients from intensive care to high dependency or from the latter to a ward bed. The only record of the current situation was limited to the paper notes of the bed managers. Furthermore, accuracy of electronic systems used for staffing levels and bed state were underused. There were gaps in information and furthermore information within the system was unreliable (together defined as 'defects'). These defects mandated bed managers' physical presence on each ward to obtain reliable data. This led to unwarranted critical care stays and resultant high rates (up to 40%) of elective operation cancellations requiring a critical care bed.The Leeds Improvement Method using principles of the Toyota Production System aimed to improve patient flow through critical care and to assess the impact on elective case activity. Problems were identified and changes were implemented over a 1-week period. The changes included measures to reduce time taken for collation of critical bed-state information and improving patient and staffing data quality collected in the electronic patient management system (EPMS) and electronic staffing record (ESR). Impact was monitored for 30 days pre-implementation and post-implementation.Following intervention, the time taken by the bed manager to gather live bed-state information decreased from 50 to 9 min; the EPMS storing correct bed-state data was improved from 71% to 0% defect; the ESR was improved from 100% to 4% defects; critical care patient step-downs occurring at night (after 20:00) improved from 80% to 20%; and the number of cancelled elective cases over a 30-day period reduced from nine to one.Implementing these organisational efficiencies can significantly improve critical care patient flow and elective case throughput.
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