SpinalRisk is the first free, evidence-based spinal pain triage tool. The roadmap takes it from a clinician-derived screening tool to a machine-learning platform that adapts to every clinical setting it operates in.
Up to 80% of the global population will experience back pain at some point in their lives. It is the leading cause of disability worldwide.
Research consistently shows that a significant proportion of spinal imaging is clinically unnecessary, exposing patients to radiation and healthcare systems to avoidable cost.
Less than 1% of low back pain presentations in primary care have a serious underlying cause. Identifying which patients need investigation is the clinical challenge SpinalRisk addresses.
No comparable evidence-based, publicly accessible decision support tool exists for spinal pain red flag screening. SpinalRisk is the first of its kind.
Each phase builds on the last. The tool is live now. Everything that follows expands its reach and sharpens its accuracy.
Clinician-derived red flag decision tree based on current systematic review evidence. Screens five risk categories and provides specific investigation recommendations with clinical rationale. Deployable in any clinical or patient-facing setting immediately.
Bayesian updating enables the tool to learn from local patient outcome data. As assessments are completed, question weighting adapts to population-level characteristics. A military clinic screening for trauma-related fracture and an aged care facility with high osteoporosis prevalence each receive a model tuned to their patients.
Patient data never leaves the hospital. Only anonymised model weights are shared across participating sites, allowing SpinalRisk to improve continuously across a global network while meeting Privacy Act, GDPR, HIPAA, and TGA medical device requirements.
Subscription-based deployment across clinical settings worldwide. Each instance runs a population-specific adaptive model, continuously refined by local and federated data. Integrated into electronic health record systems via SMART on FHIR protocols.
Each clinical environment has distinct patient populations and risk profiles. The adaptive model tunes itself to each.
Fast structured screening at triage. High volume, time-critical environment where standardised red flag assessment reduces missed pathology and unnecessary imaging from the waiting room.
Waiting room self-assessment or in-consult screening. Supports evidence-based imaging decisions and provides clinician-ready documentation for referral letters and investigation requests.
High trauma exposure, young fit population with specific musculoskeletal risk profiles. Adaptive model learns the fracture and overuse patterns unique to military service members.
Elevated osteoporosis, cancer, and infection risk in older populations. MRI prioritised over CT for patients over 60 due to cumulative radiation exposure. The model weights fracture and malignancy screening accordingly.
Physiotherapist, movement educator, and the clinical mind behind SpinalRisk. Stuart built the red flag decision tree from his own analysis of systematic review evidence, then turned it into a free tool that anyone with back or neck pain can use to determine if they need a scan.
His broader mission through Movement First International is public health education and movement promotion, with courses, content, and clinical tools designed to make evidence-based care accessible to everyone.
Whether you are a health system, defence force, aged care provider, or technology partner, there is a conversation to be had about how SpinalRisk fits your clinical environment.