Outpatient neurology coding: feasibility and implementation to improve access to services

Full-time PhD studentships are available, funded by the NIHR Applied Research Collaboration (ARC) North West Coast. The purpose of the NIHR ARC funding is to support applied Health Care, Social Care and/or Public Health research relevant to the needs of the diverse communities served by the NIHR ARC and its local health and care system and be implementable across the local region. The research should be generalisable and have wide applicability across health and care nationally, as well as within the local health and care system where it is conducted.

Outpatient neurology services in the UK face overwhelming demand that far outstrips clinical capacity, leading to restricted access and long waiting times for patients. Coupled with variations in the number of neurologists per capita between regions (ranging from approx. 1 consultant neurologist per 30,000 in London to 1 per 210,000 in Lancashire), as well as in geographical location of clinical services and staff (eg hub vs spoke vs local services), patterns of outpatient referral and pathways of care, this situation undoubtedly exacerbates health inequalities. COVID-19 has magnified these problems. Demand management and service planning – including development of appropriate alternative pathways of care or other changes (eg geographical) in the delivery of services – are severely hampered for want of formal outpatient neurology coding. This absence of coding across the UK, other than in a very small number of units where a variety of approaches are starting to be used, is now recognised as a key priority needing to be addressed nationally for neurology services. Some work is being undertaken within neurosciences centres within the North West Coast region towards prospective outpatient categorisation based on ICD-10 codes. Various barriers exist, including clinician engagement in hard-pressed clinical settings, validation of diagnosis lists, including against accepted coding systems and implementation in local IT systems with different electronic patient records. This project will involve a survey of neurology services where informal or formal coding activities are starting to be undertaken, followed by the validation of proposed working diagnosis lists using natural language processing against SNOMED-CT and/or ICD-10, leading on to initial testing in neurology clinics using manual entry or automated extraction of relevant data to facilitate more widespread uptake of outpatient neurology coding across the UK.

The PhD is currently expected to encompass the following:

1. Survey of neurology services in the UK of approaches to outpatient neurology coding. This will enable characterisation of the varying approaches being adopted, current diagnosis or ‘problem’ lists being utilised, the stage of development of IT-integration, and barriers to implementation (including workforce and technological). This will inform and provide an excellent platform for the development of the subsequent work.

2. A study using natural language processing to investigate alignment of diagnosis lists (principal working diagnosis and secondary diagnoses and comorbidities) from the two NWC neuroscience centres – the Walton Centre and Lancashire Teaching Hospitals – to SNOMED-CT and ICD-10 categories. This will establish the utility of the approach as well as ensuring the completeness of SNOMED-CT for this application.

3. Pilot testing and implementation of ‘live’ prospective coding, by manual entry into an IT-integrated system, or by automated extraction (by NLP) from the clinic letter as it is generated.

For further information, please refer to our webpages. If you would like to be considered for this opportunity please contact the project supervisor including a covering letter, you CV and a fully completed research proposal (not more than 1000 words excluding references) related to the title you are applying for should be returned by email to [Email Address Removed] quoting the studentship reference number LUARC2021