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  1. Melissa J Ng1,
  2. Jane M Anderson2,
  3. Alexis J Joannides2
  1. 1University of Cambridge
  2. 2Department of Neurosciences, Addenbrooke's Hospital


Objectives Management of emergency referrals is an important component of neurology inpatient practice. On this background, we have developed and implemented a robust electronic framework for referral recording and analysis.

Design Codes representing all possible referral outcomes were defined and refined in response to user feedback. Further datasets for presenting symptoms, candidate anatomical syndrome and working diagnosis were developed to enable further epidemiological analysis.

Methods Aggregate referral data from the first year of implementation (1/7/13 to 30/6/14) were analysed with respect to demographics, underlying pathology and management outcome.

Results A total of 1896 referrals were recorded during the study period, with a mean of 5.9±0.22 per day and a constant activity level throughout the year. Weekly and daily variations were observed, with lower levels of activity outside working hours. Age distribution showed a peak at 50–69 years. The commonest diagnostic categories included cerebrovascular pathology, epilepsy/loss of consciousness, and headache/facial pain, which together represented 49.8% of referrals. Of these, headache-related disorders most often required further neurological management (61% of cases), thus representing a greater area of clinical activity.

Conclusions Overall, our results demonstrate the feasibility of efficient referral analysis through structured electronic data capture, with potential implications for service evaluation and epidemiological research.

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