Search for a command to run...
<h3>Introduction</h3> Metastatic Spinal Cord Compression (MSCC) is a time-critical oncological emergency affecting 5–10% of patients with advanced cancer. Rapid recognition and intervention are essential to prevent irreversible neurological impairment and loss of function (Cole & Patchell, 2008; NICE, 2023). Despite established pathways, delayed diagnosis remains common due to subtle, non-specific, or evolving early symptoms (Levack et al., 2002). To address this, Amie Technologies developed Pal-PCS, an artificial intelligence (AI)-driven MSCC detection tool that flags early indicators of MSCC using routine clinical and patient-reported data. <h3>Aims</h3> To explore clinician perspectives on the usability, acceptability, and perceived value of the preliminary MSCC AI detection system across oncology, haematology, and palliative care services. <h3>Methods</h3> A mixed-Methods survey combining qualitative and quantitative questions was distributed to oncology, haematology, and palliative care clinicians in secondary and community settings. Quantitative data were summarised descriptively; qualitative responses underwent inductive thematic analysis (Braun & Clarke, 2006) to identify key themes and areas for refinement. <h3>Results</h3> Clinicians reported strong agreement with system outputs, viewing it as a valuable and supportive tool for identifying early MSCC cases. Three main themes emerged: the tool supports, not replaces, clinical judgment; it helps prioritise patients for timely review; and it can enhance multidisciplinary decision-making. Participants highlighted the importance of intuitive design and clear responsibility for actioning alerts to ensure timely response, reduce missed cases, and maintain safety. Suggested improvements included clarifying symptom timeframes. Overall, clinicians saw clear potential to improve assessment and remote monitoring with Pal-PCS. <h3>Conclusion</h3> The Pal-PCS system shows strong potential to enhance early recognition and prioritisation in oncological emergencies through collaboration between clinicians and digital innovation. <h3>Impact</h3> Embedding co-designed AI tools in cancer and palliative care pathways could transform early detection of oncological emergencies, promote consistent triage, and improve safety and outcomes while reducing system-level risk. <h3>References</h3> Braun V, Clarke V. Using thematic analysis in psychology. <i>Qualitative Research in Psychology </i>2006;<b>3</b>(2):77–101. https://doi.org/10.1191/1478088706qp063oa Cole JS, Patchell RA. Metastatic epidural spinal cord compression. <i>The Lancet Neurology</i> 2008;<b>7</b>(5):459–466. https://doi.org/10.1016/S1474-4422(08)70089-9 Levack P, Graham J, Collie D, Grant R, Kidd J, Kunkler I, Gibson A, Hurman D, McMillan N, Rampling R, Slider L, Statham P, Summers D. Don’t wait for a sensory level—listen to the symptoms: A prospective audit of the delays in diagnosis of malignant cord compression. <i>Clinical Oncology</i> 2002;<b>14</b>(6):472–480. https://doi.org/10.1053/clon.2002.0098 National Institute for Health and Care Excellence. (2023). Spinal metastases and metastatic spinal cord compression: <i>NG234</i>. https://www.nice.org.uk/guidance/NG234