Using health informatics to improve neurological care
Healthcare data can be used to improve neurological care
Clinicians lack confidence in ‘high-level’ neurology service data, particularly when these have been generated where there may be a lack of adequate validation.
There is a pressing need to improve both the quality of data and the approach to its analysis. The impact of neurological disorders on individuals, carers, and the UK health economy is widely underestimated. There is a gap between the overwhelming level of outpatient demand and consultant neurologist capacity putting significant pressure on key NHS service delivery targets so this is a really key area in which to apply the analysis described above.
A Lancaster University PhD student, jointly supervised by Professor Hedley Emsley and Prof Jo Knight, and funded by an EPSRC PhD studentship, is working on a dataset of several thousand prospectively recorded consecutive outpatient neurology clinic appointments, to be cross-linked with business intelligence data.
Fran Biggin has found there to be significant differences in waiting times for outpatient neurology clinic appointments between different neurological conditions. Fran analysed routine data from NHS neurology clinic appointments between 2016 and 2019 following linkage to hospital administrative data. She found that five diagnostic categories accounted for 62% of all patients seen within the study period, the most common of which was headache disorders. In addition, waiting times from referral to appointment varied by diagnostic category. 65% of patients with a seizure/epilepsy disorder were seen within the 18-week referral to treatment target, compared with 38% of patients with a movement disorder such as Parkinson’s disease. This work has been published in BMJ Neurology Open.
Fran’s work on builds on her recently published scoping review in BMC Neurology which set out to gain a broad picture of the scope of how routine healthcare data have been utilised. She found there to be a disproportionately large body of literature pertaining to relatively rare disorders, and a correspondingly small body of literature describing more common conditions.
Fran is further investigating the referral of common conditions such as headache, and will be exploring how multi-level models can identify unexpected levels of referral.