Digital Health (Jan 2023)

An automated, web-based triage tool may optimise referral pathways in elective orthopaedic surgery: A proof-of-concept study

  • Alexandra L. Stanley,
  • Thomas C. Edwards,
  • Martin D. Jaere,
  • Johnathan R. Lex,
  • Gareth G. Jones

DOI
https://doi.org/10.1177/20552076231152177
Journal volume & issue
Vol. 9

Abstract

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Introduction Knee pain is caused by various pathologies, making evaluation in primary-care challenging. Subsequently, an over-reliance on imaging, such as radiographs and MRI exists. Electronic-triage tools represent an innovative solution to this problem. The aims of this study were to establish the magnitude of unnecessary knee imaging prior to orthopaedic surgeon referral, and ascertain whether an e-triage tool outperforms existing clinical pathways to recommend correct imaging. Methods Patients ≥18 years presenting with knee pain treated with arthroscopy or arthroplasty at a single academic hospital between 2015 and 2020 were retrospectively identified. The timing and appropriateness of imaging were assessed according to national guidelines, and classified as ‘necessary’, ‘unnecessary’ or ‘required MRI’. Based on an eDelphi consensus study, a symptom-based e-triage tool was developed and piloted to preliminarily diagnose five common knee pathologies and suggest appropriate imaging. Results 1462 patients were identified. 17.2% ( n = 132) of arthroplasty patients received an ‘unnecessary MRI’, 27.6% ( n = 192) of arthroscopy patients did not have a ‘necessary MRI’, requiring follow-up. Forty-one patients trialled the e-triage pilot (mean age: 58.4 years, 58.5% female). Preliminary diagnoses were available for 33 patients. The e-triage tool correctly identified three of the four knee pathologies (one pathology did not present). 79.2% ( n = 19) of participants would use the tool again. Conclusion A substantial number of knee pain patients receive incorrect imaging, incurring delays and unnecessary costs. A symptom-based e-triage tool was developed, with promising performance and user feedback. With refinement using larger datasets, this tool has the potential to improve wait-times, referral quality and reduce cost.