Improving the detection and documentation of suspected cauda equina syndrome: a quality improvement project
Maiar Elhariry1,2, Tahir Khaleeq2, Rachna Prem2
1Sandwell and West Birmingham Hospitals NHS Trust, Birmingham, UK maiar.elhariry@yahoo.com.
Background:
Cauda equina syndrome (CES) is a rare but critical neurological emergency. Prompt diagnosis and accurate documentation are essential to avoid permanent neurological impairment. This quality improvement project aimed to improve the accuracy and completeness of neurological examination documentation for patients presenting with suspected CES.
Methods:
Using three Plan-Do-Study-Act (PDSA) cycles, a digitally prompted documentation tool '#CES' was developed and implemented in the electronic patient record system. The tool was designed in alignment with the 2019 GIRFT Spine Report and 2023 GIRFT National CES Pathway. It included red flag symptoms and neurological assessment criteria from the ASIA classification. A teaching session on its use was delivered in January before the second data collection period (19 September 2024-20 October 2024), and it was later incorporated into resident doctor induction in December before the third data collection period (5 January 2025-5 February 2025).
Results:
In the initial cycle (May-August 2024), documentation was inconsistent, particularly for sensory (0%) and reflex (4.9%) examination. Following implementation and a teaching session (September 2024), documentation improved in the second cycle (September-October 2024): sensation (81.3%), reflexes (57.1%), though tone and motor documentation declined. In the third cycle (January-February 2025), full template use rose to 80% (from 25%) and documentation further improved: sensation (94.1%), motor (64.7%), reflexes (64.7%), tone (64.7%), vascular (58.8%).
Conclusions:
A CES-specific, digitally prompted template significantly improved documentation quality. Integration of GIRFT red flags, clinician codesign and repeated education enhanced uptake and accuracy. Further PDSA cycles will focus on sustainability, time-to-diagnosis metrics and addressing clinician workload concerns.
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