English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background Referrals from primary care to rheumatology services have increased significantly in recent years. Many of these patients have non-inflammatory conditions, usually with pain, and may require specialist treatment such as physiotherapy or psychological intervention. If patients could be effectively triaged at the point of referral, clinical services could be better aligned to patients’ needs, but referrals do not always contain enough information to triage patients effectively. Validated patient-completed questionnaires are available which are designed to aid patient identification, for instance for fibromyalgia and inflammatory back pain. We designed an electronic questionnaire for patients to complete prior to their first attendance to inform the clinical consultation and to explore whether they could be more appropriately triaged to specialist clinics. Methods We constructed a questionnaire consisting of the modified ACR diagnostic questionnaire for fibromyalgia, the ASAS screening questionnaire for inflammatory back pain, a validated questionnaire for identification of patients with inflammatory arthritis, PHQ8 (depression) and GAD 7 (anxiety). The questionnaire contained conditional branching to ensure that only relevant questions were answered. A user-centred design approach was taken to optimise patient experience and response rates. A link to the questionnaire was sent via SMS to unselect new patients shortly after receipt of a referral from their GP. Completed questionnaires were imported into the hospital record. Results Initial response rates were around 35%. Through a process of continuous iteration informed by user feedback this increased to 80% over a 4 months period. Key factors in optimisation were the appearance of the text (displaying from ‘NHS’ rather than an apparently random number), the language which was used to maximise patients perception of the value of completion, the use of patients’ first names in the SMS and the content and timing of automated reminder messages. Over a 4 month after optimisation 664 patients completed the questionnaire. Of these 38% and 25% scored in the moderate-severe ranges for depression and anxiety respectively. 74% of patients met the paper criteria for fibromyalgia (which is not diagnostic in itself prior to clinical assessment). Of those patients scoring in the moderate-severe ranges for depression and anxiety, 92% and 88% respectively met the questionnaire criteria for fibromyalgia. Conclusion This work suggests that collecting information prior to the first appointment can add value to the process. In our department screening tools for psychological distress, widespread pain and somatisation are used to inform the consultation. We are currently analysing this data against the patients’ final diagnoses. Combination of screening tools may enable patients to be stratified with enough predictive value into specific pathways for inflammatory and non-inflammatory conditions, some of which could be community-based. We will be applying machine learning tools to determine the optimum predictive value of these questionnaires in combination. Disclosures W. Chen None. M. Thrower None. T. Garrood None.

P91 Bringing patients into the referral process: an evaluation of pre-appointment questionnaires in a rheumatology new patient pathway