Knee pain is common, representing a significant socioeconomic burden. Caused by a variety of pathologies, its evaluation in primary-care is challenging. Subsequently, an over-reliance on magnetic resonance imaging (MRI) exists. Prior to orthopaedic surgeon referral, many patients receive no, or incorrect, imaging. Electronic-triage (e-triage) tools represent an innovative solution to address this problem. The primary aim of this study was to ascertain whether an e-triage tool is capable of outperforming existing clinical pathways to determine the correct pre-hospital imaging based on knee pain diagnosis. Patients ≥18 years with a new presentation of knee pain were retrospectively identified. The timing and appropriateness of imaging was assessed. A symptom-based e-triage tool was developed, using the Amazon LEXbotplatform, and piloted to predict five common knee pathologies and suggest appropriate imaging. 1462 patients were identified. 17% of arthroplasty patients received an ‘unnecessary MRI’, whilst 28% of arthroscopy patients did not have a ‘necessary MRI’, thus requiring a follow-up appointment, with a mean delay of three months (SD 2.6, range 0.2-20.2). Using NHS tariffs, a wasted cost through unnecessary/necessary MRIs and subsequent follow-up appointments was estimated at £45,816. The e-triage pilot was trialled with 41 patients (mean age:58.4 years, 58.5% female). Preliminary diagnoses were available for 34 patients. Using the highest proportion of reported symptoms in the corresponding group, the e-triage tool correctly identified three of the four knee pathologies. The e-triage tool did not correctly identify anterior cruciate ligament injuries (n=3). 79.2% of participants would use the tool again. A significant number of knee pathology patients received incorrect imaging prior to their initial hospital appointment, incurring delays and unnecessary costs. A symptom-based e-triage tool was developed, with promising pilot data and user feedback. With refinement, this tool has the potential to improve wait-times and referral quality, whilst reducing costs.
